US20170344706A1

US20170344706A1 - Systems and methods for the diagnosis and treatment of neurological disorders

Info

Publication number: US20170344706A1
Application number: US15/457,634
Authority: US
Inventors: Elizabeth B. Torres; Jorge Jose-Valenzuela
Original assignee: Rutgers State University of New Jersey; Indiana University Research and Technology Corp
Current assignee: Rutgers State University of New Jersey; Indiana University Research and Technology Corp
Priority date: 2011-11-11
Filing date: 2017-03-13
Publication date: 2017-11-30
Also published as: US20190333629A1; WO2013071285A1; US11270790B2; US20140336539A1; US10176299B2

Abstract

Systems and methods for data compression which facilitate the diagnosis and treatment of neurodevelopmental and neurodegenerative disorders. The methods comprise performing the following operations by a computing device: generating Normalized Data (“ND”) from Original Data (“OD”) that defines a Normalized Waveform (“NW”) that is unitless and scaled from zero to one; processing ND to extract Micro-Movement Data (“MMD”) defining a Micro-Movement Waveform (“MMW”) comprising a plurality of MMD points; and generating compressed data comprising a stochastic signature of MMW. Each MMD point determined based on a value of a peak of NW and a value representing an average of all data point values between a first valley of NW immediately preceding the peak and a second valley of NW immediately following the peak. The stochastic signature is defined by empirically estimated values of at least one parameter representing a Probability Distribution Function (“PDF”) of a continuous family of PDFs.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Patent Ser. No. 62/198,930 filed on Jul. 30, 2015, and is a continuation-in-part of U.S. patent application Ser. No. 14/354,796 filed Apr. 28, 2014, which is a U.S. National Phase of International Patent Application Serial No. PCT/US2012/064805 filed Nov. 13, 2012, which claims priority under 35 U.S.C. §119(e) to U.S. Patent Ser. No. 61/648,359 filed on May 17, 2012, U.S. Patent Ser. No. 61/581,953 filed on Dec. 30, 2011, and U.S. Patent Ser. No. 61/558,957 filed on Nov. 11, 2011. The content of the above applications are incorporated by reference in their entirety.

GOVERNMENT RIGHTS

The invention was made with Government support under Grant No. 0941587 awarded by the National Science Foundation. The Government has certain rights in the invention.

FIELD OF THE INVENTION

This document relates generally to neurodevelopmental and neurodegenerative disorders (e.g., autism spectrum disorders). More particularly, this document relates to systems and methods for diagnosis and treatment of neurodevelopmental and neurodegenerative disorders.

BACKGROUND OF THE INVENTION

Present advancements in genetic and epigenetic research highlight different sub-types in the Autism Spectrum Disorders (“ASD”) of both known and unknown etiological origins. These new developments pose at least two fundamental challenges: 1) (Classification): how to distinguish different types of autism objectively; and 2) (Objective outcome measure): how to treat different types of autism differently and objectively track individual cognitive and treatment progress. Current methods are ineffective at addressing these two objectives.

SUMMARY

The present document generally relates to implementing systems and methods for (a) detecting and analyzing a neurological disorder in a human or animal subject and/or (b) data compression. In some scenarios, the systems and methods can be used in a medical context. For example, the systems and methods can be used to facilitate diagnosis and treatment of neurodevelopmental and neurodegenerative disorders. The disorders can include, but are not limited to, Autism Spectral Disorders (“ASD”).
The methods involve performing first operations, by a computing device, to generate normalized data from original data (e.g., sensor data specifying a raw neural or bodily rhythm created in part by a human or animal subject's physiological system). The normalized data defines a normalized waveform that is unitless and scaled from zero to one. In some scenarios, the normalized data defines a normalized waveform representing events of interest in a continuous random process capturing rates of changes in fluctuations in amplitude and timing of an original raw waveform defined by the original data.
The normalized data is processed by the computing device to extract micro-movement data. The micro-movement data defines a micro-movement waveform comprising a plurality of micro-movement data points. Each micro-movement data point is determined based on (a) a value of a peak of the normalized waveform and (b) a value representing an average of all data point values between a first valley of the normalized waveform immediately preceding the peak and a second valley of the normalized waveform immediately following the peak. A peak (local maximum) is automatically detected as a change in the slope of the curve from positive to negative (/ \). A valley (local minimum) is automatically detected as a change in the slope of the curve from negative to positive (\ /).
Compressed data is generated by the computing device that comprises a stochastic signature of the micro-movement waveform, i.e., the entire micro-movement waveform is reduced to the stochastic signature. The stochastic signature is defined by the empirically estimated values of two (2) parameters representing a probability distribution function of a continuous family of probability distribution functions.
In some scenarios, the stochastic signature is obtained by: performing statistical data binning using the micro-movement data; processing the binned micro-movement data to generate a frequency histogram; generating probability distribution function waveforms using different sets of variable values; comparing the probability distribution function waveforms to the frequency histogram to identify a probability distribution function waveform from the probability distribution function waveforms that most closely matches the shape and the dispersion of the frequency histogram; and considering the variable value used for generating the probability distribution function waveform as the stochastic signature. Vertical columns of the frequency histogram show how many micro-movement data points are contained in each of a plurality of statistical data bins.
In those or other scenarios, the methods also involve performing operations, by the computing device, to estimate moments of a continuous family of probability distribution functions best describing a continuous random process. The moments can include, but are not limited to, a first moment comprising a mean value, a second moment comprising a variance value, a third moment comprising skewness, and/or a fourth moment comprising kurtosis. Families of probability distribution functions can include, but are not limited to, the continuous Gamma family of probability distribution functions.
The stochastic signature is used to obtain at least one of a Noise-to-Signal Ratio (“NSR”) for a signal defined by the original data and a level of randomness in the original data. The stochastic signature is mapped on a parameter plane to determine noise and randomness classifications of a subject's neural or bodily rhythms defined by the original data. The stochastic signature may optionally be used as a seed value to an encryption algorithm for encrypting sensitive information prior to being communicated over a network communications link.
In those or other scenarios, the methods further involve: causing the computing device or a remote computing device to operate in a first session state (baseline state) in which first testing operations are performed to stimulate movement by a human or animal subject in accordance with first testing parameters; (subsequent testing steps will be measured relative to the baseline values and to preceding values, e.g., in a given subsequent step . . . ) selecting or generating second testing parameters different from the first testing parameters based on the stochastic signature (i.e., generating a stochastic trajectory); and transitioning the session state of the computing device or the remote computing device from the first session state to a second session state in which second testing operations are performed to stimulate movement by the human or animal subject in accordance with the rates of change from first to second testing parameters (and subsequent steps). The transitioning is controlled by the human or animal subject's nervous system evolving with treatment of a neurological disorder. As such, the rate of change in the therapy/intervention process is fully driven in parametric form by the individual's nervous systems' rate of change.

DESCRIPTION OF THE DRAWINGS

Embodiments will be described with reference to the following drawing figures, in which like numerals represent like items throughout the figures, and in which:

FIG. 1 is an illustration of an exemplary computing device implementing the present solution.

FIG. 2 is an illustration of an exemplary system in which the computing device of FIG. 1 can be employed.

FIG. 3A provides a flow diagram of (a) an exemplary method for detecting and analyzing a neurological disorder in a human subject and (b) an exemplary method for data compression. FIG. 3B is a continuation of the flow diagram of FIG. 3A.

FIG. 4A provides a plurality of graphs showing exemplary sensor data of a representative ASD participant. FIG. 4B provides a plurality of graphs showing exemplary sensor data of a representative control subject.

FIG. 5 provides a graph showing exemplary sensor data and normalization procedure.

FIG. 6 provides a graph showing an exemplary micro-movement waveform extracted from raw movement data.

FIG. 7 is a graph showing an exemplary histogram representative of multiplicative random process.

FIG. 8 is a graph showing an exemplary Gamma waveform fit to a micro-movements histogram.

FIG. 9 shows an exemplary classification map.

FIG. 10 is an illustration of an exemplary reference map.

FIGS. 11A, 11B and 11C provides a graph showing tracked changes in a human subject's stochastic signature as the intervention is guided by the changes in the subject's nervous system's output.

FIG. 12 is a flow diagram of an exemplary method for selectively and dynamically changing a session state of a computing system.

FIGS. 13A, 13B, 13C, 13D, 13E, 13F, 13G, 13H and 13I show the basic pointing task and examples of representative hand trajectory and speed profiles. Specifically, FIG. 13A shows a schematic of the forwards-and-back cycle of pointing behavioral task. Subjects seat in a chair comfortably in front of a touch screen. They touch the screen by moving the hand forward towards the location of a circle, the target presented on a black background. They retract the hand spontaneously, without instruction, in a continuous loop forward, towards the circle and back to rest. The size of the circle was 5 cm. The schematic trajectories have the location of the global speed maximum in each segment of the loop. The trials are activated by the screen touch, so the subjects follow their own comfortable pace and the flow of motion is continuous. FIGS. 13B, 13C and 13D show the trajectories in three dimensions for the hand continuously touching the screen and retracting from it several times in a row. FIGS. 13E, 13F and 13G show the corresponding speed profiles from the instructed touches and uninstructed retractions. Relevant behavioral landmarks are highlighted: the target touch (black dot) and the local and global speed maxima (grey dots). From top to bottom representative subjects are low-functioning nonverbal; high-functioning verbal; typical control. FIGS. 13H and 13I provides the temporal speed profile (zoomed in with the dashed rectangle) for one full (forward-and-back) motion of LF ASD and from control, respectively. Notice the presence of kinetic s-Peaks in the LF ASD and absence of those in the control.

FIGS. 14A, 14B, 14C and 14D provide s-Peaks (rastergrams-like) visualization showing temporal micro-dynamics within a single forward-and-back motion (s-PeaksPeak vector) and across continuous motion repetitions (s-Peak matrix). More specifically, FIG. 14A provides a s-PeaksPeak vector: Upper panel is the hand speed as a function of time in a single forward and back motion for low functioning ASD subject aligned to the touch point (set at time=0). The s-Peaks are local and global speed maxima. Dots mark s-Peaks as the local peaks in the reaching period toward the target, before the touch or in the retracting period. Black dots show speed maxima positions in each forward and retracting cycle. Horizontal time-axis spans from −1,000 ms to +1,000 ms relative to the touch at time 0. FIGS. 14B, 14C and 14D show the s-Peak matrix “rastergram” plotted for the three representative subjects in FIG. 13. Vertical axis is the number of continuous repeats of full motions (100 in this example) with the corresponding s-Peaks vector per repeat. They form a s-Peaks matrix. Each row of the s-Peaks matrix is a s-Peak vector as in (FIG. 14A). The curve is the averaged speed profile across trials (used here to highlight the loss of information when averaging.) The s-Peaks rate per millisecond is calculated every successive 20 frames and shown in the bottom panel.

FIGS. 15A, 15B, 15C, 15D and 15E provide graphs showing differences in s-Peaks synchronicity correspond to differences in spoken language abilities. FIGS. 15A, 15B and 15C show C_(T)as a function of bin widths _(T)for representative subjects labeled by their clinical diagnose: (FIG. 15A) Low-functioning; (FIG. 15B) High-functioning; and (FIG. 15C) control. Insets show the slopes from the fitted C_(T)-curves. FIG. 15D shows the averaged C_(T)-trajectories for the ensemble with different clinical subtypes with computed error bars. For clarity, only early and late are shown in the C_(T)-trajectories. FIG. 15E shows bar plots for the second derivate of C(r) for each subgroup. The p value for comparison between low-functioning and high-functioning ASD is 0.0011. p=0.0017 between high-functioning ASD and TD adults. p=0.0001 between low-functioning ASD and TD control. FIG. 15F shows bar plots for the distance to the C_r(T)curve for each subgroup. The p value for comparison between low-functioning and high-functioning ASD is 0.0071. p values for comparison between high-functioning ASD and TD adults and for comparison between low-functioning ASD and TD adults are smaller than 0.0001.

FIGS. 16A, 16B, 16C, 16D, and 16E is a continuation of FIGS. 15A-15E, showing differences in s-Peaks synchronicity according to population cross-correlation and Fourier analyses. More specifically, FIG. 16A provides second derivative of the C(T)-curves and FIG. 16B provides the distance to the total random curve for each representative subject. Each x-axis position indicates the clinical label of the subgroup. FIG. 16C-16E provide fast Fourier transform periodicity—synchronization analyses of s-Peaks occurrences across the s-Peaks vectors in the matrix aligned to the touch. FIG. 16C provides autocorrelation of “s-Spike” chopped vector (bin size 24 frames/104 ms) as a function of time lag for each subject. Bin size of twenty four (24) frames is selected according to FIG. 15D, where the slope-based subtypes were separated in the population cross-correlation. FIG. 16D provides the power spectrum of s-Spike's chopped vector calculated from the fast Fourier transform of chopped s-Peaks autocorrelation (maximum time lag: 30 s). Maximum time lag is limited by the 30 s-time window of the buffering-saving data cycles along the continuous flow of behavior. FIG. 16E provides the power spectrum of movement trajectory (along the maximally changing front-back axis direction of the positional trajectory in the system's world axes) calculated from fast Fourier transform of the movement's trajectory autocorrelation (maximum time lag: 30 s).

FIGS. 17A, 17B, 17C, 17D, 17E and 17F show blind clustering using the peripheral-inter-peak interval (p-IPI) distribution analysis in the parameter plane. FIGS. 17A, 17B and 17C provide frequency histograms of p-IPIs for representative subjects. Upper panels show combined s-Peaks histogram intervals during reaching and retracting periods. The histogram's bin size is set as two (2) frames per eight (8) ms, optimized to produce a clear exponential fit. Fits are based on p-IPI values below ten (10) frames per forty (40) ms (in this range all histograms are exponentially distributed.). Bottom panels in FIGS. 17A, 17B and 17C show residual p-IPIs histograms outside of the exponential fit (p-IPI values above 10 frames/40 ms). Insets zoom in the bottom panels to clearly show that multiple humps of long range p-IPI's present in control are missing from ASD. The parameter plane is constructed with the mean p-IPI value and the parameter R from the p-ISI distribution. FIG. 17D show that three clusters emerged according to the subject's position on the parameter plane (using K-clusters method 8). Cluster members were grey shaded according to the three centroids identified by the algorithm. FIG. 17E provides second derivative of the C(T) curves and FIG. 17F shows distance to Poisson reference curve C_r(T) for each subject in each cluster. Black lines denote the average values in each cluster.

FIGS. 18A, 18B, 18C, 18D and 18E provide clinical labeling verification for clustering in FIG. 17 and familial link. More particularly, FIG. 18A shows points in FIG. 17D are labeled based on their descriptive diagnosis of verbal-ability (see legend). Inset shows the center of mass for each subtype (not including the high-functioning ASD outlier with near zero R). FIG. 18B provides counts of subjects in each cluster. FIG. 18C provide characterization of maturation in s-Peaks statistics across typical and atypical development. Light circles are ASD subjects 10-15 years old; Dark circles are ASD subjects between 16 and 30 years old; Light stars are 3-4 years old TD children; Dark stars are control subjects 20-27 years old. FIG. 18D provides the location of 21 parents of the 19 ASD subjects in FIG. 18C. Note the underlying gray shaded symbols representing the typical young controls. Also that most parents are located away from the controls, largely covering the low-to-mid functioning ASD range, along with the locations of the 3-4 year old TD range in FIG. 18C. FIG. 18E provides oriented Euclidean distance of 21 parents (coded as in FIG. 18D) and 8 young adult controls (diamonds) to the centroid of TD 3-4 year old children on the parameter plane. X-axis is the difference of mean p-IPI and Y-axis is the oriented Euclidean distance. A negative value means that the subject is to the left of the TD 3-4 year old centroid.

FIGS. 19A, 19B, 19C, 19D, 19E, 19F and 19G provide speed profile and s-Peaks computations from sensor-collected positional data for Subject 2 (high-functioning ASD). FIGS. 19A, 19B and 19C provide hand positions plotted as a function of time in the three orthogonal directions. Data collected during the same time window as in FIG. 19C (10 seconds recordings at the sampling rate of 240 frames/s using the Polhemus Liberty system (Polhemus, Colchester, Vt.)). Black dots denote the target touch located at the position peaks in the Y direction. FIGS. 19D, 19E and 19F show the velocity for each direction was calculated from the first derivative of position with respect to time. The smoothed results obtained from using the triangulation smoothing algorithm. FIG. 19G provides the speed magnitude calculated from the smoothed velocity profiles along the three directions. Dots mark the local and the global peaks in the speed profile, termed here “peripheral-Spikes” (s-Peaks-Spikes).

FIGS. 20A, 20B, 20C, 20D, 20E and 20F provide description of the smoothing algorithm with window bin selection. FIG. 20A shows the triangular smoothing algorithm applied using a sliding window of width 2d+1. Weights were distributed using a symmetrical triangle as shown in the figure. ‘Sum’ is the sum of weights within that window. FIGS. 20B and 20C provide triangular and rectangular, respectively, smoothing applied to an artificial periodic data set (y=sin(irt/5)+0.10. The smoothing window size varied from 0 to 30. Note that the triangular smoothing preserves the location of the peaks whereas the rectangular one does not. FIGS. 20D and 20E show the smoothing window size effects on the p-IPI distribution parameters: Mean p-ISI (FIG. 20D) and R parameter (FIG. 20E) for different representative subjects: control subjects; high-functioning ASD subjects; mid-functioning ASD subjects; and low-functioning ASD subjects. The mean p-IPI curves for each subtype group are plotted in the insets. The window size value was chosen as 25 frames (104 ms) (dashed line). This corresponds to different subtypes which are well separated and the R values had not yet saturated. FIG. 20F shows the speed profile with triangular smoothing with 25 frames (104 ms) time window, applied to the velocity vector along each direction and compared to the speed profile calculated from the raw velocities data.

FIGS. 21A, 21B, 21C, 21D, 21E, 21F, 21G, 21H, AND 21I provides a plurality of graphs showing simulation results to quantify speed smoothness. FIG. 21A shows simulated speed profiles with SNR=0 dB, 10 dB to 15 dB. Local s-Peaks were marked with green dots. FIG. 21B shows the corresponding simulated s-Peaks matrix. FIG. 21C gives the simulated population cross-correlation function (C_(T)) as a function of bin width (r). The experimental C_(T)is labeled by connected orange dots. The green dashed lines are the analytically calculated curves for a random (Poisson) process having the same firing rate and length as in the trials for each case. The solid curves are the quadratic polynomials fits to the C_(T)-curves. FIG. 21D shows the simulated Inter-peak intervals (IPI) frequency histograms with bin size set at two (2) frames. Fits were based on s-IPI values below ten (10) frames. FIG. 21E shows relative frequency histograms of residual s-IPIs outside the exponential fit for the three (3) cases. FIGS. 21F, 21G, 21H, AND 21I are the parameters calculated for the simulation as a function of SNR: (F) negative second derivative of the fitted C_(T)curve with distance of the C_(T)curve (log value), mean IPI value (log value), and log value of R calculated from interval frequency histogram. The four (4) parameters calculated change monotonically with SNR.

FIGS. 22A, 22B, 22C, 22D, 22E and 22F show FFT analysis of trajectory and s-Peaks chopped vectors. s-Peaks chopped vector autocorrelation as in FIG. 19G: FIG. 22A: low functioning; FIG. 22B: high functioning; FIG. 22C: control. FIGS. 22B, 22C and 22D provide power spectrum of s-Peaks calculated directly from s-Peaks vector and by calculating FFT of s-Peaks autocorrelation, LF ASD in FIG. 22D, HF ASD in FIG. 22E, and control in FIG. 22F.

DETAILED DESCRIPTION OF THE INVENTION

It will be readily understood that the components of the embodiments as generally described herein and illustrated in the appended figures could be arranged and designed in a wide variety of different configurations. Thus, the following more detailed description of various embodiments, as represented in the figures, is not intended to limit the scope of the present disclosure, but is merely representative of various embodiments. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by this detailed description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.
Reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present invention should be or are in any single embodiment of the invention. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present invention. Thus, discussions of the features and advantages, and similar language, throughout the specification may, but do not necessarily, refer to the same embodiment.
Furthermore, the described features, advantages and characteristics of the invention may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize, in light of the description herein, that the invention can be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the invention.
Reference throughout this specification to “one embodiment”, “an embodiment”, or similar language means that a particular feature, structure, or characteristic described in connection with the indicated embodiment is included in at least one embodiment of the present invention. Thus, the phrases “in one embodiment”, “in an embodiment”, and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
As used in this document, the singular form “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. As used in this document, the term “comprising” means “including, but not limited to”.
As used herein, “Autistic Spectrum Disorder (“ASD”) refers to autism and similar disorders. Examples of ASD include disorders listed in the Diagnostic and Statistical Manual of Mental Disorders (“DSM-V”). Examples include, without limitation, autistic disorder, Asperger's disorder, pervasive developmental disorder, childhood disintegrative disorder, and Rees disorder. Known ASD diagnostic screenings methods include, without limitation: Modified Checklist for Autism in Toddlers (“M-CHAT”), the Early Screening of Autistic Traits Questionnaire, and the First Year Inventory; the M-CHAT and its predecessor CHAT on children aged 18-30 months, Autism Diagnostic Interview (“ADI”), Autism Diagnostic Interview-Revised (“ADI-R”), the Autism Diagnostic Observation Schedule (“ADOS”) The Childhood Autism Rating Scale (“CARS”), and combinations thereof. Known symptoms, impairments, or behaviors associated with ASD include without limitation: impairment in social interaction, impairment in social development, impairment with communication, behavior problems, repetitive behavior, stereotypy, compulsive behavior, sameness, ritualistic behavior, restricted behavior, self-injury, unusual response to sensory stimuli, impairment in emotion, problems with emotional attachment, impaired communication, and combinations thereof.
As used herein, “diagnose” refers to detecting and identifying a disease/disorder in a subject. The term may also encompass assessing or evaluating the disease/disorder status (severity, classification, progression, regression, stabilization, response to treatment, etc.) in a patient. The diagnosis may include a prognosis of the disease/disorder in the subject. In a particular embodiment, the diagnosis may determine whether a subject is a low functioning, mid functioning, high functioning, or normal individual.
As used herein, the term “prognosis” refers to providing information regarding the impact of the presence of a disease/disorder on a subject's future health (e.g., expected morbidity or mortality). In other words, the term “prognosis” refers to providing a prediction of the probable course and outcome of a disease/disorder or the likelihood of recovery from the disease/disorder.
The term “treat” as used herein refers to any type of treatment that imparts a benefit to a patient afflicted with a disease, including improvement in the condition of the patient (e.g., in one or more symptoms), delay in the progression of the condition, etc.
The terms “micro-rhythm data” and “micro-movement data”, as used herein, refer to normalized data points (e.g., NormPVIndex₁, . . . , NormPVIndex_N). For example, a micro-movement data point constitutes a single normalized data point (e.g., the value of NormPVIndex₁). The micro-movement data defines a micro-movement waveform. The definition of these terms will become more evident as the discussion progresses.
The present disclosure concerns systems and methods for objectively providing a diagnoses, a current state, and/or a prognosis for a neurological disorder in a human or animal subject. The neurological disorder can include, but is not limited to, an autism spectral disorder. The methods involve measuring a raw bodily rhythm (or motion pattern) of the human or animal subject. The raw bodily rhythm is measured using sensors coupled to the human or animal subject. The raw bodily rhythm may be a result of the performance of typical human or animal functions (e.g., heart rate, breathing, pumping blood, moving with intent upon instructions, moving spontaneously without instructions, etc.) or a result of being stimulated via a visual/auditory/tactile stimulus. In some scenarios, the stimulus can be provided by a computing device. The computing device is also referred to herein as an artificial agent.
In the artificial agent scenarios, the human or animal subject is not provided instruction on how to interact with the artificial agent. The artificial agent provides a stimulus (e.g., a real-time video of the subject) when the subject contacts a region of interest (e.g., a virtual region of interest of a displayed multi-dimensional space). The stimulus is provided to stimulate movement or motion by the human or animal subject. Data specifying a raw bodily rhythm or motion pattern of the human or animal subject's stimulated movement or motion is obtained by the computing device. This data can include, but is not limited to, sensor measurement data (e.g., acceleration data, position data, speed/velocity data, motion sensor data, longitude/latitude data, height-from-surface data, ElectroCardioGram (“ECG”) sensor data, Respiratory Inductance Plethysmography (“RIP”) sensor data). A difference in the raw bodily rhythm pattern of the subject (e.g., particularly fluctuations in the millisecond range) and a standard raw bodily rhythm pattern (e.g., a previously acquired raw bodily rhythm pattern of an individual absent of a neurological disorder) and/or the presence of a raw bodily rhythm pattern associated with a neurological disorder indicates whether the tested subject has the neurological disorder. The methods may further involve measuring other aspects of the human or animal subject (e.g., facial patterns) upon the human or animal subject's interaction with the artificial agent or other stimulus source.
In some scenarios, the artificial agent is a computing device displaying dynamic media and/or a Graphical User interface (“GUI”) on a display screen. The computing device can include, but is not limited to, a robot, a three dimensional (“3D”) animate, a personal computer, a laptop computer, a desktop computer, a personal digital assistant, a smart phone or any other electronic device having input and output components (e.g., a speaker, a display screen, a keypad and/or a touch screen). An exemplary hardware and software architecture for the artificial agent is discussed in detail below in relation to FIG. 1.
The present document also concerns systems and methods for determining an ability of a therapy to modulate (e.g., inhibit or treat) a neurological disorder (e.g., an autism spectral disorder) in a human or animal subject. The methods involve: measuring a raw neural/bodily rhythm pattern of the human or animal subject (e.g., fluctuations in the millisecond range) after administering the therapy (e.g., a pharmaceutical based therapy or a non-pharmaceutical therapy) to the human or animal subject; and/or measuring a raw neural/bodily rhythm pattern of the human or animal subject prior to the administration of the therapy (e.g., as a baseline). The modulation of the raw neural/bodily rhythm pattern of the human or animal subject after administration of the therapy (e.g., to a standard motion pattern) indicates that the therapy modulates the neurological disorder (e.g., autism spectral disorder). As previously noted, in some scenarios, these modulations are measured in the micro-movements extracted from the raw physiological rhythms. In some scenarios, the neural rhythm data is obtained from detecting activity in the brain and Central Nervous System (“CNS”). The neural rhythm data can include, but is not limited to, local field potential signals, ElectroEncephaloGram (“EEG”) data and/or functional Magnetic Resonance Imaging (“fMRI”) data. The bodily rhythm data is obtained from detecting activity in the Peripheral Nervous System (“PNS”).
The present disclosure further concerns systems and methods for lessening the improper raw bodily rhythm pattern of a human or animal subject with a neurological disorder (e.g., an autism spectral disorder). The methods may involve having the subject interact with an artificial agent. The artificial agent can include but is not limited to, a computing device (e.g., a robot and/or avatar) programmed to encourage the human or animal subject to react and co-adapt with movement patterns that the artificial agent is endowed with. The raw bodily rhythm patterns can be gradually changed so as to objectively reassess the degrees of resistance or compliance of the human or animal subject's somatosensory systems.
Exemplary System Architecture
Referring now to FIG. 1, there is provided an illustration of an exemplary computing device 100. The computing system 100 is generally configured to perform operations for facilitating the objective diagnosis and treatment of neurodevelopmental and neurodegenerative disorders. As such, the computing system 100 comprises a plurality of components 102-112. The computing system 100 can include more or less components than those shown in FIG. 1. However, the components shown are sufficient to disclose an illustrative embodiment implementing the present invention.
The hardware architecture of FIG. 1 represents one (1) embodiment of a representative computing device configured to facilitate the diagnosis and treatment of neurodevelopmental and neurodegenerative disorders. As such, the computing system 100 implements methods of the present solution.
As shown in FIG. 1, the computing system 100 includes a system interface 112, a user interface 102 (e.g., a keyboard for data input and a display for data output), a Central Processing Unit (“CPU”) 104, a system bus 106, a memory 108 connected to and accessible by other portions of the computing system 100 through system bus 106, and hardware entities 110 connected to system bus 106. At least some of the hardware entities 110 perform actions involving access to and use of memory 108, which can be a Random Access Memory (“RAM”), a disk driver and/or a Compact Disc Read Only Memory (“CD-ROM”). System interface 112 allows the computing system 100 to communicate directly or indirectly with external devices (e.g., sensors, servers and client computers).
In FIG. 1, the computing device 100 comprises sensors 150. The present solution is not limited in this regard. For example, in other scenarios, the sensors are separate devices from the computing device 100. A communications link (wired or wireless) is provided for enabling communications between the computing device 100 and sensors. In all cases, sensors 150 are coupled to a human or animal subject for obtaining data from at least one physiological relevant signal of the subject. The sensor can include, but is not limited to, an accelerometer, a gyroscope, a motion sensor, a vibration sensor, a position sensor, a restoration sensor, and/or a medical sensor (e.g., an electromyography sensor, an electrocardiogram sensor, an RIP sensor, an MRI sensor, etc.).
Hardware entities 110 can include microprocessors, Application Specific Integrated Circuits (“ASICs”) and other hardware. Hardware entities 110 can include a microprocessor programmed to facilitate the diagnosis and treatment of neurodevelopmental and neurodegenerative disorders.
As shown in FIG. 1, the hardware entities 110 can include a disk drive unit 116 comprising a computer-readable storage medium 118 on which is stored one or more sets of instructions 114 (e.g., software code) configured to implement one or more of the methodologies, procedures, or functions described herein. The instructions 114 can also reside, completely or at least partially, within the memory 108 and/or the CPU 104 during execution thereof by the computing system 100. The components 108 and 104 also can constitute machine-readable media. The term “machine-readable media”, as used here, refers to a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions 114. The term “machine-readable media”, as used here, also refers to any medium that is capable of storing, encoding or carrying a set of instructions 114 for execution by the computing system 100 and that cause the computing system 100 to perform any one or more of the methodologies of the present disclosure.
Notably, the present solution can be implemented in a single computing device as shown in FIG. 1. The present solution is not limited in this regard. Alternatively, the present solution can be implemented in a distributed network system. For example, the present solution can take advantage of multiple CPU cores over a distributed network of computing devices in a cloud or cloud-like environment. The distributed network architecture ensures that the computing time of the statistics and enhanced functionality is reduced to a minimum, allowing end-users to perform more queries and to receive reports at a faster rate. The distributed network architecture also ensures that the implementing software is ready for being deployed on an organization's internal servers or on cloud services in order to take advantage of its scaling abilities (e.g., request more or less CPU cores dynamically as a function of the quantity of data to process or the number of parameters to evaluate).
Referring now to FIG. 2, there is provided an illustration of an exemplary system 200. System 200 is a network based system in which computing device 100 can be deployed in some scenarios. In this network based scenario, computing device 100 is communicatively coupled to a server 204 and other computing devices 208 ₁, . . . , 208 _Nvia a network 202 (e.g., the Internet or Intranet). Computing devices 208 ₁, . . . , 208 _Ncan be the same as, similar to, or different than computing device 100. During operation, computing devices 100, 208 ₁, . . . , 208 _Nmay write data to or read data from database 206. Each computing device 100, 208 ₁, . . . , 208 _Nincludes, but is not limited to, a robot, a three dimensional (“3D”) animate, a personal computer, a laptop computer, a desktop computer, a personal digital assistant, a smart phone or any other electronic device having input and output components (e.g., a speaker, a display screen, a keypad and/or a touch screen). Each of the listed devices is well known in the art, and therefore will not be described herein. In some scenarios, the present solution comprises software that is at least partially installed and run on the computing device 100, computing device 208 ₁, . . . , 208 _Nand/or server 204.
Exemplary Methods for Facilitating Diagnosis/Treatment of Neurological Disorders
Referring now to FIG. 3, there is provided a flow diagram of an exemplary method 300 for detecting and analyzing a neurological disorder in a human or animal subject. Method 300 begins with step 302 and continues with step 304 where at least one sensor (e.g., sensor 150 of FIG. 1) is coupled to the human or animal subject for obtaining data from at least one physiological relevant signal. The sensor can include, but is not limited to, an accelerometer, a gyroscope, a motion sensor, a vibration sensor, a position sensor, a restoration sensor, and/or a medical sensor (e.g., an electromyography sensor, an electrocardiogram sensor, an RIP sensor, an MRI sensor, etc.).
Next in step 306, a first computing device (e.g., computing device 100 of FIGS. 1-2) collects sensor data generated by the sensor. The sensor data specifies a raw neural/bodily rhythm created in part by the human or animal subject's physiological (e.g., nervous) system. For example, in some scenarios, the sensor data relates to kinematics motion parameters continuously registered as a time series of changes in signals generated by the human or animal subject's nervous system. The raw bodily rhythm can include, but is not limited to, voluntary bodily rhythms, involuntary bodily rhythms, and autonomic bodily rhythms. For example, the raw bodily rhythm defines respiratory rhythms, muscle rhythms and/or heart beating rhythms. The neural rhythm defines activity in a subject's brain and/or CNS. The sensor data can be obtained from a variety of medical tests. The medical tests include, but are not limited to, an EEG test, an fMRI test, an MRI test, an ECG test, and/or an RIP test.
Graphs plotting exemplary sensor data are provided in FIG. 4. In the scenario of FIG. 4, the sensor data comprises samples of raw head motions extracted from Resting State fMRI (“RS-fMRI”) data, as shown by graphs A1, A2, B1 and B2. Displacement and rotation kinematics were extracted from raw sensor data using a Statistical Parametric Mapping (“SPM8”) method from raw RS image files (e.g., files having a Neuroimaging Informatics Technology Initiative (“NifTI”) format) provided in a database (e.g., such as an Autism Bain Imaging Data Exchange (“ABIDE”) database). This extraction yielded three (3) positional parameters and three (3) orientation parameters. Graphs A1 and A2 plot representative ASD participant's linear displacements and angular rotations of his(her) head registered with respect the a first frame. Graphs B1 and B2 plot representative control subject's linear displacements and angular rotations of his(her) head registered with respect the a first frame.
The sensor data also comprises data defining speed profiles, as shown by graphs A3, A4, B3 and B4. The speed profiles were obtained by computing a Euclidean norm of each three dimensional velocity vector (Δx, Δy, Δz) displacement at each point of application (x, y, z) from frame to frame. E.g., for three hundred (300) frames, a speed profile is defined by the following mathematical equation (1).
speed_frame=√{square root over ((Δx)²+(Δy)²+(Δz)²)} (1)
The position data may be filtered using a triangular filter to preserve the original temporal dynamics of the data (i.e., the timing of the spike) while smoothing the sharp transitions from frame to frame.
Other exemplary sensor data is shown in FIG. 5. The sensor data comprises data defining the rate of change of a hand's rotation. As such, the x-axis represents time and the y-axis represents angular velocity. Accordingly, the scale of the graph's x-axis is in seconds, and the scale of the graph's y-axis is in degrees per second. The plotted data points for angular velocity define an original raw waveform 500. Waveform 500 comprises a plurality of peaks 502 and a plurality of valleys 504. Each peak 502 is defined by a data point at which the waveform's slope changes from a positive slope to a negative slope. In contrast, each valley 504 is defined by a data point at which the waveform's slope changes from a negative slope to a positive slope.
Once the sensor data has been collected, the first computing device optionally encrypts the same so as to comply with at least the Health Insurance Portability and Accountability Act (“HIPAA”) confidentiality requirements, as shown by step 308. The encryption is achieved using a chaotic, random or pseudo-random number based algorithm. Any known or to be known chaotic, random or pseudo-random number based algorithm can be used herein without limitation. A seed value for the chaotic, random or pseudo-random number based algorithm can be selected from a plurality of pre-defined seed values or dynamically generated during operations of the first computing device. The term “seed value”, as used herein, refers to a starting value for generating a sequence of chaotic, random, or pseudo-random integer values. The seed value(s) can be selected or generated based on the sensor data and/or information relating to the human or animal subject (e.g., an identifier, an address, a phone number, an age, a medical diagnosis, a medical symptom, information contained in a medical history, a stochastic signature value, a noise signal ratio value, a moment value, any other value determined in a previous iteration of method 300, etc.).
Subsequently, optional step 310 is performed where the sensor data is communicated over a network (e.g., network 202 of FIG. 2) from the first computing device to a remote second computing device (e.g., computing device 208 ₁, . . . , 208 _Nor server 204 of FIG. 2) for storage in a data store (e.g., memory 108 of FIG. 1 or database 206 of FIG. 2) and subsequent processing. At the second computing device, the sensor data may be decrypted if it was previously encrypted by the first computing device prior to being communicated over the network, as shown by step 312. Methods for decrypting data are well known in the art, and therefore will not be described herein. Any known or to be known decryption technique can be used herein without limitation.
The second computing device also performs operations to normalize the sensor data, as shown by step 314. This step is very important when dealing with parameters of different units and scales. The normalization is performed to obtain normalized data defining a normalized waveform that is unit less and scaled from zero (0) to one (1). The normalized waveform represents events of interest in a continuous random process capturing rates of changes in fluctuations in amplitude and timing of an original raw waveform (e.g., waveform 500 of FIG. 5) defined by the sensor data (e.g., sensor data 500 of FIG. 5). Methods for normalizing data are well known in the art. Any known or to be known data normalizing method can be used herein without limitation.
In more general terms, the normalization is performed to standardize the different resolutions and/or scales/units of the time series waveforms defined by the sensor data. For example, a heart rate waveform has a millisecond scale. A velocity waveform has a centimeter per second scale. An acceleration waveform has a meter per second squared scale. The different units of these waveforms are standardized in a waveform which is normalized from zero (0) to one (1).
In some scenarios, the sensor data normalization is achieved using the Euclidean distance so that all parameters have the same scale. The following mathematical equation (2) is used to implement a unity-based normalization.
$\begin{matrix} X_{i, 0 to 1} = \frac{X_{i} - X_{Min}}{X_{Max} - X_{Min}} & (2) \end{matrix}$
where X_irepresents each data point i, X_MIN represents the minima among all the data points, X_MAXrepresents the maxima among all the data points, X_i, owl represents the data point i normalization between zero (0) and one (1). Alternatively, the following mathematical equation (3) can be used to produce a set of normalized data with zero (0) being the central point.
$\begin{matrix} X_{i, - 1 to 1} = \frac{X_{i} - (\frac{X_{Max} + X_{Min}}{2})}{(\frac{X_{Max} - X_{Min}}{2})} & (3) \end{matrix}$
where X_irepresents each data point i, X_MIN represents the minima among all the data points, X_MAXrepresents the maxima among all the data points, X_i, _tot represents the data point i normalization between zero (0) and one (1).
In other scenarios, the sensor data normalization is achieved using the following mathematical equation (4).
$\begin{matrix} NormPVIndex = \frac{SpeedMax}{SpeedMax + AvrgSpeed} & (4) \end{matrix}$
where NormPVIndex represents a normalized data point, SpeedMax represents a value of a peak (e.g., peak 500 of FIG. 5), and AvrgSpeed represents an average of all data point value between a first valley (e.g., valley 504A of FIG. 5) immediately preceeding the peak and a second valley (e.g., valley 504B of FIG. 5) immediately following the peak.
In a next step 315, the second computing device processes the normalized data to extract micro-rhythm data or micro-movement data defining a micro-rhythm or movement waveform. The terms “micro-rhythm data” and “micro-movement data”, as used herein, refer to normalized data points (e.g., NormPVIndex₁, . . . , NormPVIndex_N). For example, a micro-movement data point constitutes a single normalized data point (e.g., the value of NormPVIndex₁). The micro-movement data defines a micro-movement waveform. An exemplary micro-movement waveform 600 is shown in FIG. 6.
Upon completing step 315, step 316 is performed where second computing device estimates (a) a stochastic signature of the micro-movement waveform and (b) moments of a continuous family of probability distribution functions best describing the continuous random process. The probability distribution function comprises a Gamma function, a Gaussian Distribution function, and/or a Log Normal Distribution function. Each of these functions is well known in the art, and therefore will not be described in detail herein. Any known or to be known Gamma, Gaussian Distribution and/or Log Normal Distribution function can be used herein without limitation.
In some scenarios, the stochastic signature estimation is obtained by: performing statistical data binning using the micro-movement data; processing the binned micro-movement data to generate a frequency histogram; and performing a Maximum Likelihood Estimation (“MLE”) process using the frequency histogram to obtain the stochastic signature. The MLE process generally involves: generating probability distribution function waveforms using different sets of variable values; comparing the probability distribution function waveforms to the frequency histogram to identify a probability distribution function waveform from the probability distribution function waveforms that most closely matches the shape of the frequency histogram; and considering the variable value used for generating the probability distribution function waveform as the stochastic signature.
Techniques for statistical data binning are well known in the art, and therefore will not be described in detail here. However, it should be understood that in some scenarios the data binning generally involves grouping each set of micro-movement data points in respective bins, where micro-movement data points of each set have the same value (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or 1.0) or fall within a specified range of values (e.g., 0.0-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, . . . , 0.9-1.0).
The binned data is used to generate a frequency table specifying the frequency of micro-movement data points in each bin (or stated differently, the total number of micro-movement data points in each bin). An exemplary frequency table is shown below.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

4 7 14 14 11 9 11 8 8 6

The frequency table is then used to generate the frequency histogram.
The frequency histogram is constructed from the frequency table. The intervals from the frequency table are placed on the x-axis and the values needed for the frequencies are represented on the y-axis. In effect, the vertical columns of the frequency histogram show how many micro-movement data points are contained in each bin. An exemplary frequency histogram 700 is provided in FIG. 7.
The frequency histogram is then used in the MLE process to obtain an estimated stochastic signature. The MLE process involves estimating a mean value and a variance value while only knowing a relatively small number of sensed micro-rhythms or micro-movements of the human or animal subject. The MLE process accomplishes this by: generating probability distribution function waveforms using different sets of mean and variance values; and comparing the probability distribution function waveforms to the frequency histogram to identify the probability distribution function waveform that most closely matches the shape of the frequency histogram.
In some scenarios, a Gamma function is used to generate the probability distribution function waveforms. The Gamma function is defined by the following mathematical equation (5).
$\begin{matrix} γ = f (x  a, b) = \frac{1}{b^{a} Γ (a)} x^{a - 1} e^{\frac{- x}{b}} & (5) \end{matrix}$
where a is the shape parameter, b is the scale parameter, and y is the Gamma function result. Different sets of values for a and b are used to generate a plurality of Gamma function waveforms which are compared to the frequency histogram. The a and b values associated with the Gamma function waveform that most closely matches the shape of the frequency histogram define the stochastic signature for the human or animal subject.
An exemplary Gamma function waveform 800 generated using mathematical equation (5) is shown in FIG. 8. The Gamma function waveform 800 is over-laid on top of the histogram 700 of FIG. 7. As can be seen from FIG. 8, the shape of Gamma function waveform 800 closely matches the shape of histogram 700. As such, the a and b values used as inputs to mathematical equation (5) define the stochastic signature for the human or animal subject.
As noted above, step 316 also involves estimating the moments of a continuous family of probability distribution functions best describing the continuous random process. In the Gamma function scenarios, two moments are estimated. A first estimated moment comprises a mean value p defined by the following mathematical equation (6).
μ=a×b (6)
A second estimated moment comprises a variance value defined by the following mathematical equation (7).
σ=a×b ² (7)
Referring again to FIG. 3A, method 300 continues with step 317. In step 317, the second computing device performs operations to obtain (a) a Noise-to-Signal Ratio (“NSR”) to track levels of noise in the sensor data and (b) a level of randomness in the underlying raw data from the estimated stochastic signature and/or moments. The NSR is defined by the following mathematical equation (8).
NSR=σ/μ (8)
In the Gamma scenarios, the above mathematical equation (8) can be re-written as the following mathematical equation (9).
NSR=(a×b ²)/(a×b)=b (9)
As evident from mathematical equation (9) the NSR is equal to the scale parameter b in the Gamma scenarios. The level of randomness L_randomis defined by the following mathematical equation (10).
L _random =a (10)
In a next step 318, the second computing device maps the stochastic signature on a parameter plane and/or on a space spanned by the estimated moments so as to generate a classification map. In the Gamma scenario, this mapping involves plotting a data point on a graph having an x-axis with numbers representing a shape parameter a of the Gamma function and a y-axis with numbers representing a scale parameter b of the Gamma function, where the Gamma function generates a waveform that most closely matched the frequency histogram. A schematic illustration of an exemplary classification map 900 (or map) is provided in FIG. 9.
After generating the classification map, step 319 is performed where the human or animal subject is localized relative to other human or animal subjects (including individuals without a neurological disorder and individuals with a neurological disorder having a pathology of known origins). This localization is achieved using a classification map. The result of the localization is a classification of whether a human or animal subject's neural/bodily rhythms are noisy and random, and therefore unpredictable. This classification is useful for diagnosis of a neurological disorder.
In some scenarios, the classification map comprises classification map 900. As noted above, the NSR is equal to the scale parameter b of the Gamma function. Therefore, along the y-axis of the clarification map 900, the higher the value the higher the noise in a signal (or stated differently, the lower the value on the value on the y-axis the higher the signal). So, the amount of noise in the random process can be quantified. Along the x-axis of the classification map 900, the closer to one (1) the more random the process and less informative the collected data is of future events (the shape value of one corresponds to the memoryless Exponential distribution, the most random probability distribution function there is). In contrast, the farther to the right from the shape value of one the more predictive the collected data is of future events. The mapping provides a means to classify for diagnosis purposes whether a human or animal subject's neural/bodily rhythms are noisy and random, and therefore unpredictable. A classification of noise and random neural/bodily rhythms indicates that the human or animal subject has a neurological disorder.
As noted above, the result of the localization is a classification of whether a human or animal subject's neural/bodily rhythms are noisy and random, and therefore unpredictable. This classification is useful for the objective diagnosis of a neurological disorder. The classification result of the localization can be used to track progress or improvement in the subject's neurological disorder if there is a concurrent therapy.
Therefore upon completing step 319, method 300 continues with optional step 320 of FIG. 3B. In optional step 320, changes in the human or animal subject's stochastic signature are tracked over a period of time (e.g., hours, days, weeks, months, years). A graph showing tracked changes in a human or animal subject's stochastic signature is provided in FIG. 11. Such tracking provides a means to detect positive or negative progression of a neurological disorder, i.e., whether the neurological disorder is getting worse or whether there has been an improvement of the neurological disorder.
In a next step 321, the second computing device performs operations to confirm whether or not the human or animal subject has a neurological disorder and/or to determine the type of neurological disorder. This confirmation/determination is achieved using a reference map comprising data points associated with a plurality of reference individuals having an neurological disorder and a plurality of reference individuals without a neurological disorder. The reference map is pre-stored in a data store (e.g., memory 108 of FIG. 1 and/or data store 206 of FIG. 2) that is accessible to the second computing device.
An exemplary reference map 1000 is shown in FIG. 10. The reference map 1000 comprises a plurality of data points 1002 (e.g., the red data points) associated with individuals without a neurological order, a plurality of data points 1004 (e.g., the blue data points) associated with individuals having schizophrenia, and a plurality of data points 1006 (e.g., the purple data points) associated with individuals having ASD. The stochastic signature values for the human subject are processed to obtain a log scale value and a log shape value. The log scale and log shape values are then plotted on the reference map 1000. The location of the data point associated with the human subject indicates (1) whether the human subject has a neurological disorder, (2) the type of neurological disorder, and/or (3) possible causes of the neurological disorder (e.g., deletion of a chromosome). For example, if the data point associated with the human subject falls in the top left corner of reference map 1000, then a diagnosis is made that the human subject has ASD with possible causes defined by the known origins of ASD in the reference individuals with similar NSRs, level of randomness, and stochastic signatures. In contrast, if the data point associated with the human subject falls in the bottom right corner of reference map 1000, then a determination is made that the human subject does not have a neurological disorder.
The results of previous steps 319-321 are then used by the second computing device to generate diagnosis information to be communicated to an entity testing the human or animal subject (e.g., a physiologist), as shown by step 322. The diagnosis information specifies (a) noise/randomness classification of the human or animal subject's neural/bodily rhythm, (b) whether the human subject has a neurological disorder, (c) the type of neurological disorder, (d) the progression of the human or animal subject's neurological disorder, and/or (e) possible causes of the human or animal subject's neurological disorder.
In some scenarios, the second computing device encrypts the diagnosis information as shown by optional step 323. The encryption is achieved in accordance with a chaotic, random or pseudo-random based algorithm for generating a numerical sequence. Any known or to be known chaotic, random or pseudo-random number based algorithm can be used herein without limitation. A seed value for the chaotic, random or pseudo-random number based algorithm can be selected from a plurality of pre-defined seed values or dynamically generated during operations of the second computing device. The seed value(s) can be selected from or generated based on the sensor data and/or information relating to the human or animal subject (e.g., an identifier, an address, a phone number, an age, a medical diagnosis, a medical symptom, information contained in a medical history, the estimated stochastic signature, a mean value, a variance value, an NSR value, a level of randomness value, a value indicating a positive or negative change in the stochastic signature, moment values, etc.). Upon completing step 322 and/or 323, step 324 is performed where the diagnosis information is communicated from the second computing device to the first computing device via the network.
At the first computing device optional step 326 may be performed. Optional step 326 involves decrypting the diagnosis information at the first computing device. Techniques for decrypting data are well known in the art, and therefore will not be described herein. Any known or to be known decryption technique can be used herein without limitation.
In a next step 328, the first computing device performs operations to present the diagnosis information to a user thereof. The diagnosis information can be presented via a display, a speaker, or other output device of the first computing device. The diagnosis information can be presented to the user in any auditory format, visual format (e.g., a textual format, a graphical format, a table format and/or a chart format), and/or tactile format (e.g., as vibration). The diagnosis information can be used to select a treatment plan that is appropriate and is likely to be most effective for the human or animal subject, and which has had a history of improving the same neurological disorder in other individuals with similar or the same test results (e.g., stochastic signatures).
In some scenarios, the method 300 continues with optional steps 330 where another iteration of method 300 may be performed. Subsequent to completing step 330, step 332 is performed. In step 332, method 300 ends or other processing is performed.
Notably, at least steps 306-316 provide a technique to compress sensor data. In this regard, the sensor data collected in step 306 is encoded using fewer bits than the original representation. The encoding is achieved by generating compressed data comprising a stochastic signature of neural/bodily rhythms defined by the sensor data. Decompression is possible in this case since the stochastic signature can be used as an input to a probability distribution function (e.g., a Gamma function, a Gaussian Distribution function, and/or a Log Normal Distribution function) so as to produce a waveform that closely matches a frequency histogram of micro-movement waveform data point values. The data compression technique can also include steps 317-319 in some cases.
Referring now to FIG. 12, there is provided a flow diagram of an exemplary method 1200 for selectively and dynamically changing a session state of a computing system (e.g., computing device 100 of FIG. 1 and/or system 200 of FIG. 2) based on a human or animal subject's stochastic signature(s). The session state change is driven in real-time by a human or animal subject's nervous system evolving with treatment of a neurological disorder. The session state change of the computing system facilitates the tailoring of neurological disorder testing based on the human or animal subject's nervous system's status.
As shown in FIG. 12, method 1200 begins with step 1202 and continues with step 1204 where a software application (e.g., instructions 114 of FIG. 1) installed on a computing device (e.g., computing device 100 of FIG. 1) is launched. In a next step 1206, the computing device receives a user software interaction to initiate a first test for determining whether a human or animal subject has a neurological disorder. In response to the first test's initiation, step 1208 is performed where testing parameters of the computing device are set to default values. As a result of using default values for the testing parameters, the computing device is transitioned to a first session state in which default testing operations are performed. The testing parameters can include, but are not limited to, parameters for causing a particular visual, auditory and/or tactile stimulus of a plurality of stimuli to be output, parameters selecting one or more probability distribution functions (e.g., a Gamma function, a Gaussian Distribution function, and/or a Log Normal Distribution function), parameters selecting a particular data binning method from a plurality of statistical data binning methods, parameters selecting a cryptographic algorithm from a plurality of cryptographic algorithms, parameters selecting seed values for a given cryptographic algorithm, and/or parameters selecting a sensor data normalization algorithm.
Upon completing step 1208, step 1210 is performed where the computing device performs operations to output a stimulus therefrom for stimulating neural/bodily rhythms by the human or animal subject. The stimulus can include a visual component, an auditory component and/or a tactile component. The computing device then obtains sensor data specifying sensed neural/bodily rhythms, as shown by step 1212. Thereafter, step 1214 is performed where operations are performed by a computing system (e.g., computing system 200 of FIG. 2) to obtain various information. This information includes, but is not limited to, (a) a stochastic signature of a micro-movement waveform, (b) the moments of a continuous family of probability distribution functions best describing the continuous random process, (c) an NSR indicating a level of noise in sensor data, and/or (d) the level of randomness in underlying raw data. Such information can be obtained by performing method 300 discussed above. As such, step 1214 involves performing some or all of the method steps discussed above in relation to FIGS. 3A-3B.
Subsequent to completing step 1214, step 1216 is performed where the computing device and/or computing system dynamically select or generate new values for the testing parameters of the computing device based on the information obtained in previous step 1214. For example, the new values can be selected or generated based on a stochastic signature of the human or animal subject. The stochastic signature values can be used as inputs into an algorithm for generating new testing parameters. Additionally or alternatively, one or more rules can be defined which indicate which values of a plurality of pre-defined values should be used as the new values when the stochastic signature values match pre-specified values.
The non-stationary nature of the nervous systems signals permits the tracking of the rate of change in the empirically estimated stochastic parameters forming a stochastic trajectory. As these empirically estimated parameters evolve (e.g., see FIG. 11 panels B-C), it is possible to obtain the frequency and amplitude of stochastic shifts on the Gamma parameter plane from the quadrants of the Gamma parameter plane delimiting the left upper quadrant of high NSR and randomness vs. the right lower quadrant of low NSR and predictable statistics. Such limits are automatically determined by obtaining the median values of the empirically obtained shape and scape parameters across the stochastic trajectory. Furthermore, stochastic rules, such as that derived in attached reference from the Behavioral and Brain Functions, permit the prediction of future values of speed and acceleration self-generated by the nervous system of the person, based on previously experienced (sensed) values in the continuous stream of micro-movements. The micro-movements scaled between zero (0) and one (1) are mapped to the actual physical values and corresponding units from the sensors in use (e.g., cm/s, m/s², deg/ms, μV, etc.). This permits the assessment of boundaries of change for each individual directly obtained as a read-out of the nervous system of the person in reaction to therapy (pharmacological and/or behavioral). As such, any changes in analytical parameters are informed and directly driven by the changes in nervous system's output and by their actual rates of change. Of note, as the changes in the signal and empirical estimated parameters reach stability (e.g., stationary changes in the right lower quadrant of low NSR and high predictability), the nervous system's output indicates that learning and adaptation has taken place. As such the effectiveness of the therapy (or of motor learning in general, e.g., sports training as in the Behavior and Brain Functions paper) is objectively assessed through this outcome measure method.
Once the new values have been selected or generated, the testing parameters are set by the computing device to the new values, as shown by step 1218. In effect, the session state of the computing device is transitioned from a first session state to a second session state in which tailored testing operations are performed. The tailored testing operations ensure that the most effective form of sensory-motor feedback is employed when re-testing the human or animal subject. The term “sensory-motor feedback”, as used herein, refers to the process performed by a human or animal that involves taking sensory information and using it to make motor actions. Included in the sensory input is the kinesthetic feedback, referred to as kinesthetic reafference, in this case, the self-generated micro-movements sensed back through peripheral afferent nerves. In some scenarios, the most effective form of motor-driven-sensory feedback is one that causes the human or animal subject's neural/bodily rhythms to converge towards neural/bodily rhythms of an individual without any neural disorder, i.e., those empirically quantified thus far in over 500 typical/healthy young individuals on the right lower quadrant of the Gamma parameter plane.
When the computing device is in its second session state, step 1220 is performed where the computing device performs operations to obtain certain information. This information includes, but is not limited to, (a) a stochastic signature of a micro-movement waveform, (b) the moments of a continuous family of probability distribution functions best describing the continuous random process, (c) an NSR indicating a level of noise in sensor data, and/or (d) the level of randomness in underlying raw data. Such information can be obtained by performing method 300 discussed above. As such, step 1220 involves performing some or all of the method steps discussed above in relation to FIGS. 3A-3B. Subsequently, step 1222 is performed where method 1200 ends or other processing is performed.
Exemplary Studies and Results Via Implementations of the Present Solution
ASD affects over one percent (1%) of school-age children. A current challenge for diagnosis based on observation is the highly heterogeneous behavioral presentation that impedes sub-typing the disorder according to severity. Behaviors being observed and scored involve highly variable movements with different levels of intent. Here, the temporal dynamics of the motor output variability from continuously flowing movements are studied at a much finer level involving millisecond time scales. The hand speed trajectories are very irregular. At a micro-level, they have millisecond-range peak fluctuations that are termed peripheral speed spikes (s-Peaks-Spikes). In fifty-nine (59) subjects (19/30 ASD with their family too), the s-Peaks-Spikes' synchronicity and periodicity patterns were examined. To this end, the above described methods were employed and augmented with new indexes to be explained next so as to first perform analytical simulations, create various possible scenarios and then test and validate the analytically obtained parameters with the actual empirical data. The empirically obtained statistics' parameters (well informed by the analytical simulations) sub-typed autism severity and revealed that 13/21 parents clustered together with their affected child, indicating a putative genetic link. These s-Peaks—previously considered just noise and traditionally smoothed out—contain important information providing new quantitative biomarkers to objectively classify ASD subtypes.
Movement abnormalities are not a core symptom of ASD, yet movement is implicitly present in each of the symptoms currently defining the disorder. Movements provide a new tool to assess ASD and to track cognitive and behavioral changes because the same brain that controls cognitive processes controls body movements. Cognition and movement coexist in a closed loop where one component impacts the other. There are two main types of cognitive processes: deliberate-conscious and automatic-unconscious (Gazzaniga, M. S., The Cognitive Neurosciences. 4th Ed., (MIT Press, 2009)). Current methods in cognitive psychology cannot access the unconscious mental processes because they rely on inferences and verbal reports about mental operations and perceptions that reach consciousness.
Movements also come in these two flavors, along with a range of flavors between these two (2) limiting cases. Some movements are deliberate, generally aimed at a goal and consciously controlled. Deliberate movements stand in contrast to spontaneous movements. Spontaneous movements are automatically carried along by the body in transition to other goal-directed movements and tend to be largely below conscious awareness. They do not permeate with the same level of intentionality or goal-directness as deliberate movements. The trajectories of spontaneous movements change as a function of the speed whereas those of deliberate movements keep their intended course despite speed changes. In natural motions—whether instrumental activities of daily living or complex choreographic sequences—the ebb and flow of these two movement classes can be unambiguously identified with the random fluctuations of movement parameters and connected with the ebb and flow of mental processes. Unlike automatic cognitive processes that cannot be reported on, spontaneous movements that also occur largely below conscious awareness can be precisely and objectively quantified, independent of any inferences. They also provide a new tool to connect body movements and cognitive abilities. The random fluctuations—particularly in the millisecond range—of trajectory parameters during deliberate and automatic pointing movements—such as the hand speed maximum and the time to attain the speed maximum—give away unequivocal differences between those with neurodevelopmental and neurodegenerative disorders (e.g., individuals with ASD) and their typically developing (TD) peers, optionally in the naturalistic settings of their classroom environment.
The dichotomy between deliberate and spontaneous motions is new, yet it is present in cognitive processes, body gestures for communication, biological motions, facial expressions, ON/OFF states of saccadic eye movements, speech, etc. Most likely the core (more primitive) automatic centers of the brain control the spontaneous motions (e.g., the brain stem, the cerebellum, basal ganglia and limbic system). These centers are severely disrupted during development in ASD. In certain individuals, they may not mature and give rise to the goal-directness necessary to leave the autistic bubble and explore the peripersonal space, much less the social medium. However, it is possible to tap into mental processes that are below conscious awareness and objectively measure the behavioral outcome in the unconscious movements. This is because the micro-movements' waveform reflect a readout of the nervous system's self-generated actions, under the voluntary, automatic and spontaneous control.
In cognitive psychology and cognitive neuroscience there are two separate cognitive systems—those that control automatic processes and those that control more deliberate ones. The two classes of movement can connect with these two cognitive categories—automatic and deliberate. When cognitive processes and movement mechanisms are studied in a close loop such that cognition impacts movement and movement impacts cognition, one can be influenced by reshaping the other. So one can make automatic cognitive processes impact spontaneous motions and deliberate cognitive processes impact deliberate motions. In the case of low-functioning non-verbal children with ASD one can very precisely and objectively track their spontaneous learning with no instructions or specific goals.
Herein, cognition and movement have been connected and this connection has been made objectively quantifiable. These objective measurements may be performed in about fifteen (15) minutes, with minimal disruption of daily routines. Notably, the task, the stimuli, the medication, etc. can be changed and the outcome very precisely measured before and after the manipulation to assess performance gains. Each child has different sensory preferences and capabilities, and different predispositions to learn. These can be measured and personalized target therapies may be identified and tailored according to the neurodevelopmental and neurodegenerative disorder or ASD type. Accordingly, the instant invention has provided an entirely objective metric of behavioral performance that is simple to use, fast to acquire, and mathematically sound.
While the instant invention has been exemplified with ASD, the methods of the instant invention can be used with other developmental/mental disabilities or neurological disorders. For example, the methods of the instant invention can be used in sports, perceptual sciences, and the performing arts. Examples of developmental/mental disabilities or neurological disorders that the instant methods can be used with include, without limitation, attention deficit hyperactivity disorder (ADHD), Parkinson's Disease, stroke (e.g., stroke in the cortex, particularly the posterior parietal cortex; Tones et al. (2010) J. Neurophysiol, 104:2375-2388), Down syndrome, William syndrome, schizophrenics, concussive injuries (e.g., sports concussion), and the like. The instant methods may also be used with other developmental disabilities to identify traits solely associated with ASD with clearly localized regions of the Gamma-plane in relation to other disorders that also impact mental capabilities. Stochastic rates of change and rules may be identified that are specific to ASD.
In a particular embodiment of the instant invention, real-time hand movements are captured from the subject (e.g., animal, particularly human (e.g., child) as he/she faces an interface (e.g. a computer monitor) and are used to trigger real-time videos (e.g., videos of self from a camera facing the subject, pre-recorded cartoon videos of the subject's preference, or the like). The videos are triggered by the subject's real-time motions of his/her hand when the motions are constrained to a virtual Region Of Interest (“vROI”) defined by the experimenter. In a particular embodiment, the subject discovers this vROI on his/her own, without instructions. The self-discovery of the vROI emerges by random exploratory motions of the hand. Once the subject discovers the motion/vROI that triggers the videos, they systematically initiate motions that will sustain the video ON or that will flicker the videos successively ON/OFF. Typically developing subjects (TD) verbally communicate the succession of events to the experimenter. They undergo an “aha!” moment that they tend to want to explain to the experimenter. In the cases of the subject with autism spectrum disorders, the progression is different because they have various degrees of functionality that range from very low-functioning non-verbal to high-functioning verbal abilities. However, they manifest changes in their motion patterns and facial expressions which are objectively quantified. These indicate substantial changes in engagement with the task and reveal the progression of the task. Their motions reveal how to attain the goal of reaching the specific vROI in space in order to attain their reward (e.g., videos of themselves or prerecorded cartoons of their preference). Without instructions, they can then proceed to control the flow of the video display as a function of the reshaping of their personal stochastic signatures of movement variability (the random fluctuations in the movement patterns that are characteristic to each individual subject). The metrics reveal which form of reward is most effective in the sense of engaging the subject in the active exploration of the environment and exiting for a moment their “autistic bubble”. Sessions typically last a few minutes and enable measurement behavior before and after this exploratory exercise.
In a particular embodiment, the method of the instant invention is a real-time co-adaptive interface between the subject and an artificial agent (e.g., a robot, three dimensional animate (e.g., avatar), screen, or any dynamics media with controllable dynamics). As above, there may be no explicit instructions or goals given to the subject. The user rather may discover the goal through random behavior that leads to active exploration of its surroundings stimulated by a reward (some change in the external stimulus). Once the exploration turns systematic, the subject can discover that it can act in tandem with the media's dynamics and control it. This sensory-feedback based control in closed loop with the external media (agent) can then be used to co-adapt the agent's and the user's motions, emotions, etc. The external media with dynamics can be a robot, a virtual three dimensional animate, sound (speech) or any kind of simpler sensory input that changes over time with some structure (i.e., that has dynamics).
As stated hereinabove, this methodology may lack instructions or well defined goals. Unlike other methods that also exploit sensory-motor feedback in closed loop (e.g. the Wii®, brain machine interfaces, etc.) the instant methodology does not require the subject to understand a priori what the goal of the task should be. The subject comes to realize the goal by randomly interacting with the environment and discovering the contingencies that evoke a rewarding experience. Since methods have been developed that can objectively quantify the subject's sensory preferences in the stochastic patterns of his motions, one can identify in real time which reward is the most effective in engaging the subject and fostering systematic exploratory behavior that potentially can lead to active control of the stimulus in closed loop. Once the active control of the stimulus is attained, one can co-adapt the motions of the subject and those of the stimulus in real time. This means that one can reshape the motions of the subject by reshaping the stimulus motions. One can reshape the stimulus motions in ways that can shift the stochastic signatures of the autistic subject towards typical patterns that are acceptable within social settings without having to explicitly tell the subject. Since the random fluctuations of velocity-dependent parameters are a readout of the subject's somatosensation, it is possible to very precisely quantify if there is resistance or compliance to co-adaptation under specific types of noise that the external media can be endowed with. In other words, a form of augmented sensory feedback has been created that can be precisely parameterized and its effectiveness tracked in real time.
The stimulus, which may be real time self-videos or videos of the subject's preference, can be replaced by other media (e.g., audio, touch-vibration-type stimuli, etc.) including an anthropomorphic robot that the motions of the subject can control in closed loop with the motions of the robot. The stimulus can also be an animated three dimensional animate embedded in a social environment so one can co-adapt the subject and the three dimensional animate within a social setting to build and to foster theory of mind.
As stated hereinabove, the methods of the instant invention may be performed with a robot or robotic interface. Robots provide an amenable platform to teach the subject because they cannot only move autonomously but respond to the subject's movements as well. Robots can detect different motion patterns in different subject populations and be trained to move with specific statistical signatures of variability that may be more appropriate for one population than for other.
Whether one moves with a specific purpose or mindlessly moves in “auto-pilot” mode, movement has inherent variability that is objectively quantifiable. All things being equal, the statistical signature of movement variability is unique to each person and is revealing of mental states and of mental illnesses. In this sense movement variability can become the bridge to connect the mind and the body and to provide appropriate means to improve social awareness.
The movement sense (kinesthesis) as other senses (vision, audition, vestibular input, etc.) is a form of sensory input that shapes the path of everything that one learns, yet movement can also channel out through its inherent NSR- and variability-patterns the most adequate form of sensory guidance to aid the system to learn to heal itself. Such motor-sensorial preferences can be extracted and exploited in a reward-reinforcement-based cooperative person-robot setting to help stimulate creative and abstract thinking through hands-on co-adaptive interactions, parts of which can occur without full awareness and without explicit instructions.
The same research program can also be carried out with children in the spectrum of both genders from four to fifteen (4-15) years of age. These children also became engaged in the closed-loop video-triggering guided by the feedback from their hand movement in real time, yet their progression towards intentionality was slower than that of their TD peers and had a reversed progression. TD exploration transitioned from random to systematic to well-structured to intended-some TD children even verbalized the contingency of arm movement and video appearance. In marked contrast ASD started abnormally systematic (mechanic) and nearly noiseless, transitioned through chaotic phases with no discernible patterns and in some cases started to acquire similar exploratory features of the TD. In the TD children these included detectable systematic fluctuations in the distance traveled by the hand as the hand crossed the virtual planes with corresponding changes in the speed profiles of the hand which switched the statistical patterns of variability of the hand peak velocity from exponential to skewed lognormal and eventually to bimodal distributions signaling speeds from two complementary space regions.
In both TD and ASD this progression strongly depended on the child's favorite form of sensory input. In some cases the real time video of themselves was quite effective in ASD whereas for other children with ASD it was not as engaging. However, the children with ASD can do the task without instructions, even low-functioning, non-verbal. There is also a form of sensory guidance that is quite effective in engaging them in exploratory behavior towards intentional acts which can be objectively measured in their spontaneous motions. Additionally the child with ASD with echolalia and the verbal child with AS in the group were extremely engaged and did show remarkable changes in a matter of seconds across multiple sessions. The beneficial effects of this training tool were strong, fast (a few minutes a day) and consistent across all the TD children but they also showed promise in the children with ASD.
Since the children can be engaged in active exploration until they discover that they control an external stimulus and pursue that control, the real time self-video can be replaced with a robot (e.g., NAO robot) that can be modified and endowed with the statistical range of movement variability from the typical children. This robot will co-adapt its movements with those of the child, initially recruited by the child. In time, once the robot detects systematicity and that it is being controlled by the child, it will be programmed to gradually shift the spontaneous components of the motions into slightly different statistical patterns until the child catches up with it and spontaneously reverts to try and control the robot. The interface may be designed in closed loop as a game to make it attractive to the child and to store the adaptation trajectory for later to be used as reference when comparing it to the trajectory of the children with ASD.
The motion detection algorithms may be developed to program the robot to detect differences between random patterns, chaotic patterns, systematic patterns, well-structured patterns and intended patterns towards an emerging, well defined goal.
This interface may present snips from real social situations and may be used to probe the TD children, for problem solving in a virtual social setting and to enhance various aspects of ToM. Well established paradigms may be used that probe the young children's abilities for pretend-play, deception, implicit false belief, understanding intention, and word-learning. The statistics of both the intended and spontaneous motions that have been harvested and parameterized in natural settings may be used to introduce as the seed and then slowly reshaped in the virtual settings in order to broaden the range of patterns present in the child's behavior as the child co-adapts with the robot. In this way, awareness of automatic body motions during problem solving in the TD children is increased, which largely contribute to the highly automatic inferences in ToM. Since metrics of performance gains have been developed and tested across multiple populations of human and non-human primates, gains in behavioral performance as the child learns can be objectively quantified. This allows for the design of a tool/device to precisely quantify the form of “automatic intelligence” and identify it with automatic behavior. The instant invention may also be used to enhance awareness in TD young children of the cognitive difficulties in others to avoid bullying situations and to foster understanding of others.
Understanding and objectively measuring movements link body and brain. Building this link computationally will be fundamental to foster proper development of the subject's mental abilities in society. The instant approach to movement control and embodied cognition facilitates the objective quantification of behaviors in naturalistic settings—such as the classroom environment and the home settings and permits the objectively quantitative tracking of movement performance and cognitive-based motor learning gains over time. Since the subject with ASD has social impairments, a framework that spontaneously—without instructions—engages them with virtual agents may first be developed. Subsequently, the actual robots may be introduced to encourage the children's active exploration and initial control over the robots, only to have the robots gradually shift the statistical signatures of variability in the subject with ASD towards typical ranges.
Since the statistical signatures are so far apart in TD and ASD, any robot can be easily programmed to use the natural statistics of movement to distinguish when it is interacting with a TD child from when it is interacting with a child that has ASD. Furthermore, since typical movements can be unambiguously classified into spontaneous and intended based on the effects of dynamics on their trajectories; but since this distinction is blurred in ASD, it will be feasible for a robot to detect motions from each child type and be programmed to respond in compliance. These natural statistical features of physical movements may be exploited to co-adapt robot and child as they interact using the appropriate noise and variability levels and to automatically (without the child's awareness) shift motor variability towards levels that promote facial expressions, body gestures and body language for non-verbal communication thus boosting social interactions. This can be achieved in closed loop using real time movements captured by motion sensors that are paired with other sensory input of the child's preference. Three novel characteristics of the paradigm include, without limitation: (1) The automatic components of the wholesome movement unit, of which the child has no awareness are targeted—rather than restricting to the study of the intended, goal-directed component; (2) A goal is not specified. Rather, the child discovers the goal of the task through exploration; and (3) The statistics of facial expressions, emotions and body movements (intended and automatic) of both TD children and children with ASD have been parameterized (thereby providing ground truth for training the robots).
The instant methods help blend TD children with peers who have ASD. In the school system the methods will raise awareness and understanding in the TD children of the motor/communication problems in ASD and promote their willingness to approach their peers socially and to interact with them and, crucially, to avoid bullying in general. In turn by measuring and gradually shifting the statistical signatures of movement variability in ASD towards TD levels, the ASD children will be better able to blend in the social scene as others will perceive them within the ranges of socially acceptable motions. Importantly the children with ASD will not have to directly imitate the motions of their TD peers through explicit instructions. They will not be instructed to intentionally do anything. Rather their automatic behaviors will be used and the statistical patterns of their spontaneous movements will be reshaped without their awareness to evoke the transition towards intended behavior related to stimuli of their preference. These children's interactions will contribute to the improvement of their social and communicative skills in the classroom settings and beyond while circumventing the known problems that children with ASD have regarding imitation, verbal communication and cognitive understanding in general.
A mobile child-machine interface system has been developed that enables one to visit the classroom settings and have TD children interact with touch screens and perform cognitive-driven tasks adapted from their curricula. The statistical patterns of natural movements—both voluntary and automatic—were first collected across a variety of natural tasks including those of the classroom settings involving the hands and upper body and others engaging all limbs, the trunk and the head in sports routines such as beginner's martial arts. All of the data was first parameterized and may be used as a source to train the robots move naturally in order to facilitate engagement with the children. Unconstrained, natural movements are recorded by the system as the child learns to perfect the task and becomes familiar with the computer environment as a whole. The initial version of this interface was in open loop. Children responded to stimuli presented on the touch screen and pointed at the correct target that matched a given sample evoked by their touch of the screen. The stimuli had perceptual and cognitive features that varied in increasing levels of complexity from purely visual (e.g., color) to more abstract (e.g., geometric shapes) and even yet to more complex features that required mental rotation to correctly match the given sample. The movements of the hand, arm, trunk and head were concurrently recorded with the hand-screen touches and video input from a camera facing the child, with all behavioral events time stamped and logged for further off-line analyses. Movements of the hand homing in on the target and immediately preceding touching the screen were intended towards the target or towards the sample to be matched. These intended movements touched those locations on the screen. Spontaneously retracting motions were automatic in that they were transitional, did not pursue a goal, were not instructed and merely supported the goal-directed component of the whole reach. These motions were being carried passively along as the body spontaneously recovered from the goal-directed portion. They engaged the full body and were very revealing of the dynamics of the task. Their hand trajectories changed dramatically with speed—unlike their intended counterpart aimed towards the targets. The latter could be well characterized as unique geodesics curves on a Riemannian manifold in that they remained on the intended track, were speed- and loads-invariant with low variability and locked in time with the trunk and head immediately preceding the reach initiation as the decision to choose a target was being made.
The instant invention may also be used to evoke automatic cognitive abilities in the children with ASD. This may be done in the context of ToM paradigms, borrowing specific scenarios from it. The instant invention may be used to specifically identify abnormal reflexes in the ASD children, known to be problematic in newborns that go on to develop ASD and AS. The data bank of statistical signatures may be parameterized in both intended and spontaneous automatic movements in TD and used as a template to detect abnormal patters and to correct them in ASD via the robot. The identification of residual reflexes or their absence thereof will guide the programming of movements that the robots will use to co-adapt with the child. It has already been shown that the individual statistical signatures of the natural movement variability in the children with ASD can be shifted towards the normal ranges and that they can perform these experiments in closed loop using real time video-based and self-motions as forms of sensory input.
Once the children with ASD are comfortable with the concept of moving in tandem with an external agent and initiating the robot's (avatar's) hand motions in closed loop with the children's hand movements, the recruitment of other robot body parts may be gradually initiated (e.g., NAO has twenty five (25) degrees of freedom) by the child. It is known that systematic changes in sensory input reshape the dynamics of their natural movements and these changes can be precisely and objectively quantified across time as the system learns new behaviors. In so doing, it has been possible to decouple DoF that are devoted to intended behavior and that are task relevant from DoF that are incidental to the task, changing spontaneously and subject to different ranges of variability with changes in dynamics. The evolution of the ASD body can be closely monitored as it engages in tandem with the robot so that eventually the child with ASD comes to spontaneously control the ASD robot, without instructions. Fun movements to play may be used that have already been tested in ASD including beginners' martial arts routines and simple instrumental reaching and grasping acts.
The lack of ToM is unique to children with high severity scores of ASD, often without spoken language. Children with Down syndrome or other mental disorders do not entirely lack ToM or pretend-play abilities. Moreover some children with ASD can develop deliberate, explicit ToM by twelve (12) years of age and solve the problems that developmental psychology have created to probe their cognitive abilities. They cannot however develop the type of ToM that is implicit, fast, automatic and intuitive. The automatic motions of the children with ASD may be monitored as they interact with their TD peers and the two robots may be used as proxies to promote social exchange. The TD robot information will provide the trajectory in normal development whereas the ASD robot will provide the error data.
The results from the instant methods will lead to new discoveries about how the mind-brain interacts with the body and spontaneously self discovers new solutions to problems. The paradigm of tapping into automatic processes that can be objectively quantified through the statistics of the variability inherently present in natural repeats of unconstrained movements will lead to the understanding of “automatic intelligence” and will broaden the understanding of the spontaneous emergence of ToM during typical development as well as through atypical development. The outcome from the instant methods will provide a set of metrics that enable one to systematically link motor variability and normal/abnormal mental development. It will also enable one to link motor variability with mental illnesses that affect cognitive disabilities specific to improper social interactions. It will create the first comprehensive parameterization of facial expression and emotion statistics ranging from infants to young adults with the corresponding set of body motions, including reflexes, automatic and intended motions. Thus, it will provide the first map identifying the statistics of facial motions and emotions with the corresponding body dynamics across a large range of social and non-social activities. This comprehensive tool will be of utility to the robotics community modeling the phenomena as well as to the clinical community trying to provide the appropriate behavioral therapies.
In accordance with the instant invention, methods for classification leading to diagnosing and/or providing a prognosis for a neurological disorder, particularly an autism spectral disorder, in a subject (e.g., animal, particularly human) are provided. In a particular embodiment, the method comprises measuring the motion pattern of the subject upon interaction with an artificial agent, wherein the motion of the subject is observed over a millisecond range (e.g., s-Peaks are observed/monitored). In a particular embodiment, the motion is detected through the use of wearable sensor. In a particular embodiment, the synchronicity and/or periodicity of the fluctuations (changes) in the millisecond range are observed. As used herein, the phrase “millisecond range” may refer to a time frame that is less than one second, particularly less than about a half (0.5) second, particularly less than about on hundred (100) milliseconds, less than about fifty (50) milliseconds, less than about twenty five (25) milliseconds, or less than about five (5) or ten (10) milliseconds. In a particular embodiment, the motion of the subject (e.g., the speed) is observed over segments of time in the millisecond range (e.g., from about one to about three (3) millisecond, from about one (1) to about five (5) milliseconds, from about one (1) to about ten (10) milliseconds, from about one (1) to about twenty five (25) milliseconds, about one (1) to about fifty (50) milliseconds, or about one (1) to about one hundred (100) milliseconds). In a particular embodiment, the subject is not provided instruction on how to interact with the artificial agent. The artificial agent provides a stimulus when the subject contacts a region of interest (e.g., a virtual region of interest such a three dimensional (3D) space). A difference in the motion pattern of the subject compared to a healthy individual and/or the presence of a motion pattern associated with an autism spectral disorder indicates that the tested subject has an autism spectral disorder. In certain embodiments, the artificial agent is a dynamic media or interface and may be a robot, three dimensional animate, speaker, or multi-touch surface screen. In a particular embodiment, the artificial agent is a screen (e.g., comprising a target) and the stimulus is a real-time video of the subject. The methods of the instant invention may further comprise measuring other aspects of the subject (e.g., facial patterns) upon interaction with the artificial agent.
In accordance with another aspect of the instant invention, the methods described herein can be used for determining the sensory capabilities and preferences of an individual. Once determined this sensory modality is used in therapies that examine the patterns of random fluctuations—particularly over a millisecond range—of movement parameters in the movement trajectories of the person's body parts (e.g. hands, head, trunk, limbs, etc.). These random fluctuations over time (over repetitions of the same behavior) serve as a form of re-afferent sensory input that the system integrates with the efferent motor output and utilizes these inputs differently as a function of cognitive complexity in closed loop with cognition. In a particular embodiment, the methods use a set of postures to trigger external media (audio, videos, real time self-videos from a webcam facing the child, or virtual variants of the child embodied in a three dimensional animate that is endowed with the child's physical motions or with noisy variants of it). In a particular embodiment the set of postures thus learned by the subject are associated with intuitive gestures for communication that operate and control external media (e.g. play, rewind, pause, fast-forward, flicker, etc.). In a particular embodiment, the method further comprises measuring a motion pattern of the subject—particularly over a millisecond range—prior to the administration of a therapy (which could be either pharmaceutical or behavioral or both; e.g., to obtain a baseline measurement). In a particular embodiment, the method comprises measuring the motion pattern of the subject—particularly over a millisecond range—upon interaction with an artificial agent as described above, after administering the therapy to the subject to measure performance gains relative to baseline values. The modulation of the motion pattern of the subject after administration of the therapy (e.g., to a normal motion pattern) indicates that the therapy modulates the autism spectral disorder. The direction of this modulation (away or towards typicality, or neutral meaning no change) is evaluated so the effectiveness of treatment can be objectively determined.
In accordance with another aspect of the instant invention, methods of reshaping the spontaneous random noise into well-controlled motion patterns of a subject with an autism spectral disorder are provided. In certain embodiments the subject is allowed to control an artificial agent which initially moves with the stochastic signatures from the dynamics extracted from the physical motions of the subject—particularly over the millisecond range. Gradually the stochastic signatures of the artificial agent (three dimensional animate or robot) are reshaped so as to harmoniously co-adapt the subject and the artificial agent. In certain embodiments, the method comprises having the subject interact with an artificial agent as described above. In a particular embodiment, the artificial agent is a robot, particularly one programmed to encourage the subject to spontaneously (without explicit instructions or goals) react and move similarly to typically developing children. The subject is in control. The changes work because they are applied to movements that are spontaneous and occur without the subject's intent. These are the movements that do not conserve their motion trajectories as the dynamics of the motion change. The movements that conserve their trajectories and remain invariant to changes in dynamics are the ones under voluntary control and will resist spontaneous changes. Therefore the technique exploits the motions that are collateral, supplemental, and “invisible” to the conscious mind.
As stated herein, the methods described throughout the instant invention can be used for diagnosing, characterizing, classifying (e.g., along a continuum spectrum), assessing, and/or treating a neurological disorder in a subject. In a particular embodiment, the subject is at least three (3) or four (4) years old. Neurological disorders include neurodevelopmental and neurodegenerative disorders. Specific examples of neurological disorders include, without limitation: Parkinson's disease, parkinsonian syndrome, Autism, Autism spectrum disorder, Huntington's disease, athetosis, dystonia, cerebellar and spinal atrophy, multiple system atrophy, striatonigral degeneration, olivopontocerebellar atrophy, Shy-Drager syndrome, corticobasal degeneration, progressive supranuclear palsy, basal ganglia calcification, parkinsonism-dementia syndrome, diffuse Lewy body disease, Alzheimer's disease, Pick's disease, Wilson's disease, multiple sclerosis, peripheral nerve disease, brain tumor, cerebral stroke, attention deficit hyperactivity disorder (ADHD), Down syndrome, William syndrome, schizophrenias, etc. In a particular embodiment, the neurological disorder is Autism, Autism spectrum disorder, or Parkinson's disease.
In a particular embodiment, the method of the instant invention comprises measuring the motion pattern of a subject—particularly within the millisecond range—upon interaction with an artificial agent, wherein a difference in the motion pattern of the subject compared to at least one control (e.g., a healthy individual and/or an individual with a neurological disorder indicates whether the subject has the neurological disorder and/or the classification or severity of the neurological disorder). In a particular embodiment, the gender and/or age of the subject and the control standards are the same. In a particular embodiment, the artificial agent provides a stimulus when the subject contacts a region of interest. The artificial agent may provide a target or cue to the subject (e.g., a target to touch, such as a dot or light). In a particular embodiment, the artificial agent provides a challenge or test (e.g., match-to-sample test) to the subject that requires the movement and selection of an answer by the subject. For example and as described herein, the artificial agent may present an object and require the subject to touch the object by distinguishing between the object and a second object which differs from the first object by color, shape, orientation, or the like. The subject may or may not be provided instruction on how to interact with the artificial agent. The artificial agent may be a dynamic media or interface. In a particular embodiment, the artificial agent provides a video (e.g., a video of movements by an individual (e.g., karate moves) which can be mimicked by the subject). Examples of an artificial agent include, without limitation a robot, three dimensional animate, speaker, screen (e.g., computer screen), touch screen, tablet (e.g., iPad), and the like. In a particular embodiment, the artificial agent is a screen (e.g., a touch screen).
In a particular embodiment, the motion pattern of the subject's arm is measured particularly within the millisecond range. However, any body part can be measured (e.g. hands, head, trunk, limbs, etc.). In a particular embodiment, the difference in size of the body parts (e.g., limb size) of subjects and controls is accounted for (e.g., normalized). The intentional or deliberate motions (e.g., those aimed at a target) of the subject may be measured and compared to standards and/or the automatic or spontaneous motions (e.g., the retracting from a target) may be measured and compared to a standard. Any parameter of the motion of the subject may be measured. Parameters that can be measured include, without limitation: speed profile, max speed, time to reach maximum speed, acceleration, max retraction speed, time to reach max retraction speed, three-dimensional path, accuracy of target touching, percentage correct (when the artificial agent provides a test), overall amount of time for motion, decision movement latency, body part rotation or positioning, and joint angle. In a particular embodiment, changes within the speed of the motion in the millisecond range are measured. The method of the instant invention may also comprise measuring and/or monitoring the facial patterns of the subject during interaction with the artificial agent. In a particular embodiment, the subject is placed in a particular position or orientation (e.g., a primed position) prior to interacting with the artificial agent.
When the artificial agent provides a stimulus (e.g., turns on a stimulus) when the subject contacts a region of interest, the stimulus may be a real-time video of the subject. In a particular embodiment, the stimulus is a video, such as a cartoon video. In yet another embodiment, the stimulus is a three dimensional animate.
As stated hereinabove, the instant invention also encompasses methods for determining the ability of a therapy to modulate a neurological disorder in a subject. In a particular embodiment, the method comprises administering the therapy to a subject and performing at least one of the above diagnostic methods of the instant invention (e.g., monitoring motion upon interaction with the artificial agent, particularly within the millisecond range) to determine whether the administered therapy modulated (e.g., treated) the neurological disorder (e.g., by comparing to standards or previously obtained standards of the subject). In a particular embodiment, the method comprises performing at least one of the diagnostic methods of the instant invention, administering the therapy to the subject, and performing a second diagnostic method of the instant invention on the subject, wherein a change in the second assay compared to the first assay indicates that the therapy modulates the neurological disorder. For example, if the results of the second assay more closely approximate the pattern of a healthy individual than the first assay, the therapy is effective against the neurological disorder.
In accordance with another aspect of the instant invention, methods of treating a neurological disorder and/or lessening the improper motion pattern of a subject with a neurological disorder such as an autism spectral disorder are provided. In a particular embodiment, the method comprises having the subject interact with an artificial agent which provides a stimulus when the subject contacts a region of interest, wherein the interaction of the subject with the artificial agent lessens the improper motion pattern associated with the neurological disorder particularly within the millisecond range.
The following example is provided to illustrate certain aspects of the present solution. The present solution is not intended to limit the invention in any way.

Example

A critical need in autism research and its treatments is to find an efficient quantitative way to categorize the disorder. In a given cohort of affected people there is high probability that not two individuals are alike, even when their ADOS (Lord et al. (2000) J. Autism Dev. Disord., 30:205-223) scores may classify them similarly. One may find, for example, that two children who are classified as medium functioning show surprising differences in their individual capabilities and predispositions that set them apart.
The active field of computational neuroscience and in particular the subfields of sensory-motor physiology and motor control may provide this bridge because behaviors that are verbally described by clinicians can be objectively quantifiable. Behaviors are composed of many movements with different levels of intent (Tones, E. B. (2012) Neurocase, 1:1-16; Tones, E. B. (2011) Exp Brain Res., 215:269-283). They flow as a continuous stream of motions that occur at different bodily levels and have underlying quantifiable sensory-motor physiology. There is thus a critical need for finding objective biometrics providing neurophysiological characterizations of different behaviors that can be done by active research groups working on autism sensory-motor neuroscience research.
Recent studies from various groups have uncovered deficits in the two-way exchange of motor command and sensory information in motor control as potentially fundamental core symptoms of ASD, across ages and in both sexes (Deitz et al. (2007) Phys. Occupation. Ther. Ped., 27:87-102; Whyatt et al. (2012) J. Autism Develop. Disorders 42:1799-1809; Torres et al. (2013) Front. Integr. Neurosci., 7:32; Torres et al. (2013) J. Neurophysiol., 110:1646-1662; Haswell et al. (2009) Nat. Neurosci., 12:970-972; Marko et al. (2015) Brain 138:784-797). However, this nascent field of sensory-motor research in autism has yet to penetrate mainstream clinical practices. Moreover, possible connections between sensory-motor control and cognitive abilities such as spoken verbal abilities have not yet been considered.
The human eye observing and qualitatively evaluating behaviors has limitations and necessarily misses subtle millisecond movements that occur largely beneath awareness. With the advent of new high resolution wearable sensors (Allet et al. (2010) Sensors 10:9026-9052; Burns et al. (2010) Conf Proc IEEE Eng Med Biol Soc., 2010:3759-3762) it is possible to obtain valuable information that otherwise escapes the observer's eyes. Using such instrumentation, important statistical signatures were found hidden in the micro-structure of motor output variability inherently present in peripheral limb movements (Jones et al. (2013) Nature 504:427431; Gepner et al. (2001) J. Autism Dev. Disord., 31:37-45; Torres et al. (2013) Front. Integr. Neurosci., 7:32). These physiological motion signals from the Peripheral Nervous Systems (PNS) unambiguously separate the course of maturation of autistic from typically developing individuals (Tones, E. B. (2013) Neurocase 19:150-165; Tones et al. (2013) Front. Integr. Neurosci., 7:32; Tones et al. (2013) Front. Integr. Neurosci., 7:46; Tones et al. (2013) J. Neurophysiol., 110:1646-1662). After four (4) years of age, peripheral kinematics undergoes a maturational change that does not occur in ASD, regardless of age, sex or ADOS scores (Tones, E. B. (2013) Neurocase 19:150-165; Tones et al. (2013) Front. Integr. Neurosci., 7:32; Tones et al. (2013) Front. Integr. Neurosci., 7:46; Tones et al. (2013) J. Neurophysiol., 110:1646-1662).
These findings enable reliable detection of autistic traits using critical points of the velocity trajectories, e.g., their absolute global maxima (Tones, E. B. (2013) Neurocase 19:150165; Tones et al. (2013) Front. Integr. Neurosci., 7:32; Torres et al. (2013) Front. Integr. Neurosci., 7:46; Torres et al. (2013) J. Neurophysiol., 110:1646-1662). Here, local, much smaller maxima were also found to be present in these hand trajectories. These local smaller peak fluctuations in the speed were found to provide finer grain detailed information about ASD.
Traditionally, when analyzing raw kinematics data, the fluctuations along the position time series are considered noise and often averaged out. In particular, in the past, local peak fluctuations in the kinematics data have not been of interest to movement neuroscientists. To assess the possible relevance of those fluctuations it is important to use smoothing techniques which preserve elements of the original temporal structure of the raw data upon smoothing that may contain relevant information. To ensure that the possibly important original peak fluctuations were not averaged out here we chose to use a triangular smoothing procedure (Simonoff, J. S. Smoothing methods in statistics (Springer, 1996)) that preserves the original peak fluctuations in the temporal structure of the raw data within the millisecond time range. The statistical structure and properties of the resulting time series of those movement fluctuations were examined (FIGS. 13-14 and 18-19).
The velocity-dependent fluctuations (both global and local) were termed Peripheral Spikes (s-Peaks-Spikes) because they are physically recorded from the peripheral limbs using high-resolution sensors that the person physically wears. Such wearable sensors “listen” to the physiological signal and noise blend that the muscles naturally amplify from motor and sensory nerves under the skin. This is in contrast to the kinematics signal estimated from observed in video sequences, which require numerous coding heuristic interpolations to recover missing frames from occlusions, or to extract discrete segments that the observer determines are of relevance. The sensors sampled at two hundred forty (240) Hz continuously capturing the actual physical movements. The analytical approach used recently provided an objective longitudinal profiling of peripheral limb movements' development across different ages in a heterogeneous cohort (Torres et al. (2013) Front. Integr. Neurosci., 7:32). Here it was aimed at blindly characterizing individuals in the heterogeneous spectrum of autism in relation to their progenitors. New statistical links not previously considered were uncovered since they emerged from direct measurements without human subjective human interventions.
A simple forward-and-back pointing paradigm was used to continuously record the s-Peaks (FIG. 13A). A modified version of the traditional pointing paradigm was used (Torres et al. (2013) Front. Integr. Neurosci., 7:32). In the continuous flow of hand motions, the deliberate reach-out segments directed towards the instructed target was distinguished from the hand-retraction segments that spontaneously took place without any instruction or visual goals. FIG. 13B-13D show sample trajectories from three representative subjects. Although at first glance the positional trajectories look very smooth, when one zoomed in the temporal dynamics covering these hand positional paths, one found millisecond range speed fluctuations that clearly differed across subjects from low-functioning non-verbal, high-functioning verbal to typical control (FIG. 13E-13G). A typically developing individual completed several cycles of forward-and-back motions, within any given time window, along continuous flow of hand movements, (e.g. 8 s in FIG. 13G). In stark contrast individuals across the spectrum of autism had systematically increasing number of s-Peaks both between and within the forward-and-back cycles. The lower the verbal/communicative skills were, the higher were the numbers of s-Peaks present in their motions. Consequently, there were fewer full cycles per unit time. Zooming in further in one full cycle of motion for a low-functioning non-verbal child and a typically developing child revealed a dramatic difference in the smoothness of the motion cycle itself (FIGS. 13A-13I).
Trial-by-trial s-Peaks were examined while searching for patterns of synchronous behavior across cycles. To this end, the touches were aligned at zero (0) ms time and a large enough time window that included both the forward and the retraction movements was taken. FIG. 14A shows a s-Peaks vector spanning one thousand (1,000) ms before the touch at the start to one thousand (1,000) ms after the touch. By stacking up these s-Peaks vectors along the continuous motion flow, a s-Peaks matrix was formed. In analogy to what is done with action potential CNS spikes we plotted the s-Peaks vectors in rastergram-like form. FIG. 14B-14D show s-Peaks matrices from different representative subjects in the cohort, including the representative typical control cases. The global speed maxima are aligned across trials relative to the touch. In the control subject (FIG. 14D) there are no s-Peaks in the acceleration or in the deceleration phases of the forward or backwards movements of each cycle. The other participants, who had a diagnosis of ASD, had very different features. The systematic increase in the lack of structure of the s-Peaks matrix coincided with the reported verbal spoken abilities of the cohort. The visualization tool revealed an orderly trend: the fewer the verbal spoken abilities, the more disordered the structure in the s-Peaks matrix.
This systematic relationship prompted further examination of various stochastic features of these continuous random processes in more detail. Trial-by-trial (population) cross-correlation and patterns of synchronization (or lack thereof) were assessed for each individual member of this cohort of sixty-five (65) individuals. Included in the cohort of sixty-five (65) subjects were: thirty (30) subjects diagnosed with ASD with ages from seven (7) to thirty (30) years old; eight (8) adult controls, six (6) typical developing (TD); three (3) to five (5) years old children, and twenty one (21) ASD's parents. All the ASD individuals were diagnosed in the spectrum by professionals/agencies qualified to do the testing. The demographic information for all participants studied is listed in TABLES 1-2. The labels from the clinical reports were used as references: low functioning (LF: no spoken language); mid functioning (MF: some spoken words) and high functioning (HF: some communicative phrases).

	TABLE 1

	ADOS Scores	GARS Scores

M/

Age

Stanford-Binet

Com +

Stereo

Com

Soc

Autism

Parent

Code

F

Yrs

NVIQ

VIQ

FSIQ

Stereo

Com

Soc

SS

Index

Mo, Fa	1	M	10	N/A	N/A	107	3	3	9	12	N/A	N/A	N/A	N/A
	2	M	10.3	42	43	40	3	4	10	14	N/A	N/A	N/A	N/A
	3	M	11.5	100	82	90	7	5	6	11	N/A	N/A	N/A	N/A
	4	F	11.5	50	43	4	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
	5	M	11.7	42	43	40	5	8	10	18	N/A	N/A	N/A	N/A
	6	M	11.7	43	43	40	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Mo, Fa	7	M	12	N/A	N/A	67	4	5	13	18	N/A	N/A	N/A	N/A
	8	F	12	N/A	N/A	60	4	8	10	18	N/A	N/A	N/A	N/A
Mo, Fa	9	M	12	N/A	N/A	95	2	5	8	13	N/A	N/A	N/A	N/A
Mo, F	10	M	12	N/A	N/A	95	1	5	7	12	N/A	N/A	N/A	N/A
Mo	11	M	13	N/A	N/A	89	2	3	7	10	N/A	N/A	N/A	N/A
	12	M	13.8	42	43	40	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Mo, Fa	13	M	14	N/A	N/A	74	3	9	18	19	N/A	N/A	N/A	N/A
	14	F	14.3	50	43	44	N/A	N/A	N/A	N/A	8	11	9	124
Mo	15	F	15	N/A	N/A	52	2	6	11	17	N/A	N/A	N/A	N/A
	16	F	15	N/A	N/A	77	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Mo	17	F	15	N/A	N/A	71	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
	18	M	15	N/A	N/A	56	3	4	10	14	N/A	N/A	N/A	N/A
	19	F	15	N/A	N/A	52	2	6	11	17	N/A	N/A	N/A	N/A
	20	F	15	N/A	N/A	7	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
	21	M	15	N/A	N/A	71	6	5	7	12	N/A	N/A	N/A	N/A
	22	F	15.8	42	43	40	N/A	N/A	N/A	N/A	13	10	11	109
Mo, Fa	23	M	14	N/A	N/A	100	100	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Mo	24	F	16	N/A	N/A	81	2	7	9	16	N/A	N/A	N/A	N/A
Mo	25	M	18	N/A	N/A	101	2	4	6	10	N/A	N/A	N/A	N/A
Mo	26	M	18	N/A	N/A	96	4	4	8	12	N/A	N/A	N/A	N/A
Mo, Fa	27	M	18	N/A	N/A	76	1	5	7	12	N/A	N/A	N/A	N/A
	28	F	19	N/A	N/A	55	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Mo	29	M	25	N/A	N/A	99	6	3	7	10	N/A	N/A	N/A	N/A
Mo, Fa	30	M	30	N/A	N/A	36	2	8	14	22	N/A	N/A	N/A	N/A

(Autism Diagnostic Observational Scale) (Lord et al. (2000) J. Autism Dev. Disord., 30: 205-223; Gotham et al. (2009) J. Autism Dev. Disord., 39:693-705) is a standard assessment tool used by clinicians as a basis for the ASD diagnosis. Module 1 of the ADOS was used for the young, non-verbal students. Module 3 was used for the adolescent students with conversational ability. Stereo is a measure of stereotyped behaviors were a higher score indicates more stereotyped behaviors; however without a cutoff for ASD diagnosis.com is the total Communication score, where four (4) is the cutoff for Autism and two (2) the cutoff for Autism Spectrum. Soc is the total Reciprocal Social Interaction Score, where four (4) is the cutoff for Autism, and two (2) the cutoff for Autism Spectrum. Corn+Soc is the combined Communication and Social Interaction score, with a score of twelve (12) being the Autism cutoff, and seven (7) the Autism spectrum cutoff. Because of their age and extremely limited verbal abilities, two (2) of the children could not be given the ADOS. Therefore the GARS 2 (Gilliam Autism Rating Scale—Second edition; Gilliam, J. (2006) GARS-2: Gilliam Autism Rating Scale-Second Edition. Austin, Tex.: PRO-ED) was used to assess these individuals. Stereo SS is the standardized score of stereotyped behaviors. Corn SS is the standardized score of Communication. Social SS is the standardized score of Social Interactions. The Autism Index is the sum of standard scores, converted to a normed index score. For the participants shaded in gray we also recorded the parents under identical circumstances. Mo is for mother and Fa is for father. Dark gray are siblings with the same Mo and Fa (two males and one female marked with an asterisk).

TABLE 2

Participant Gender Age

1 M 3

2 M 4.3

3 F 4.3

4 F 4.8

5 M 4.8

6 M 5.1

7 F 21

8 F 22

9 F 24

Table 2: Information from TD participants. Typically developing children (1-6) and typical controls (shaded in gray).
Results
An orderly consistent increase in the number of s-Peaks and their randomness emerged from the analysis that corresponded well with the low-, mid-, high-functioning ASD diagnostic classification (FIGS. 13E-13F). These were much less or entirely absent in the TD subjects. Zooming in the movement temporal dynamics within one full motion segment cycle, dramatic differences in the number of s-Peaks appearing were identified (FIGS. 13A-13I). FIGS. 14B-14D further showing this trend in the raster grams-like plots constructed from the s-Peaks.
These panels show the increasing lack of structure in the spike trains from controls (well structured) to high functioning (some structure) to low functioning (random). The reports on the precise quantification and characterization of the degree of randomness and spoken abilities are provided.
A measure to assess the s-Peaks' repetitiveness or synchronicity across cycles has been provided by calculating the population cross-correlation C(τ) of the binned s-Peaks cycle vectors as a function of binning size “τ”. As illustrated in the methods, the degree of synchronicity was characterized with the second derivative of the C(τ) curves, i.e. the second derivative of the C(τ) curve decreases as the synchronicity increases. The empirical C(τ)curve was also compared to the ‘total-random’ curve C_r(τ) calculated using a simulated Poisson random s-Peaks process having the same occurrence rate and cycle length as in the empirical data. The empirical case was verified against the results from the numerical simulations and analytical approach to also determine empirical boundaries for the extreme cases (FIG. 20 simulation and FIG. 15 empirical results).
FIGS. 15A-15C show the C(τ) curves with their quadratic fits, compared to their corresponding ‘total random’ curves C_r(τ) (dashed lines analytically derived as limiting cases), for three representative subjects with different spoken verbal abilities. The representative control subject's curve has a decreasing slope as r grows, deviating away from its corresponding total random curve, indicating partial s-Peaks synchronicity. The curves for the representative subjects with HF- and LF-ASD show a flat and increasing slope, respectively, indicating that the decrease in s-Peaks' synchronicity correlates well with a systematic reduction in spoken language ability. Notice that the curve for the LF-ASD subject almost overlaps with its totally random curve, indicating high randomness (i.e. the lack of any synchronicity) of s-Peaks occurrences in this case.
FIG. 15D verifies the link between s-Spike's synchronicity and spoken verbal abilities in the ensemble. Averaged C(τ) curves for subgroups classified by spoken verbal ability (8 LF, 8 MF, 14 HF and 8 controls), are plotted with error bars early and late in binned time. Note that the averaged curves are well separated and automatically ordered matching the spoken verbal ability classification. FIG. 15E further illustrates this correspondence in the two dimensional (“2D”) parameter plane: second derivatives of the C(τ) curves and by the maximum deviation in-between the C(τ) and the C_r(τ) curves. Average subgroups positions with different spoken verbal abilities are plotted in the inset, showing the correlation between s-Peaks synchronicity and spoken verbal abilities. The curves for ASD subjects have positive second derivatives compared to the negative values for control subjects and the curves for ASD subjects have less deviation from the random curve compared to the control subjects.
FIG. 21 shows results of the autocorrelation and Fourier spectrum analyses for the three representative subjects supporting the increase of s-Peaks synchronicity correlating to the increasing of spoken verbal abilities. The movements studied here are natural, not forced to be synchronized. For example, the movement's durations might vary from trial to trial. This might contribute to the lack of synchronicity of the s-Peaks. To address this issue, further s-Peaks Interval Separation analyses was
performed as follows below.
As discussed in the Methods section, micro-dynamics fluctuations were also studied. Specifically, the temporal properties of the s-Peaks in the speed profiles was characterized using the inter s-Peaks intervals (s-IPIs) in analogy with the neuronal action-potential inter-spike intervals (IPIs). The analyses commonly done for neuronal spikes in the central nervous system were adapted here in the new data type obtained from the raw kinematics read out from the peripheral limbs.
Frequency histograms were built with the s-IPIs in the full forward-and-backward cycles (kinetic s-IPIs). As shown in FIGS. 17A-17C, across all subjects, small s-IPIs values are exponentially distributed. The exponential contribution of the s-IPIs show the total s-Peaks randomness. The non-exponential components of the s-IPI's distributions were separated to illustrate their “away from full randomness” contributions (lower panels in FIGS. 17A-17C). These residual s-IPI histogram contributions capture the systematic differences found across subjects with different spoken verbal abilities. The histogram for the representative subject with LF-ASD rarely had any residual s-IPIs components (FIG. 17A) having almost all s-IPI values falling in the exponential region. This result provides further support to the s-Peaks randomness in LF-ASD obtained from the synchronization analysis described above.
In contrast to the LF-ASD, the s-IPI histograms for the representative subjects with HF-ASD and the TD adult had significant residual s-IPIs contributions away from the exponential region (FIGS. 17B-17C). The residual histogram hump found in the s-IPIs for the HF-ASD subject falls at the exponentially dominated tail, while the hump for the control subject falls farther away from the exponential range, having larger s-IPI values. The hump with larger interval values mostly corresponds to the smoother speed behaviors during ballistic movements with rare kinetic s-Peaks (FIG. 13I and FIG. 14D).
As discussed in the Methods, to better visualize quantitatively the differences among subjects from the histograms, a parameter space defined by two statistical parameters was constructed. The first parameter is simply the mean value of the kinetic s-IPIs. To quantify the appearance of humps away from the exponential region in the histograms we introduced a second parameter R weighted by the separation values. This two-dimensional plane with axes R (vertical) and mean s-IPI value (horizontal) provides a concise map representation for each individual localized by a specific point in the plane as shown in FIG. 17D. The results from the cohort studied (30 with ASD and 8 adult controls) automatically showed three (3) visually distinctive clusters in the phase diagram. The K-means cluster algorithm was used for coloring the points in varying shades of gray (cluster 1 has the lowest mean s-IPI and lowest R). FIGS. 17E-17F show the decrease in population cross-correlation C(τ), with the curve's second derivative and the increase in its distance from randomness as the cluster index increases. This parameter plane provides a quantitative ensemble description further supporting the systematic traits found in the s-IPIs histograms and the s-Peaks' synchronicity discussed in previous sections.
As a test of this approach, the correspondence of the automatic subject clustering was compared and verified and it was found independently in the s-IPI parameter plane analysis described above with the level of ASD severity as clinically determined by the spoken verbal ability. In FIG. 18A, the subjects are coded based on the degree of spoken verbal abilities (from lacking to being able) and shape-coded based on the cluster index. The number of subjects from each clinical subgroup fell a posteriori into each gray-shaded cluster on the parameter plane illustrated in FIG. 18B: all LF-ASD subjects (8/8) fell into cluster 1; most HF-ASD subjects (12/14) fell into cluster 2; MF-ASD subjects fell either in cluster 1 (3/8) or cluster 2 (5/8); all control subjects (8/8) fell into cluster 3. The HF-ASD subject falling into cluster 1 with zero R-value is an outlier in our cohort.
The overall results show the agreement of the phase plane location with the subject's spoken verbal abilities. The average subject's location in each clinical subgroup (excluding the outlier high functioning subject) is shown in the inset in FIG. 18A. These results clearly provide strong evidence for a systematic increase from the bottom left to the top right corners in the parameter plane corresponding with the degree of spoken abilities. The millisecond time range analysis of the s-Peaks separations presented here enables the screening of subjects with ASD, unambiguously distinguishing them from adult controls, and further providing clear quantitative information about the severity level of the ASD. This classification feature is explored next in relation to age and maturational stages.
Most studies of ASD focus on children, as it is difficult to recruit older subjects. Here a wider range of ages was included: from seven (7) to thirty (30) years old. Under this broader range of ages it was then asked if the s-Peaks signatures had any systematic maturational trend that changed with ageing. Age is an important factor in predicting cognitive milestones in TD controls. Yet in ASD, the developmental process is atypical and may follow different individual cognitive trajectories. To address the question on possible maturational stages, six (6) TD young individuals (3-5 years old) were included in the cohort, in addition to the eight (8) TD adults previously discussed. The statistical signatures based on the single speed maxima in the pointing motion task identified had unveiled an important transitional maturation threshold in the statistical signatures taking place after five (5) years of age (Tones et al. (2013) Front. Integr. Neurosci., 7:32). In that study the three (3) to four (4) year olds and the adult groups served as limiting typical subgroups to set anchors on the Gamma parameter space. The individuals with ASD in the present study have a broader age spectrum (from 7 to 30). This allowed one to more clearly explore whether or not the group with ASD matures towards the same patterns found in typical adults or not. Those patterns would also describe about locations of affected subjects in the s-IPI parameter plane relative to TD young subjects before reaching full maturation.
Note that the parameter plane in FIG. 18C clearly shows a separation between TD young children and typical adults, providing the maturation evolution trajectory of TD subjects from bottom left to top right in the plane. On the other hand, subjects with ASD, across the broader range of ages, remained in the same lower left region of the plane, clustered together with the three (3) to five (5) year old TD children. They did not show any evidence of transitional maturation registered in typical development. This is clearly shown in the inset of FIG. 18C with average positions for younger ASD subjects up to fifteen (15) years of age clustering together. This result indicates that, within the biometrics defined here and in terms of movements the ASD subject's age might be developmentally irrelevant: a 30 year old ASD subject may fall in the same region as a ten (10) year old or a fifteen (15) year old subject. This indicates that the nervous system with ASD is continuously coping with the disorder and evolving along atypical developmental trajectories. The result further emphasizes that grouping individuals with ASD by age, as it is traditionally done, may blur relevant information about the levels of maturity of their coping systems and their unpredictable longitudinal evolution.
Behavioral phenotyping of ASD is currently done by observation and subjective verbal reports. These methods preclude correlating genetic information with behavioral phenotypes. The new biometrics introduced here allows one to examine, whenever possible, the patterns of parents' motions and those of their affected children. The literature suggests high heritability of ASD based on reports of significant concordance of ASD in twins (Ronald et al. (2011) Amer. J. Med. Genet., Part B, 156:255-274) and the high recurrence risk (Ozonoff et al. (2011) Pediatrics 128:e488-e495) in families. ASD risk has been reported to increase with increasing relatedness (Sandin et al. (2014) JAMA 311:1770-1777). The inventories in use to phenotype ASD have high clinical and genetic heterogeneity even in affected siblings (Yuen et al. (2015) Nat. Med., 21:185-191) posing a significant challenge to bridge the gap between the current phenotyping and the underlying genetics. Bridging this gap would be critical to the development of target treatments in ASD.
Here, the question of whether the s-Peaks' patterns found in their children are similar to those present in their parents' movements was addressed. Previous studies reported an increasing risk of having communication and social difficulties (known as “broad phenotype”) in non-diagnosed ASD relatives (Bishop et al. (2006) Amer. J. Med. Genet., Part B, 141:117-122). The quantitative metrics introduced here for movement assessments enables one to address a possible relationship with millisecond time precision.
To address this question, twenty one (21) available parents of fourteen (14) ASD participants were included. The corresponding results are plotted in FIG. 18D. The parents' signatures were mostly localized together with the ASD subjects, away from the typical adult controls in the background (same nomenclature as in FIG. 17D). The figure shows thirteen (13) out of the twenty one (21) parents (11 out of 14 mothers, 2 out of 7 fathers) localized in the same ASD region clustered as well with their affected child, clearly away from typical adults. This result can also be seen in FIG. 18E where an oriented Euclidean norm was used to plot the oriented distance for each parent and typical young controls from the TD three (3) to five (5) year old cluster centroid (negative values indicates falling to the left of the centroid). Notice that most of their parent's s-Peaks statistics never got past the most random and the noisiest regimes of the TD three (3) to five (5) years old.
Herein, a new data type (s-Peaks) derived from raw kinematic measurements was introduced with new statistical metrics characterizing movement signatures at millisecond time scales. A methodology is provided to characterize the micro-dynamics of various aspects of the sensory motor physiology underlying natural behaviors. Although a cohort of individuals with ASD was used to instantiate the methods and the data type, the same framework can be applied to a broad range of neurological disorders, particularly those that are today diagnosed by observation and subjective inventories.
The s-Peaks data revealed here captures the internally generated physiological signals at the periphery directly extracted from the output of high precision wearable sensors physically attached to the body. By analogy, the wearable sensors can operate in similar way as peripheral EEG sensors reading out activity from large number of motor and sensory nerve ensembles. This is in contrast to motions analyzed using camera-based systems, positioned externally to the physical body. The kinematics signal derived from camera-based systems rely on hand coding and learning algorithm procedures (e.g., Moeslund et al. (2006) Computer Vision and Image Understanding, 104:90-126) used to computerize observed behaviors. In such cases a great deal of human heuristics and decision making interventions are required to parse out, code and classify behavioral information through learning algorithms that require training and testing. During those processes of hand coding behaviors the human eye inevitably may miss subtle information due to fatigue, confirmation biases and limited detection capacity.
The metrics provided herein yield a different way to phenotype disorders. In the cohort with ASD discussed here the biometrics not only unambiguously distinguished ASD from typical controls, but also found ASD subtyping that strongly correlated with spoken language abilities. Previous studies have not separated such levels of severity using clinical inventories (Hilton et al. (2007) Res. Autism Spectrum Dis., 1:164-173; Hilton et al. (2011) Autism:1362361311423018). Here, the importance of quantitatively examining sensory-motor problems in autistic and other neurological disorders has been strengthened.
This form of random and noisy action tremor found here across subjects and trending with spoken language is very intriguing because it is also present in most of the parents that were examined from a random draw. The results presented here shed light on relations between movements as a form of active senging (kinesthetic sensory feedback) and the scaffolding of cognitive abilities (specifically, spoken abilities).
Lastly, the methods presented here can also be used in animal research that currently describes behaviors by observation. Animal models of autism and other neurological disorders are based on observation and description of the phenotype that genetic manipulations may give rise to. By employing the objective behavioral phenotyping provided herein, behavioral neuroscience and genetics can be bridged so as to design target therapies for autism and other disorders of the nervous system.
Methods
The subjects performed a basic pointing task paradigm as illustrated in FIG. 13A. They comfortably sat down in front of a touch screen. They were instructed to point to the target in the center of the screen but were not instructed to retract the hand. The subject spontaneously chose any motion after the touch. They moved at their own comfortable pace. The target disappeared when touched and reappeared later to initiate next reach. Their hand motions were continuously captured at two hundred forty (240) Hz sampling resolution (Polhemus Liberty, Colchester, Vt.).
1. Speed Profiles and Smoothing Data Approach
FIGS. 19A-19B show the step by step procedure followed to obtain the speed profiles. Target touch points are located as peaks along the Y-axis (FIG. 19B). Hand movement velocity for each direction was calculated as the first time derivative of the position and smoothed out by the triangular smoothing algorithm with bin size of twenty five (25) frames/one hundred four (104) ms (FIGS. 19C-19D). The triangular smoothing procedure preserves the positions of the peaks along the x-axis. This is important for the temporal analyses on synchronicity and periodicity that was performed on the empirical data. The speed profiles were obtained from the square root of the sum of squares of the three velocity components along the X-Y-Z orthogonal axes. s-Peaks were termed the local millisecond range peak fluctuations within the temporal speed profile.
FIG. 19 provides the rational of the triangular smoothing algorithm. Often an un-weighted sliding-average (e.g. rectangular) is used as a smoothing algorithm to filter out high frequency noise. That algorithm simply replaces each point in the signal with an average of n-adjacent points, where n is a positive integer called the smoothening width. That way of averaging washes out potentially important temporal information from the original raw data set. It is clear that it affects the millisecond peak's locations along the x-axis. But that is precisely the temporal dynamics that is being analyzing. The triangular smoothing method, however, does allow one to preserve those peak locations in present in the original speed profiles. The triangular smoothing algorithm with bin size 2d+1 (d=12) was implemented using the following moving triangular window (FIG. 20A):
$v^{'} (i) = \frac{\sum_{k = - d}^{d} (v (k + i) \cdot (d + 1 - \langle k \rangle))}{\sum_{k = - d}^{d} (d - 1 - \langle k \rangle)}$
Here, v(i) is the i^thelement of the original speed profile and v′(i) is the i^thelement of the smoothed profile: k is the summation index, going from d to d. In this case, the total number of elements is twenty five (25) with width d=12, centered at element 13 and running from d=−12 (element 1 in the sliding window) to d=12 (element 25 in the window). This builds up a symmetric weighted sum around the central point.
FIGS. 20B-20C compare the outcomes from using the two alternative smoothing methods. Note that the triangular smoothing approach better preserves the peak shape and structure of the profile, especially the peak's locations which play an essential role in the temporal series analysis.
The s-Spike's dynamic analysis depends on the specific optimal choice for smoothing bin size. Multiple possible scenarios were numerical simulated to ascertain the robustness of the methods used here and the stability of the results obtained for a range of bin width values.
FIGS. 20D-20E show the results dependence on the choice of smoothing bin width for the s-Peaks temporal analysis. To examine the stability of the results, the mean p-IPI was calculated and the R metric was defined from the p-IPI distribution (FIG. 16) using different triangular smoothing bin widths. The simulations showed that the subject's separation into different subtypes remained stable for a range of bin widths (before the R values saturate). The twenty five (25) frames bin width was selected such that the separation among subtypes was clear and R was not yet saturated for the control subjects.
The effects of taking the 25-frames-triangular-smoothing algorithm on the speed profiles are shown in FIG. 20F. One line is the speed calculated from the raw velocity's data and the other line is the speed calculated from the smoothed velocities. The algorithm gets rid of very high frequency fluctuations while retaining the s-Peaks within the milliseconds time range.
2. Definitions of the s-Peaks Vector and s-Peaks Matrix
An s-Peaks vector includes the full forward-and-backward motion cycle. The s-Peaks matrix is built from these s-Peaks vectors aligned along the touch. They were defined to capture the s-Peaks temporal information cycle by cycle, and relative to the touching point in each cycle (the intended goal of the task.). The i^ths-Peaks trial vector is defined as:
$s_{i} (j) = {\begin{matrix} 1, s - Spike occurs at j^{th} sampling point \\ 0, no s - Spike at j^{th} sampling point \end{matrix}, (j = 1, 2, \dots, N_{j}) for i^{th} cycle$
The s-Peaks matrix is then M(i,j)=s₁(j) (i=1, 2, . . . , N_i; j=1, 2, . . . , N_j). N_iis the number of full forward and back cycles (at least 100 cycles). N_jis the number of frames in each trial vector. A traditional rater gram representation was used to visualize the s-Peaks cycle vector (FIG. 14A) and s-Spike matrix in (FIGS. 14B-14D). N_jis set as eight hundred (800) (−3.4 s), with four hundred (400) frames (−1.7 s) before and after the touching point for all subjects. All s-Peaks vectors for one experimental session are given by the rows of the s-Peaks matrix M. FIG. 14A provides a way to visualize the s-Peaks vectors and the s-Peaks matrices (FIGS. 14B-14D) across representative subjects. Each dot represents a s-Spike. The matrix provides the s-Peaks rastergram. The s-Peaks-Spikes' mean ‘firing rate’ across trials was also calculated every twenty (20) frames and shown in the figure. The s-Peaks vectors were chopped into successive bins (named as s-Peaks cycle chopped vectors) in some of the following analyses. In the case of the s-Peaks cycle chopped vectors, is the s-Peaks cycle chopped vectors sT for i^thcycle with chopped bin size equal to r. If any s-Peaks appear in the k^thbin (number of s-Peaks inside the kth bin>0) s; (k)=1: Otherwise, sT (k)=0.
3. Numerical Simulation and Analytical Analysis of the Population Cross-Correlation Function
For a homogeneous Poison random spike train, the possibility for each sample data point to have a spike equals ‘r’. For two uncorrelated spike trains (S_i, S_j) of length N and firing rate r, the cross-correlation of the two trains is zero. If one adds one zero in the same position in both trains, the cross correlation function can be calculated from its definition:
$C_{i, j} = \frac{\sum_{l = 1}^{N + 1} (S_{i} (l) - \overline{S_{i}}) \cdot (S_{j} (l) - \overline{S_{j}})}{\sqrt{\sum_{l = 1}^{N + 1} {(S_{i} (l) - \overline{S_{i}})}^{2} \cdot {(S_{i} (l) - \overline{S_{i}})}^{2}}}$ $with S_{i} (N + 1) = S_{j} (N + 1) = 0,^{\overline{S_{i}} = \overline{S_{j}} = \frac{rN}{N + 1} \sim r}, N  1.$
The two trains are thus not correlated and the probability for each element (element 1 to N) to be one (1) is r (p_i(1)=p_j(1)=r). The probability for one (1) pair of elements to be (S_i(l),S_j(l))=(1,1) is P(1,1)=p_i(1)=p_j(1)=r². Similarly, P(0,1)=P(1,0)=r(1−r) and P(0,0)=(1−r)². Hence, within N sample pairs, the number of pairs (1, 1), (0, 1), (1, 0), (0, 0) would be r²N, r(1−r)N, r(1−r)N, (1−r)²N respectively. It can be shown that
$\sum_{l = 1}^{N} (S_{i} (l) - \overline{S_{i}}) \cdot (S_{j} (l) - \overline{S_{j}}) = 0.$
The correlation equation numerator can then be written as
$\sum_{l = 1}^{N + 1} (S_{i} (l) - \overline{S_{i}}) \cdot (S_{j} (l) - \overline{S_{j}}) = \sum_{l = 1}^{N} (S_{i} (l) - \overline{S_{i}}) \cdot (S_{j} (l) - \overline{S_{j}}) + r^{2} = r^{2} . Furthermore, \sum_{l = 1}^{N} {(S_{i} (l) - \overline{S_{i}})}^{2} = \sum_{l = 1}^{N + 1} {(S_{j} (l) - \overline{S_{j}})}^{2} = r (1 - r) N + r^{2} .$
The denominator equals
$\sqrt{\sum_{l = 1}^{N + 1} {(S_{i} (l) - \overline{S_{i}})}^{2} \cdot \sum_{l = 1}^{N + 1} {(S_{j} (l) - \overline{S_{j}})}^{2}} = r (1 - r) N + r^{2} . Hence, C_{i, j} = \frac{r}{(1 - r) N + r}$
For spike trains with correlations,
$\sum_{l = 1}^{N} (S_{i} (l) - \overline{S_{i}}) \cdot (S_{j} (l) - \overline{S_{j}}) > 0, Then$ $C_{i, j} > \frac{r}{(1 - r) N + r} .$
For a spike train of length N_oand firing rate r_ochopped into successive bins with bin width τ, the chopped vector will have the length
$N_{τ} = \frac{N}{τ} .$
Each bin is assigned as one when there is at least one s-Peaks in the bin and zero otherwise. The probability of having one for each element is r_τ=1−(1−r₀)^r.
Together with the equations derived above, the population cross-correlation function among the uncorrelated poison spike trains chopped into bins with length
τ (with 0 added at the end of each train) is
$C_{r} (τ) = \frac{r_{τ}}{(1 - r_{τ}) N_{τ} + r_{τ}}, N_{τ} = \frac{N}{τ}, r_{τ} = 1 - {(1 - r_{τ})}^{τ} .$
The curve increases from zero (0) to one (1) with a positive second derivative. Since the firing rate r is small, C_r(τ) can be approximated as rτ²/N.
For synchronized spike trains, for τ=1, C(τ)˜C_r(τ); when τ→∞, C(τ)=C_r(τ)=1 and C(τ)>C_r(τ) for aτ value in between. Hence, the second derivative of the C(τ) curve for synchronized spike trains will be smaller than that for unsynchronized ones: C(τ) grows faster at lower τ values and slower at larger τ when compared to the behavior for totally random trains (C_r(τ)). Based on this analysis, the synchronicity of the spike trains have been quantified using the second derivative of C(τ) and its deviation (distance) from the simulated curve for total random trains (C_r(τ)).
In FIG. 20, the results for a numerically generated Poison random spike trains and synchronized spike trains are shown. The homogenous Poison spike train was generated with firing rate equal to r/sampling point: r for each trial is random
numbers generated between zero (0) and one tenth (0.1) (average value r_o=0.05). Each trial has five hundred (500) sampling points with three hundred (300) generated trials. The synchronized spike train was generated with a time dependent firing rate. Within each trial, the firing rate is
r=cos(4πt/500)r ₀ +r ₀,
with mean firing rate equal to
{tilde over (r)}=r ₀=0.05
FIGS. 20A-20B show the rastergram for both cases.
The p-IPI-like distribution is plotted in FIGS. 20C-20D. A good exponential fit on the IPIs was found below forty (40) with the residual IPI distribution shown in the bottom panels. The synchronized spike train shows a hump around IPI=400 outside the exponential fit region, similar to the control subject case shown in FIG. 15C.
The random spike train exhibits a single exponential distribution, as found in the low functioning ASD case shown in FIG. 15A. The cross-correlation measure analysis, as applied to the empirical data, also applies to the numerically generated spike trains (ranging from totally random to partly synchronized spike trains). FIG. 20E shows the difference between the C(τ) curves in the two extreme limiting cases: the random spike train has a C(τ) with an increasing slope at small r while the synchrony spike train case shows a decreasing slope (shown in the insets). The curve for random spike train agrees well with the analytical result (dashed line).
4. Population Cross-Correlation s-Peaks Matrix Analysis
s-Peaks cycle cross-correlations for any two pairs of chopped vectors were calculated from,
$C_{i, j} (τ) = \frac{\sum_{l = 1}^{N + 1} (S_{i}^{τ} (l) - \overline{S_{i}^{τ}}) \cdot (S_{j}^{τ} (l) - \overline{S_{j}^{τ}})}{\sqrt{\sum_{l = 1}^{N + 1} {(S_{i}^{τ} (l) - \overline{S_{i}^{τ}})}^{2} \cdot \sum_{l = 1}^{N + 1} {(S_{i}^{τ} (l) - \overline{S_{i}^{τ}})}^{2}}}$
s_i ^τ(s_j ^τ) is the i^th(j^th). s-Peaks cycle chopped vector (chopped bin size equal τ), with a zero (0) added at the end of the vector, s_i ^τ (s_j ^τ) is the mean value of the i^th(j^th) s-Peaks cycle chopped vector.
Averaging c_i,j(τ) across all cycle pairs (all i, j for i≠j) provides the population cross-correlation function C(τ) of the process. C(τ) was calculated for various values of τ across subjects. As demonstrated in the following section, the shape of the C(τ) curve as a function of τ, especially its second derivative can be related to the s-Peaks synchronicity across repetitions. Based on this, the C(τ) curve was fitted with a quadratic polynomial function ƒ(τ)+p₁τ²+p₃and the degree of synchronicity was determined via the fitted second derivative p₁.
The shape of the C(τ) curve also depends on conditions other than synchronicity, like the s-Peaks firing rate and cycle length. To address this question, a total random poison spike train process was simulated with the same firing rate and trial length for each subject calculating the distance from the empirical C(τ) the total randomness C_r(τ) curve. The curves' separation distance was defined as the maximum separation of the two curves (considering the region between one (1) sample point (0.004 s) to seventy (70) sample points (0.3 s)).
Simulating the total random process (homogeneous Poison process) and a partially synchronous process helped set proper analytical bounds to better understand the empirical data. The C(τ) obtained for the homogeneous Poison random process agrees well with the analytically calculated curve for total random spike train (C_r(τ)), i.e. increasing slowly for small bin size and the growth rate increases as τ increasing (positive second derivative). At the other extreme, the simulated C(τ) for the partially synchronous spike train increases faster for small bin size with a growth rate decreasing as τ increases (negative second derivative), clearly deviating from the random process curve. Based on these simulated results, the s-Peaks synchronicity was interpreted in terms of the second derivative of the C(τ) curve and its distance to the C_r(τ)curve. Second derivative was fitted with the curve before the population cross-correlation value reach six tenths (0.6) and with τ smaller than fifty (50) frames. The relationship between s-Peaks synchronization and the level of spoken verbal abilities was investigated (FIG. 15).
5. FFT Analysis of Trajectories and s-Peaks Chopped Vectors
In addition to studying the s-Spike's synchronicity across repetitions (cycles), s-Peaks periodicities along the whole continuous movement profiles were considered by calculating autocorrelations of s-Peaks' occurrences in the full speed profile (FIG. 22). An s-Peaks full-profile vector includes s-Peaks in the whole speed profile continuously. By chopping the s-Peaks full-profile vector into successive bins with bin width size τ=24 frames (100 ms), the s-Peaks full profile chopped vector (S^τ(l)) was obtained.
The (unbiased) autocorrelation function was calculated by
$R (m) = \frac{\sum_{m = 0}^{N_{s} - m - t} S^{τ} (n + m) S^{τ} (n)}{N_{s} - m} .$
Here N, is the total bin number in the vector; m is the time lag varying from 0 to seven thousand two hundred (7,200) frames/three hundred (300) bins/thirty (30) seconds. Maximum time lag of thirty (30) seconds is limited by the thirsty (30) second-time window of the data cycles.
The periodicity was also checked using a power spectrum analysis. For a given output X, including both signal and noise, the power spectrum of the pure signal can be calculated from the FFT of the autocorrelation function (Wiener-Khinchin theorem; Wiener, N. (1930) Acta mathematica 55:117-258):
PW _s(w)=|F _s(w)|² =FR(w)/√{square root over (2π)},
Where PW_s(w) is the power spectrum of the pure signal with FR(w) the Fourier transform amplitude of the signal autocorrelation and F_S(w) is the Fourier transform amplitude of the pure signal. FIGS. 22D-22F shows the s-Peaks-Spikes' power spectra for the three (3) representative subjects calculated from the s-Peaks full-profile chopped vectors' (bin width size r=24 frames) autocorrelations, with time lag from zero (0) to thirsty (30) seconds, as well as the power spectra calculated directly from the FFT of the spike train. As shown in the figure the magnitude of the autocorrelation FFT gives a smoothed version of the power spectrum of the spike trains.
6. Distribution Analysis of s-Peaks Time Intervals (the s-IPIs)
The s-IPIs is defined as the time intervals between nearest-neighbor s-Peaks. The distribution of s-IPIs during the reaching and retracting periods (kinetic s-IPIs) was constructed for each subject in the cohort. As shown in FIGS. 17A-17C, small s-IPIs (below 10 frames/40 ms) was exponentially distributed. Exponentially distributed intervals correspond to randomly scattered s-Spikes separations (Ross, S. M. (1987) Introduction to probability and statistics for engineers and scientists. New York, N.Y.: Wiley) and hence the remaining s-IPIs values falling outside of the exponential fit contained important information away from full randomness.
The R metric introduced here quantifies the proportion of s-IPI outliers (durations) from the exponential distribution: If R=0, all s-IPIs are exponentially distributed, suggesting total randomness of the s-Spikes occurrences. Otherwise, having a larger proportion of outliers corresponds to further deviation from total randomness. However, the unbiased proportion of s-IPIs cannot distinguish, for example, one distribution with two small outlier intervals of length t from one with single outlier interval of length 2t. The speed profile of the latter case is actually smoother, or less random. Based on this, R is defined as:
$R = \frac{\sum_{j}^{} n_{j, out} \times {pISI}_{j}^{}}{\sum_{i}^{} n_{i} \times {pISI}_{i}} (j > 10) .$
Here pIPI_iis the s-IPI interval at the i^thbin, where the bin width is two (2) frames (˜8 ms). n_iis the s-IPIs count in the i^thbin; n_{j, out}is the s-IPIs count away from the exponential fit. In cases where there is not enough small s-IPIs for a good exponential fit or the speed profile is very smooth with rare small s-IPIs present, n_{j, out}=n_j. R has the same dimension as s-IPI (ms in this case). Notice that R is normalized, so as to be independent of the number of trials included in the distribution.
A parameter plane with R was built as a vertical axis and the mean kinetic s-IPI value as its horizontal axis (FIG. 17D). Each subject (across both ASD and TD groups) was located as a point in the phase diagram. The points in the plane are separated into separated clusters based on K-means cluster analysis (Theodoridis, S. (2010) Introduction to pattern recognition: a MATLAB approach. Burlington, Mass.: Academic Press). The positions (the cluster indexes) of the subjects were compared to their clinical diagnoses. This parameter plane analysis was further extended to the discussion of age development and potential parental link.
7. Power Spectrum Analysis of s-Peaks Vectors
FIGS. 21A-21C show the autocorrelation of s-Peaks chopped vectors for three (3) sample subjects (LF ASD, HF ASD, control) as in FIG. 16B. FIGS. 22D-22E show the power spectrum of the s-Peaks chopped vectors (as in FIG. 15I). The lines are the power spectrum calculated directly from the FFT of the spike train and the coded thicker lines are the FFT from the autocorrelation function. As shown in the figure the magnitude of the autocorrelation FFT gives a smoothed version of the power spectrum of the spike trains. FIGS. 22D-22E clearly show the periodicity difference of the s-Peaks occurrences across three representative subjects.
While certain of the preferred embodiments of the present invention have been described and specifically exemplified above, it is not intended that the invention be limited to such embodiments. Various modifications may be made thereto without departing from the scope and spirit of the present invention, as set forth in the following claims.
All of the apparatus, methods, and algorithms disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the invention has been described in terms of preferred embodiments, it will be apparent to those having ordinary skill in the art that variations may be applied to the apparatus, methods and sequence of steps of the method without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain components may be added to, combined with, or substituted for the components described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those having ordinary skill in the art are deemed to be within the spirit, scope and concept of the invention as defined.
The features and functions disclosed above, as well as alternatives, may be combined into many other different systems or applications. Various presently unforeseen or unanticipated alternatives, modifications, variations or improvements may be made by those skilled in the art, each of which is also intended to be encompassed by the disclosed embodiments.

Claims

1. A method for data compression, comprising:

performing operations, by a computing device, to generate normalized data from original data defining neural or bodily rhythms of a subject, the normalized data defining a normalized waveform that is unitless and scaled from zero to one;

processing, by the computing device, the normalized data to extract micro-movement data defining a micro-movement waveform comprising a plurality of micro-movement data points, each said micro-movement data point determined based on a value of a peak of the normalized waveform and a value representing an average of all data point values between a first valley of the normalized waveform immediately preceding the peak and a second valley of the normalized waveform immediately following the peak; and

generating, by the computing device, compressed data comprising a stochastic signature of the micro-movement waveform, said stochastic signature defined by empirically estimated values of two parameters representing a probability distribution function of a continuous family of probability distribution functions.

2. The method according to claim 1, wherein the original data comprises sensor data specifying a raw neural or bodily rhythm created in part by a human subject's physiological system.

3. The method according to claim 1, wherein the normalized data defines a normalized waveform representing events of interest in a continuous random process capturing rates of changes in fluctuations in amplitude and timing of an original raw waveform defined by the original data.

4. The method according to claim 1, further comprising performing operations, by the computing device, to estimate moments of a continuous family of probability distribution functions best describing a continuous random process.

5. The method according to claim 4, wherein the moments include at least one of a first moment comprising a mean value, a second moment comprising a variance value, a third moment comprising skewness, and a fourth moment comprising kurtosis.

6. The method according to claim 4, wherein the probability distribution functions comprise a function from a continuous Gamma family of probability distribution functions.

7. The method according to claim 1, wherein the stochastic signature is obtained by:

performing statistical data binning using the micro-movement data;

processing the binned micro-movement data to generate a frequency histogram;

generating probability distribution function waveforms using different sets of variable values;

comparing the probability distribution function waveforms to the frequency histogram to identify a probability distribution function waveform from the probability distribution function waveforms that most closely matches a shape and a dispersion of the frequency histogram; and

considering the variable value used for generating the probability distribution function waveform as the stochastic signature.

8. The method according to claim 7, wherein vertical columns of the frequency histogram show how many micro-movement data points are contained in each of a plurality of statistical data bins.

9. The method according to claim 1, further comprising using the stochastic signature to obtain at least one of a Noise-to-Signal Ratio (“NSR”) for a signal defined by the original data and a level of randomness in the original data.

10. The method according to claim 1, further comprising mapping the stochastic signature on a parameter plane to determine noise and randomness classifications of a subject's neural or bodily rhythms defined by the original data.

11. The method according to claim 1, further comprising using the stochastic signature as a seed value to an encryption algorithm for encrypting sensitive information prior to being communicated over a network communications link.

12. The method according to claim 1, further comprising:

causing the computing device or a remote computing device to operate in a first session state in which first testing operations are performed to stimulate movement by a human subject in accordance with first testing parameters;

selecting or generating second testing parameters different from the first testing parameters based on the stochastic signature; and

transitioning the session state of the computing device or the remote computing device from the first session state to a second session state in which second testing operations are performed to stimulate movement by the human subject in accordance with the second testing parameters.

13. The method according to claim 12, wherein the transitioning is controlled by the human subject's nervous system evolving with treatment of a neurological disorder.

14. The method according to claim 1, further comprising receiving by the computing device the original data which was sent from a remote device over a network.

15. A system, comprising:

a computing device configured to

generate normalized data from original data defining neural or bodily rhythms of a subject, the normalized data defining a normalized waveform that is unitless and scaled from zero to one,

process the normalized data to extract micro-movement data defining a micro-movement waveform comprising a plurality of micro-movement data points, each said micro-movement data point determined based on a value of a peak of the normalized waveform and a value representing an average of all data point values between a first valley of the normalized waveform immediately preceding the peak and a second valley of the normalized waveform immediately following the peak, and

generate compressed data comprising a stochastic signature of the micro-movement waveform, said stochastic signature defined by empirically estimated values of two parameters representing a probability distribution function of a continuous family of probability distribution functions.

16. The system according to claim 15, wherein the original data comprises sensor data specifying a raw neural or bodily rhythm created in part by a human subject's physiological system.

17. The system according to claim 15, wherein the normalized data defines a normalized waveform representing events of interest in a continuous random process capturing rates of changes in fluctuations in amplitude and timing of an original raw waveform defined by the original data.

18. The system according to claim 15, wherein the computing device is further configured to estimate moments of a continuous family of probability distribution functions best describing a continuous random process.

19. The system according to claim 18, wherein the moments include at least one of a first moment comprising a mean value, a second moment comprising a variance value, a third moment comprising skewness, and a fourth moment comprising kurtosis.

20. The system according to claim 18, wherein the probability distribution functions comprise a function from a continuous Gamma family of probability distribution functions.

21-32. (canceled)