CN115426935A

CN115426935A - Systems, devices and methods for determining and/or assessing brain related conditions based on pupillary light responses

Info

Publication number: CN115426935A
Application number: CN202180029067.6A
Authority: CN
Inventors: 伊加尔·罗滕施特赖希; 伊法特·希尔
Original assignee: Tel HaShomer Medical Research Infrastructure and Services Ltd
Current assignee: Tel HaShomer Medical Research Infrastructure and Services Ltd
Priority date: 2020-02-11
Filing date: 2021-02-11
Publication date: 2022-12-02
Also published as: EP4103038A4; IL295444A; WO2021161318A1; EP4103038A1

Abstract

Systems, devices, and methods are provided herein for monitoring the progression of, determining, and/or assessing a brain-related condition of a subject based on Pupil Light Response (PLR) to color light stimuli, particularly by classifying the PLR based on one or more PLR parameter values, wherein the classification allows for monitoring the progression of, determining, and/or assessing the brain-related condition.

Description

Systems, devices and methods for determining and/or assessing brain related conditions based on pupillary light responses

Technical Field

Systems, devices, and methods are provided herein for monitoring the progression of, determining, and/or assessing a brain related condition of a subject based on a Pupil Light Response (PLR) to a color light stimulus.

Background

The Pupillary Light Reflex (PLR) controls the amount of Light entering the eye through the Pupil in response to the contraction and expansion of the Light. The pupillary light reflex, which constricts the iris in response to light, reflects the function of the retina and Retinal Ganglion Cells (RGCs). The afferent arm of the PLR is mediated by intrinsic photosensitive retinal ganglion cells (iprgcs) which account for about 1% of total RGCs. iprgcs regulate pupil size by integrating external signals from rod and cone cells and internal signals from melanopsin light transduction. The axons of the iprgcs reach the anterior apical (protectal) nucleus, where they synapse with apical anterior neurons projecting to the Edinger-Westphal nucleus. From there, the preganglionic parasympathetic fibers travel with the oculomotor nerve and synapse at ciliary ganglion cells. Postganglionic parasympathetic neurons innervate the iris sphincter muscle, releasing acetylcholine at the neuromuscular junction, causing the pupil to constrict. The re-dilation of the pupil is mediated by inhibition of parasympathetic innervation of the pupil sphincter and contraction of the iris dilator muscles. The retina is an extension of the brain because the axons of RGCs that form the optic nerve synapse directly with neurons in several brain regions. Unlike the brain, the retina is susceptible to direct and non-invasive imaging with high resolution and sensitivity due to the transparency of the eye. In clinical practice, retinal measurements are commonly used to assess degeneration and vascular changes in several clinical settings (e.g., diabetes, hypertension, and neurological diseases).

A color pupillometer utilizing a color multifocal pupillometer may be used to assess PLR for various radiation stimuli presented at different locations in the Visual Field (VF). These methods are useful for diagnosing various ocular diseases, such as Retinitis Pigmentosa (RP). For example, international application publication No. WO 2017/123710 relates to systems and methods for objective visual field examination and diagnosis of patients with retinitis pigmentosa and other ocular diseases.

Currently, there is no objective and reliable test for continuous and sensitive assessment of brain function in neurological diseases, nor is such a test available for early detection of various types of brain-related conditions. In some cases, PLR is used as a prognostic indicator in neurological intensive care units, general intensive care units, and comatose patients to attempt and at least partially detect neurological damage. However, such manual pupillary examination is challenged by uncontrolled background light, overly simplified light response recordings (i.e., only very few parameters are detected), differences between observers, the use of only white light stimuli, lack of control over light source intensity or size, and, furthermore, relative pupillary deficits are not applicable when both pupillary responses are affected.

Accordingly, there is a need in the art for non-invasive, cost-effective, safe, reliable, accurate, and objective methods, devices, and systems that can be performed continuously in real-time to allow early detection and/or assessment of the progress of various brain-related conditions.

SUMMARY

According to some embodiments, provided herein are methods, devices and systems that allow for early detection and/or assessment of the progress of various brain-related conditions based on various analyses of Pupillary Light Reflex (PLR). In some embodiments, the systems, devices, and methods utilize highly sensitive measurements of PLR using color pupillometry under various conditions (e.g., with various wavelengths (e.g., red and blue light), various intensities (e.g., dim light and bright light), various irradiation locations in the field of view (e.g., central and/or peripheral), etc.) and process the obtained or selected PLR measurements (e.g., with various PLR parameters, parameter values, and/or features derived therefrom) to allow determination of a brain-related condition of a subject and/or its progression. In some embodiments, processing the selected PLR measurements utilizes various machine learning algorithms and Artificial Intelligence (AI) tools for determining a likelihood that the subject has a brain-related condition and/or assessing a state or progression of the brain-related condition. Thus, according to some embodiments, the systems, devices and methods allow for non-invasive, reliable and sensitive early detection (diagnosis) of chronic or acute brain-related (neurological) conditions, as well as continuous objective monitoring of brain function in these subjects.

According to some embodiments, the systems, devices and methods disclosed herein are advantageous in that they allow for a non-invasive, objective means for detecting and assessing the progress of various brain-related conditions while being accurate, reliable, accurate, reproducible, cost-effective and capable of being performed continuously and in real-time. Thus, the disclosed systems, devices, and methods may advantageously operate without subjective input, allow for early detection or diagnosis of neuropathology, provide high diagnostic accuracy (e.g., greater than about 95%), allow for detection of acute brain-related conditions (neurological conditions), may allow for accurate and accurate monitoring or assessment of the progression (or recovery) of brain-related conditions, do not rely on binocular disparity, and are easy to monitor continuously.

The disclosed systems, devices, and methods are further advantageous, according to some embodiments, because they can provide stimulation at various light (e.g., red and blue), various intensities (e.g., dark blue and bright blue), various focal regions/foci of the field of view to obtain PLR-related measurements by stimulating various types of photoreceptor cells, e.g., by targeting the red or blue visual pathway (including, e.g., cone cells (stimulated by, e.g., red light having a wavelength of about 624 nm), rod cells (stimulated by, e.g., dim blue light having a wavelength of about 485 nm), and/or melanopsin (also known as iPRGC, stimulated by, e.g., bright blue light having a wavelength of about 485 nm)).

In further embodiments, the systems, devices, and methods disclosed herein are advantageous in that they can be used to accurately and objectively identify and/or assess a wide range of brain-related conditions, including acute or chronic conditions, such as, but not limited to: according to some embodiments, the systems, devices and methods are further advantageous as they allow for the adjustment of the analysis of various general parameters (e.g., patient specific parameters (e.g., age, sex, drugs, history, etc.)) to improve the accuracy thereof.

According to some embodiments, by utilizing the systems, devices, and methods provided herein to accurately and objectively determine a brain-related condition and/or assess or monitor its progress or state, appropriate treatments can be provided to a subject, thereby improving subject outcomes. For example, early detection of brain-related conditions and accurate continuous monitoring of subjects with neurological diseases (e.g., AD, PD, MS, TBI, brain tumors, ICP, stroke, PTC, etc.) are paramount to increasing the chance of good outcome in the subject by intervention (e.g., drugs or other types of suitable treatments) as early as possible. According to some exemplary embodiments, early diagnosis and accurate continuous monitoring of patients with acute neurological diseases (e.g., stroke) is crucial to increase the chance of good outcome through early intervention.

According to some embodiments, the systems, devices, and methods may further be used to identify, predict, assess, or monitor responses to various treatments. In some embodiments, monitoring disease progression and assessing treatment response are essential for selecting the best treatment for each patient and for developing or determining additional treatments.

According to some embodiments, therefore, provided herein are systems, devices and methods that allow for objective and reliable testing of continuous, sensitive assessment of brain function in various neuro-related conditions. In some embodiments, reliable early diagnosis and accurate continuous monitoring of patients with acute neurological diseases is provided, and a triage (triage) is provided.

According to some embodiments, there is provided a non-invasive method of monitoring the progression, determining and/or assessing a brain related condition of a subject based on Pupillary Light Response (PLR) to colored light stimuli, the method comprising:

determining a baseline pupil size of an eye of a subject;

applying blue and/or red light stimuli to one or more regions of the visual field of the eye, the light stimuli configured to evoke a response in the pupil;

obtaining values for one or more parameters associated with a change in pupil size induced in response to a light stimulus;

normalizing the values of one or more parameters based on the baseline pupil size; and

classifying the PLR based on the one or more parameter values, wherein the classification allows monitoring progression of, determining, and/or assessing the brain-related condition.

According to some embodiments, the brain-related condition may be selected from: brain tumors, optic neuritis, neurodegenerative diseases, traumatic brain injury, stroke, intracranial lesions, intracranial pressure, and pseudobrain tumors.

According to some embodiments, the neurodegenerative disease may be selected from: alzheimer's Disease (AD), multiple Sclerosis (MS), parkinson's Disease (PD), and cognitive decline associated with Fragile X.

According to some embodiments, the one or more parameters may be selected from: pupil contraction percentage (PPC), pupil Response Latency (PRL), maximum Contraction Velocity (MCV), MCV Latency (LMCV), pupil relaxation percentage (PPR), maximum Relaxation Velocity (MRV), MRV Latency (LMRV), maximum acceleration of contraction (MCA), MCA Latency (LMCA), maximum acceleration of relaxation (MRA), MRA Latency (LMRA), maximum deceleration of relaxation (MRD), maximum deceleration of relaxation (LMRD), curve Area (AC), maximum pupil contraction Latency (LMP), maximum deceleration of contraction (MCD), MCD Latency (LMCD), maximum pupil size (Max _ PS), minimum pupil size (Min _ PS), and any combination thereof. Each possibility is a separate embodiment.

According to some embodiments, the method may further comprise applying a curve fit to the data associated with pupil size in response to the light stimulus.

According to some embodiments, the classifying may include applying at least one algorithm to one or more selected parameter values and obtaining the brain-related condition.

According to some embodiments, the classification may include applying one or more selected parameter values to one or more machine learning algorithms, wherein the selected parameters are associated with the selected brain-related condition.

According to some embodiments, the light stimuli may include 1 to 228 individual light stimuli, each of which may be applied to a different location of the visual field.

According to some embodiments, the optical stimulus may be in a wavelength range from about 410nm to about 520nm and/or from about 550nm to about 700 nm.

According to some embodiments, wherein the light stimulus may comprise high intensity and/or low intensity light.

According to some embodiments, the light stimulus may be presented for a period of time of about 0.1 to about 10 seconds.

According to some embodiments, the region of the field of view may include a central field of view ranging between approximately 0-10 degrees.

According to some embodiments, the region of the field of view may include a peripheral field of view greater than about 10 degrees.

According to some embodiments, the method may further comprise providing an initial predetermined light stimulus with an initial illumination, duration, and location of the field of view configured to determine a likelihood that the subject has a brain-related condition, and wherein applying the blue and/or red light stimulus to the one or more regions of the field of view of the eye is based at least in part on the likelihood determined in the initial illumination.

According to some embodiments, applying blue and/or red light stimuli to one or more regions of the visual field of the eye may include selecting a subset of the light stimuli based on their location relative to the visual field.

According to some embodiments, applying blue and/or red light stimuli to one or more regions of the visual field of the eye may include one or more of: selecting a wavelength of each individual light of the light stimulus, selecting an intensity of each individual light of the light stimulus, selecting a ratio of the blue light stimulus to the red light stimulus, selecting a duration of illumination of each individual light of the light stimulus, or any combination thereof.

According to some embodiments, applying blue and/or red light stimuli to one or more regions of the visual field of the eye may comprise applying blue and/or red light stimuli in at least two intervals.

According to some embodiments, the at least two intervals may be about 2 seconds to about 120 seconds apart.

According to some embodiments, each interval may include a different subset of light stimuli, a different wavelength of light stimuli, and/or a different intensity of light stimuli.

According to some embodiments, a baseline pupil size may be determined for each individual stimulus.

According to some embodiments, the method may further comprise positioning the subject's eye at an eye fixation device (ocular fixation) such that the subject's non-test eye is occluded.

According to some embodiments, the method may comprise determining the risk of AD or developing alzheimer's disease, and the calculated value may be determined based on at least: MCV parameters in a central region of the field of view in response to a high intensity blue light stimulus, PRL parameters in response to blue light, PRL parameters in response to red light, LMCA parameters in response to blue light, LMCA parameters in response to red light, LMCD parameters in response to blue light, LMCD parameters in response to red light, LMP parameters in response to blue light, LMP parameters in response to red light, MCV parameters in response to blue light, or any combination thereof.

According to some embodiments, the condition may be Parkinson's Disease (PD), and the calculated value may be determined based on at least one of: a PPR parameter in a central region of the field of view in response to a high intensity blue light stimulus, a PPC parameter in the central region of the field of view in response to a low intensity blue light stimulus, an MCA parameter in the central region of the field of view in response to a low intensity blue light stimulus, a PPC parameter in a peripheral region of the field of view in response to a low intensity blue light stimulus, an MCA parameter in the peripheral region of the field of view in response to a low intensity blue light stimulus, and a PPC parameter in the central region of the field of view in response to a red light stimulus.

According to some embodiments, the condition may be a brain tumor, and the calculated value may be determined based on at least one of: a PPR parameter in a peripheral region of the visual field in response to the high intensity blue light stimulus and a PPC parameter in the peripheral region of the visual field in response to the low intensity blue light stimulus.

According to some embodiments, the condition may be a fragile X carrier, and the calculated value may be determined based on at least one of: a PPR parameter in a peripheral region of the field of view in response to a high intensity blue light stimulus, an LMCA parameter in a peripheral region of the field of view in response to a low intensity blue light stimulus, and an LMCA parameter in a central region of the field of view in response to a red light stimulus.

According to some embodiments, the condition may be Multiple Sclerosis (MS), and the calculated value may be determined based on at least one of: a PPC parameter in a peripheral region of the visual field in response to a red light stimulus, a PPC parameter in a peripheral region of the visual field in response to a low intensity blue light stimulus, an MRV parameter in a peripheral region of the visual field in response to a red light stimulus, an MRV parameter in a peripheral region of the visual field in response to a low intensity blue light stimulus, and an MCV parameter in a peripheral region and/or a central region of the visual field in response to a red and/or blue light stimulus, and a PPR at a central and/or peripheral location in response to a strong and long duration blue light.

According to some embodiments, the condition may be optic neuritis and may be determined based on at least one of: the PPC parameter in the peripheral and/or central region of the field of view in response to red and/or bright blue stimuli and the PPR parameter in the peripheral region of the field of view in response to blue stimuli, and/or the calculated value is the number of test targets having abnormal PPC in response to blue and red stimuli.

According to some embodiments, the condition may be an intracranial lesion, and the calculated value may be determined based on at least one of: PLR parameters in the peripheral and/or central regions of the visual field in response to high intensity blue light stimuli, PPC parameters in the nasal region of the visual field in response to low intensity blue light, PPR parameters in the nasal region of the visual field in response to low intensity blue light.

According to some embodiments, the condition may be a pseudobrain tumor, and the calculated value may be determined based on at least one of: PPC parameters in the peripheral and/or central regions of the visual field in response to red and/or blue light stimuli, and MRV parameters in the peripheral and/or central regions of the visual field in response to red and/or blue light stimuli.

According to some embodiments, the condition may be a stroke, and the calculated value may be determined based on at least one of: a PPR parameter in a peripheral region and/or a central region of the visual field in response to the high-intensity blue light stimulus, a PPC parameter in a peripheral region of the visual field in response to the low-intensity blue light stimulus, and an MRV parameter in a peripheral region of the visual field in response to the low-intensity blue light stimulus.

According to some embodiments, the method may further comprise controlling the emission wavelength, intensity and duration of individual light stimuli or subsets of light stimuli.

According to some embodiments, when obtaining values for more than one parameter, the method repeats the steps of determining a baseline pupil size, applying blue and/or red light stimuli, and obtaining values to obtain a value for each parameter.

According to some embodiments, the method may further comprise inputting (applying) one or more selected values of at least one of the one or more parameters to a machine learning algorithm configured to classify the subject as having a brain-related condition or not having a brain-related condition.

According to some embodiments, the method may further comprise classifying the brain-related condition as a type and/or severity level and/or progression of the condition using a machine learning algorithm based at least in part on the selected value of the at least one parameter.

According to some embodiments, there is provided a pupillometer device for monitoring the progress of, determining and/or assessing a brain related condition of a subject based on pupillary light responses to a colored light stimulus, the pupillometer device comprising:

a plurality of color beam emitters configured to produce red and/or blue light stimuli at predetermined locations of a field of view;

at least one camera configured to detect pupillary responses; and

a control unit in communication with the plurality of color beam emitters and the at least one camera, wherein the control unit is configured to:

determining a baseline pupil size of an eye of a subject;

normalizing values of the one or more parameters based on the baseline pupil size; and

classifying PLRs based on the one or more parameter values, wherein the classifying results in monitoring progression of, determining, and/or assessing the brain-related condition.

According to some embodiments, the control unit may be in communication with a server or memory module that includes instructions for monitoring the progress of, identifying, and/or assessing brain-related conditions.

According to some embodiments, the control unit may be configured to classify one or more selected values of at least one of the one or more parameters as being associated with the brain-related condition and/or the progression of the brain-related condition based on a machine learning algorithm.

According to some embodiments, there is provided a system for monitoring the progression, determining and/or assessing a brain-related condition of a subject based on pupillary light responses to colored light stimuli, the system comprising:

at least one hardware processor; and

a non-transitory computer-readable storage medium having program code stored thereon, the program code executable by at least one hardware processor to:

receiving data associated with red and/or blue light stimuli generated at a predetermined location of a visual field of a subject;

receiving data associated with a pupil size of a subject;

determining values of one or more parameters associated with a change in pupil size induced in response to a light stimulus;

inputting (applying) one or more selected values of at least one of the one or more parameters to an algorithm configured to classify the subject as having a brain-related condition or not based at least in part on at least one value of the one or more parameters.

According to some embodiments, the algorithm is a machine learning algorithm configured to classify the brain-related condition as a type and/or severity level and/or progression of the condition based at least in part on the values of the one or more parameters.

Certain embodiments of the present disclosure may include some, all, or none of the above advantages. One or more other technical advantages may be readily apparent to one skilled in the art from the figures, descriptions, and claims included herein. Moreover, while specific advantages have been enumerated above, various embodiments may include all, some, or none of the enumerated advantages.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. In case of conflict, the patent specification, including definitions, will control. As used herein, the indefinite articles "a" and "an" mean "at least one" or "one or more" unless the context clearly dictates otherwise.

Brief Description of Drawings

Some embodiments of the present disclosure are described herein with reference to the accompanying drawings. It will be apparent to one of ordinary skill in the art from the description taken in conjunction with the drawings how some embodiments may be practiced. The drawings are for illustrative purposes and are not intended to show structural details of the embodiments in more detail than is necessary for a fundamental understanding of the disclosure. For purposes of clarity, some objects depicted in the drawings are not drawn to scale. Furthermore, two different objects in the same figure may be drawn at different scales. In particular, the scale of some objects may be greatly exaggerated compared to other objects in the same figure.

Optional elements/components and optional stages may be included within the block diagrams and flow diagrams within the dashed boxes.

In the drawings:

fig. 1A shows a schematic diagram of a system for monitoring the progression of, determining and/or assessing a brain-related condition of a subject, according to some embodiments of the invention;

fig. 1B illustrates a front view of a pupillometer device according to some embodiments of the present invention;

FIG. 1C illustrates an exemplary distribution map of the locations of light stimuli for a pupillometer device according to some embodiments of the present invention;

fig. 2 illustrates a flow chart of functional steps in a non-invasive method for monitoring the progression of, determining and/or assessing a brain-related condition in a subject, according to some embodiments of the present invention;

3A, 3B, 3C, and 3D illustrate graphs of exemplary parameter and value calculations according to some embodiments of the invention;

FIGS. 4A and 4B show graphs of velocity of pupil size as a function of time, according to some embodiments of the invention;

FIGS. 5A and 5B illustrate graphs of acceleration of pupil size as a function of time, according to some embodiments of the invention;

FIG. 6 illustrates a graph of sudden changes in pupil size (lurch) as a function of time, according to some embodiments of the invention;

fig. 7 shows a flow chart of functional steps in a non-invasive method of monitoring the progress of, determining and/or assessing a brain-related condition of a subject using a machine learning algorithm, according to some embodiments of the present invention;

FIG. 8 shows a schematic diagram of a feature vector extraction process for exemplary parameters measured from a patient's eye, according to some embodiments of the invention;

fig. 9 shows a graphical representation of the 95% Confidence Interval (CI) that yields AUC for a single pupillometer parameter (R PRL in this example). According to some embodiments of the present invention, given a complete sample set, an exemplary training set is generated by having a sample with replacement, while the test set consists of samples that are not included in the training set;

figure 10 shows two bar graphs of the mean AUC (bar) for each pupillometric parameter and STD value (black line) for each pupillometric parameter (bar) for eye-0 (top graph) and eye-1 (bottom graph) generated using an exemplary bootstrapping process (shown in figure 9) according to some embodiments of the invention;

fig. 11 illustrates the relative weights given by the machine learning model to each of the 54 eyepoints, in accordance with some embodiments of the invention. As described above, the weights are the average over 100 trials to generate 95% CI. The upper and lower rows show the weights of the B _ PRL and R _ PRL parameters, respectively. The left and right columns show the results for the left and right eye, respectively. Color-coded bars represent the weight of an eyepoint, where higher weights correspond to darker colors;

fig. 12 illustrates the distribution of different AUC values within confidence intervals, according to some embodiments of the invention. For each eye, there were 95 horizontal lines, each corresponding to a different AUC value, which is the result of independent experiments (total 100 experiments per eye);

fig. 13 illustrates that attenuated melanopsin-mediated PPR is observed in the central and inferior fields of View (VF) of PD patients in response to bright blue light, according to some embodiments of the present invention;

fig. 14A and 14B illustrate cone-mediated PPC observed in central and peripheral VF (i.e., in response to red light) attenuated in PD patients, according to some embodiments of the invention;

fig. 15A, 15B, and 15C illustrate reduced rod-mediated PPC and MCA observed in central VF and peripheral VF (i.e., in response to dim blue light) in PD patients, according to some embodiments of the invention;

fig. 16 shows the results of PPC parameters measured at various locations of the visual field of the left (OS) or right (OD) eye of a control subject, according to some embodiments of the invention;

FIG. 17 shows the results of PPC parameters measured at different locations of the visual field of the left (OS) or right (OD) eye of an MS patient, according to some embodiments of the invention;

FIG. 18 shows results of PPC parameters measured at various locations of a visual field of a left eye (OS) or a right eye (OD) of an MS patient, according to some embodiments of the invention;

fig. 19 shows results for MRV parameters measured at various locations in the field of view of the left eye (OS) or right eye (OD) of control subjects in response to blue or red light irradiation, according to some embodiments of the invention;

fig. 20 shows the results with respect to MRV parameters measured at various positions of the visual field of the left eye (OS) or the right eye (OD) of the MS patient in response to blue or red light irradiation. Each number in the pupillometer "map" represents the MRV measured at that retinal location. The color coding is set to resemble the output of a hummphrey perimeter, where white represents a "normal" value (based on the average of age-matched controls at each test point location), and darker gray represents a value lower than the normal value. The darkest colors were used for the test spots where the MRV was 5 SEs below the mean of the controls in these spots. Yellow, a target whose PPC is higher than the mean of the controls;

fig. 21 shows the results with respect to MRV parameters measured at various positions of the visual field of the left eye (OS) or the right eye (OD) of the MS patient in response to blue or red light irradiation;

figure 22 shows results for MCV parameters measured at various locations in the visual field of the left eye (OS) or right eye (OD) of control subjects in response to blue or red light irradiation;

fig. 23 shows the results with respect to MCV parameters measured at various positions of the visual field of the left eye (OS) or the right eye (OD) of the MS patient in response to blue or red light irradiation;

FIG. 24 shows results for MCV parameters measured at different positions of the visual field of the left eye (OS) or the right eye (OD) of an MS patient in response to blue or red light irradiation;

figure 25 shows attenuated rod and cone mediated PPC during ON episodes;

fig. 26 shows a reduction in pupillary response (PPC) to blue light in ON patients and correlates with severity of visual field examination defects;

figure 27 shows a progression analysis of PLR and visual function in representative ON patients after treatment;

fig. 28 shows a progression analysis of PLR and visual function in representative ON patients after treatment with methylprednisolone (Solu-Medrol);

figure 29 shows ROC analysis of PPR results for high intensity blue light in ON subjects;

fig. 30A and 30B show ROC analysis of PPR results for high intensity blue light in ON subjects; figure 30A is a significantly higher PRP in eyes with optic neuritis and contralateral eyes (NON) or healthy eyes (control). The ON eye is affected more than the contralateral eye. Fig. 30B is a ROC analysis with AUC of 100% for ON detection using PPR for high intensity blue light.

Fig. 31A-31F show the assessment of focal PPC changes at various retinal locations during ON and after treatment. The representative 18 year old male was tested during the acute ON episode (

panels

31A, 31C, 31E) and after 5 days of SOLU-MEDROL treatment (

panels

31B, 31D, 31F). The patient's vision during the ON episode was 0.3 and improved to 0.18 after treatment (logMAR ETDRS). The reduced PPC for blue light (31C) in the overall VF associated with the Humphrey visual field examination during ON (31A) improved after treatment (31D). PPC for red light was less affected (31E) and there was essentially no change after treatment (31F);

FIG. 32 shows color pupillometry measurements correlated with the severity of VF loss during an ON episode; four subjects (2 males (M) and 2 females (F)) of a given age (age, YO) were tested; the top row is the color pupillary visual field map for the PPC parameter: the bottom row is the Humphrey visual field examination result;

fig. 33A-33B show PLR results for assessing brain pathology in fragile X carriers illustrated in example 5, fig. 33A shows PPR parameters, fig. 33B shows LMCA parameters;

fig. 34 shows test point locations for PPR for high intensity blue light. Test point locations are highlighted in yellow: a center (C); a mid-periphery (M); a periphery (P);

FIG. 35 shows ROC curves for PPR in the central retinal target (see FIG. 34) in the right eye of a tumor patient who was not exposed to the visual organ (optical appatatus) prior to surgery;

fig. 36 shows VF test points used to assess transient PLR in patients and controls with tumors that do not contact visual organs. CN test target highlighted in red; temporal side (T); center-nasal (CN); central-temporal (CT); nasal side (N), superior (S); a lower part (I);

FIG. 37 shows ROC curves for Maximal Contraction Velocity (MCV) measured after red light stimulation presented at a VF target CN in the right eye of a patient and a control with a brain tumor without touching visual organs prior to surgery;

FIG. 38 shows an MRI scan of patient (# 1), showing preoperative olfactory sulcus meningioma, red arrow pointing to the optic nerve and visual cross, white arrow pointing to the tumor mass;

fig. 39 shows VF test points used to assess sustained PLR in patients with tumors in contact with visual organs; the test point position of the color pupil visual field inspection meter; test points are highlighted in yellow: a center (C); a mid-periphery (M); a periphery (P);

fig. 40 shows ROC curves for PPR against high-intensity blue light in peripheral visual field targets (see fig. 39) in the right eyes of patients and controls with tumors in contact with visual organs;

fig. 41 shows VF test points for evaluating transient PLR tests in patients and controls with tumors in contact with visual organs. The center and upper test targets are highlighted in red; temporal side (T); center-nasal (CN); central-temporal (CT); nasal side (N), superior (S); a lower part (I);

figure 42 shows an MRI scan of a patient (p # 7) with recurrent temporal lobe glioblastoma in contact with the right eye prior to surgery. Red arrows point to the optic nerve and the optic chiasm, white arrows point to the tumor mass;

fig. 43 shows light stimulus presentation at nasal and temporal locations. And (5) presenting light stimulation. In each eye, the subject was tested first for red light stimulation and then for dark blue light stimulation, the red and dark blue stimuli being presented sequentially in the following order: CN-N-T-CT for OD, and CT-T-N-CN for OS (FIGS. 43A, 43B). The duration of stimulation was 1 second and the inter-stimulation interval was 3 seconds. The PLR was then recorded for three consecutive bright blue stimuli, each appearing for 8 seconds with 8 seconds between stimuli, using the sequence N-C-T for OD and T-C-N for OS (43C, 43D). After all the tests (red, dim and bright blue) were completed for one eye, a second eye was tested. C-center, N-nasal, T-temporal, CN-center nasal, CT-center-temporal;

FIGS. 44A-44L show patients diagnosed with supratentorial right temporal brain metastases of lung cancer (# 4, group I). Fig. 44A-44b a T2 MRI scan (44A) and T1 with gadolinium (44B) showed cerebral edema in the right temporal lobe (blue arrow) with direct pressure on the right optic nerve (black arrow) and right optic tract. Fig. 44C-fig. 44D: four months post-operative (post OP) T2 MRI scans (44C) showed significant improvement in cerebral edema and mass effect on the right optic nerve (black arrow). T1 MRI with gadolinium revealed recurrent tumors of the right frontal lobe, directly compressing the right optic nerve (black arrow, 44D). 44E: fundus imaging before OP. 44F is the pre-OP SD-OCT peripapillary RNFL thickness. Humphrey visual field tests were performed pre-operatively (44G) and 4 months post-OP (44H). The percent maximal pupil constriction (PPC in%) in response to dim blue light was recorded before OP (44I) and 4 months after OP (44K). Red indicates PPC values 2 Standard Errors (SE) below the mean of the controls. Green indicates PPC values within 2 SE of the mean of the controls; 44J, 44L are the percent pupil recovery (PPR in%) recorded before (44J) and 4 months after (44L) OP. Red indicates a PPR value 2 SEs higher than the mean of the controls (faster pupil recovery). Green indicates PPR values within 2 SE of the mean of the controls;

fig. 45A-45Q show patients diagnosed with right temporal lobe glioblastoma (# 10, group II). Functional MRI T1+ GAD scans showed brain edema around the right temporal augmentation lesion (red arrow), involving right optic nerve radiation (bright white, 45A-45C). MRI scans were performed post OP (45D) and 3 months post-surgery (45E-45F). 45G is fundus imaging before OP. 45H is the RNFL thickness around the papilla of the SD-OCT before OP. The Humphrey SITA standard visual field test was performed before OP (45I), 4 days after OP (45J) and 3 months after OP (45K). The maximum percent pupil constriction (PPC in%) in response to dim blue light was recorded before OP (45L), 4 days after OP (45N) and 3 months after OP (45P). Red represents a value 2 Standard Error (SE) below the mean of the controls, green represents a value within 2 SE of the mean of the controls; percent pupil recovery (PPR in%) was recorded before OP (45M), 4 days after OP (45O) and 3 months after OP (45Q). Red represents a value that is 2 Standard Errors (SEs) higher (faster recovery) than the mean of the controls, green represents a value within 2 SEs of the mean of the controls;

fig. 46A-46L show one patient (# 18, group III), who was diagnosed with a convex meningioma located on the frontal right side. Functional MRI T1+ GAD scans showed large right frontal lobe meningiomas (red arrows in 48A and 48B) and cerebral edema in the frontal and right temporal lobes. MRI scans 4 weeks after surgery confirmed the improvement of cerebral edema and tumor mass effects (48C, 48D). 48E is fundus imaging before OP. 48F is the SD-OCT peripapillary RNFL thickness before OP. The humphey SITA standard visual field test was performed before OP (48G) and 7 days after OP (48H). The maximum percent pupil constriction (PPC in%) in response to dim blue light was recorded before OP (48I) and 7 days after OP (48K). Red represents a value 2 Standard Error (SE) lower than the mean of the controls, green represents a value within 2 SE of the mean of the controls; percent pupil recovery (PPR in%) was recorded before OP (48J) and 7 days after OP (48L). Red represents a value that is 2 Standard Errors (SEs) higher (faster recovery) than the mean of the controls, green represents a value within 2 SEs of the mean of the controls;

figure 47 shows rod-mediated PPC results (in response to blue light stimulation) in representative pseudoneoplastic brain (PTC) patient # 1; and

fig. 48 shows cone-mediated PPC results in PTC patient #1 in response to red light stimulation.

Detailed Description

The principles, uses and implementations taught herein may be better understood with reference to the accompanying description and drawings. Those skilled in the art will be able to implement the teachings herein without undue effort or experimentation, upon perusal of the description and drawings presented herein. In the drawings, like reference numerals refer to like parts throughout.

In the following description, various aspects of the present invention will be described. For purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced without the specific details presented herein. In addition, well-known features may be omitted or simplified in order not to obscure the present invention.

As used herein, the terms "brain-related condition" and "CNS-related condition" may be used interchangeably. These terms relate to conditions that affect the function and/or structure of the brain and/or associated tissues. According to some embodiments, brain-related conditions may affect various neurological functions, such as cognitive, functional, or behavioral functions. In some embodiments, the brain-related condition may be an acute disease or a chronic disease. In some embodiments, the brain-related condition is a neurodegenerative disease. In some embodiments, the neurodegenerative disease may be selected from, but is not limited to: alzheimer's Disease (AD), parkinson's Disease (PD), multiple Sclerosis (MS), cognitive impairment of fragile X carriers, and the like. In some embodiments, the brain-related condition is a nerve-related condition, such as an optic nerve-related condition, including, for example, optic Neuritis (ON). In some embodiments, the condition may be a tumor in various regions of the brain. In some embodiments, the tumor does not affect the visual system. In some embodiments, the tumor may affect the visual system. In some embodiments, the brain-related condition is intracranial pressure (ICP), which can be caused by various conditions (e.g., brain tumors, edema, contusions, hematomas, abscesses, and the like). In some embodiments, the brain-related condition is an ischemic condition. In some embodiments, the brain-related condition is Traumatic Brain Injury (TBI). In some embodiments, the brain-related condition may be selected from a tumor, a lesion, ICP, a neurodegenerative disease, an injury, an ischemic condition, a neurologically-related condition, optic neuritis, inflammation, infection, and the like, or any combination thereof.

As used herein, the term "subject" is interchangeable with the term "patient" or "individual". According to some embodiments, the subject in need thereof is a subject having or suspected of having (being afflicted with/suffering from) a brain and Central Nervous System (CNS) -associated condition. According to some embodiments, the subject is symptomatic. According to some embodiments, the subject is asymptomatic. In some embodiments, a "control" subject is a subject that does not have, or is not diagnosed with, a corresponding condition.

As used herein, the term "determining a brain-related condition" refers to identifying/detecting/diagnosing a subject as having a brain-related condition, and/or a type of brain-related condition. In some embodiments, determining the brain-related condition further comprises determining the probability/likelihood/risk of the subject to suffer from the brain-related condition.

As used herein, the term "assessing a brain-related condition" relates to assessing or classifying the brain-related condition of a subject. As used herein, the terms "monitoring a brain-related condition" and "monitoring the progress of a brain-related condition" may be used interchangeably. These terms relate to ranking or comparing the severity (progression) of a brain-related condition in a subject (e.g., over a period of time, or between at least two time points). In some embodiments, these terms relate to assessing, grading, or comparing the severity of a brain-related condition in response to a treatment or treatment regimen. In some embodiments, these terms also relate to determining the efficacy of a treatment of a selected treatment on the state or severity of a brain-related condition. In some embodiments, the severity can diminish over time (i.e., an improvement in the brain-related condition or its outcome). In some embodiments, the severity may increase over time (i.e., deterioration of the brain-related condition or its outcome).

As used herein, the term "treating" includes, but is not limited to, any one or more of the following: eliminating, ameliorating, suppressing, attenuating, blocking, inhibiting, alleviating, delaying, stopping, alleviating, or preventing the brain-related condition and/or symptoms associated therewith. Each possibility is a separate embodiment.

As used herein, the term "PLR" refers to the "pupillary light response" or "pupillary light reflex" of a subject. These terms relate to the change in various parameters associated with the pupil's response when stimulated (irradiated) by light. In some embodiments, the light is colored light. In some embodiments, the stimulus may include various variables, including, for example and without limitation: different wavelengths (e.g., red, blue), different beam sizes, different intensities, different irradiation durations, different time periods between irradiations, stimulated field of view regions (areas or points), etc., or any combination thereof. In some embodiments, the parameter may be measured, detected, calculated, and/or determined directly or indirectly. In some embodiments, changes in parameter values may be detected, measured, calculated, and/or determined. In some embodiments, the change in pupillary response is compared to the subject's baseline response under similar or different conditions. In some embodiments, changes in the pupillary response parameter (or value thereof) may be compared between different stimuli and/or compared to a baseline response. In some embodiments, a change in a pupillary response parameter (or value thereof) can be compared to a corresponding response of a control subject under similar conditions.

As described herein, different intensities of blue light may be used for stimulation, high intensity blue light, which is also referred to as intense blue light or bright blue light; and low intensity blue light, which is also referred to as dim blue light.

Referring to fig. 1A, a schematic diagram of a system for monitoring the progression of, determining and/or assessing a brain-related condition of a subject, according to some embodiments of the present invention, is shown. According to some embodiments, the system may comprise a pupillometer device 100 for monitoring the progress of, determining and/or assessing a brain related condition of a subject. According to some embodiments, pupillometer device 100 may include a plurality of color beam emitters 106, the color beam emitters 106 being configured to produce red and/or blue light stimuli at predetermined locations of the subject's field of view. According to some embodiments, the system and/or pupillometer device 100 may include at least one camera 108 configured to detect pupillary responses. According to some embodiments, the system may include at least one hardware processor 110. According to some embodiments, the at least one hardware processor 110 may be in communication with the at least one camera 108 and/or color beam emitter 106. According to some embodiments, the system may include a non-transitory computer-readable storage medium having program code stored thereon in communication with the processor 110. According to some embodiments, the program code may be configured to be executable by at least one hardware processor 110.

According to some embodiments, the pupillometer device 100 may include a test chamber 102, the test chamber 102 being configured to support the head of the subject during the test and to provide light stimulation in the field of view of the subject. According to some embodiments, the test bay 102 may include a Ganzfeld dome apparatus. According to some embodiments, the test bay 102 may be portable. According to some embodiments, pupillometer device 100 may be multifocal. According to some embodiments, the test bay 102 may include an eye-fixation device 104 configured to support a subject's head. According to some embodiments, the test chamber 102 and/or the eye fixation device 104 may include a chin rest. According to some embodiments, the eye fixation devices 104 of the test compartment 102 may help position the eyes toward the color beam emitters 106. In some embodiments, the eye fixation device 104 can include additional or fewer components for positioning the subject's eye. According to some embodiments, the eye fixation device 104 may be configured such that the subject's non-test eye is occluded. According to some embodiments, the test capsule 102 and/or the pupillometer device 100 may include a frame that includes a wall 112. According to some embodiments, the wall 112 may be positioned across the ocular fixation device 104. According to some embodiments, the wall 112 may be positioned about 330mm from the eye fixation device 104. According to some embodiments, a plurality of color beam emitters 106 may be arranged around the inner surface of the wall 112. According to some embodiments, the distance between the plurality of color beam emitters 106 and the eye fixation device 104 may be about 330mm.

Referring to fig. 1B, a front view of a pupillometer device according to some embodiments of the invention is shown. According to some embodiments, the plurality of color beam emitters 106 may be positioned in a grid pattern at a plurality of locations arranged around the wall 112 and/or the interior surface of the test bay 102. According to some embodiments, the color beam emitters 106 may be positioned and driven to produce light stimulation in substantially the entire visual field of the subject's eye located at the eye fixation device 104. According to some embodiments, color beam emitters 106 may include about 1 to about 1000 color beam emitters. According to some embodiments, color beam emitters 106 may include about 7 to about 76 color beam emitters. According to some embodiments, and as described in more detail elsewhere herein, the color beam emitters 106 may be in communication with a processor 110.

Reference is made to fig. 1C, which illustrates an exemplary distribution diagram of the locations of light stimuli for a pupillometer device according to some embodiments of the present invention. According to some embodiments, the color beam emitters 106 may be positioned in a grid pattern, such as that shown in FIG. 1C. According to some embodiments, each color beam emitter 106 may be positioned at a test point location (e.g., test point locations 1 through 76, as shown in fig. 1C). According to some embodiments, each color beam emitter 106 may be positioned at a test point location of the field of view of the subject's eye. According to some embodiments, the color beam emitters 106 may be positioned relative to the eye fixation device 104 such that light emitted by the color beam emitters 106 is emitted at one or more regions of the subject's field of view. According to some embodiments, the region of the field of view may include a central field of view ranging between 0-10 degrees. According to some embodiments, the region of the field of view may include a peripheral field of view ranging between 11-160 degrees.

According to some embodiments, the color beam emitter 106 may include one or more LED lights 116. According to some embodiments, each color beam emitter 106 may be configured to emit blue and/or red light. According to some embodiments, the red stimulus emission wavelength may range between 550nm to about 700nm, or from about 620nm to about 630nm, for example. According to some embodiments, the red stimulus emission wavelength may be about 624nm. According to some embodiments, the blue stimulus emission wavelength may range between about 410nm to about 520nm or from about 480nm to about 490nm. According to some embodiments, the blue stimulus emission wavelength may be about 485nm. According to some embodiments, the color beam emitters 106 may comprise ultraviolet LED lights. According to some embodiments, the color beam emitters 106 may comprise infrared light LED lights. According to some embodiments, the processor 110 may control the wavelength of light emitted by one or more of the plurality of color beam emitters 106. According to some embodiments, the processor 110 may control the wavelength of light emitted by each of the plurality of color beam emitters 106.

According to some embodiments, the processor 110 may control the individual color beam emitters 106. According to some embodiments, and as described in more detail elsewhere herein, the processor 110 may be configured to supply power to a selected subset of the color beam emitters 106. According to some embodiments, the subset of color beam emitters 106 may include 1 to 76 color beam emitters 106. According to some embodiments, the processor 110 may be configured to control the wavelength, duration, and/or intensity of the emitted light of each individual color beam emitter 106.

According to some embodiments, the processor 110 may control the intensity of light emitted by one or more of the plurality of color beam emitters 106. According to some embodiments, the processor 110 may control the intensity of light emitted by each of the plurality of color beam emitters 106. According to some embodiments, the intensity of light emitted by color beam emitter 106 can be between 50 and 7000cd/m ² Within the range of (a). According to some embodiments, the color stimulus may comprise high intensity and/or low intensity light. According to some embodiments, the high intensity may range from 5500 to 6500cd/m ² In between. According to some embodiments, the low intensity may range between 100 and 200cd/m ² In between. According to some embodiments, the red stimulus may include high intensity and/or low intensity red light. According to some embodiments, the red stimulus may be in a range between high intensity red light and/or low intensity red light. According to some embodiments, the blue stimulus may include high intensity and/or low intensity blue light. According to some embodiments, the blue stimulus may vary between high intensity red light and/or low intensity red light.

According to some embodiments, the processor 110 may control the duration of light emitted by one or more of the plurality of color beam emitters 106. According to some embodiments, the processor 110 may control the duration of light emitted by each of the plurality of color beam emitters 106. According to some embodiments, the plurality of color beam emitters 106, individual color beam emitters 106, and/or a subset of the plurality of color beam emitters 106 may be illuminated for a duration in the range of 0.1 to 120 seconds. According to some embodiments, the plurality of color beam emitters 106, individual color beam emitters 106, and/or a subset of the plurality of color beam emitters 106 may be illuminated for a duration ranging from 1 to 8 seconds. According to some embodiments, the plurality of color beam emitters 106, individual color beam emitters 106, and/or a subset of the plurality of color beam emitters 106 may be illuminated for a duration in the range of 0.5 to 3 seconds.

According to some embodiments, the pupillometer device may include a backlight in communication with the processor 110. According to some implementationsFor example, the processor 110 may control the powering of backlights. According to some embodiments, the processor 110 may control the duration of backlight illumination. According to some embodiments, the processor 110 may control the wavelength of the backlight. According to some embodiments, the wavelength of the backlight may be in the range of about 410nm to about 520nm or from about 480nm to about 490nm. According to some embodiments, the processor 110 may control the intensity of the backlight. According to some embodiments, the intensity of the backlight may be between 0 and 5cd/m ² Within the range of (a). According to some embodiments, the intensity of the backlight may be between 0 and 0.5cd/m ² Within the range of (a). According to some embodiments, the intensity of the backlight may be about 0.4cd/m ² 。

According to some embodiments, the pupillometer device may include a fixation light (fixation light) 114, the fixation light 114 configured to maintain the subject's gaze during the administration of the color stimuli. According to some embodiments, the fixation light 114 may be configured to facilitate exposure of light-sensitive eye structures of the eye to color stimuli of the color beam emitter 106. According to some embodiments, the fixation light 114 may include a light such as an LED light. According to some embodiments, the fixation lamp may comprise a white lamp. According to some embodiments, the fixation light 114 may include indicia, such as points or objects configured to contrast with the interior wall 112 of the test bay 102. According to some embodiments, the fixation lamp 114 may be located in the center of the field defined by the plurality of color beam emitters 106. According to some embodiments, the fixation light 114 may be located opposite the eye fixation device 104. According to some embodiments, fixation light 114 may be located near camera 108. According to some embodiments, the fixation light 114 may be located near the camera 108 such that the pupil of a subject gazing toward the fixation light 114 may appear to be gazed at the camera 108. According to some embodiments, the camera 108 is located below the fixation light 114.

According to some embodiments, camera 108 may be configured to record one or more images and/or videos of the pupil of the subject. According to some embodiments, camera 108 may comprise a video camera. According to some embodiments, camera 108 may comprise a high-resolution video camera. According to some embodiments, camera 108 may include a high-resolution infrared camera. According to some embodiments, camera 108 may include multiple cameras and/or recording devices and/or sensing devices. According to some embodiments, camera 108 may be any device having an array of sensing devices (e.g., pixels) capable of detecting radiation in the ultraviolet wavelength band, the visible wavelength band, or the infrared wavelength band. According to some embodiments, camera 108 may be an infrared camera. According to some embodiments, camera 108 may have any resolution. According to some embodiments, the camera 108 may be an omnidirectional camera or a panoramic camera. According to some embodiments, camera 108 may include one or more optical components, such as mirrors, fish-eye lenses, or any other type of lens. According to some embodiments, processor 110 may be configured to control and/or operate camera 108.

According to some embodiments, the processor 110 may be configured to execute instructions associated with methods for monitoring the progression of, determining, and/or assessing a brain-related condition of a subject based on a Pupillary Light Response (PLR) to a colored light stimulus, such as a colored light beam emitter 106.

Referring to fig. 2, a flow diagram of functional steps in a non-invasive method for monitoring the progression of, determining and/or assessing a brain-related condition of a subject according to some embodiments of the present invention is shown. According to some embodiments, the method 200 may be based on a Pupillary Light Response (PLR) to colored light stimuli. According to some embodiments, at step 202, method 200 may include determining a baseline pupil size of an eye of the subject. According to some embodiments, at step 204, method 200 may include applying blue and/or red light stimuli to one or more regions of the visual field of the eye, the light stimuli configured to evoke a response in the pupil. According to some embodiments, at step 206, method 200 may include determining values of one or more parameters associated with the induced changes in pupil size in response to the light stimulus. According to some embodiments, at step 208, method 200 may include classifying the PLR based on one or more parameter values, where classifying may include monitoring the progress of, determining, and/or evaluating the brain-related condition.

According to some embodiments, the brain-related condition is selected from: brain tumor, optic neuritis, neurodegenerative disease, traumatic brain injury, stroke, intracranial lesion, intracranial pressure, pseudocerebroma. According to some embodiments, the brain-related condition may comprise an existing brain condition and/or a level of risk of the brain condition. According to some embodiments, the risk level of the brain condition may be genetic. According to some embodiments, the neurodegenerative disease is selected from: alzheimer's Disease (AD), multiple Sclerosis (MS), parkinson's Disease (PD), cognitive decline associated with Fragile X in Fragile X carriers, and the like, or combinations thereof.

According to some embodiments, method 200 may include positioning the subject and/or fixing the position of the subject relative to the pupillometer device, as shown in fig. 1A. According to some embodiments, the method 200 may include positioning the subject using the ocular fixation device 104. According to some embodiments, the method 200 may include positioning the subject's eye at the eye fixation device 104 such that the subject's non-test eye is occluded. According to some embodiments, the method may include blocking the subject's non-test eyes, for example, by using eye shields. According to some embodiments, the method may include powering a backlight of the pupillometer device. According to some embodiments, the method may comprise controlling the wavelength and/or intensity of a backlight of the pupillometry device. According to some embodiments, the method may include powering a fixation light 114 of the pupillometer device. According to some embodiments, the method may include controlling the wavelength and/or intensity of the fixation light 114 of the pupillometer device.

According to some embodiments, method 200 includes operating camera 108. According to some embodiments, the method 200 includes recording the pupil of the subject. According to some embodiments, method 200 includes recording the pupil of the subject over a time ranging from 1 to 30 seconds. According to some embodiments, method 200 includes recording the pupil of the subject over a time ranging from 1 to 20 seconds. According to some embodiments, the method includes storing data, such as one or more images and/or one or more videos, recorded using the camera 108. According to some embodiments, and as described in more detail elsewhere herein, the method may include extracting one or more parameters from data received from the camera 108.

According to some embodiments, at step 202, method 200 may include determining a baseline pupil size of an eye of the subject. According to some embodiments, a baseline pupil size of the subject's eye is determined based at least in part on data received from camera 108. According to some embodiments, a baseline pupil size of the subject's eye is determined based at least in part on the size of the subject's pupil recorded prior to the subject's pupil responding to the light stimulus produced by the color beam emitter 106. According to some embodiments, data that may be recorded after application (and/or powering) of blue and/or red light stimuli and before the pupil of the subject begins to respond to the light stimuli may be used to determine a baseline pupil size of the eye of the subject. According to some embodiments, the method may include determining a baseline pupil size of the subject's eye for each light stimulus produced by the color beam emitter 106. For example, for a series of two or more light stimuli separated by a discontinuity (break), a first baseline pupil size may be associated with a first stimulus produced by a color beam emitter, while a second baseline pupil size may be associated with a second stimulus produced by a color beam emitter.

According to some embodiments, at step 204, method 200 may include applying blue and/or red light stimuli to one or more regions of the visual field of the eye, the light stimuli configured to evoke a response in the pupil. According to some embodiments, method 200 may include initiating recording and/or operation of camera 108 upon application of blue and/or red light stimuli, thereby determining a baseline for the size of the pupil using the time until the pupil of the subject begins to respond to the light stimuli. According to some embodiments, the application of the blue and/or red stimuli may vary between having a high intensity and having a low intensity. According to some embodiments, the method may include applying a blue light stimulus configured to stimulate rod cells of the eye of the subject and/or ipRGC of the eye of the subject. According to some embodiments, the method may include applying a high intensity blue light stimulus, thereby stimulating the ipRGC of the eye of the subject. According to some embodiments, the method may include applying a low intensity blue light stimulus, thereby stimulating rod cells of the eye of the subject. According to some embodiments, the method may include applying a red light stimulus configured to stimulate cone cells of an eye of the subject.

According to some embodiments, applying the blue and/or red light stimulus may include illuminating a subset of the color beam emitters. According to some embodiments, a plurality of colored light stimuli may be applied. According to some embodiments, the subset of color beam emitters may include 4 to 76 color light stimuli. According to some embodiments, a subset of color beam emitters may include 1 to 228 individual small/focal point (e.g., in the range of about 0.1-16 degrees) light stimuli at different locations of the field of view. According to some embodiments, the subset of color beam emitters may include a designated portion of the color beam emitters that are associated with a particular brain condition.

According to some embodiments, the subset of color beam emitters may comprise a designated portion of the color beam emitters that are associated with a particular brain condition, wherein different subsets of color beam emitters may be located at different positions in the field of view of the subject. According to some embodiments, the number of color beam emitters of a subset may vary depending on the particular brain condition being tested. According to some embodiments, the position of one or more color beam emitters (in the field of view of the subject) of a subset may vary depending on the particular brain condition being tested. According to some embodiments, the wavelength of one or more of the color beam emitters of the subset may vary depending on the particular brain condition being tested. According to some embodiments, the duration of the color beam emitters of the subset that are applied may vary depending on the particular brain condition being tested. According to some embodiments, the intensity of the applied color beam emitters of a subset may vary depending on the particular brain condition being tested. According to some embodiments, the ratio between the different wavelengths of the color beam emitters in the subset, for example the ratio between red and blue stimuli, may vary depending on the particular brain condition being tested.

According to some embodiments, method 200 may include controlling the emission wavelength, intensity, and duration of individual light stimuli or subsets of light stimuli. According to some embodiments, applying the blue and/or red light stimuli to one or more regions of the visual field of the eye comprises selecting a subset of the light stimuli based on at least one of a location of the light stimuli relative to the visual field and optionally a type of brain-related condition. According to some embodiments, the blue and/or red light stimulus may be applied such that prior to testing, there is no a priori knowledge of the subject's brain-related condition (i.e., the subject is not suspected of having a brain-related condition). In some embodiments, the blue and/or red light stimulus may be applied such that prior to the test, there is a priori knowledge of, or at least suspicion of, the subject's brain-related condition. According to some embodiments, applying blue and/or red light stimuli to one or more regions of the visual field of the eye comprises one or more of: selecting a wavelength of each individual light of the light stimulus, selecting an intensity of each individual light of the light stimulus, selecting a ratio of the blue light stimulus to the red light stimulus, selecting a duration of illumination of each individual light of the light stimulus, or any combination thereof.

According to some embodiments, the illumination duration of each individual light (or in other words, each individual color beam emitter) and/or subset of color beam emitters of the light stimulus may be in a range between 0.1 and 10 seconds. According to some embodiments, the duration of the illumination may be in a range between 1 second and 8 seconds. According to some embodiments, the individual color beam emitters of a subset may have different illumination durations. According to some embodiments, the individual color beam emitters of a subset may have the same illumination duration.

According to some embodiments, the method may comprise applying blue and/or red light stimuli at a ratio of red to blue color stimuli of 1 to 1. According to some embodiments, the ratio of red to blue color stimuli can be 1, 2, 5,1, 6, 1, 50, 1, 60, 1. According to some embodiments, the method may comprise applying blue and/or red light stimuli at a ratio of blue to red color stimuli of 1 to 1. According to some embodiments, the ratio of blue to red color stimuli may be 1, 2,6, 1, 50, 1.

According to some embodiments, the method may comprise applying a blue and/or red light stimulus at the central and/or peripheral visual field. According to some embodiments, the central field of view may range between 0 and 10 degrees. According to some embodiments, the central field of view may range between 0 and 15 degrees. According to some embodiments, the peripheral field of view may be greater than 10 degrees. According to some embodiments, the peripheral field of view may be greater than 15 degrees. According to some embodiments, the peripheral field of view may range between 10 degrees and 60 degrees. According to some embodiments, the method may include applying blue and/or red light stimuli at the central and/or peripheral visual fields, wherein the position of the light stimuli relative to the visual fields may be based at least in part on the type of brain condition.

According to some embodiments, the method may comprise applying blue and/or red light stimuli to one or more regions of the visual field of the eye in a series of two or more intervals. According to some embodiments, applying blue light and/or red light stimuli to one or more regions of the visual field of the eye comprises applying blue light and/or red light stimuli at two or more intervals. According to some embodiments, each interval in which blue and/or red light stimuli are applied may include the same, different, and/or similar subsets of colored light stimuli. According to some embodiments, each interval of applying blue and/or red light stimuli may include the same and/or different stimulus durations, ratios between different wavelengths, wavelengths of light stimuli, intensities of light stimuli, and locations of light stimuli in the visual field. According to some embodiments, the number of intervals and/or the subset of color stimuli in each subset may vary based at least in part on the type of brain condition. According to some embodiments, the intervals may be 2 seconds to 120 seconds apart. According to some embodiments, the intervals may be between 2 seconds and 8 minutes apart. According to some embodiments, the time between each successive interval may vary. According to some embodiments, between two illumination intervals of the color beam emitter, the method may comprise illuminating any one or more of the fixation light and/or the backlight. According to some embodiments, between two illumination intervals of the color beam emitter, the method may comprise determining a baseline size of the pupil of the subject. According to some embodiments, each interval comprises a different subset of the light stimuli, a different wavelength of the light stimuli, and/or a different intensity of the light stimuli.

According to some embodiments, the method 200 may include providing an initial predetermined light stimulus upon initial illumination. According to some embodiments, the method may include determining a baseline size of the pupil prior to the initial illumination. According to some embodiments, the initial illumination may be configured to determine a likelihood that the subject has a brain-related condition. According to some embodiments, the initial illumination may include at least one or any of a predetermined duration of the color light stimulus activated, a location of a field of view of the color light stimulus, a wavelength of the color light stimulus, and/or a subset of color beam emitters. According to some embodiments, providing the initial predetermined light stimulus upon initial illumination may include illuminating at least one color beam emitter. According to some embodiments, providing the initial predetermined light stimulus upon initial illumination may include illuminating a designated subset of color beam emitters. According to some embodiments, the method may include applying blue light and/or red light stimuli to one or more regions of the visual field of the eye, and applying blue light and/or red light stimuli to one or more regions of the visual field of the eye is based at least in part on the likelihood determined in the initial illumination. According to some embodiments, the method may comprise selecting any one or more of a duration of the stimulus, a ratio between different wavelengths, a wavelength of the light stimulus, an intensity of the light stimulus, a position of the light stimulus in the visual field, a number of illumination intervals, and a time between each illumination based at least in part on the determined likelihood in the initial illumination.

According to some embodiments, at step 206, the method 200 may include determining values of one or more parameters associated with the changes in pupil size induced in response to the light stimulus. According to some embodiments, method 200 may include determining values of one or more parameters related to the changes in pupil size induced in response to the light stimulus based at least in part on data received from camera 108. According to some embodiments, the one or more parameters may be selected from: pupil contraction percentage (PPC), pupil Response Latency (PRL), maximum Contraction Velocity (MCV), MCV Latency (LMCV), pupil relaxation percentage (PPR), maximum Relaxation Velocity (MRV), MRV Latency (LMRV), maximum systolic acceleration (MCA), MCA Latency (LMCA), maximum Relaxed Acceleration (MRA), MRA Latency (LMRA), maximum Relaxed Deceleration (MRD), maximum relaxed deceleration Latency (LMRD), area of Curve (AC), maximum Pupil contraction Latency (LMP), maximum systolic deceleration (MCD), MCD Latency (LMCD), maximum Pupil size (Max _ PS), minimum Pupil size (Min _ PS, in some cases only for blue light), and any combination thereof. Each possibility is a separate embodiment. According to some embodiments, the one or more parameters may include a number of test targets having aberrant PPC in response to blue and/or red light stimuli.

According to some embodiments, the method may comprise a multi-dimensional analysis of a plurality of pupillary response parameters for each location. According to some embodiments, the method may include a multi-dimensional analysis of a plurality of pupil response parameters for each position of the illuminated color beam emitter. According to some embodiments, the method may comprise modeling the size of the pupil of the subject as a function of time for each position. According to some embodiments, each illumination interval of the color beam emitter subset may generate at least 20, at least 30, or at least 40 pupillary response parameters for each location. According to some embodiments, each illumination interval of the color beam emitter subset may generate 35 pupillary response parameters for each location.

According to some embodiments, a non-invasive method is provided for determining, assessing and/or monitoring the progression of a brain-related condition in a subject based on Pupillary Light Response (PLR) to a plurality of color small/focal point (0.1-16 degrees) light stimuli.

According to some embodiments, there is provided a non-invasive method of monitoring the progression, determining and/or assessing a brain related condition of a subject based on Pupillary Light Response (PLR) to colored light stimuli, the method comprising the steps of:

(a) Determining a baseline pupil size of an eye of a subject;

(b) Applying blue and/or red light stimuli to one or more regions of the visual field of the eye, the light stimuli configured to evoke a response in the pupil;

(c) Obtaining values for one or more parameters related to the induced change in pupil size in response to the light stimulus;

(d) Normalizing the values of the one or more parameters based on the baseline pupil size; and

(e) Classifying the PLR based on one or more parameter values;

wherein the classification results in monitoring of the progression of, determining and/or assessing a brain-related condition.

determining a baseline pupil size of an eye of a subject;

determining values of one or more parameters related to the induced change in pupil size in response to the light stimulus;

classifying the PLR based on one or more parameter values, wherein the classification allows monitoring the progression of, determining and/or assessing the brain-related condition.

Reference is made to fig. 3A, 3B, 3C, and 3D, which illustrate graphs of exemplary parameter and value calculations according to some embodiments of the present invention. According to some embodiments, the method may include modeling the size of the pupil of the subject as a function of time, such as shown in fig. 3A, 3B, 3C, and 3D. According to some embodiments, the method may include applying a curve fitting algorithm to the model of the subject's pupil size as a function of time. According to some embodiments, the method comprises applying a curve fitting algorithm to the model of the subject's pupil size as a function of time for each individual illumination interval. According to some embodiments, curve fitting may include calculating a trend line curve, e.g., a best fit model, a polynomial equation, etc., for a model of the subject's pupil size as a function of time. According to some embodiments, curve fitting may include smoothing a model of the subject's pupil size as a function of time. According to some embodiments, smoothing the curve may include removing tremors and/or small oscillatory movements of the subject's pupil. For example, according to some embodiments, applying a curve fitting algorithm may include using an Extreme Optimization Numerical Libraries (Extreme Optimization kernels) and/or curve fitting functions. According to some embodiments, the method may include normalizing pupil size as a function of time and/or curve fitting a model of pupil size as a function of time. According to some embodiments, the normalization may be based at least in part on the determined baseline size of the subject's pupil.

According to some embodiments, the method may comprise extracting values of one or more parameters from a curve fit of a model of the subject's pupil size as a function of time. According to some embodiments, the method may include extracting values for PPC, PPR, AC, PRL, LMP and/or MCV from a curve fit of a model of the subject's pupil size as a function of time. According to some embodiments, the values of the one or more parameters may be extracted from a curve fit of a model of the subject's pupil size as a function of time. According to some embodiments, and as shown in fig. 3B, values for one or more of PPC, PPR, AC, PRL, and/or LMP may be extracted from a curve fit of a model of the subject's pupil size as a function of time. According to some embodiments, and as shown in fig. 3C, values for one or more of PPC, MCV, and/or PPR may be extracted from a curve fit of a model of the subject's pupil size as a function of time. According to some embodiments, the value of AC may comprise an area captured between a baseline size of the pupil and a fitted curve of the pupil size model as a function of time. According to some embodiments, the value of the MCV may include a slope of a curve of the model between a baseline size of the pupil and a minimum point of the model. According to some embodiments, the value of PPC may comprise a size difference between a baseline size of the pupil and a pupil size at a minimum point of the model.

According to some embodiments, the pupil diameter may be automatically recorded at a suitable frequency (e.g., 30 Hz). For each stimulus, the percent pupil constriction (PPC, in%) can be automatically determined by program instructions after normalization based on the initial pupil size (baseline pupil size) measured at the beginning of each stimulus using the following formula:

from this, the maximum contraction speed (MCV in pixels/sec) and MCV latency (LMCV in seconds) can be determined.

The percent pupil recovery (% PPR in%) for bright blue light stimuli can be determined at 3.7 seconds after the start of light based on the initial pupil size measured at the start of each stimulus using the following formula:

the normalized minimum pupil size (NMPS, in%) may be determined based on the initial pupil size measured at the beginning of each stimulus, using the following formula during the maximum pupil constriction phase:

reference is made to fig. 4A and 4B, which illustrate graphs of velocity of pupil size as a function of time, in accordance with some embodiments of the present invention. According to some embodiments, the method may comprise deriving a model of the velocity of the pupil size as a function of time. According to some embodiments, the method may comprise deriving the model of the velocity of the pupil size as a function of time based at least in part on a curve fit of the model of the size of the pupil size as a function of time and/or the model of the size of the pupil size as a function of time. According to some embodiments, and as shown in fig. 4A and 4B, values for one or more of MCV, MRV, LMRV, and/or LMCV may be extracted from a model of the velocity of pupil size as a function of time.

Reference is made to fig. 5A and 5B, which show graphs of acceleration of pupil size as a function of time, in accordance with some embodiments of the present invention. According to some embodiments, the method may include deriving a model of the acceleration of the pupil size as a function of time. According to some embodiments, the method may comprise deriving the model of the acceleration of the pupil size as a function of time based at least in part on a curve fit of the model of the size of the pupil size as a function of time and/or the model of the size of the pupil size as a function of time. According to some embodiments, the method may include deriving a model of acceleration of the pupil size as a function of time based at least in part on the model of velocity of the pupil size as a function of time. According to some embodiments, and as shown in fig. 5A and 5B, values for one or more of LMCA, MCA, LMRD, MRD, LMRA, and/or MRA may be extracted from a model of acceleration of pupil size as a function of time.

Referring to fig. 6, a graph illustrating sudden changes in pupil size as a function of time is shown, according to some embodiments of the present invention.

According to some embodiments, the method may include determining and/or calculating values of one or more parameters using a size model as a function of time, a curve fit of the size model as a function of time, a velocity of the pupil size as a function of time, and an acceleration of the pupil size as a function of time. According to some embodiments, the method may comprise determining and/or calculating values for one or more parameters for each position of a coloured beam emitter in the field of view.

According to some embodiments, the method may comprise applying one or more values of one or more parameters to one or more machine learning algorithms, for example a classification algorithm. According to some embodiments, the one or more machine learning algorithms may be configured to classify the subject as having a brain-related condition or not having a brain-related condition.

According to some embodiments, the algorithm to be used in the method or system of the present disclosure is a machine learning algorithm. Examples of machine learning algorithms include AdaBoost, discriminant analysis, K-nearest neighbors classifier (KNN), support Vector Machine (SVM) classifier, logistic regression classifier, neural network classifier, gaussian Mixture Model (GMM), nearest centroid classifier, linear regression classifier, decision tree classifier, and random forest classifier, classifier integrations (ensemble of classifiers), or any combination thereof.

According to some embodiments, at step 208, method 200 may include classifying the PLR based on one or more parameter values. According to some embodiments, the classification allows monitoring the progression of, determining and/or assessing brain-related conditions. According to some embodiments, the method may include classifying the brain-related condition as a type and/or severity level and/or progression of the condition based at least in part on the one or more parameters and the at least one calculated value using one or more machine learning algorithms.

According to some embodiments, the method may include obtaining one or more models, such as a size model as a function of time, a curve fit of the size model as a function of time, a model of the velocity of the pupil size as a function of time, and a model of the acceleration of the pupil size as a function of time. According to some embodiments, each of the one or more models is associated with a response to a stimulus using red light and/or blue light. According to some embodiments, the method may comprise determining values for one or more parameters of one or more models. According to some embodiments, the number of values of the determined one or more parameters may correspond to the number of color beam emitters present in the illuminated subset. For example, in some embodiments, for color beam emitters for which the subset includes 54 virtual field positions, the method may include determining 54 values for the specified parameter. For example, for a color beam emitter whose subset includes 54 virtual field locations, the method may include determining 54 CMP values measured at 54 different points in the subject's retina.

According to some embodiments, the method may comprise applying the determined values of the one or more parameters to one or more machine learning algorithms configured to identify or assess the progress of the brain-related condition. According to some embodiments, the method may comprise applying the determined values of the one or more parameters to one or more machine learning algorithms configured to identify a risk of the subject with respect to a brain-related condition prior to onset of the disease. According to some embodiments, the Confidence Interval (CI) for the identification or assessment of the progression of the brain-related condition is at least 80%. According to some embodiments, the Confidence Interval (CI) for the identification or assessment of the progression of the brain-related condition is at least 90%. According to some embodiments, the Confidence Interval (CI) for the identification or assessment of the progression of the brain-related condition is at least 95%.

According to some embodiments, the method may comprise determining a pupil diameter for each light stimulus. According to some embodiments, the processor 110 is configured to calculate values of one or more parameters for each light stimulus responsive to each location. According to some embodiments, the method may comprise calculating values of one or more parameters for the red and blue stimuli separately and/or independently. According to some embodiments, and as described in more detail elsewhere herein, the method may include normalizing the values of the one or more parameters. According to some embodiments, the method may include normalizing the values of the one or more parameters based at least in part on a baseline size of the pupil. According to some embodiments, the method may comprise applying the calculated values of the one or more parameters to a machine learning algorithm.

Referring to fig. 7, a flow diagram illustrating functional steps in a non-invasive method of monitoring the progress of, determining and/or assessing a brain-related condition of a subject using a machine learning algorithm is shown, according to some embodiments of the present invention. According to some embodiments, the machine learning algorithm is configured to perform a method for monitoring the progress of, determining and/or assessing a brain-related condition of a subject, such as method 700 depicted in fig. 7. According to some embodiments, at step 702, the method may comprise obtaining values of one or more parameters for each light stimulus. According to some embodiments, at step 704, the method may include selecting one or more values of one or more parameters related to a particular brain condition. According to some embodiments, at step 706, the method may include classifying the values of the selected one or more parameters as being indicative of a brain condition.

According to some embodiments, at step 702, the method may comprise obtaining values of one or more parameters for each light stimulus. According to some embodiments, the method may comprise obtaining values of one or more parameters for each light stimulus in the form of an array. According to some embodiments, each value within the array may correspond to a location in the field of view, such as locations 1-76 shown in FIG. 1C. According to some embodiments, the method includes obtaining at least two arrays associated with each of the subject's left and right eyes, respectively.

According to some embodiments, at step 704, the method may include selecting one or more values of one or more parameters related to a particular brain condition. According to some embodiments, and as described in more detail elsewhere herein, a specified parameter may indicate a particular brain condition. According to some embodiments, the values of certain parameters may be normalized.

According to some embodiments, at step 706, the method may comprise classifying the values of the selected one or more parameters as being indicative of a particular brain condition. According to some embodiments, the method may include classifying the values based at least in part on a threshold value associated with each particular value. According to some embodiments, the method may include classifying values based at least in part on thresholds associated with particular values of a parameter related to a particular brain condition or state thereof. According to some embodiments, the threshold value may be correlated (compared) to a control value. In a particular example, the threshold value may be 1, 2, 4, or more SE or SD higher or lower than the corresponding control value. In one particular example, the threshold value may be determined based on ROC AUC analysis. According to some embodiments, the threshold value may comprise a normalized value, above which a parameter value is indicative of a specific brain condition. According to some embodiments, and as described in more detail elsewhere herein, different brain conditions may be associated with one or more different parameters. For example, as shown in table 1A below.

TABLE 1A

TABLE 1A-continuing.

According to some embodiments, the condition may be Alzheimer's Disease (AD) or a high risk of AD, and the calculated value may be determined based at least on the MCV parameter in the central region of the field of view in response to the high intensity blue light stimulus to determine the presence or absence of alzheimer's disease (or risk) using the calculated value.

Early detection of Alzheimer's Disease (AD) in preclinical stages, or identification of patients at risk, may be beneficial for implementation of preventive measures and for drug-modified disease research purposes. The identification of sensitive and specific biomarkers in the very early stages of AD neuropathology is crucial for the development of interventions aimed at preventing this disease. Although amyloid and tau-associated biomarkers hold promise for early diagnosis of AD, they do not provide sufficient sensitivity or accuracy in identifying asymptomatic disease stages. Some methods developed to detect these biomarkers involve highly invasive CSF measurements or cannot be widely used due to the high cost of amyloid imaging using PET scanning. Retinal studies have been the target for detecting neurodegenerative biomarkers in AD patients. More and more data suggests that in neurodegenerative diseases, ocular findings may precede lesions in the brain. Studies have shown that iprgcs are significantly lost in post-mortem AD eyes and impaired PLRs are reported in sporadic AD and in asymptomatic carriers of autosomal dominant genetic mutations in AD. Abnormal PLR is associated with cognitive performance in AD and Mild Cognitive Impairment (MCI) subjects, and with CSF markers of AD. These studies measure PLR in response to white light stimulating the entire retina.

According to some embodiments, in the differential diagnosis and/or risk (inheritance) of PD, the condition may be Parkinson's Disease (PD), and the calculated value may be determined based on at least one of: a PPR parameter in a central region of the field of view in response to a high intensity blue light stimulus, a PPC parameter in the central region of the field of view in response to a low intensity blue light stimulus, an MCA parameter in the central region of the field of view in response to a low intensity blue light stimulus, a PPC parameter in a peripheral region of the field of view in response to a low intensity blue light stimulus, an MCA parameter in the peripheral region of the field of view in response to a low intensity blue light stimulus, and a PPC parameter in the central region of the field of view in response to a red light stimulus.

Parkinson's Disease (PD) is the second most common degenerative disease in the CNS, affecting 700 to 1000 million people worldwide. Currently, although early intervention is critical for PD, particularly before motor symptoms appear, only a few sensitive and reliable biomarkers can help predict whether a mutation carrier will develop PD. Biomarkers currently used for PD include those based on early pre-motor clinical symptoms (e.g., REM sleep behavior disorder and hyposmia) and dopaminergic system imaging (e.g., FDOPA PET CT and DAT-SPECT). However, these biomarkers are either expensive (brain imaging), invasive (e.g., CSF obtained by lumbar puncture), of unproven value, or lack sensitivity and/or specificity. Patients with PD exhibit circadian rhythm disturbances, depressed mood and cognitive disorders controlled by a homeostatic network (homeostatic network). These pathways are affected by light passing through the hypothalamic tract, which is almost completely driven by the ipRGC. Previous studies have shown significant differences in PLR recorded between PD patients and healthy controls in response to white light stimulation, including longer PLR latency and smaller amplitude contractions. A dose-response relationship between maximal pupil diameter and morning levodopa dose was observed in PD patients. However, these studies tested PLR for full field stimulation (i.e., irradiation of light stimulation across the retina). In another study, PD patients showed normal rod-mediated PLR and reduced cone and melanopsin-mediated PLR using full-field blue light and red light stimulation. However, no correlation was observed between changes in PLR and clinical symptom severity, quality of sleep, RNFL thickness, or drug dose.

According to some embodiments, the condition may be a brain tumor, and the calculated value may be determined based on at least one of: a PPR parameter in a peripheral region of the visual field in response to the high intensity blue light stimulus and a PPC parameter in a peripheral region of the visual field in response to the low intensity blue light stimulus.

Intracranial tumors are a major cause of morbidity and mortality. Clinical symptoms are caused by a tumor mass effect, an increase in intracranial pressure, or a hormonal effect. Identification of PLR, and in particular PLR interocular asymmetry, is used clinically as part of neurological assessments of these patients. Attenuated PLR was demonstrated in patients with brain tumors that compress the midbrain and in patients with postgeniculate injury (postgenic injury) using white or orange light stimulation. Automated pupillometry is capable of sensitive and quantitative measurement of PLR, and several studies report its use for the detection of increased intracranial pressure, brain injury, motion-related concussions, and analgesic management.

Intracranial pressure (ICP) is determined by the volume of its contents, brain, blood and cerebrospinal fluid (CSF). If the volume of one of these components increases, ICP will begin to increase. Increased ICP can lead to brain damage, a life threatening condition. It may be the result of brain injury and other pathologies (e.g., pseudobrain tumors). There is currently no reliable, sensitive, objective and non-invasive method to diagnose elevated ICP.

According to some embodiments, the condition may be a fragile X carrier and/or a fragile X related cognitive decline, and the calculated value may be determined based at least on one of: a PPR parameter in a peripheral region of the visual field in response to a high intensity blue light stimulus, an LMCA parameter in a peripheral region of the visual field in response to a low intensity blue light stimulus, and an LMCA parameter in a central region of the visual field in response to a red light stimulus.

According to some embodiments, the condition may be Multiple Sclerosis (MS), and the calculated value may be determined based on at least one of: a PPC parameter in a peripheral region of the field of view in response to a red light stimulus, a PPC parameter in a peripheral region of the field of view in response to a low intensity blue light stimulus, an MRV parameter in a peripheral region of the field of view in response to a red light stimulus, an MRV parameter in a peripheral region of the field of view in response to a low intensity blue light stimulus, and an MCV parameter in a peripheral region and/or a central region of the field of view in response to a red light and/or blue light stimulus, and a PPR at a central and/or peripheral location in response to a high intensity and/or long duration blue light.

Multiple Sclerosis (MS) is a chronic, autoimmune, inflammatory, progressive disease of the central nervous system that also is a major cause of disability. MS is initiated by T cells targeting self-antigens in the CNS. The first lesions are the typical demyelinating focal regions in the white matter, called plaques. Depending on the location of these plaques, the resulting clinical neurological manifestations are notoriously variable, often due to immune cell invasion of the blood brain barrier. This process ultimately leads to CNS homing in the brain parenchyma and sustained activation of CNS-resident innate immune cells (macrophages and microglia), with consequent demyelination and neurodegeneration. About 75% of MS patients develop Optic Neuritis (ON), an inflammatory optic neuropathy associated with demyelination and RGC degeneration. The relationship between inflammatory lesions, demyelination and the neurodegenerative changes most associated with clinical disability is poorly understood. In recent years, the number of available treatments for MS, which are primarily directed to brain inflammation, has increased dramatically, and new treatments are in different stages of development. Due to the large variability in disease manifestation among patients, treatment strategies are often based on personalized approaches determined by the prognosis and treatment risk of the individual patient. Melanopsin-mediated reduction of PLR in MS patients has been shown to be significantly associated with thinning of the GCIP layer portion of the retina in MS patients (particularly in patients with a history of acute ON).

According to some embodiments, the condition may be Optic Neuritis (ON), and the calculated value may be determined based ON at least one of: the PPC parameter in the peripheral and/or central region of the visual field in response to red and/or blue stimuli and the PPR parameter in the peripheral region of the visual field in response to blue stimuli, and/or the calculated value is the number of test targets having abnormal PPC in response to blue and red light.

Optic Neuritis (ON) is an acute inflammation of the optic nerve. It is a common manifestation of multiple sclerosis with clinical symptoms including: sudden painful visual loss, diminished sensation of lightness, decreased color perception, and a loss of central vision. Visual field assessment is important for diagnosis and follow-up. Current visual field examination tests have significant limitations because the test is subjective and relies heavily on the cooperation and attention of the subjects.

According to some embodiments, the condition may be an intracranial lesion, and the calculated value may be determined based on at least one of: parameters in the peripheral and/or central regions of the visual field in response to high intensity blue light stimuli, PPC parameters in the nasal region of the visual field in response to low intensity blue light, PPR parameters in the nasal region of the visual field in response to low intensity blue light.

According to some embodiments, the performance of a classification model (i.e., diagnostic performance) or its ability to distinguish between different conditions may be obtained by AUC, which is the area under the receiver operating characteristic curve (ROC). According to some embodiments, the method may include calculating a ROC AUC for values of one or more parameters related to the brain condition. According to some embodiments, the method may comprise calculating a ROC AUC for values of one or more parameters for a particular location and/or area in the field of view, wavelength of light stimulation of one or more color beam emitters, and/or intensity of light stimulation. According to some embodiments, the Confidence Interval (CI) may be based at least in part on AUC. According to some embodiments, the Confidence Interval (CI) may be an AUC confidence interval.

According to some embodiments, the classification algorithm may comprise an AdaBoost algorithm. According to some embodiments, a classification algorithm may be trained on a training set comprising a plurality of models and labels. According to some embodiments, the training set model may include an array of pupillary responses to red light stimuli. According to some embodiments, the training set model may include an array of pupillary responses to blue light stimuli. According to some embodiments, the training set model may include an array of pupillary responses for the left and/or right eye. According to some embodiments, the training set model may include an array of pupillary responses associated with one or more combinations of parameters. According to some embodiments, the missing data of the training set may be evaluated using a mean replacement strategy that assigns an overall responder mean to all missing responses. According to some embodiments, the method may comprise predicting the accuracy of the different parameters. According to some embodiments, machine learning algorithms are used to determine the prediction accuracy of different parameters. According to some embodiments, the training of the algorithm may include training such that each model may be introduced to a single feature type (or, in other words, a single parameter) in the training set. According to some embodiments, the same hyper-parameters and training scheme may be used in all models.

According to some embodiments, each model may be tested to quantify its ability to distinguish between different tags. According to some embodiments, the tag may comprise one or more of the following: a subject with a brain condition, an offspring of parents with a particular type of brain condition, and an offspring of parents with a probable brain condition. According to some embodiments, the tag may comprise one or more of: a medical record of the subject, a medical record of at least one parent of the subject, a questionnaire administered to the subject and/or at least one parent of the subject. According to some embodiments, the label may include one or more diagnostic tests performed on the subject. For example, for early stage alzheimer's disease, cognitive assessments may be performed, such as the Rey auditory speech learning test, the test for immediate and delayed recall and recognition, the numerical symbol test, the part a and/or part B of the trail Making test and/or the forward and backward numerical spans.

According to some embodiments, as exemplified herein, abnormal retinal structure and PLR measurements are significantly associated with cognitive decline and early changes in brain structure in healthy subjects at high risk of developing dementia. According to some embodiments, such measurements may be used as biomarkers for early detection of cerebral neurodegenerative diseases.

According to some embodiments, the systems, devices, and methods may utilize color multifocal pupillometry and various Machine Learning (ML) and Artificial Intelligence (AI) algorithms (e.g., adaBoost) to allow for the determination or prediction of a subject's risk of developing a brain-related condition, the detection or identification of a condition, the assessment of a condition, and/or the monitoring of the progression of a condition.

According to some embodiments, methods are provided for predicting the presence of a validated family history of AD in a person without clinical cognitive impairment. According to some embodiments, as exemplified herein, methods for predicting AD may be based, at least in part, on parameters related to PLR latency, in particular pupillary response latency as measured by a multifocal pupillometer. According to some embodiments, as illustrated herein, and without wishing to be bound by any theory or mechanism, there is a link between AD and the afferent pathway of the pupil.

According to some exemplary embodiments, the methods disclosed herein, and the devices and systems performing the methods, may allow for identification of subjects at risk for AD, even years prior to the onset of the condition.

According to some embodiments, for ML analysis of parameters, each tested parameter may produce any number of independent features based on the value of the respective parameter measured at each different illumination point in the retina, which is to be input into the algorithm and may be part of the analysis.

In some embodiments, each parameter may consist of any number of independent features (e.g., in the range of 1-228), depending on the values measured/determined at the various illumination points. In one example, each parameter may include 54 independent features based on the measured/determined parameter values at 54 different illumination points in the retina. According to some embodiments, the prediction accuracy of the different parameters may be determined using a suitable algorithm, such as, but not limited to, the Adaboost algorithm. According to some embodiments, multiple classifiers may be trained, each classifier receiving a different set of features (e.g., 54 features) corresponding to a single parameter. According to some embodiments, for each eye, any number of models may be trained based on red illumination (e.g., in the range of 1-35). Alternatively or additionally, any number of models may be trained based on blue illumination.

In some exemplary embodiments, in the training of AD models (as illustrated by example 1 herein), 17 models are trained for red light and 18 models are trained for blue light, resulting in a total of 35 different models. In some embodiments, missing data may be evaluated using a suitable tool (e.g., a mean replacement strategy). According to some embodiments, the feature may be a scale or gradient in the range [0,1], e.g., using min-max normalization.

In some embodiments, such predictions as to the presence of AD family history can exhibit an area under the ROC curve (AUC) of about 89 ± 6 at a 95% Confidence Interval (CI), as exemplified herein.

According to some embodiments, melanopsin-mediated MCV (i.e., MCV determined in response to strong blue light) is significantly lower at the central retina (p =0.015, adjusted for age and sex) but not significantly lower at the peripheral retina, where ROC AUC =85% (p = 0.00001), in subjects at risk for AD (based on family history (FH +)). According to some embodiments, the PLR parameter is significantly and directly related to the following features: 1. thickness of the inner retinal layer (mGCL and mpil, p < 0.015); 2. hippocampal volume (pearson correlation, r =0.245, p =0.001 for the whole population, fh +: p =0.333, p =0.0001, fh-: r =0.319, p = 0.048) and; 3. cognitive function test scores for executive function (where lower scores reflect better function: r = -0.230, p =0.001 fh +: p = -0.319, p =0.0001, fh-: r = -0.252, p =0.034, adjusted for age and gender for the whole population) and memory (r =0.273, p =0.00004 fh +: p =0.278, p =0.001, fh-: r =0.302, p =0.01 for the whole population.

According to some embodiments, B _ PRL (pupillary response latency determined in response to blue light stimuli) and R _ PRL (pupillary response latency determined in response to red light stimuli) are predictive parameters for a high risk of AD, as exemplified below. According to some embodiments, B PRL and R PRL provide very high discriminatory (diagnostic) performance, where AUC is 0.89 ± 0.03, ci is [0.83,0.95] (eye-0), AUC is 0.83 ± 0.03, ci is [0.77,0.89] (eye-1) for B PRL, AUC is 0.89 ± 0.03, ci is [0.81,0.93] (eye-0), AUC is 0.86 ± 0.03, ci is [0.81,0.91] (eye-1) for R PRL. According to some embodiments, additional parameters that exhibit high differential diagnostic values for AD for both eyes may include B _ LMCA, R _ LMCA, B _ LMCD, R _ LMCD, and B _ LMP. According to some embodiments, an additional parameter that exhibits a high discrimination value for eye-1 is R _ LMP.

According to some embodiments, to predict the risk of AD, one or more of the following parameters may be used: b _ PRL, R _ PRL, B _ LMCA, R _ LMCA, B _ LMCD, R _ LMCD, B _ LMP, R _ LMP, B-MCV, or any combination thereof. Each possibility is a separate embodiment. According to some embodiments, a deviation or change in the value of one or more parameters in one or both eyes, for example under specified conditions (e.g., stimulation wavelength, stimulation intensity, duration, location on VF, etc.), may be predictive of an increased risk of developing AD, AD status, and/or AD progression. According to some embodiments, the color pupillometry parameters associated with the systolic phase of the PLR are more discriminative than the parameters associated with the relaxed phase.

According to some embodiments, based on the determined PLR parameters, cost-effective and non-invasive biomarkers for PD are provided that can be used to detect earliest pre-locomotive signs of impending disease, predict motor symptoms and development of PD in high risk subjects. Furthermore, such biomarkers can be used to assess the progression of PD status and/or PD pathology at all stages thereof.

According to some embodiments, the decreased PPC, AC, and/or MCA in the central VF and/or the peripheral VF determined in response to the dimmed blue light stimulus is predictive of a PD condition, as illustrated below.

According to some embodiments, as exemplified below, decreased PPC and/or MCV in central VF determined in response to red light stimulation is predictive of a PD condition.

According to some embodiments, the decreasing PPR in the center VF in response to bright blue light predicts a PD condition, as illustrated below.

According to some embodiments, to predict risk of PD, detect PD, and/or assess progression of a PD condition, one or more of the following parameters may be used: attenuated PPCs in the central and/or peripheral VF in response to dim blue light, attenuated (reduced) ACs in the central and/or peripheral VF in response to dim blue light, attenuated (reduced) MCAs in the central and/or peripheral VF in response to dim blue light, attenuated PPCs in the central VF in response to red light, attenuated MCVs in the central VF in response to red light, attenuated PPRs in the central VF in response to bright blue light, or any combination thereof. Each possibility is a separate embodiment. According to some embodiments, a deviation or change in the value of one or more of these parameters may be predictive of an increased risk of developing PD, PD status, and/or PD progression.

According to some embodiments, based on the determined PLR parameters, cost-effective and non-invasive biomarkers for MS are provided. Such biomarkers can be used to assess the status of MS and/or the progression of MS pathology.

According to some embodiments, for predicting the risk of, detecting and/or assessing the progression of or monitoring a condition of MS, one or more of the following parameters may be used: PPC in response to a decrease (reduction) of red light in the peripheral VF, MRV in response to a decrease (reduction) of red light in the peripheral VF, PPC in response to a decrease (reduction) of dim blue light in the peripheral VF, MRV in response to a decrease (reduction) of dim blue light in the peripheral VF, MCV in response to a decrease (reduction) of red light in the overall VF, MCV in response to a decrease (reduction) of bright blue light in the overall VF, or any combination thereof. Each possibility is a separate embodiment. According to some embodiments, a deviation or change in the value of one or more of these parameters may be predictive of an increased risk of developing MS, MS status, and/or MS progression.

According to some embodiments, to detect ON and/or assess the progress of an ON condition or monitor an ON condition, one or more of the following parameters may be used: an attenuated PPC in the peripheral VF in response to bright blue light, an attenuated PPC in the field of view in response to red light, an attenuated PPC in the field of view in response to dim blue light, or any combination thereof. Each possibility is a separate embodiment.

According to some embodiments, melanopsin-mediated PPR (in response to bright blue light) in peripheral VF of an optic neuritic eye is decreased compared to a control with a ROC AUC of about 91% (p = 0.001). According to some embodiments, in most visual field test targets of ON eyes, the rod and cone mediated Percent Pupil Constriction (PPC) in response to red and blue light may be more than 2 Standard Errors (SE) below the mean of controls, even in patients with normal Best Corrected Vision (BCVA).

According to some embodiments, the melanopsin-mediated pupillary response (i.e., response to bright blue light) at the peripheral retina can be used as a biomarker to detect ONs and/or assess their progression or the efficacy of treatment thereof.

According to some embodiments, the systems, devices and methods may be used to detect or diagnose cognitive decline associated with fragile X carriers based on PLR responses to different light stimuli.

According to some embodiments, for detecting cognitive decline associated with fragile X carriers and/or for assessing progress or monitoring their condition, one or more of the following parameters may be used: an attenuated PPR in the peripheral VF in response to bright blue light, an attenuated LMCA in the field of view in response to red light, an attenuated LMCA in the field of view in response to dim blue light, or any combination thereof. Each possibility is a separate embodiment.

According to some embodiments, the systems, devices, and methods may be used to detect or diagnose a brain tumor or a symptom associated with a brain tumor. In some embodiments, the systems, devices and methods may allow for non-invasive objective diagnosis and monitoring of patients with focal brain tumors, with intracranial pathologies, which may be brain tumors that have no apparent contact with or are in contact with visual organs.

According to some embodiments, to detect brain tumors and/or to assess the progression of or monitor a tumor condition, one or more of the following parameters may be used: mean normalized pupil size at 3.7 seconds after cessation of blue light at central and peripheral locations was significantly higher compared to control subjects, where ROC analysis showed that in brain tumors without significant contact to the visual organs, the PLR parameter had the largest area under the curve (AUC) in the central retina, while in brain tumors with contact to the visual organs, the PLR parameter had the largest area under the curve in the peripheral retina (90.8%, p =0.003 and 89.3%, respectively, p = 0.003). As illustrated below, in brain tumors that contact visual organs, exemplary ROC analysis showed that the maximum contraction rate in response to red light stimulation recorded in the peripherally superior test target had the greatest AUC (96.4%, p = 0.006).

According to some embodiments, for detecting brain tumors and/or assessing the progression of tumor conditions or monitoring tumor conditions (non-contact eye system), a Maximum Contraction Velocity (MCV) parameter in response to red light stimulation in the central nasal side (CN) target region may be used. As illustrated below, this parameter shows the maximum AUC (84.1%, p = 0.007), with a cutoff (threshold) of 11.97 pixels/sec (sensitivity =0.778, 1-specificity = 0.143) that provides the most reliable prediction.

According to some embodiments, to detect brain tumors and/or to assess the progression of a tumor condition or to monitor a tumor condition (in contact with the visual system), one or more of the following parameters may be used: normalized pupil size, percent pupil constriction reduced (PPC), maximum constriction velocity reduced (MCV), maximum relaxation velocity in response to reductions in blue and red light in upper VF (MRV), measured at 3.7 seconds after blue light cessation in peripheral VF test target, or any combination thereof. Each possibility is a separate embodiment.

According to some embodiments, for brain tumors that contact the optic tract and/or midbrain, in order to detect brain tumors and/or assess the progression of or monitor a tumor condition, one or more of the following parameters may be used: significantly lower PPC values in nasal and central-temporal VF test targets in the right eye in response to dim blue light, significantly lower PPC values in nasal and central-nasal test targets in the left eye in response to dim blue light, faster pupil recovery in response to bright blue light (larger PPR values) of VF across the right eye, larger PPR values in nasal and central VF locations in the left eye in response to bright blue light, or any combination thereof. Each possibility is a separate embodiment.

According to some embodiments, for an on-axis brain tumor involving visual radiation, to detect the brain tumor and/or assess the progression of the tumor condition or monitor the tumor condition, one or more of the following parameters may be used: significantly greater PPR values at all VF test points in the right eye in response to bright blue light stimuli, significantly lower PPCs at nasal, central-temporal, and/or temporal locations in the right eye in response to dim blue light, or any combination thereof. Each possibility is a separate embodiment.

According to some embodiments, for brain tumors that have no apparent contact with vision or the PLR system, the following parameters may be used in order to detect the brain tumor and/or to assess the progression of the tumor condition or to monitor the tumor condition: significantly greater PPR values in response to bright blue light stimuli in nasal VF test subjects in the right eye compared to controls.

According to some embodiments, as exemplified herein, the systems, devices, and methods disclosed herein allow for objective and non-invasive diagnosis and monitoring of various brain lesions. According to some embodiments, PLR (primarily mediated by cone cells) for red light is not significantly affected in tumor patients. According to some embodiments, a significantly attenuated rod-mediated PLR is indicative of a patient having a tumor that contacts the PLR and visual pathway. According to some embodiments, abnormal sustained melanopsin-mediated PLR is indicative of a tumor patient. According to some embodiments, the MCV and LMCV parameters are normal in brain tumor patients compared to control subjects.

According to some embodiments, the systems, devices, and methods disclosed herein allow for the identification or detection of focal intracranial lesions by determining local melanopsin-mediated persistent PLR for high intensity blue stimulation. In some embodiments, the detection of a brain tumor involving the visual pathway may further comprise identifying a decrease in (attenuation of) rod-mediated PLR (i.e., response to dim blue light).

According to some embodiments, the systems, devices, and methods can be used to detect, diagnose, and/or assess the function of visual pathways mediating PLR, as well as therapeutic responses in PTC patients. According to some embodiments, analysis of PLR for focal blue and red light stimulation provides a diagnosis and/or assessment of visual pathway-mediated PLR function in PTC patients. According to some embodiments, analysis of PLR for focal blue and red light stimulation provides a diagnosis and/or assessment of therapeutic response in PTC patients.

According to some embodiments, to detect Pseudobrain Tumors (PTC) and/or to assess the progression of or monitor a PTC condition, one or more of the following parameters may be used: reduced PPC, reduced MRV, or both. Each possibility is a separate embodiment. In some embodiments, these parameters may be significantly lower in PTC subjects compared to controls (e.g., greater than or equal to 4SD below the mean of the controls).

According to some embodiments, to detect PTC response to treatment and/or to assess the progress of or monitor treatment efficacy, improvement in pupillary response to blue stimuli (but not red) is shown, primarily in the center of the visual field, following treatment (e.g., with acetazolamide).

According to some embodiments, the systems, devices, and methods may be used to detect, diagnose, and/or assess a stroke condition in a subject.

According to some embodiments, to detect stroke and/or assess progression or monitor stroke condition, one or more of the following parameters may be used: lower PPR in the central and peripheral VFs in response to high intensity (e.g., about 6000cd/m ^ 2) and long duration (e.g., about 8 seconds) blue light, lower PPC and lower MRV in the peripheral locations in response to low intensity (e.g., about 170cd/m ^ 2) blue light, or any combination thereof. Each possibility is a separate embodiment. In some embodiments, in a stroke condition, normal PPC, normal MCV, normal LMCV and/or normal MRV are detected throughout VF in response to red light (an intensity of approximately 100cd/m ^ 2). In this context, normal refers to a control subject without stroke.

In the description and claims of this application, the words "comprise" and "have" and their various forms are not limited to the members of the list with which they may be associated.

Unless specifically stated otherwise as apparent from the present disclosure, it is appreciated that according to some embodiments, terms such as "processing," "computing," "calculating," "determining," "estimating," "evaluating," "determining," or the like, may refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulates and/or transforms data represented as physical quantities (e.g., electronic quantities) within the computing system's registers and/or memories into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices.

Embodiments of the present disclosure may include apparatuses for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random Access Memories (RAMs), electrically programmable read-only memories (EPROMs), electrically Erasable and Programmable Read Only Memories (EEPROMs), magnetic or optical cards, or any other type of media suitable for storing electronic instructions, and capable of being coupled to a computer system bus.

The processes and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the desired method. The required structure for a variety of these systems will appear from the description below. In addition, embodiments of the present disclosure are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the disclosure as described herein.

Aspects of the disclosure may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The disclosed embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

As used herein, the term "about" may be used to specify a value of a quantity or parameter within a continuous range of values about (and including) a given (stated) value. According to some embodiments, "about" may specify a parameter value between 80% and 120% of a given value. According to some embodiments, "about" may specify a parameter value between 90% and 110% of a given value. According to some embodiments, "about" may specify a parameter value between 95% and 105% of a given value.

It is appreciated that certain features of the disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the disclosure which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the disclosure. Features described in the context of an embodiment are not considered essential features of that embodiment unless explicitly so specified.

Although the stages of the methods according to some embodiments may be described in a particular order, the methods of the present disclosure may include some or all of the described stages performed in a different order. The method of the present disclosure may include some or all of the stages described. Unless explicitly specified as such, a particular stage in a disclosed method is not considered an essential stage of the method.

While the present disclosure has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, the present disclosure is intended to embrace all such alternatives, modifications and variances which fall within the scope of the appended claims. It is to be understood that the disclosure is not necessarily limited in its application to the details of construction and the arrangement of components and/or methods set forth herein. Other embodiments may be practiced, and embodiments may be performed in various ways.

The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. Citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present disclosure. The section headings used herein are for ease of understanding the specification and should not be construed as necessarily limiting.

Examples of the invention

Example 1-early detection of Alzheimer's Disease (AD) Using Multi-factor analysis of PLR on color light stimulation

Materials and methods:

the participants: the study included 186 participants, 125 of which were offspring of AD patients (FH +) and 61 of which were age-matched controls (FH-), aged 44-71 years. FH + is defined as the offspring of parents with disease onset before age 70 in their father or with disease onset before age 75 in their mother diagnosed with probable AD (probable AD). FH-is defined as (at the time of acceptance by the participants or at the time of death) the offspring of cognizant normal fathers over 70 years of age and mothers over 75 years of age. Parental AD was determined based on medical records and Dementia Questionnaire (Dementia questonaire). The DQ questionnaire is conducted for potential participants with respect to their parents. According to the NINCDS-ADRDA standard, a diagnosis of possible AD is given by reviewing medical records and questionnaires. If due to some information a diagnosis cannot be made that the parent is likely to be AD or that the parent is defined to have normal cognition (i.e., not suffering from dementia and its subtypes, MCI, subjective cognitive impairment or decline, or not using dementia-related drugs), the offspring are excluded from the study. The study included only participants who did not themselves exhibit AD symptoms or cognitive impairment using cognitive assessment as detailed below.

Each participant was tested for both eyes (left eye (eye-0) and right eye (eye-1)) using a multifocal color pupillometer, resulting in a total of 372 eyes, 250 of which were FH +, and 122 of which were FH-.

Cognitive assessment of participants: all patients received a cognitive assessment comprising the following tests: (1) the Rey auditory speech learning test (RAVTL), the test for immediate and delayed recall and recognition, (2) the digital symbol test, (3) part a and part B of the wiring test, and (4) forward and backward digital spans.

A pupillometer: PLR was tested using a color multifocal pupillometer that enabled early detection and mapping of functional defects in iPRGC and in rod and cone photoreceptors at different locations of the retina, and differential identification of the affected visual pathway (i.e., rod, cone photoreceptor, or intrinsic iPRGC activation).

Color multifocal pupil measurement: the pupillometer includes a light source having 76 LEDs (2 mm) ² Diameter) of the Ganzfeld dome device. Each eye was tested individually and the non-tested eyes were blocked to prevent PLR due to light perception by the contralateral eye. The participant is asked to look at a dim white fixation at the center of the dome. Red (624 nm) and blue (485 nm) light stimuli were presented from each LED for 1 second. Pupil diameter was photographed by a computerized infrared high resolution camera for 4 seconds. The software automatically measures the pupil diameter for each stimulus and calculates the response at each locationThe percentage of pupil contraction, the maximum contraction and expansion rate, and the maximum contraction and expansion rate latency for each stimulus. Pupillometer results are the percent pupil constriction in response to low intensity blue and red light stimuli.

A plurality of pupillary response parameters were calculated and analyzed for each participant's eyes: (1) curve Area (AC), (2) maximum systolic acceleration (MCA), (3) maximum systolic deceleration (MCD), (4) maximum systolic velocity (MCV), (5) MCA Latency (LMCA), (6) MCD Latency (LMCD), (7) MCV Latency (LMCV), (8) Maximum Relaxed Acceleration (MRA), (9) Maximum Relaxed Deceleration (MRD), (10) Maximum Relaxed Velocity (MRV), (11) MRA Latency (LMRA), (12) maximum relaxed deceleration Latency (LMRD), (13) MRV Latency (LMRV), (14) maximum pupil response latency (from light flash to maximum systole, LMP), (15) maximum pupil contraction (Max _ PC), (16) minimum pupil size (Min _ PS — for blue light only), (17) Pupil Response Latency (PRL), and (18) percentage of pupil maximum relaxation (PPR or PRP).

Each of the above parameters was calculated independently using red and blue light, resulting in a total of 35 independent parameters.

Retinal evaluation: all subjects received a comprehensive ophthalmic examination to exclude ocular pathologies (e.g., glaucoma) including assessment of Best Corrected Vision (BCVA), color vision (Farnsworth D15 test), pupillary reflex, intraocular pressure (Goldmann applanation tonometry), and slit lamp biomicroscopy of the anterior and posterior segments of the eye and a dilated fundoscopy (dilated fundus exposure). Eyes with a BCVA of 20/50 or less or eyes with ocular lesions were excluded.

Statistical analysis and machine learning algorithm

For the purpose of classifying the AD family history, the significance of each of the 35 pupillometer parameters was analyzed using a color multifocal pupillometer.

Each pupillometer parameter yields 54 independent features (variables) based on the pupillometer values triggered at 54 different illuminated points in the retina.

Referring now to fig. 8, a schematic diagram of a feature vector extraction process from parameters of a patient's eye is shown. In the example shown in fig. 8, a pupillary response latency (R _ PRL) vector for red light is generated. This process is repeated for each patient's eyes.

Next, using the AdaBoost algorithm, a plurality of classifiers are trained, where each classifier receives a different set of 54 features corresponding to a single pupillometer parameter. This process is repeated independently for each eye.

For each eye, 17 models were trained based on red light and 18 models based on blue light, resulting in a total of 35 different models. Note that the classifier can be trained using a combination of inter-parameter features.

Missing data was evaluated using a mean-substitution strategy that assigned an overall responder mean to all missing responses and was a deterministic degenerate form of a linear function with no auxiliary variables. All features were rescaled to the range of [0,1] using min-max normalization.

The discriminatory performance of a classification model is measured by AUC, which is the area under the receiver operating characteristic curve (ROC).

To evaluate AUC confidence intervals, nonparametric resampling was used. The general algorithm of nonparametric bootstrapping applied includes: (1) Randomly and repeatedly sampling n observations (n =186 × 80% = 149) from 186 samples to obtain a bootstrap dataset; (2) Training a classifier using the bootstrapped version of the data, testing it on observations that are not part of the bootstrapped dataset to derive an AUC measure; (3) Repeat steps 1 and 2 one hundred times to obtain an estimate of the confidence interval.

Referring to fig. 9, a graphical representation of the generation of Confidence Intervals (CI) for a single pupillometer parameter (R _ PRL in this example) is shown. Given a complete sample set, a training set is created by having a put back sample, while a test set consists of all samples not selected in the training set. The model was evaluated on the test set (average size of 88 different samples) using AUC measures. This process was repeated N =100 times to infer 95% CI, as detailed above.

As a result:

the curves themselves and the AUC of the ROC curve for each of the 70 training models are given in table 1B below.

For each of the 69 training models, the Confidence Interval (CI) of 100 iterations, the mean of ROC ± STD AUC values were used. Each row in table 1B represents one pupillometer parameter. Each parameter occurs twice, with a prefix of 'B _' or 'R _', which indicates the use of blue or red light, respectively.

TABLE 1B

As detailed in table 1B, and as shown in fig. 10, 11 and 12, the models of B _ PRL and R _ PRL achieved very high discrimination performance, with AUC of 0.89 ± 0.03, ci of [0.83,0.95] (eye-0), AUC of 0.83 ± 0.03, ci of [0.77,0.89] (eye-1) for B _ PRL (PRL parameters measured using blue light), and AUC of 0.89 ± 0.03, ci of [0.81,0.93] (eye-0), AUC of 0.86 ± 0.03, ci of [0.81,0.91] (eye-1) for R _ PRL (PRL parameters measured using red light). Other models to achieve high discrimination for both eyes are B _ LMCA, R _ LMCA, B _ LMCD, R _ LMCD and B _ LMP. R _ LMP achieves a high discrimination value only for eye-1.

Fig. 10 shows the average AUC (bar) for each pupillometer parameter and STD value (black line) for each pupillometer parameter for eye-0 (top) and eye-1 (bottom) measured using the bootstrapping procedure (shown in fig. 9).

The parameter showing the highest AUC value is a parameter related to the pupil light reflex latency, indicating an effect on the afferent branch of the pupillary response (afferent limbus).

In fig. 11, the two most significant parameters (B _ PRL, R _ PRL) are focused on and the weight given by the machine learning model to each of the 54 eyepoints is visualized. Relative weights are given to each of the 54 eyepoints by the machine learning model. As described above, the weights are the mean over 100 trials to generate 95% CI. The up and down rows show the weights of the B _ PRL and R _ PRL parameters, respectively, and the left and right columns show the left and right eye results, respectively. Color coding uses darker values to represent higher weights.

Fig. 12 shows the distribution of different AUC values within the confidence interval for the most significant parameter (B _ PRL), in the left eye, CI: [80.63, 97.97], mean. + -. Std:90.51 ± 4.87, and in the right eye, CI: [76.84, 94.48], mean. + -. Std:88.11 +/-4.91. CI: 95% confidence intervals based on 100 iterations. For each eye, there were 95 horizontal lines, each corresponding to a different AUC value, which is the result of independent experiments (total 100 experiments per eye).

Thus, the results presented above show that a learning model based on color pupillary visual field examination can predict the presence or absence of an AD family history with performance of 0.89 + -0.03 ROC AUC, 0.81,0.93 for 95% CI (left eye) and 0.86 + -0.03 ROC AUC, 0.81,0.91 for 95% CI (right eye). It is noted that the color pupillary visual field examination parameters associated with the constricted arm of the PLR are more discriminative than the parameters associated with the relaxed branch.

Overall, the results show that AD family history status can be predicted with high discrimination values using the color pupillary visual field examination PLR parameter. This suggests that subtle changes in pupil constriction associated with AD can be detected using simple non-invasive tests long before (even years) onset of disease.

Example 2-early detection of Parkinson's Disease (PD) Using Multi-factor analysis of colored light stimuli presented at Central and peripheral retinal locations

Typically, the main pathological findings in the brain of PD patients are the selective deletion of dopaminergic neurons in the compact part of the substantia nigra and the presence of cytoplasmic eosinophilic inclusion bodies (called lewys) that are immunoreactive with alpha-synuclein. The loss of dopamine in the nigrostriatal pathway is responsible for most motor symptoms, which are hallmarks of the disease. However, the pathological process is not limited to the substantia nigra, but involves additional brain regions (e.g. olfactory bulb, lower brainstem and nerve nuclei) and more structures outside the brain. As the disease progresses, the major motor symptoms of PD and myriad non-motor non-dopaminergic symptoms, including cognitive decline, mental disorders, autonomic failure, and sleep and sensory disorders, severely degrade the quality and function of life of the patient. In addition to α -synuclein-associated neurodegeneration, cerebrovascular mechanisms may also be involved in the pathophysiology of PD, thereby promoting disease progression. Dopamine is a key neuromodulator in the retina. It is released by a unique group of amacrine cells present in the inner layer of the retina and activates dopamine receptors distributed throughout the retina, including retinal pigment epithelium, photoreceptors, muller glia, bipolar cells, horizontal cells, and ganglion cells. Dopamine modulates the receptive field of retinal ganglion cells and thus affects spatial contrast sensitivity and color perception, and plays a key role in the regulation of photoadaptation and circadian rhythm by regulating melatonin production. Various dopamine-dependent mechanisms result in increased signal flow through the cone pathway during the day and inhibition of the rod circuit. At night, dopamine enhances the rod cell visual pathway, promoting vision in dim light. Dopamine also has multiple nutritional roles in retinal function associated with cell survival. Autopsy studies showed low levels of retinal dopamine, dopaminergic retinal neurons (DACs) apoptosis, and accumulation of phosphorylated alpha-synuclein in the retina. Dopamine depletion is accompanied by decreased color perception, decreased light sensitivity, and decreased contrast sensitivity. Optical Coherence Tomography (OCT) imaging indicates changes in retinal structure. Some studies report thinning of the inner retinal layers including the retinal nerve fiber layer (RNFL, axon containing retinal ganglion cells forming the optic nerve), ganglion cell layer (GCL, containing ganglion cell bodies), and inner plexiform layer (IPL, containing ganglion cell dendrites, amacrine cells, and bipolar cell exons) in PD patients. Other studies report swelling of photoreceptor and retinal ganglion cell layers.

Method: PD patients (mean age ± standard deviation: 61.29 ± 11.54 years) and healthy controls of similar age (65.07 ± 11.22 years, p = 0.29) were tested. As detailed in example 1, under mesopic (mesopic) photopic conditions, pupillary responses (PLR) to small (0.43 °) red and blue light stimuli presented at central (4.2 °) and peripheral (21 °) visual field positions were measured using a color pupillometer.

Subjects received a complete neurological assessment, as well as MDS-unified parkinson's disease rating scale (MDS-UPDRS), montreal cognitive assessment (MoCA), sleep assessment according to PD sleep scale (PDSS-2), complete ophthalmic examination, color vision testing, best-corrected vision, refraction, and spectral domain optical coherence tomography (SD-OCT) imaging.

17 PD patients (mean age. + -. Standard deviation: 61.29. + -. 11.54 years) and 26 healthy controls of similar age (65.07. + -. 11.22 years, p = 0.29) were tested for PLR.

The results indicate that the percentage of rod-mediated maximal pupil constriction (PPC) at central and peripheral retinal locations is significantly lower in PD patients compared to controls (all p < 0.04). Cone-mediated PPC is less affected. Sustained, intrinsic melanopsin-mediated pupillary response recovery (PPR) in the central retina was significantly faster in PD compared to controls (p = 0.01).

In addition, 3 patients with mild PD (69.6. + -. 3.1 years) and 19 age-similar controls (69.6. + -. 7.5 years) were tested for focal PLR. In PD patients, a significantly lower Percentage of Pupil Constriction (PPC) (7.0 ± 3.0% versus 17.7 ± 1.7%, p = 0.03) in response to focal blue light stimulation (rod-mediated PLR) was recorded in the central retina compared to controls. In contrast, the (cone-mediated) PLR measured at this location for red light was not significantly different between groups (11.9 ± 2.7% versus 17.1 ± 1.7%, p = 0.25).

Next, PLR was tested in both eyes at four central and four peripheral targets in the visual field by stimulation with dim and bright red (624 nm) and blue (485 nm) light in 17 PD patients and 30 controls (similar in age and sex) aged 34-72 years.

The results presented in fig. 13 and table 2 show that attenuated melanotropin-mediated PPR was observed in central VF in PD patients in response to bright blue light.

Table 2:

the results presented in fig. 14A, fig. 14B, and table 3 below show that cone-mediated PPC and MCV attenuated in central VF (i.e., in response to red light) were observed in PD patients.

TABLE 3

The results presented in fig. 15A, fig. 15B, fig. 15C, and table 4 below show that reduced rod-mediated PPC, AC, and MCA in central VF and peripheral VF (i.e., in response to dim blue light) were observed in PD patients.

TABLE 4

The results indicate that intrinsic and extrinsic light responses in iprgcs are affected differently in central and peripheral retinal locations in PD patients, and further differentiate between rods and cones.

Overall, the results clearly show that various PLR parameters are indicative of PD status and can therefore be used as early sensitivity biomarkers for PD.

EXAMPLE 3 different PLR parameters in Multiple Sclerosis (MS) patients

As detailed in example 1, three chronic MS patients (3f &1m, mean ± SD:33.5, 12.2 YO) and 26 healthy age-matched controls (15f &11m,35.4 ± 12.4 YO) were tested for PLR under different conditions as initial endpoints.

The subjects further received Humphrey 24-2 visual field, BCVA, SD-OCT, color vision and VEP tests (secondary endpoints).

The results are presented in fig. 16-24. Fig. 16-18 show results for PPC parameters measured at various positions in the visual field of the left eye (OD) or right eye (OS) of control subjects (fig. 16) or MS patients (fig. 17-18).

Fig. 19-21 show results for MRV parameters measured at various locations in the visual field of the left eye (OD) or right eye (OS) of control subjects (fig. 19) or MS patients (fig. 20-21) in response to blue or red light irradiation.

Fig. 22-24 show results for MCV parameters measured at various positions in the visual field of the left eye (OD) or right eye (OS) of control subjects (fig. 22) or MS patients (fig. 23-24) in response to blue or red light irradiation.

The results clearly show that PPC and MRV are significantly lower for red (intensity of about 1000cd/m 2) and dim blue (intensity of about 170cd/m 2) at peripheral visual field targets/locations in MS patients compared to controls. The results further show that the MCV for red and blue is reduced over the entire VF (not just peripherally).

Even though the results of hummphrey visual field examination in chronic MS patients were normal, chronic MS patients exhibited diminished rod and cone-mediated PLR at the peripheral retinal locations, suggesting that chronic damage to the retinal circuits may be present at these locations that was not detected by conventional visual field examination.

The results clearly show that different PLR parameters can be used to identify that MS is a subject and can be used as predictors of the condition.

EXAMPLE 4 color pupillary visual field examination for objective diagnosis and monitoring of optic neuritis

The objective of this study was to characterize the rod, cone and melanopsin mediated pupillary responses (PLR) to small focal colored light stimuli that occur at peripheral and central retinal locations in patients with optic neuritis.

Method: 11 patients with acute Optic Neuritis (ON) (8F and 3M, mean age ± standard deviation: 31 ± 9 years) 7 women and 3 men, and 26 healthy controls of similar age (35.4 ± 12.4 years, p = 0.42) 15 women and 11 men were tested at several visits (visit): v1 (ON episode (within 48 hours post-diagnosis), V2 (end of steroid treatment (+ 5 d), V3 (+ 1 mo), V4 (+ 3 months).

Pupillary Light Responses (PLR) were recorded for small (0.43) red and blue light stimuli (485 nm and 625nm peak, respectively) presented at 54 locations in a 24-2 field of view. In addition, the melanopsin-mediated sustained pupillary response (percent pupillary recovery, PPR) was evaluated at both central and peripheral VF sites. All patients received Optical Coherence Tomography (OCT) imaging, standard visual field examination (humphey SITA standard protocol) and determined their Best Corrected Vision (BCVA).

As a result, the: the results are presented in fig. 25-30, 31 and 32, which show: rod and cone mediated PPC attenuated during ON episodes (fig. 25), pupillary response to blue light was reduced in ON patients and correlated with severity of visual field defects (fig. 26), analysis of the progression of PLR and visual function in representative ON patients after treatment (fig. 27), analysis of the progression of PLR and visual function in representative ON patients after treatment with methylprednisolone (Solu-Medrol) (fig. 28) and ROC analysis of the results (fig. 29). FIGS. 30A-30B show ROC analysis of PPR results for high intensity blue light in ON subjects; figure 30A shows significantly higher PRP in eyes with optic neuritis and contralateral eyes (NON) or healthy eyes (control). The ON eye is affected more than the contralateral eye. Figure 30B shows ROC analysis with AUC of 100% for ON detection using PPR for high intensity blue light.

Fig. 31 shows the assessment of focal PLR changes at various retinal locations during ON and after treatment. A representative 18 year old male was tested during the acute ON episode (A, C, E) and after 5 days of SOLU-MEDROL treatment (B, D, F). The patient's vision during the ON episode was 0.3 and improved to 0.18 after treatment (logMAR ETDRS). The reduced PLR (C) for blue light in the entire VF associated with the hummphrey visual field examination during ON (a) improved after treatment (D). PLR for red light is less affected (E) and there is essentially no change after treatment (F). Each number in the pupillometer "map" represents the Percent Pupil Constriction (PPC) measured at that retinal location. The color coding is set to resemble the output of a Humphrey perimeter, where white represents a "normal" value (based on the average of age-matched controls at each test point location), and darker gray represents a value lower than the normal value. The darkest color was used for the test points where the PPC was 5 SEs below the mean of the controls in these points. Yellow indicates a target with a PPC above the mean of the controls.

Notably, during ON onset, significantly reduced melanopsin-mediated PLR was recorded in the peripheral retina of eyes with ON (n = 8) compared to healthy eyes (n =23, p = 0.00015). Melanopsin-mediated PLR at the peripheral retina distinguishes with high sensitivity and specificity between eyes with ON and controls (ROC AUC =91.3%, p = 0.01). Furthermore, in areas with VF deficiency identified by hummphrey perimetry, significantly attenuated rod-mediated PLR was recorded during the ON episode in the affected eye (representative patients are presented in panels a and C of fig. 31). In that

After treatment with (methylprednisolone sodium succinate), rod-mediated restoration of pupillary response was observed, mainly at the central retinal location, but not at the peripheral central location (fig. 31, panel D). During ON episodes, the pericentral retinal location of cone-mediated PLR is reduced, but it is less severely affected than rod-mediated PLR (fig. 31, panel E). There was no significant change in cone-mediated PLR following SOLU-MEDROL treatment (fig. 31, panel E).

In addition, the color pupillary perimetry values correlate with the severity of VF loss during the ON episode, as shown in fig. 32, which shows that the color pupillary perimetry values correlate with the severity of VF loss during the ON episode. Four subjects (3 males (M) and 1 female (F)) of a given age (age, YO) were tested. Top row-color pupil visual field inspection map of PPC parameter. Bottom row-Humphrey visual field examination results.

Thus, as shown, decreased melanotropin-mediated PPR (in response to bright blue light) was recorded in peripheral VF in ON eyes compared to controls, ROC AUC =91.1% (p = 0.001). In most visual field test targets of the optic neuritic eye (mean + -SE: 60% + -12% and 55% + -10% test targets, respectively), the rod and cone cell mediated Percentage Pupillary Constriction (PPC) in response to red and blue light was more than 2 Standard Errors (SE) below the mean of the controls, even in patients with normal BCVA. Furthermore, although normal BCVA and VEP P100 were recorded in the contralateral eyes, substantially lower rod and cone mediated PPC values (2 SE below the mean of controls) were found in the contralateral eyes of all patients (mean SE: 33% + -9% and 30% + -7% of the test targets, respectively). The peripapillary OCT of both eyes was within the normal range.

Overall, the results clearly show that significantly lower rod, cone and melanopsin mediated PLR were recorded in both the ON and contralateral eyes of the patients, even in eyes with normal BCVA. Melanopsin-mediated pupillary responses to blue light at the peripheral retina can therefore be used as a highly sensitive surrogate functional biomarker for detecting ONs.

Example 5 brain pathology diagnosis of Fragile X Carriers

5 female healthy controls (63-79 YO, mean ± SD:70 ± 5yo, p = 0.62) and 6 female carriers (where n =3 was analyzed, 67-70, mean ± SD:69 ± 2YO, other 3 ages were less than 47 YO) were tested by pupillometric analysis as detailed in example 1. Subjects were further subjectively tested by visual contrast sensitivity and vernier threshold measurements.

The results are presented in fig. 33A and 33B. As shown, a diminished PPR response to bright blue in the peripheral region of the field of view was observed in fragile X carriers. In other words, attenuated melanopsin PPR was found in fragile X carriers. In addition, the results show that reduced LMCA in response to blue and red light in the central and peripheral fields of view was observed in fragile X-carriers. In other words, attenuated LMCA in rods and cones was observed in fragile X-carriers.

Overall, the results indicate that cognitive decline associated with fragile x carriers can be diagnosed early by mapping retinal circuit functions that mediate pupillary light responses at different retinal locations in response to stimuli presented at various wavelengths.

EXAMPLE 6 diagnosis and monitoring of patients with brain tumors and/or focal intracranial lesions

The objective of this study was to identify rod, cone and melanopsin mediated Pupillary Light Responses (PLR) presented at peripheral and central retinal locations in brain tumor patients for small focal color light stimulation.

For this, 18 patients and 32 age-matched controls were tested.

The brain edema group [6 women, 12 men, age 56.94 ± 13.12 (mean ± SD) ] was recruited based on a clinical diagnosis of brain tumors made by a committee-certified neurosurgeon based on pathology, which was clinically and radiologically expected to affect ICP or stress the visual pathway. Exclusion criteria were concurrent eye disease and any other condition affecting pupillary response. Data were recorded for all patients, including sex, gene mutation, snellen BCVA and 24-2 hummphrey visual field examination.

Study protocol

Each patient received examinations within 72 hours before surgery and within 1 week, 6 weeks and 3 months after surgery.

Pupillary response measurement:

the light stimulus occurred 330mm from the patient's eye. The study was carried out in a dim room (0.04 cd/m 2). The non-test eyes were covered with an eye mask. For the mesopic background conditions, a uniform white background light with an intensity of 0.04cd/m2 was used. After 2 minutes of accommodation under either apparent light condition, a small colored light stimulus from the LED (Goldmann size III,0.43 °) was presented on the different VF test points within 30 VF. To determine the optimal stimulation light intensity and duration required for focal excitation (focal ablation) of rod, cone and melanopsin-mediated Pupillary Light Reflex (PLR), PLR was recorded at 3 light intensities, at different locations and 2 wavelengths. The PLR for red light (625 + -15 nm) was tested first at each VF location and then the PLR for blue light (485 + -20 nm) was tested at the same light intensity using the same sequence. The light intensity was determined by measurement with an LS-100 luminance meter (Konica Minolta). All stimulation light intensities tested were well below the recommendations outlined in IEC 62471 for light biosafety of lamps and lamp systems and ICNIRP guidelines for exposure limits of incoherent visible and infrared radiation. Pupil diameter was recorded in real time by a computerized infrared high resolution camera at a frequency of 30 Hz. The PLR parameters were analyzed using custom software.

Rod and cone mediated PLR examination conditions

To evaluate rod and cone mediated PLR, stimulation duration was between 1 second and inter-stimulation interval was 4 seconds, respectively. The stimulation intensity of the red light stimulation was 1000cd/m ² The stimulus intensity of the blue light stimulus is 170cd/m ² 。

Melanopsin-mediated PLR examination conditions

To evaluate melanopsin-mediated PLR, red and blue light stimuli (6000 cd/m) ² ) Presentation is 8 seconds and the inter-stimulus interval is a 16 second interval.

Cerebral edema protocol

Subjects were acclimated to intermediate light conditions for two minutes prior to testing, which was conducted in a dark room. Using rod and cone mediated PLR examination conditions, light stimulation was presented in 30 degrees VF from 6 VF targets (0.43 degrees, goldmann size III). To evaluate melanopsin-mediated PLR, three VF targets were examined with blue light intensity. Both eyes were examined separately. The non-test eyes are occluded. Based on our previous studies, several PLR parameters were examined, including: percent Pupil Constriction (PPC); maximum shrinkage speed (MCV); MCV Latency (LMCV); maximum relaxation speed (MRV); MRV Latency (LMRV), PPR.

Optical coherence tomography of SD-OCT-spectral domain

Spectral optical coherence tomography (SD-OCT) enables in vivo real-time non-contact scanning of the eye and provides cross-sectional and volumetric images with resolution approaching that of histology. SD-OCT testing was performed to rule out retinal disease in controls and quantitatively assess retinal and optic nerve structures in patients. Retinal nerve fiber layer thickness, optic nerve head and macular ganglion cell layer thickness were obtained using Heidelberg Spectralis SD-OCT (Heidelberg Engineering, germany). Measurements were taken using a 3.4mm circular scan around the optic nerve using the standard protocol and segmentation algorithm of the device. All scans were acquired by experienced operators and checked by independent operators for correct centering and segmentation.

Statistical analysis

Statistical analysis was performed using IBM SPSS Statistics for Windows version 24.0 (Armonk, NY: IBM Corp). Age and gender of the study groups were compared using the t-test and fishers precision test, respectively. Test points where the device failed to record pupillary responses due to blinking were not included in the analysis.

Multiple comparisons were performed using the Bonferroni correction, and paired t-tests were performed to compare controls and patients at different VF goals and parameters. The area under the ROC curve (AUC) and p-value of PPR, PPC, MRV, MCV in response to blue and red light for each test point were calculated.

As a result, the

Pupillary light response measurement in patients with brain tumors

18 patients (mean age 56.94. + -. 13.12,6 females, 12 males) and 32 age-similar controls (mean age 53.62. + -. 12.21, 19 females, 13 males) were recruited. The mean age and gender of the subjects in both groups were not statistically significant (p =0.385 and p =0.140, respectively). Patients were divided into two groups according to tumor location: the first group includes patients whose tumors have not been in contact with the visual organs. The second group includes patients who contact the visual organs. SD-OCT measurements of ganglion cell and inner plexiform layer (GCL-IPL) and Retinal Nerve Fiber Layer (RNFL) thickness in all patients were within normal limits compared to published data (references 18, 19): group 1 (mean. + -. SD: 74.54. + -. 6.18 and 108.75. + -. 22.54, respectively); group 2 (mean. + -. SD: 79.79. + -. 5.25 and 97.57. + -. 9.88, respectively).

Group 1-tumor patients not exposed to the visual organ

10 patients with brain tumors that did not significantly contact the visual organs (Table 2, mean age 58.5. + -. 14.37,4 females, 6 males) and 32 age-similar controls (mean age 53.62. + -. 12.21, 19 females, 13 males) were recruited. The average age and gender of the subjects in both groups were not statistically significant (p =0.35 and p =0.468, respectively).

Due to their serious condition, most of these patients have completed the first visit (up to 24 hours prior to brain surgery).

Continuous PLR analysis of tumor patients not in contact with visual organs

Table 5 gives the mean normalized pupil size measured 3.7 seconds after cessation of blue light in the right eyes of patients and controls in 3 VF test subjects (as shown in fig. 34). In the control group, the normalized pupil size in all VF targets is ≦ 0.75 pixels. In contrast, in patients, the pupil size is significantly larger (> 0.8 pixels).

TABLE 5

Table 5 presents the normalized pupil size measured 3.7 seconds after blue light cessation. Data are expressed as mean ± SE. Student's two-tailed T-test p-values for comparisons between patients and controls with bonferroni correction are shown in parentheses.

ROC analysis of the results shown in fig. 35 revealed that the normalized pupil size measured 3.7 seconds after blue light cessation in the central retinal test target had the greatest AUC (90.8%, p = 0.003). The cutoff value (threshold) for the normalized pupil size that provided the most reliable prediction was 0.757 (sensitivity =0.857, 1-specificity = 0.143).

Transient PLR analysis of tumor patients not exposed to visual organs.

Table 6 gives an analysis of the 3 instantaneous PLR parameters (pupil contraction percentage (PPC), maximum Contraction Velocity (MCV), maximum Relaxation Velocity (MRV)) recorded in the target CN. Significantly lower transient PLR parameters were recorded in the patients compared to preoperative controls (table 6).

The data in table 6 are expressed as mean ± SE. Student's two-tailed t-test p-values for comparisons between patients and controls with bonferroni correction are shown in parentheses.

TABLE 6

VF test points for assessing transient PLR in tumor patients and controls who did not contact the visual organs are shown in fig. 36. CN assay target highlighted in red; temporal side (T); center-nasal- (CN); central-temporal- (CT); nasal side (N), superior (S); the following (I).

ROC analysis presented in fig. 37 revealed that the maximal contraction velocity recorded in response to red light stimulation in the CN target had the largest AUC (84.1%, p = 0.007). The cutoff value that provides the most reliable prediction is 11.97 pixels/sec. (sensitivity =0.778, 1-specificity = 0.143).

Case-patient #1

Patient No. 1 (female, 52 years old) was diagnosed with olfactory sulcus meningioma. Her MRI scan revealed frontal lobe brain tumors, no direct contact between the tumor and the optic chiasm or optic nerve (fig. 38). Pre-operative, pupillary visual field examination tests revealed a reduction in the instantaneous PLR, mainly for red light, in the central VF test point CN of the right eye. The recorded PPC, MCV and MRV in response to red stimuli differed from the mean of the controls by more than 2 SEs (table 7). PLR parameters recorded in response to blue light stimulation were within 2 SEs of the mean of the controls. Three months after surgery, PLR values were significantly higher compared to baseline measurements. Specifically, PLR parameters in response to red light were within 2 SEs of the mean of the controls 3 months after surgery. The mean normalized pupil size of patients after onset of blue light recorded in the central VF target before surgery was 0.83, significantly higher compared to the mean of controls (0.7 ± 0.02), indicating a defect in sustained melanopsin-mediated PLR. The value of this parameter was 0.79 within 2 SE of the mean of the controls, 3 months after surgery.

TABLE 7

Group 2-tumor patients in contact with visual organs

8 patients (mean age 55. + -. 12.02,2 females, 6 males) and 32 age-similar controls (mean age 53.62. + -. 12.21, 19 females, 13 males) were tested. The mean age and gender of the subjects in both groups were not statistically significant (p =0.78 and p =0.12, respectively). Due to the severity of their condition, most of these patients have completed the first visit (up to 24 hours before brain surgery).

Continuous PLR analysis of tumor patients exposed to visual organs

Table 8 gives the mean normalized pupil size measured 3.7 seconds after cessation of blue light in the right eyes of patients and controls in 3 VF test subjects (described in detail in fig. 39). In the control group, the normalized pupil size in all VF targets is ≦ 0.75. In contrast, pupil size was significantly larger (> 0.8, all p < 0.021) in the patient group.

Table 8-data are expressed as mean ± SE. Student's two-tailed T-test p-values for comparisons between patients and controls with bonferroni correction are shown in parentheses.

TABLE 8

The ROC analysis presented in fig. 40 revealed that the normalized pupil size measured 3.7 seconds after blue light cessation had the largest AUC (89.3%, p = 0.003) in the peripheral VF test target. The cutoff value for the normalized pupil size that provides the most reliable prediction is 0.817 (sensitivity =0.875, 1-specificity = 0.214).

Transient PLR analysis of tumor patients exposed to visual organs

Table 9 gives the parameters analyzed for the instantaneous PLR (percent pupil constriction (PPC), maximum Constriction Velocity (MCV), maximum Relaxation Velocity (MRV)) recorded in the upper (MS) and Center (CN) VF test target (highlighted in red in fig. 41) in the right eye. Significantly lower PLR values were recorded in the upper VF test target only of the preoperative patients compared to the controls (table 9). The other parameters of the instantaneous PLR recorded in the center VF target and the other VF test targets were not statistically significant compared to the controls.

TABLE 9Data are expressed as mean ± SE. Student's two-tailed t-test p-values for comparisons between patients and controls with bonferroni correction are shown in parentheses.

TABLE 9

ROC analysis revealed that the maximal systolic velocity recorded in response to red light stimuli in the upper VF test target had the largest AUC (96.4%, p = 0.006). The cutoff value that provides the most reliable prediction is 11.97 pixels/sec. (sensitivity =1, 1-specificity = 0.143)

Case-patient #7

Patient #7 was clinically diagnosed with glioblastoma in the right temporal lobe and irradiated in the right eye (see figure 42). Pupillary visual field examination testing revealed that pupillary responses to blue and red light were diminished in the upper VF test site shown in figure 41 in the right eye at visit 1. In the upper VF target, the PPC, MCV, and MRV values were lower than the mean of the controls (Table 10). PLR parameters recorded in the central VF test target were within 2 SEs of the mean of the controls (table 10). Three months after surgery, the PLR parameters of patients recorded in response to red light in the upper VF test target were above baseline and within 2 SEs of the mean of the control range. In contrast, the response to blue light is lower than baseline.

Analysis of the patient's sustained PLR revealed that the normalized pupil size at 3.7 seconds after cessation of blue light in the right eye was significantly greater in the 3 VF test subjects compared to the mean of the controls (table 11). Three months after surgery, the normalized pupil size of the patient is no greater than 0.74.

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TABLE 11

The presented results demonstrate the feasibility of evaluating the central-peripheral gradient of pupillary response kinetics for non-invasive objective diagnosis and monitoring of brain tumors. Multifactorial analysis of PLR for various stimuli presented at various retinal locations revealed that PLR deficiency differed between the two brain tumor groups. Thus, of the three VF test targets tested, both groups showed abnormal sustained melanopsin-mediated PLR. In contrast, in tumor patients who did not contact the visual organs, significantly lower transient PLR was recorded in the central retina, whereas in tumor patients who contacted the visual organs, significantly lower transient PLR was recorded in the peripheral (superior retina), but not in the central retina.

It should be noted that all patients underwent normal eye examination and had normal values for SD-OCT, RNFL and GCL-IPL thickness, indicating that the defects measured in PLR originated from changes in the brain, not in the retina.

The results suggest that PLR is primarily affected in the right eye of patients, which is relevant to clinical diagnosis of these patients that reveals tumors on the right side of the brain affecting the right eye.

And (4) conclusion: focal intracranial lesions can be detected by local melanopsin-mediated persistent PLR against central blue stimuli. Patients with brain tumors involving the visual pathway have additional defects in cone-mediated PLR. Quantification of PLR for central and peripheral focus color stimulation can be a new non-invasive objective diagnostic tool for focal intracranial lesions.

Example 7-diagnosis and monitoring of intracranial lesions patients

The objective of this study was to characterize the Pupillary Light Response (PLR) in intracranial diseased patients for small focal color light stimuli presented at peripheral and central retinal locations. For this purpose, 18 brain tumor patients and 32 age-similar controls were recruited. Patients were divided into 3 groups: group I included patients with brain tumors that contacted the optic tract and/or midbrain, group II included patients with brain tumors involving visual radiation, and group III included patients with brain tumors that did not have apparent contact with the visual or PLR system (n =6 per group). Under mesopic vision photopic adaptation conditions, color pupillometry was used to measure PLR for small (0.43 °) blue and red light stimuli presented at peripheral (21 °) and central (4.2 °) visual field positions. All subjects received a complete ophthalmic examination, a standard humphey automated visual field examination (24-2), color vision testing, best corrected vision, refraction, and spectral domain optical coherence tomography (SD-OCT) imaging. All patients received a brain MRI examination.

The inclusion criteria for the control group were normal eye examination, no history of ocular disease, normal color vision (Farnsworth/Lanthon D-15 test), best Corrected Vision (BCVA) of 20/20, no use of local or systemic drugs that could adversely affect PLR and normal humrey 24-2 visual Field examination tests (humrey Field Analyzer II, SITA 24-2.

These patients were enrolled based on diagnosis and good performance status of brain lesions (ability to sit and follow the instructions of the technician for PLR and visual field examination tests). All patients received standard MRI brain protocols (T1, without and with gandolinuim, T2, FLAIR) prior to inclusion. The neurological state of the patient is determined by a certified neurosurgeon. The ocular and brain exclusion criteria of patients are concurrent ocular diseases and any other condition affecting PLR. Controls were tested in a single visit. All patients received the examination within 72 hours prior to surgery. Three patients in group I were tested again within 3-5 days and 3 months after surgery. Two patients in group II had visits 3-5 days and 3 months after completion of the OP test. All other patients felt not good enough or did not wish to return for a subsequent ophthalmic examination.

At each visit, participants were subjected to BCVA, humphrey visual Field examination (Humphrey Field Analyzer II, SITA 24-2 protocol), spectral domain optical coherence tomography (SD-OCT), and color pupil visual Field examination.

SD-OCT

Macular and peripapillary scans were performed using Heidelberg Spectralis SD-OCT (Heidelberg Engineering, heidelberg, germany). Horizontal high resolution transfoveal line scans were obtained using automatic real-time tracking (ART), averaging 8 images. Each volume scan consists of 25 horizontal b-scans in high resolution mode, with a distance of 237 μm between b-scans, and 512 a-scans per b-scan. Parapapillary measurements were performed using a 3.45mm circular scan centered on the optic disc with 1536 a scan points.

All scans were acquired by experienced operators and checked for correct centering and segmentation.

Color pupil visual field inspection

Under mesopic conditions (0.04 cd/m) ² ) ^20,23 Next, PLR was measured using color pupillometry. Prior to testing, the patient was acclimated to ambient light conditions for two minutes and the non-tested eyes were covered with a black eye mask. Participants were asked to look at a fixation device white light (6 cd/m) located in the center of the device ² ). Focused color light stimuli (0.43 °, goldmann size III) were presented in sequence at two central (4.24 °), nasal and temporal (21.21 °) VF locations. First, red light stimulation (624 nm. + -. 5nm, 1000cd/m) is presented ² 1 second stimulation duration), followed by dimming (170 cd/m) ² ) Blue light (485 nm + -5 nm) stimulation (1 second stimulation duration, FIG. 43 panels A, D). The inter-stimulus interval was 3 seconds. After completion of this sequence, bright (6000 cd/m) appeared in the central (4.24 °), nasal and temporal (21.21 °) VF locations ² ) Blue light stimulation was 8 seconds (fig. 43 panels B, C), with an inter-stimulation interval of 8 seconds. The nasal and temporal locations were selected to match the Humphery 24-2 location in each eye.

The light intensity was measured using an LS-100 luminance meter (Konica Minolta, tokyo, japan) and is well below the recommendations outlined in IEC 62471 for the photobiosafety of lamps and lamp systems and the ICNIRP guide for the exposure limits of incoherent visible and infrared radiation.

The patient is tested for both eyes. Except for patient #4 who was very tired and only completed testing for the left eye. The controls were tested on only one eye, and 16 subjects were randomly assigned for the right eye and 16 subjects for the left eye.

The pupil diameter was automatically recorded at a frequency of 30 Hz. For each stimulus, the percent pupil constriction (PPC in%) was automatically determined by the software after normalization based on the initial pupil size measured at the beginning of each stimulus using the following formula:

maximum contraction speed (MCV in pixels/sec) and MCV latency (LMCV in seconds) were determined as described previously (Haj Yahia S et al, invest opthalmol Vis Sci 2018.

The percent pupil recovery (% PPR in%) for bright blue light stimuli was determined at 3.7 seconds after the start of light based on the initial pupil size measured at the start of each stimulus using the following formula.

A normalized minimum pupil size (NMPS, in%) is determined during the maximum pupil constriction phase based on the initial pupil size measured at the beginning of each stimulus using the following formula:

tests in which subjects blinked during the pupil constriction phase were automatically excluded and the test subjects were automatically retested.

Statistical analysis

Statistical analysis was performed using IBM SPSS Statistics for Windows version 25.0 (Armonk, NY: IBM Corp). Test points where the PLR was recorded to fail more than 4 times were not included in the analysis. Age and gender of the patient groups were compared to controls using the t-test and fishers precision test, respectively. The left eye and right eye pupillary visual field inspection data of the control were mirrored to the right eye and left eye, respectively, for statistical analysis.

The general linear model used to compare the mean of the pupillary visual field examination metrics between groups was adjusted for age and gender. A simple comparison with a control as a reference group was calculated. Robustness of discrimination between control and patient groups using PPC and PPR was tested by calculating ROC AUC for data adjusted by age and gender. If P <0.05, the difference is considered significant.

As a result, the

Focused dim blue light stimulation reduction PLR for brain lesion patients：

Patients with brain tumors that contacted the midbrain or visual organs (group I), patients with brain tumors that did not contact the midbrain or visual organs but contacted other parts of the visual pathway (group II), and patients with lesions that did not have significant contact with the midbrain or visual pathway (group III) were recruited.

SD-OCT thickness measurements of the ganglion cell layer and inner plexiform layer (GCL-IPL) and Retinal Nerve Fiber Layer (RNFL) were within normal limits in all patients.

The red PPC did not differ significantly between study groups at most VF test point locations. In contrast, significantly lower PPC values were recorded in the nasal and central-temporal VF test subjects in the right eye and in the nasal and central-nasal test subjects in the left eye in response to dim blue light in group I patients (table 12). In group II, significantly lower PPC were recorded only at nasal, central-temporal and temporal locations in the right eye. No significant difference in PPC for dim blue light stimulation was observed in both eyes of group III in any of the VF test subjects. Furthermore, no significant difference in the PLR parameters MCV or LMCV was observed in both patient groups in response to dim blue light stimulation.

ROC analysis showed that group I patients had good diagnostic accuracy (ROC AUC > 82%) compared to controls using PPC values for dark blue light for the binocularly nasal VF target and the right eye central temporal side test target. In group II, a ROC AUC of >78% was obtained in all VF test targets, with significantly lower PPC measured in patients compared to controls (table 12).

TABLE 12

VF-visual field, N-number of subjects measuring PLR at a given test site, ^# mean-percent pupil constriction (PPC in%), ^￡ p-value-comparison of patient groups with controls, sexThe identity and age were adjusted. ^€ ROC AUC (expressed in%) was calculated only for test targets with significant differences between the patient groups and the controls. * p is a radical of<0.05。

As detailed above, bright blue light stimulation was present for 8 seconds to induce sustained PLR. The Percent Pupil Recovery (PPR) was recorded at 3.7 seconds after the start of the light. The selection of this point in time is based on the following findings: the average PPR in the control group was 96% or more at 3.7 seconds after the onset of dim blue light stimulation (Table 13). In contrast, the average PPR at this time point was significantly lower after the bright blue stimulus was present in the control group (average PPR ≦ 76%, table 13).

Watch 13

* Dim blue light stimulation: light intensity-170 cd/m ² Present for 1 second; ^# bright blue light stimulation: light intensity-6000 cd/m ² Presenting for 8 seconds; VF-visual field, N-number of subjects measuring PLR at a given test site, ^￡ NMPS-normalized minimum pupil size (measured at the maximum pupil constriction stage, expressed as the mean and standard deviation in%), ^€ PPR — percent pupil recovery, measured at 3.7 seconds after the onset of light (expressed as mean and standard deviation in%).

All VF locations tested in the right eye and nasal and central VF locations in the left eye, group I patients showed significantly faster pupil recovery (greater PPR values) for bright (strong/high intensity) blue light stimulation compared to controls (table 14). ROC analysis showed that this parameter had good diagnostic accuracy (> 85.9%) at all VF sites tested in the right eye and excellent diagnostic accuracy at the nasal test point in the left eye (ROC AUC =96.8%, p = 0.001). The patients in group II showed significantly greater PPR values for bright blue light stimulation compared to the control, ROC AUC (. Gtoreq.85.6%) for all VF test points in the right eye. In the nasal VF test target in the right eye, group III patients exhibited significantly greater PPR values (88 ± 5% versus 76 ± 9%, p = 0.005) compared to controls, and this parameter distinguished group III patients from controls with excellent sensitivity and specificity (ROC AUC =95.3%, p = 0.001).

TABLE 14

Case-patient #4

Patient #4, 59YO male presented with headache and complained of vision problems. He was clinically diagnosed with supratentorial right temporal brain metastases of lung cancer. MRI scans showed brain edema in the right frontal and temporal lobes, causing direct pressure on the right optic nerve and right optic tract (red arrows in fig. 44 panels a-B). Left eye vision is 20/20 and right eye vision is 20/25, with normal SD-OCT and fundus imaging (fig. 44 Panel E). Visual field inspection testing (Humphrey SITA standard algorithm) revealed a large VF loss, MD = -19.54dB and PSD =10.30dB in the left eye and MD = -29.74dB and PSD =3.29dB in the right eye (fig. 44 panels F, G). The maximum Percent Pupil Constriction (PPC) for dim (low intensity) blue light was more than 2 Standard Errors (SE) below the mean of the controls for most test points in both eyes (fig. 44 panel I). The Percent Pupil Recovery (PPR) for bright blue light stimuli was more than 2 SE higher than the mean of the controls (faster pupil recovery) in all test subjects in the right eye and in the central and temporal subjects in the left eye. In the nasal test subjects in the left eye, the PPR was within 2 SE of the mean of the controls (fig. 44 panel J).

MRI scans 4 months after OP confirmed significant improvement in brain edema and mass effects on the right optic nerve (fig. 44 panel C). T1 MRI with gadolinium revealed recurrent tumors of the right frontal lobe, directly compressing the right optic nerve (fig. 44 panel D). The left eye has 20/20 of vision and the right eye has no light sensation. Peripheral VF loss was observed in the left eye using the Humphrey SITA standard algorithm in the area primarily superior temporal side, where MD = -4.58db, psd =6.67db. No light sensation was observed in the right eye using the Humphrey SITA standard and FASTPAC stimulation V algorithm (fig. 44 panel H). In all test points in the right eye, the PPC for dim blue light was more than 2 Standard Errors (SE) lower than the mean of the controls. In contrast, PPC for dim blue light in the left eye improved significantly and the PPC values were within 2 SE of the mean of the controls in all VF test subjects in the left eye, which correlates with pressure relief on the left eye after tumor removal (fig. 44 panel K). In all test subjects in the right eye, PPR for bright blue stimuli remained more than 2 SE higher than the mean of the controls. In the left eye, the PPR in the center of the left eye and nasal target was within 2 SEs of the mean of the controls, but the PPR in the temporal VF was lower than 2 SEs of the mean of the controls (fig. 44 panel L), which correlates to the superior temporal defect in this area by hummphrey visual field examination.

Case-patient #10 (group II)

Patient #10, 38YO, clinically diagnosed as right temporal lobe glioblastoma. Functional MRI T1 with gadolinium showed brain edema around the right temporal enhancement lesion involving right side visual radiation (red arrows in panels a-C of fig. 45). The vision of both eyes was 20/20, and the hummphrey visual field examination was almost normal (left eye MD = -0.85db, psd = -2.11db, right eye MD = -0.28db, psd = -1.91db, fig. 45 panel I). Fundus imaging and SD-OCT showed normal optic papilla and RNFL thickness (fig. 45 panels G, H). Pupillary perimetry testing in response to dim blue light revealed a reduced PPC that was below 2 Standard Errors (SE) of the mean of the controls in three of the four VF test points of the right eye and in only a single (central) test point of the left eye (fig. 45 panel L). In contrast, the PPR for bright blue was more than 2 SE higher than the mean of the controls in all test subjects in the right eye and in two of the three test spots in the left eye (fig. 45 panel M). MRI scans performed immediately after OP showed improvement in tumor mass effect and cerebral edema in the right temporal lobe with complete removal of tumor (fig. 45 panel D). 4 days after surgery, the vision was 20/16. The Humphrey visual field examination showed a decrease in sensitivity in the upper left quadrant, MD = -1.69, psd = -5.4 in the right eye, MD = -1.55, psd = -6.65 in the left eye (fig. 45 panel J). Pupillary visual field examination tests revealed complete recovery of PPC for dim blue light in both eyes, with all recorded PPC values within 2 SE of the mean of the controls (fig. 45 panel N). In contrast, attenuated PLR for bright blue light was recorded in both eyes, where the PPR was more than 2 SEs higher than the mean of the controls in all tested targets (fig. 45 panel O). Three months after surgery, MRI scans were performed due to nerve degeneration, showing tumor recurrence in the right temporal lobe and peripheral cerebral edema, involving right optic nerve radiation and mass effect on the right midbrain (fig. 45 panels E, F). The binocular vision is 20/20. Fundus imaging and OCT showed normal optic papilla and RNFL thickness. Visual field examination tests confirmed incomplete bilateral upper left quadrant views (fig. 45 panel K), MD = -4.76, psd =11.7 in right eye, MD = -4.97, psd =11.44 in left eye. At the central VF test point in both eyes, the PPC for dim blue light was lower than 2 SE of the mean of the controls (fig. 45 panel P). In all VF test points, normal PPR values for bright blue light were recorded in the right eye (within 2 SE of the mean of the controls), but in all test subjects, the PPR in the left eye was still higher than normal (fig. 45 panel Q), which correlates with a lump effect in the left brain.

Case-patient #18 (group III)

Patient #18, 61YO, presented with headache and a state of confusion, but did not complain of vision problems. Functional MRI T1+ GAD scans showed large right lateral frontal convex meningiomas (red arrows in panels a-B of fig. 46) and cerebral edema in the frontal and right temporal lobes, with signs of increased ICP. Four weeks after tumor removal, MRI scans showed improvement in brain edema and mass effects (fig. 46 panels C-D). Before operation, the vision of both eyes is 20/20. Fundus imaging and SD-OCT showed normal optic papilla and RNFL thickness (fig. 46 panels E, F). Both eyes were examined for a humphey visual field that was normal (fig. 46 panel G). In contrast, color pupillometry tests performed before OP showed significantly lower PPC for dim blue light in the right and central visual field test targets in both eyes (fig. 46 panel I), which correlates with a mass on the right side of the brain. Furthermore, in both eyes in two of the three test subjects, the PPR in response to bright blue light was significantly lower than normal (fig. 46 panel J). BVCA remained 20/20 7 days after tumor removal and both eyes were examined for Humphrey visual fields as normal (FIG. 46 panel H). Fundus imaging and SD-OCT showed normal disk and RNFL thickness (data not shown). Color pupillary visual field examination revealed that in most of the tested subjects in both eyes, PLR improved and PPC was normal in response to dim blue light stimuli (fig. 46 panel K). In both eyes, the center and left test target, the PPR for bright blue light was significantly lower than normal (fig. 46 panel L).

The results indicate that the SD-OCT thickness of the macular ganglion cells and the inner plexiform layer, as well as the peripapillary retinal nerve fiber layer, are within normal limits in all patients. Patients in groups I and II showed reduced rod-mediated PLR in the right eye. The highest ROC AUC was measured in the nasal test targets (AUC =86.8%, p =0.006 and AUC =84.0%, p =0.011 for group I and group II, respectively). In contrast, the rod-mediated PLR was not significantly different between patient group III and the control. In response to bright blue light stimulation in the right eye, patients showed significantly faster pupil recovery, with a high (> 85%) ROC AUC measured in all test targets of group I and group II as well as in the nasal test target of group III (AUC =95.3%, p = 0.001). In the vast majority of the tested subjects, the cone-mediated PLR was not statistically significant in all patient groups from the control.

The results demonstrate the feasibility of color pupillary visual field examination for non-invasive objective diagnosis and monitoring of brain lesions. Multifactorial analysis of blue and red stimulated PLR presented at various retinal locations at different light intensities revealed that PLR deficiency differed between the three patient groups. Thus, although PLR (primarily mediated by cone cells) for red light was not significantly affected in patients, significantly attenuated rod-mediated PLR was recorded in tumor patients exposed to PLR and visual pathways. In addition, all patient groups showed abnormal sustained melanopsin-mediated PLR. All patients showed normal SD-OCT, RNFL and GCL-IPL thickness, indicating that PLR deficiency originated from changes in the brain, not changes in retinal levels.

The results indicate that patients with brain tumors have normal MCV and LMCV. These findings highlight the advantage of analyzing various PLR parameters to identify diagnostic specific PLR biomarkers.

Interestingly, color pupillary visual field examination can sensitively detect focal PLR defects in patients with brain tumors, even in patients without significant visual loss. Despite the relatively small sample size, ROC AUC >85% was obtained for PLR measurements in all patient groups (including group III, whose tumors had no apparent association with PLR of the visual pathway, and BCVA and Humphrey visual field examination was normal).

Overall, the results indicate that focal intracranial lesions can be detected by local melanopsin-mediated persistent PLR against high intensity blue stimuli. Patients with brain tumors involving the visual pathway have additional defects in rod-mediated PLR. Quantification of PLR for central and peripheral color stimuli can be a new non-invasive objective diagnostic tool for focal intracranial lesions.

Example 8-evaluation of pupillary reflex in response to Focus color light stimulation in acute Pseudobrain Tumors (PTC)

4 Pseudocerebroma (PTC) patients (all women, age: 26.2 + -4.3, mean + -SD) and 8 healthy age-matched controls (5 women, 3 men, age 29.2 + -6.3) were tested.

Ophthalmic evaluation included complete ophthalmic examination, color vision, optical coherence tomography (SD-OCT), blue pupil focus (485nm, 170cd/m) ² ) And red (624nm, 1000cd/m) ² ) Response (PR) -light stimuli presented at 54 targets in 24-2VF were recorded by a color pupillometer. The percent change in pupil size (PPC) and maximum relaxation rate (MRV) of the patients were compared to PR of the controls. Patients were tested within 48 hours of diagnosis and then tested 1 week and 2 months after acetazolamide dryness.

The results presented in FIGS. 47 and 48 indicate that significantly lower PPC and MRV (4 SD below the mean of the controls) were recorded at the first visit in the three patients. Acetazolamide treatment improved pupil response to blue stimuli, but not red, suggesting a residual effect on cone-mediated PLR. The pupillary response is improved primarily in the center of the field of view. Thus, rod and cone mediated PLR is affected in PTC.

Overall, the results indicate that multifactorial analysis of PLR for focal blue and red light stimulation can allow objective, non-invasive, sensitive assessment of the function of the visual pathways mediating PLR in PTC patients and their response to treatment.

Claims

1. A non-invasive method of monitoring the progression of, determining and/or assessing a brain-related condition of a subject based on Pupillary Light Response (PLR) to colored light stimuli, the method comprising:

determining a baseline pupil size of an eye of a subject;

obtaining values for one or more parameters related to the induced change in pupil size in response to the light stimulus;

classifying the PLR based on the one or more parameter values;

wherein the classification results in monitoring the progression of, determining and/or assessing the brain-related condition.

2. The method of claim 1, wherein the brain-related condition is selected from the group consisting of: brain tumors, optic neuritis, neurodegenerative diseases, traumatic brain injury, stroke, intracranial lesions, intracranial pressure, and pseudobrain tumors.

3. The method of claim 2, wherein the neurodegenerative disease is selected from the group consisting of: alzheimer's Disease (AD), multiple Sclerosis (MS), parkinson's Disease (PD) and cognitive decline associated with Fragile X.

4. The method of any of claims 1-3, wherein the one or more parameters are selected from: pupil contraction percentage (PPC), pupil Response Latency (PRL), maximum Contraction Velocity (MCV), MCV Latency (LMCV), pupil relaxation percentage (PPR), maximum Relaxation Velocity (MRV), MRV Latency (LMRV), maximum Contraction Acceleration (MCA), MCA Latency (LMCA), maximum Relaxation Acceleration (MRA), MRA Latency (LMRA), maximum Relaxation Deceleration (MRD), maximum relaxation deceleration Latency (LMRD), curve Area (AC), maximum pupil contraction Latency (LMP), maximum Contraction Deceleration (MCD), MCD Latency (LMCD), maximum pupil size (Max _ PS), minimum pupil size (Min _ PS), and any combination thereof.

5. The method according to any one of claims 1-4, further comprising applying a curve fit to the data associated with pupil size in response to the light stimulus.

6. The method according to any one of claims 1-5, wherein said classifying comprises applying at least one algorithm to one or more selected parameter values and obtaining a brain-related condition.

7. The method of any of claims 1-6, wherein the light stimulus comprises 1 to 228 individual light stimuli, each light stimulus being applied to a different location of the visual field.

8. The method of any of claims 1-7, wherein the optical stimulus comprises a wavelength ranging from about 410nm to about 520nm and/or from about 550nm to about 700 nm.

9. The method according to any of claims 1-8, wherein the light stimulus comprises high intensity light and/or low intensity light.

10. The method of any of claims 1-9, wherein the light stimulus is presented for a period of about 0.1 to 10 seconds.

11. The method of any of claims 1-10, wherein the region of the field of view comprises a central field of view ranging between about 0-10 degrees.

12. The method of any of claims 1-11, wherein the region of the field of view comprises a peripheral field of view greater than about 10 degrees.

13. The method according to any one of claims 1-12, further comprising providing an initial predetermined light stimulus with an initial illumination, duration, and location of the field of view, the initial predetermined light stimulus configured to determine a likelihood that the subject has a brain-related condition, and wherein applying the blue and/or red light stimulus to one or more regions of the field of view of the eye is based at least in part on the determined likelihood in the initial illumination.

14. The method of any one of claims 1-13, wherein applying blue and/or red light stimuli to one or more regions of a visual field of an eye comprises selecting a subset of the light stimuli based on their location relative to the visual field.

15. The method of any one of claims 1-14, wherein applying the blue and/or red light stimulus to one or more regions of the visual field of the eye comprises one or more of: selecting a wavelength of each individual light of the light stimulus, selecting an intensity of each individual light of the light stimulus, selecting a ratio of a blue light stimulus to a red light stimulus, selecting a duration of illumination of each individual light of the light stimulus, or any combination thereof.

16. The method of any of claims 1-15, wherein applying the blue and/or red light stimulus to one or more regions of the visual field of the eye comprises applying the blue and/or red light stimulus in at least two intervals.

17. The method of claim 16, wherein the at least two intervals are about 2 to 120 seconds apart.

18. The method according to any of claims 15-17, wherein each interval comprises a different subset of light stimuli, a different wavelength of light stimuli, and/or a different intensity of light stimuli.

19. The method according to any one of claims 1-18, wherein the baseline pupil size is determined for each individual stimulus.

20. The method of any one of claims 1-19, further comprising positioning the subject's eye at an eye fixation device such that the subject's non-test eye is occluded.

21. The method according to any one of claims 1-20, wherein the method comprises determining a risk of developing alzheimer's disease, and the calculated value is determined based on at least one of: MCV parameters in a central region of the field of view in response to a high intensity blue light stimulus, PRL parameters in response to blue light, PRL parameters in response to red light, LMCA parameters in response to blue light, LMCA parameters in response to red light, LMCD parameters in response to blue light, LMCD parameters in response to red light, LMP parameters in response to blue light, LMP parameters in response to red light, MCV parameters in response to blue light, or any combination thereof.

22. The method of any one of claims 1-20, wherein the condition is parkinson's disease and the calculated value is determined based on at least one of: the PPR parameter in a central region of the field of view in response to the high intensity blue light stimulus, the PPC parameter in the central region of the field of view in response to the low intensity blue light stimulus, the MCA parameter in the central region of the field of view in response to the low intensity blue light stimulus, the PPC parameter in a peripheral region of the field of view in response to the low intensity blue light stimulus, the MCA parameter in the peripheral region of the field of view in response to the low intensity blue light stimulus, and the PPC parameter in the central region of the field of view in response to the red light stimulus.

23. The method according to any one of claims 1-20, wherein the condition is a brain tumor and the calculated value is determined based on at least one of: a PPR parameter in a peripheral region of the visual field in response to the high intensity blue light stimulus and a PPC parameter in a peripheral region of the visual field in response to the low intensity blue light stimulus.

24. The method according to any one of claims 1-20, wherein the condition is a fragile X carrier and the calculated value is determined based on at least one of: the PPR parameter in the peripheral region of the field of view in response to the high intensity blue light stimulus, the LMCA parameter in the peripheral region of the field of view in response to the low intensity blue light stimulus, and the LMCA parameter in the central region of the field of view in response to the red light stimulus.

25. The method of any of claims 1-20, wherein the condition is Multiple Sclerosis (MS), and the calculated value is determined based on at least one of: PPC parameters in the peripheral region of the visual field in response to red light stimuli, PPC parameters in the peripheral region of the visual field in response to low-intensity blue light stimuli, MRV parameters in the peripheral region of the visual field in response to red light stimuli, MRV parameters in the peripheral region of the visual field in response to low-intensity blue light stimuli, and MCV parameters in the peripheral and/or central regions of the visual field in response to red light stimuli and/or blue light stimuli, and PPR at central and/or peripheral locations in response to strong + long duration blue light.

26. The method of any one of claims 1-20, wherein the condition is optic neuritis and the calculated value is determined based on at least one of: the PPC parameter in the peripheral and/or central region of the field of view in response to the red light stimulus and/or the bright blue light stimulus, and the PPR parameter in the peripheral region of the field of view in response to the blue light stimulus, and/or the calculated value is the number of test targets having abnormal PPC in response to the blue light and the red light.

27. The method according to any one of claims 1-20, wherein the condition is an intracranial lesion and the calculated value is determined based on at least one of a PLR parameter in a peripheral region and/or a central region of the field of view in response to high intensity blue light stimulation, a PPC parameter in a nasal region of the field of view in response to low intensity blue light, a PPR parameter in a nasal region of the field of view in response to low intensity blue light.

28. The method according to any one of claims 1-20, wherein the condition is a pseudobrain tumor and the calculated value is determined based on at least one of a PPC parameter in a peripheral region and/or a central region of the visual field in response to a red light stimulus and/or a blue light stimulus, and an MRV parameter in a peripheral region and/or a central region of the visual field in response to a red light stimulus and/or a blue light stimulus.

29. The method of any of claims 1-20, wherein the condition is stroke and the calculated value is determined based on at least one of: a PPR parameter in a peripheral region and/or a central region of the visual field in response to the high-intensity blue light stimulus, a PPC parameter in a peripheral region of the visual field in response to the low-intensity blue light stimulus, and an MRV parameter in a peripheral region of the visual field in response to the low-intensity blue light stimulus.

30. The method of any one of claims 1-29, further comprising controlling emission wavelength, intensity, and duration of individual light stimuli or subsets of light stimuli.

31. A method as claimed in any one of claims 1 to 30, wherein when values for more than one parameter are obtained, the steps of determining a baseline pupil size, applying a blue and/or red stimulus and obtaining a value are repeated to obtain a value for each parameter.

32. The method according to any one of claims 1-31, further comprising inputting one or more selected values of at least one of the one or more parameters to a machine learning algorithm configured to classify a subject as having a brain-related condition or not having a brain-related condition.

33. The method of claim 32, further comprising classifying, using the machine learning algorithm, the brain-related condition as a type and/or severity level and/or progression of the condition based at least in part on the one or more parameters and their selected values.

34. A pupillometer device for monitoring the progress of, determining and/or assessing a brain related condition of a subject based on pupillary light responses to a colored light stimulus, the pupillometer device comprising:

at least one camera configured to detect pupillary responses; and

determining a baseline pupil size of an eye of a subject;

classifying the PLR based on the one or more parameter values and at least one feature value, wherein the classification allows monitoring the progress of, determining and/or assessing the brain-related condition.

35. The device of claim 34, wherein the brain-related condition comprises: brain tumors, optic neuritis, neurodegenerative diseases, traumatic brain injury, stroke, intracranial lesions, intracranial pressure, and pseudobrain tumors.

36. The device of claim 35, wherein the neurodegenerative disease is selected from alzheimer's disease, multiple sclerosis, parkinson's disease, and cognitive decline associated with fragile-X.

37. The apparatus of any one of claims 34-36, wherein the one or more parameters are selected from: pupil contraction percentage (PPC), pupil Response Latency (PRL), maximum Contraction Velocity (MCV), MCV Latency (LMCV), pupil relaxation percentage (PPR), maximum Relaxation Velocity (MRV), MRV Latency (LMRV), maximum acceleration of contraction (MCA), MCA Latency (LMCA), maximum acceleration of relaxation (MRA), MRA Latency (LMRA), maximum deceleration of relaxation (MRD), maximum deceleration of relaxation (LMRD), curve Area (AC), maximum pupil contraction Latency (LMP), maximum deceleration of contraction (MCD), MCD Latency (LMCD), maximum pupil size (Max _ PS), minimum pupil size (Min _ PS), and any combination of these.

38. The apparatus according to any of claims 34-37, further comprising applying a curve fit to the data associated with pupil size in response to the light stimulus.

39. The apparatus of any of claims 34-38, wherein said classifying comprises inputting one or more selected parameter values to one or more machine learning algorithms.

40. The device of any one of claims 34-39, wherein the light stimulus comprises 1 to 228 individual light stimuli, each light stimulus being applied to a different location of the visual field.

41. The device according to any of claims 34-40, wherein the optical stimulus comprises wavelengths ranging from 410nm to 520nm and/or from 550nm to 700 nm.

42. The apparatus according to any of claims 34-41, wherein the light stimulus comprises a high intensity and/or a low intensity.

43. The method of any one of claims 34-42, wherein the light stimulus is presented for a period of about 0.1 to 10 seconds.

44. The apparatus of any of claims 34-43, wherein the region of the field of view comprises a central field of view ranging between about 0-10 degrees.

45. The apparatus of any of claims 34-44, wherein the region of the field of view comprises a peripheral field of view greater than about 10 degrees.

46. The device according to any of claims 34-45, wherein the control unit is further configured to select the subset of light stimuli based at least on their position relative to the visual field.

47. The device according to any of claims 34-46, wherein the control unit is further configured to select a wavelength of each individual light of the light stimulus, an intensity of each individual light of the light stimulus, a ratio of blue light stimulus to red light stimulus and/or a duration of illumination of each individual light of the light stimulus.

48. The device according to any of claims 34-47, wherein the control unit is further configured to apply the blue and/or red light stimulus to one or more regions of the visual field of the eye, the applying the blue and/or red light stimulus to one or more regions of the visual field of the eye comprising applying in at least two intervals.

49. The apparatus according to claim 48, wherein the at least two intervals are between 2 and 120 seconds apart.

50. The device of any of claims 48-49, wherein each interval comprises a different subset of light stimuli, a different wavelength of light stimuli, and/or a different intensity of light stimuli.

51. The device according to any one of claims 34-50, wherein the control unit is in communication with a server or memory module containing instructions for identifying, assessing and/or monitoring the progress of a brain-related condition.

52. The device according to any of claims 34-51, wherein the control unit is configured to classify one or more selected values of at least one of the one or more parameters as being associated with a brain-related condition and/or a progression of a brain-related condition based on a machine learning algorithm.

53. A system for monitoring the progression of, determining and/or assessing a brain-related condition of a subject based on pupillary light response to a colored light stimulus, the system comprising:

at least one hardware processor; and

a non-transitory computer-readable storage medium having program code stored thereon, the program code executable by the at least one hardware processor to:

receiving data relating to red and/or blue light stimuli generated at predetermined locations of a visual field of a subject;

receiving data associated with a pupil size of a subject;

inputting one or more selected values of at least one of the one or more parameters to an algorithm configured to classify the subject as having a brain-related condition or not based at least in part on at least one value of one or more parameters.

54. The system according to claim 53, wherein the algorithm is a machine learning algorithm configured to classify the brain-related condition as a type and/or severity level and/or progression of the condition based at least in part on the values of the one or more parameters.