WO2024081800A1 - Mesure de volumes cérébraux pour modélisation d'état de maladie neurologique - Google Patents

Mesure de volumes cérébraux pour modélisation d'état de maladie neurologique Download PDF

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Publication number
WO2024081800A1
WO2024081800A1 PCT/US2023/076710 US2023076710W WO2024081800A1 WO 2024081800 A1 WO2024081800 A1 WO 2024081800A1 US 2023076710 W US2023076710 W US 2023076710W WO 2024081800 A1 WO2024081800 A1 WO 2024081800A1
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disorder
brain
individual
computer readable
readable medium
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PCT/US2023/076710
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Kelly Michelle LEYDEN
Anisha KESHAVAN
Daniel Jon PETERSON
Erwan Rivet
William A. Hagstrom
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Octave Bioscience, Inc.
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/5602—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/032—Transmission computed tomography [CT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/037—Emission tomography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/501—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of the head, e.g. neuroimaging or craniography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data

Definitions

  • MRI magnetic resonance imaging
  • a trained medical expert e.g., a radiologist
  • Differences in baseline brain volumes can be made challenging to interpret by biases associated with image acquisition hardware, imaging protocol, and image quality, thereby rendering the analysis of brain volume change inconclusive. Therefore, there is an existing need in the art for quality metrics that can account for variables in MRI imaging acquisition as well as image quality.
  • SUMMARY [0004] Disclosed herein are methods for obtaining a set of brain images, obtaining a quality metric of the images, and generating volumetric measurements of one or more brain regions in the images using the quality metric.
  • the method includes modeling the volumetric measurement of the brain region(s) using the quality metric and comparing the modeled volumetric measurement of the brain region(s) to a reference for determining a state of disorder in an individual.
  • Use of the quality metric of the set of brain images is advantageous for establishing individualized volume-based brain charts. Such individualized volume-based brain charts can accommodate heterogeneous image quality, as well as images taken over time.
  • methods disclosed herein provide the advantage of more accurately determining the 1 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO percentile of a volumetric measurement of one or more brain regions in the individual at one or more time points. Such percentiles can be used, for example, in research studies or to guide physician decision support systems for the management of patients having a neurological disorder.
  • the disclosure relates to a method including: obtaining a set of brain images captured with magnetic resonance imaging (MRI) from an individual at one or more time points; generating a volumetric measurement of one or more brain regions in the set of brain images at each of the one or more time points; generating a quality metric of the set of brain images; modeling the volumetric measurement of the one or more brain regions using at least the quality metric; comparing the modeled volumetric measurement of the one or more brain regions to one or more reference percentiles of volumes of the one or more brain regions at each of the one or more time points to determine a percentile for the individual; and determining a state of disorder for the individual using the determined (e.g., estimated) percentile.
  • MRI magnetic resonance imaging
  • the method further includes responsive to the determination of a worsened state of disorder, providing a therapy to the individual.
  • providing the therapy includes providing one or more therapies selected from the list including corticosteroids, plasma exchange, ocrelizumab (Ocrevus®), IFN- ⁇ (e.g., Avonex®, Betaseron®, Rebif®, Extavia®, and Plegridy®), Glatiramer acetate (e.g., Copaxone® and Glatopa®), anti-VLA4 (e.g., Tysabri and natalizumab), dimethyl fumarate (e.g., Tecfidera® and Vumerity®), teriflunomide (Aubagio®), monomethyl fumarate (BafiertamTM), ozanimod (Zeposia®), siponimod (Mayzent®),
  • the disclosure provides a non-transitory computer readable medium including instructions that, when executed by a processor, cause the processor to: obtain a set of brain images captured with MRI from an individual at one or more time points; generate a volumetric measurement of one or more brain regions in the set of brain images at each of the one or more time points; generate a quality metric of the set of brain images; obtain one or more reference percentiles of volumes of the one or more brain regions; model the volumetric measurement of the one or more brain regions using at least the quality metric; and compare 2 ACTIVE/124820102.8 Attorney Docket No.
  • the quality metric is a contrast- to-noise ratio (CNR).
  • the state of disorder includes a rate of relapse, expression of symptoms, appearance or disappearance of active lesions, severity of atrophy, a response of an individual to a therapy, a degree of disorder-related disability, or a risk of developing a disorder.
  • the reference percentiles are estimated from volumetric measurements of the one or more brain regions in a set of brain images from a reference population of individuals at one or more time points.
  • modeling includes performing covariate adjustment.
  • the volumetric measurement includes an uncorrected volumetric measurement.
  • the method further includes: generating a representation of the modeled volumetric measurement, optionally wherein the method further includes plotting the representation of the modeled volumetric measurement and the reference percentiles of volumes.
  • generating a volumetric measurement includes using NeuroQuant software, Icometrix icobrainMS, Combinostics software, Corticometrics software, Brain Reader software, Quantib software, Qynapse software, FreeSurfer software, FSL software, or SIENA software.
  • generating the CNR includes generating a CNR between the gray and white matter of the brain.
  • obtaining a set of brain images captured at one or more time points includes obtaining a set of brain images captured at one 3 ACTIVE/124820102.8 Attorney Docket No.
  • the determined state of disorder includes a worsened state of disorder for a higher determined (e.g., estimated) percentile for the individual in comparison to a determined state of disorder for a lower determined (e.g., estimated) percentile for the individual.
  • the determined state of disorder includes a worsened state of disorder for a lower determined (e.g., estimated) percentile for the individual in comparison to a determined state of disorder for a higher determined (e.g., estimated) percentile for the individual.
  • obtaining a set of brain images captured at one or more time points includes obtaining a set of brain images captured at two, three, four, or five or more time points
  • the reference percentiles include a volumetric measurement of the one or more brain regions in a set of brain images from a reference population of individuals having the disorder at two, three, four, or five or more time points.
  • obtaining a set of brain images captured at five or more time points includes obtaining a set of brain images captured at six, seven, eight, nine, or ten or more time points, and wherein the reference percentiles include a volumetric measurement of the one or more brain regions in a set of brain images from a reference population of individuals having the disorder at six, seven, eight, nine, or ten or more time points.
  • determining the state of disorder for the individual using the determined (e.g., estimated) percentile further includes: generating a slope of the determined (e.g., estimated) percentile of the one or more brain regions of the individual across the two, three, four, five, six, seven, eight, nine, or ten or more time points; and determining the state of disorder using the generated slope for the individual.
  • determining the state of disorder using the generated slope for the individual further includes: obtaining a slope of the estimated percentile of the one or more brain regions from a reference population of individuals having the disorder across the two, three, four, five, six, seven, eight, nine, or ten or more time 4 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO points; and comparing the generated slope for the individual to the generated slope for the reference population of individuals.
  • the determined state of disorder includes a worsened state of disorder responsive to a comparison that the generated slope for the individual is greater than the generated slope for the reference population of individuals.
  • the determined state of disorder includes an improved state of disorder responsive to a comparison that the generated slope for the individual is less than the generated slope for the reference population of individuals.
  • the method further includes responsive to the determination of an improved state of disorder, continuing a therapy for the individual.
  • a set of brain images captured with MRI includes images captured with a 1.5 Tesla (T) MRI scanner, a 3T MRI scanner, or a 7T MRI scanner.
  • a set of brain images captured with MRI includes images captured with a 1.5T MRI scanner, a 3T MRI scanner, and/or a 7T MRI scanner.
  • a set of brain images captured with MRI includes images captured with a protocol including T1-weighting, T2-weighting, or T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) imaging.
  • a set of brain images captured with MRI includes images captured with a protocol including T1- weighting, T2-weighting, and/or T2-weighted FLAIR imaging.
  • the scanner is obtained from a manufacturer.
  • MRI includes structural MRI.
  • the individual is an individual at risk of developing or is diagnosed as having a neurological disorder.
  • the neurological disorder is Multiple Sclerosis (MS), Alzheimer’s disease (AD), Parkinson’s disease (PD), a traumatic central nervous system (CNS) injury, Down syndrome (DS), glaucoma, amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), or Huntington’s disease (HD).
  • MS Multiple Sclerosis
  • AD Alzheimer’s disease
  • PD Parkinson’s disease
  • CNS central nervous system
  • DS Down syndrome
  • ALS amyotrophic lateral sclerosis
  • FTD frontotemporal dementia
  • HD Huntington’s disease
  • the neurological disorder is MS.
  • the state of neurological disorder is the state of MS.
  • the state of MS includes MS atrophy, MS disease activity, MS disease progression, MS severity, MS relapse, and/or MS remission.
  • the reference population of individuals having the disorder includes a population of individuals having a neurological disorder.
  • the neurological disorder is MS, AD, PD, a traumatic CNS injury, DS, glaucoma, ALS, FTD, or HD.
  • the neurological disorder is MS.
  • the one or more reference percentiles are reference percentiles of age-expected volumes of the one or more brain regions.
  • the one or more reference percentiles are reference percentiles from a generalized additive model or an additive variance component model.
  • the generalized additive model includes sex-stratified location, scale, and/or shape metrics by age of the one or more brain regions.
  • the generalized additive model including sex-stratified location, scale, and shape metrics is the generalized additive model for location, scale, and shape (GAMLSS).
  • the additive variance model includes modeling conducted first by fitting a mean-only model, followed by modeling with a more flexible additive model (e.g., a GAMLSS).
  • the one or more brain regions includes the ventricles, the cerebrospinal fluid volume, the intracranial space, the thalamus, the white matter, the gray matter, and/or the total brain.
  • the one or more brain regions includes the ventricles. [0047] In some embodiments of either of the foregoing aspects, the one or more brain regions includes the intracranial space. 6 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO [0048] In some embodiments of either of the foregoing aspects, the one or more brain regions includes the ventricles and the intracranial space. [0049] In some embodiments of either of the foregoing aspects, the one or more brain regions includes the cerebrospinal fluid volume. [0050] In some embodiments of either of the foregoing aspects, the state of disorder includes a severity of atrophy.
  • FIG. 1 is an overall system environment for generating brain charts for patients, in accordance with an embodiment.
  • FIG. 2 depicts a block diagram of the model, in accordance with an embodiment.
  • FIG. 3 is a flow process for generating percentile(s) of the volume of one or more brain regions from an individual, in accordance with an embodiment.
  • FIGs. 4A-4E are a set of graphs showing the correlation of five quality metrics, including “T1_fber” (FIG. 4A), “T1_summary_gm_stdv” (FIG. 4B), “T2_inu_med” (FIG. 4C) “T2_summary_bg_p95” (FIG. 4D) and “T1_summary_gm_p05” (FIG. 4E), respectively, with the volumetric measurement of the whole brain in a set of images captured with structural magnetic resonance imaging (MRI).
  • MRI structural magnetic resonance imaging
  • FIG. 5A-5B are a set of graphs showing the total ventricle volume (top rows, respectively), estimated cross-sectional percentile (y-axis, middle row) of the volumetric measurement of the total ventricle volume with and without the contrast-to-noise ratio (CNR) correction (middle rows, respectively), and T1 CNR measurements over MRI acquisitions (bottom rows, respectively) in two individuals (FIG. 5A and FIG. 5B, respectively) having multiple sclerosis (MS).
  • FIG. 5A and FIG. 5B respectively
  • FIG. 6 is a set of graphs showing the total ventricle volume (top row), estimated percentile of the volumetric measurement of the total ventricle volume with and without the CNR correction (middle row), and T1 CNR measurements over MRI acquisitions (bottom row) in an individual having MS.
  • FIG. 7 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO
  • FIG. 7 is a set of graphs showing the total ventricle volume (top row), estimated percentile of the volumetric measurement of the total ventricle volume with and without the CNR correction (middle row), and T1 CNR measurements over MRI acquisitions (bottom row) in an individual having MS.
  • FIGs. 7 is a set of graphs showing the total ventricle volume (top row), estimated percentile of the volumetric measurement of the total ventricle volume with and without the CNR correction (middle row), and T1 CNR measurements over MRI acquisitions (bottom row) in an individual having MS.
  • FIG. 8A-8C are a set of graphs showing the total ventricle volume (top rows, respectively), estimated longitudinal percentiles (y-axis, middle row) of the volumetric measurement of the total ventricle volume with and without the CNR correction (middle rows, respectively), and T1 CNR measurements over MRI acquisitions (bottom rows, respectively) in three individuals (FIG. 8A, FIG. 8B, and FIG. 8C, respectively) having MS.
  • the baseline ventricle volume top rows, respectively, leftmost data point, indicated by ‘x’
  • the second measurement of ventricle volume was used to estimate the centiles for the third timepoint for the individual having MS (middle rows, respectively).
  • FIG. 9 is a set of graphs showing the total ventricle volume (top row), estimated longitudinal percentiles of the volumetric measurement of the total ventricle volume with and without the CNR correction (middle row), and T1 CNR measurements over MRI acquisitions (bottom row) in an individual having MS.
  • the baseline ventricle volume (top row, leftmost data point, indicated by ‘x’) is used as an input to the algorithm to generate the normative curves for the individual having MS (middle row).
  • FIG. 10 is a set of graphs showing the total ventricle volume (top row), estimated longitudinal percentiles of the volumetric measurement of the total ventricle volume with and without the CNR correction (middle row), and T1 CNR measurements over MRI acquisitions (bottom row) in an individual having MS.
  • the baseline ventricle volume (top row, leftmost data point, indicated by ‘x’) is used as an input to the algorithm to generate the normative curves for the individual having MS (middle row).
  • FIG. 11 is a set of graphs showing the total ventricle volume (top row), estimated longitudinal percentiles of the volumetric measurement of the total ventricle volume with and without the CNR correction (middle row), and T1 CNR measurements over MRI acquisitions (bottom row) in an individual having MS.
  • the baseline ventricle volume (top row, leftmost data point, indicated by ‘x’) is used as an input to the algorithm to generate the normative curves for the individual having MS (middle row).
  • 8 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO DETAILED DESCRIPTION Definitions [0063] Terms used in the claims and specification are defined as set forth below unless otherwise specified.
  • additive model for location, scale, and shape refers to models used to generate the brain charts described in Bethlehem et al. (“Brain charts for the human lifespan.” Nature (2022): 1-11.), the models and methods of which are incorporated herein in their entirely by reference.
  • additive variance component model refers to models used to describe differing variability in measurements that are associated with observed factors (e.g., age or other subject demographics).
  • brain measurements may depend on the amount of variation explainable by age, as well as the amount of variance explained by the other modeled factors.
  • the term “brain region(s)” is used according to its plain and ordinary meaning and refers to a brain anatomical region following standard neuroanatomy hierarchies (e.g., a functional, connective, or developmental region).
  • Exemplary brain regions include, but are not limited to the, intracranial space; cerebrospinal fluid volume; one or more ventricles; the ventricles and the intracranial space; brainstem; medulla oblongata; medullary pyramids; olivary body; inferior olivary nucleus; rostral ventrolateral medulla; respiratory center; dorsal respiratory group; ventral respiratory group; pre-Bötzinger complex; Bötzinger complex; paramedian reticular nucleus; cuneate nucleus; gracile nucleus; intercalated nucleus; area postrema; medullary cranial nerve nuclei; inferior salivatory nucleus; nucleus siponimod; dorsal nucleus of vagus nerve; hypoglossal nucleus; solitary nucleus; pons; pontine nuclei; pontine cranial nerve nuclei; chief or pontine nucleus of the trigeminal nerve sensory nu
  • OVB-006WO paramedian pontine reticular formation cerebellar peduncles, superior cerebellar peduncle, middle cerebellar peduncle, inferior cerebellar peduncle, cerebellum, cerebellar vermis, cerebellar hemispheres, anterior lobe; posterior lobe; flocculonodular lobe; cerebellar nuclei; fastigial nucleus; interposed nucleus; globose nucleus; emboliform nucleus; dentate nucleus; tectum; corpora quadrigemina; inferior colliculi; superior colliculi; pretectum; tegmentum; periaqueductal gray; parabrachial area; medial parabrachial nucleus; lateral parabrachial nucleus; subparabrachial nucleus (Kölliker-fuse nucleus); rostral interstitial nucleus of medial longitudinal fasciculus; midbrain reticular formation; dorsal
  • Brain regions and specific parts of brain regions may be referred to according to their rostral/caudal, dorsal/ventral, medial/lateral, and/or anterior/posterior positions within the brain region with respect to the skull.
  • “one or more brain regions” 11 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO includes the ventricles.
  • “one or more brain regions” includes the intracranial space.
  • “one or more brain regions” includes the ventricles and the intracranial space.
  • “one or more brain regions” includes the cerebrospinal fluid volume.
  • the word “comprise,” or variations such as “comprises” or “comprising” will be understood to imply the inclusion of a stated word or group of words but not the exclusion of any other word or group of words.
  • the terms “metrics by age” and “age-expected” refer to the process of a normalization of data by age, which is a technique that is used to allow populations of individuals to be compared when the age profiles of the individuals are different.
  • the terms “contrast-to-noise ratio” or “CNR” refer to the relationship of signal intensity differences between two regions in an image, scaled to image noise.
  • contrast-to-noise ratio is: , where SA and SB are signal intensities for signal-producing structures “A” and “B” in the brain region of interest, and where ⁇ o is the standard deviation of the pure image noise.
  • subject is used interchangeably and encompass an organism, human or non-human, and male or female.
  • neurological disorder and “neurological disease” refer interchangeably to a disorder characterized by progressive loss of the number (e.g., by cell death), structure, and/or function of neurons.
  • a neurological disease may be associated with genetic defects (e.g., a mutation in a gene), protein misfolding, defects in protein degradation, programmed cell death, membrane damage, or other processes.
  • genetic defects e.g., a mutation in a gene
  • protein misfolding e.g., protein misfolding, defects in protein degradation, programmed cell death, membrane damage, or other processes.
  • exemplary, non-limiting neurological disorders include Multiple Sclerosis (MS), Alzheimer’s disease, Parkinson’s disease, a traumatic central nervous system (CNS) injury, Down syndrome (DS), glaucoma, Amyotrophic Lateral Sclerosis (ALS), Frontotemporal Dementia (FTD), and Huntington’s disease.
  • the neurological or neurological disease is any one of Absence of the Septum Pellucidum, Acid Lipase disease, Acid Maltase Deficiency, Acquired Epileptiform Aphasia, Acute Disseminated Encephalomyelitis, Attention-Deficit / Hyperactivity Disorder (ADHD), Adie’s Pupil, Adie’s syndrome, Adrenoleukodystrophy, Agenesis of the corpus callosum, Agnosia, Aicardi syndrome, acquired immunodeficiency syndrome (AIDS), Alexander disease, Alper’s disease, Alternating Hemiplegia, Anencephaly, 12 ACTIVE/124820102.8 Attorney Docket No.
  • OVB-006WO Alterans Hereditary Neuropathy, Hereditary Spastic Paraplegia, Heredopathia Atactica Polyneuritiformis, Herpes Zoster, Herpes Zoster Oticus, Hirayama syndrome, Holmes-Adie syndrome, Holoprosencephaly, HTLV-1-associated Myelopathy, Hughes syndrome, Huntington’s disease, Hydranencephaly, Hydrocephalus, Hydromyelia, Hypernychthemeral syndrome, Hypersomnia, Hypertonia, Hypotonia, Hypoxia, Immune-mediated Encephalomyelitis, Inclusion Body Myositis, Incontinentia Pigmenti, Infantile Hypotonia, Infantile Neuroaxonal Dystrophy, Infantile Phytanic Acid Storage disease, Infantile Refsum disease, infantile spasms, an inflammatory myopathy, Iniencephaly, Intestinal Lipodystrophy, intracranial cysts, intracranial hypertension, Isaac’s syndrome, Joubert syndrome, Kearns-Sa
  • the neurological disease is MS.
  • FLAIR-weighting or grammatical derivatives thereof refer to a magnetic resonance imaging (MRI) sequence with an inversion recovery set to null fluids.
  • FLAIR weighting can be used in brain imaging to suppress cerebrospinal fluid effects on the image, so as to bring out the periventricular hyperintense lesions, such as MS plaques.
  • an observable nuclear spin polarization e.g., magnetization
  • This field makes the magnetic dipole moments of the sample process at the resonance frequency of the nuclei.
  • nuclear spins process randomly about the direction of the applied field. They become abruptly phase coherent when they are hit by radiofrequency (RF) pulses at the resonant frequency, created orthogonal to the field.
  • RF radiofrequency
  • the RF pulses cause the population of spin-states to be perturbed from their thermal 15 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO equilibrium value.
  • the generated transverse magnetization can then induce a signal in an RF coil that can be detected and amplified by an RF receiver.
  • spin-lattice relaxation The return of the longitudinal component of the magnetization to its equilibrium value is termed “spin-lattice relaxation” while the loss of phase-coherence of the spins is termed “spin-spin relaxation.”
  • T1- weighting or grammatical derivatives therein are a basic pulse sequence in MRI.
  • T2 relaxation also known as “spin-spin relaxation” or “transverse relaxation,” refer to the progressive dephasing of spinning dipoles resulting in decay in the magnetization in the transverse plane.
  • T2-weighting or grammatical derivatives therein are a basic pulse sequence in MRI.
  • gray matter refers to neural tissue of the brain that contains mostly cell bodies, though may contain some nerve fibers, has a brownish gray color, and forms most of the cortex and nuclei of the brain.
  • gray matter can include a portion of adjacent matter (e.g., nerve fibers, also known as “white matter”) of about + 0.2 mm (e.g., about + 0.1 mm); or “gray matter” can be used to mean the signal in regions of an image marked as gray matter using an automated or manual annotation.
  • white matter refers to neural tissue that consists largely of nerve fibers (e.g., myelinated nerve fibers) and has a whitish color, though may contain some gray matter.
  • white matter can include a portion of adjacent matter (e.g., gray matter) of about + 0.2 mm (e.g., about + 0.1 mm) or “white matter” can be used to mean the signal in regions of an image marked as white matter using an automated or manual annotation.
  • the terms “multiple sclerosis” or “MS” refer to an inflammatory disorder of the central nervous system in which the insulating covers (myelin) of nerve cells in the brain and spinal cord are damaged. This damage disrupts the ability of parts of the nervous system to communicate, resulting in a wide range of signs and symptoms, including physical, mental, and psychiatric.
  • MS includes MS or a related disorder, and optionally refers to all types and stages of MS, including, but not limited to: benign MS, relapsing remitting MS, secondary progressive MS, primary progressive MS, progressive relapsing MS, chronic progressive MS, transitional/progressive MS, rapidly worsening MS, clinically-definite MS, malignant MS, also known as Marburg’s Variant, and acute MS.
  • MS encompasses all forms of MS including relapsing-remitting MS (RRMS), secondary 16 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO progressive MS (SPMS), primary-progressive MS (PPMS), and progressive relapsing MS (PRMS).
  • Conditions related to MS which are exemplary neurological disorders, include, e.g., Devic’s disease, also known as Neuromyelitis Optica; acute disseminated encephalomyelitis, acute demyelinating optic neuritis, demyelinative transverse myelitis, Miller-Fisher syndrome, encephalomyelradiculoneuropathy, acute demyelinative polyneuropathy, tumefactive multiple sclerosis, and Balo’s concentric sclerosis.
  • the term “obtaining a set of brain images” encompasses obtaining one or more brain images captured from an individual. Obtaining one or more brain images can encompass performing steps of capturing the one or more images e.g., using a MRI device.
  • the phrase can also encompass receiving one or more images, e.g., from a third party that has performed the steps of capturing the one or more images from the individual.
  • the one or more images can be obtained by one of skill in the art by a variety of known ways including being stored on a storage memory.
  • the term “obtaining one or more images” can also include having (e.g., instructing) a third party obtain the one or more images.
  • a “percentile” refers to groups into which a sample can be divided according to the distribution of values of a particular variable. For example, a percentile refers to each of the 100 equal groups (e.g., 1 st percentile up to 100 th percentile).
  • Percentiles may be referenced herein within a range, such as a range between a 25 th percentile and 75 th percentile.
  • quality metric is meant to include any useful characteristic useful for determining image quality.
  • Quality metrics may include a contrast-to-noise ratio (CNR), sharpness, noise, dynamic range, tone reproduction, color accuracy, distortion, vignetting, exposure accuracy, lateral chromatic aberration, lens flare, color moire, artifacts, degree of image intensity inhomogeneity, signal-to-noise ratio (SNR), signal properties of areas outside the image foreground (e.g., air), fractions of estimated tissue in the image and their location (including tissue probability maps), and degree of partial voluming.
  • CNR contrast-to-noise ratio
  • SNR signal-to-noise ratio
  • a “reference” is meant any useful reference used to compare volume of one or more brain regions longitudinally, between individuals, or both.
  • the reference can be any sample, standard, standard curve, or level that is used for comparison purposes.
  • the reference can be a normal reference sample or a reference standard or level.
  • a “reference sample” can be, for example, a control, e.g., a predetermined negative control value such as a “normal control” or a prior sample taken from the same individual; a sample from a normal healthy individual, such as an individual not having a neurological disorder; a sample from an individual that has a neurological disorder; or a sample from an individual that has been treated for a neurological 17 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO disorder.
  • a “reference standard or level” is meant a value or number derived from a reference sample.
  • a “normal control value” is a predetermined value (e.g., volume value) indicative of non-disordered state, e.g., a value expected in a healthy control individual. Typically, a normal control value is expressed as a range (“between X and Y”), a high threshold (“no higher than X”), or a low threshold (“no lower than X”).
  • An individual having a measured value within the normal control volume for a particular brain region is typically referred to as “within normal limits” for the volume of that brain region.
  • a normal reference standard or level can be a value or number derived from a normal individual not having a neurological disorder; a individual that has a neurological disorder; or an individual that has been treated for a neurological disorder.
  • the reference sample, standard, or level is matched to the sample individual sample by at least one of the following criteria: age, weight, sex, disorder stage (e.g., diagnosed as having a neurological disorder (e.g., MS)), and overall health.
  • reference percentiles of age-expected volume of one or more brain regions can be obtained from a Brain Chart, as described in Bethlehem et al. (“Brain charts for the human lifespan.” Nature (2022): 1-11.), which is incorporated herein in its entirely by reference.
  • state of disorder refers to any of a flare event associated with a disorder, such as a neurological disorder, a rate of relapse (e.g., an annualized rate of relapse), exacerbation or quiescence, or a measurable variable that is informative of the disorder activity.
  • a rate of relapse e.g., an annualized rate of relapse
  • exacerbation or quiescence or a measurable variable that is informative of the disorder activity.
  • state of disorder may also be used herein to mean an appearance or disappearance of active lesions, severity of atrophy, a response of an individual to a therapy, a degree of disorder-related disability, and a risk (e.g., likelihood) of an individual developing a disorder at a subsequent time.
  • FIG. 1 depicts a system environment overview for generating brain charts for patients, in accordance with an embodiment.
  • the system environment 100 provides context in order to introduce a patient 110, an image generation system 120, and a brain chart system 130 for 18 ACTIVE/124820102.8 Attorney Docket No.
  • FIG. 1 depicts one patient 110 for whom a state of disorder 140 is predicted
  • the system environment 100 includes two or more patients such that that brain chart system 130 predicts states of disorder 140 for the two or more patients (e.g., a predicted state of disorder for each of the two or more patients).
  • a predicted state of disorder 140 can be useful for guiding treatment for the patient 110.
  • the predicted state of disorder 140 can indicate any of disease atrophy, disease activity, disease reversal, disease progression, disease severity, disease relapse, and disease remission, which can be used to guide whether a patient 110 is to be provided an intervention.
  • the patient 110 was previously diagnosed with a disease.
  • the predicted state of disorder 140 for the patient 110 can be useful for determining a presence or absence of the disease.
  • the patient is suspected of having a disease. Therefore, the predicted state of disorder 140 for the patient 100 shown in FIG. 1 can be useful for diagnosing the patient with the disease.
  • the disease is a neurodegenerative disease, such as multiple sclerosis (MS). Additional examples of diseases are described herein.
  • the image generation system 120 captures one or more images from the patient 110.
  • the image can be obtained by a third party, e.g., a medical professional.
  • the image can be obtained in a hospital setting or a medical clinic.
  • the image generation system 120 captures one or more images of the patient 110. In various embodiments, the image generation system 120 captures one or more images from a particular anatomical location of the patient 110. In particular embodiments, the image generation system 120 captures one or more images of the patient’s brain.
  • the imaging generation system 120 includes an imaging device for capturing the one or more images.
  • the imaging device can be one of a computed tomography (CT) scanner, magnetic resonance imaging (MRI) scanner, positron emission tomography (PET) scanner, x-ray scanner, an ultrasound imaging device, or a light microscope, such as any of a brightfield microscope, darkfield microscope, phase-contrast microscope, 19 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO differential interference contrast microscope, fluorescence microscope, confocal microscope, or two-photon microscope.
  • the imaging device is a MRI scanner that captures MRI images.
  • the imaging device is a MRI scanner that captures a set of two dimensional (2D) images, such as a 2D stack of MRI images.
  • the image generation system 120 captures various sets of one or more images of the patient 110.
  • the image generation system 120 may capture a first set of images of the patient 110 prior to administering an agent.
  • the image generation system 120 may further capture a second set of images of the patient 110 after administering the agent.
  • an agent include a contrast agent, such as a MRI contrast agent (e.g., gadolinium). Therefore, the first set of images and the second set of images can represent pre- contrast and post-contrast images, respectively, captured from the patient 110.
  • the image generation system 120 captures images of the patient 110 at a single timepoint.
  • the image generation system 120 captures images of the patient 110 at multiple timepoints. In various embodiments, the image generation system 120 captures images of the patient 110 across at least 2 timepoints, at least 3 timepoints, at least 4 timepoints, at least 5 timepoints, at least 6 timepoints, at least 7 timepoints, at least 8 timepoints, at least 9 timepoints, at least 10 timepoints, at least 11 timepoints, at least 12 timepoints, at least 13 timepoints, at least 14 timepoints, at least 15 timepoints, at least 16 timepoints, at least 17 timepoints, at least 18 timepoints, at least 19 timepoints, at least 20 timepoints, at least 21 timepoints, at least 22 timepoints, at least 23 timepoints, at least 24 timepoints, at least 25 timepoints, at least 30 timepoints, at least 35 timepoints, at least 40 timepoints, at least 45 timepoints, at least 50 timepoints, at least 60 timepoints, at least 70 time
  • the brain chart system 130 analyzes the images captured by the image generation system 120 to generate the predicted state of disorder 140 for the patient 110.
  • the brain chart system 130 can include one or more computers. Therefore, in various embodiments, the steps described in reference to the brain chart system 130 are performed in silico.
  • the brain chart system 130 analyzes images of a patient 110 captured at a single timepoint to determine the predicted state of disorder 140 (e.g., referred to herein as cross-sectional modeling).
  • the brain chart system 130 analyzes images of a patient 110 captured across two or more timepoints to determine the predicted state of disorder 140 (e.g., referred to herein as longitudinal modeling).
  • the brain chart 20 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO system 130 analyzes the images to generate a volumetric measurement of a brain region of the patient.
  • the brain chart system 130 can further generate a quality metric representing a measure of the image quality of one or more images and models the volumetric measurement of the brain region while accounting for the quality metric.
  • the modeled volumetric measurement that accounts for the quality metric may represent a more accurate measurement of the volume of the brain region.
  • the brain chart system 130 determines a percentile for the patient using the modeled volumetric measurement, which is useful for predicting the state of disorder of the patient.
  • the predicted state of disorder 140 of the patient can then be used to guide treatment or therapy for the patient 110.
  • the imaging generation system 120 and the brain chart system 130 are employed by different parties.
  • a first party operates the imaging generation system 120 to capture one or more images derived from the patient 110 and then provides the captured one or more images to a second party which implements the brain chart system 130 to determine the predicted state of disorder 140.
  • the imaging generation system 120 and the brain chart system 130 are employed by the same party.
  • a volumetric brain model e.g., a brain chart
  • the method including: obtaining a set of brain images captured with MRI from an individual at one or more time points; generating a volumetric measurement of one or more brain regions in the set of brain images at each of the one or more time points; generating a quality metric (e.g., the contrast to noise ratio (CNR)) of the set of brain images; modeling the volumetric measurement of the one or more brain regions using at least the quality metric; comparing the modeled volumetric measurement of the one or more brain regions to one or more reference percentiles of volumes of the one or more brain regions at each of the one or more time points to determine a percentile for the individual; and determining a state of disorder for the individual using the determined (e.g., estimated) percentile.
  • CNR contrast to noise ratio
  • a “volumetric brain model” refers to the one or more determined (e.g., estimated) percentiles, which account for the quality metric, of volume of one or more brain regions of an individual.
  • the volumetric measurement is an uncorrected volumetric measurement. 21 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO
  • the one or more sets of brain images include images captured from an individual.
  • the images captured from the individual include MRI, such as structural MRI, of one or more brain regions of the individual’s brain. Structural MRI can be used to generate a volumetric measurement of the one or more brain regions in the set of brain images.
  • the volumetric measurement is generated using NeuroQuant software, Icometrix icobrainMS, Combinostics, Corticometrics, Brain Reader, Quantib, Qynapse, FreeSurfer, FSL, SIENA, or any suitable software or segmentation package.
  • the volumetric measurement is generated using NeuroQuant software.
  • the volumetric measurement is generated using Icometrix icobrainMS software.
  • the volumetric measurement is generated using Combinostics software.
  • the volumetric measurement is generated using Corticometrics software.
  • the volumetric measurement is generated using Brain Reader software.
  • the volumetric measurement is generated using Quantib software.
  • the volumetric measurement is generated using Qynapse software. In some embodiments, the volumetric measurement is generated using FreeSurfer software. In some embodiments, the volumetric measurement is generated using FSL software. In some embodiments, the volumetric measurement is generated using SIENA software.
  • a quality metric of the set of brain images may be generated.
  • the quality metric refers to a characteristic useful for determining image quality.
  • a quality metric may be a measure of meaningful signal present in an image in relation to less meaningful noise in the image.
  • One example of such a quality metric is the CNR.
  • the quality metric may be a particular centile intensity of voxels in an image, which can be indicative of the quality of the image.
  • centile intensity of voxels in an image refers to the intensity value at that particular centile across the voxels in the image (e.g., 100 th centile being the maximum intensity, and 0 th centile being the minimum intensity).
  • the particular centile intensity may be any of the 5 th centile intensity, 10 th centile intensity, 15 th centile intensity, 20 th centile intensity, 25 th centile intensity, 30 th centile intensity, 35 th centile intensity, 40 th centile intensity, 45 th centile intensity, 50 th centile intensity, 55 th centile intensity, 60 th centile intensity, 65 th centile intensity, 70 th centile intensity, 75 th centile intensity, 80 th centile intensity, 85 th centile intensity, 90 th centile intensity, or 95 th centile intensity.
  • a quality metric refers to a ratio of a signal within a certain anatomical location present in the image to a signal outside the anatomical location in the image.
  • the anatomical location can be the patient’s head. Therefore, the quality metric can refer to the 22 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO ratio of the intensity of voxels in the image of the patient’s head in relation to the intensity of voxels in the image outside of the patient’s head.
  • a quality metric refers to a correction value for non-uniformity arising from the MRI scanner hardware.
  • the quality metric refers to a correction value that corrects for the non-uniformity arising from the MRI scanner hardware.
  • the quality metric includes any one of CNR, a 5 th centile intensity of the gray matter voxels in an image (referred to in the examples as “T1_summary_gm_05”), a foreground-to-background energy ratio (FBER; e.g., the image energy in the foreground of an image of the head compared to that outside the head; referred to in the examples as “T1_fber”), a deviation of intensity of gray matter voxels in an image (referred to in the examples as “T1_summary_gm_stdy”), a median of an intensity non- uniformity (INU) correction as estimated in T2-weighted FLAIR images (referred to in the examples as “T2_inu_med”), and a 95 th centile intensity of the voxels in the image that are outside of the head (referred to in the examples as “T2_summary_bg_p95”).
  • CNR CNR
  • the quality metric is a CNR. In some embodiments, the quality metric is T1_summary_gm_05. In some embodiments, the quality metric is T1_fber. In some embodiments, the quality metric is T2_inu_med. In some embodiments, the quality metric is T2_summary_bg_p95. In some embodiments, the quality metric is T1_summary_gm_stdv. [00101] In some embodiments, the quality metric is a CNR. [00102] In some embodiments, the volumetric measurement of the one or more brain regions is modeled using a quality metric. In some embodiments, modeling includes performing covariate adjustment.
  • modeling the volumetric measurement using the quality metric includes employing a modeling approach.
  • the modeling approach involves employing a generalized additive model, such as a generalized additive model for location, scale, and shape (GAMLSS).
  • a GAMLSS model represents univariate distributional models, such that parameters of the distribution can be modeled as additive functions of explanatory variables.
  • the quality metric can serve as an input to the generalized additive model.
  • the generalized additive model may include one or more pre-trained weights associated with the quality metric.
  • the generalized additive model may include additional pre-trained weights associated with the 23 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO inputs.
  • the generalized additive model appropriately weighs the quality metric according to the associated pre-trained weights.
  • the modeling approach involves employing an additive variance component model.
  • Reference percentiles can be estimated from volumetric measurements of the one or more brain regions in a set of brain images from a reference population of individuals at one or more time points.
  • the reference percentiles can be an estimated from a volumetric measurement of the one or more brain regions in a set of brain images from a reference population of individuals having the disorder (e.g., MS) at one time point.
  • the reference percentiles can be estimated from a volumetric measurement of the one or more brain regions in a set of brain images from a reference population of individuals having the disorder at two, three, four, or five or more time points. In some embodiments, the reference percentiles can be estimated from a volumetric measurement of the one or more brain regions in a set of brain images from a reference population of individuals having the disorder at six, seven, eight, nine, or ten or more time points. [00107] In some embodiments, the one or more reference percentiles are reference percentiles of age-expected volumes of the one or more brain regions. [00108] In some embodiments, the one or more reference percentiles are reference percentiles from a generalized additive model or an additive variance component model.
  • the generalized additive model includes sex-stratified location, scale, and/or shape metrics by age of the one or more brain regions. In some embodiments, the generalized additive model includes sex-stratified location, scale, and shape metrics is the generalized additive model for location, scale, and shape (GAMLSS).
  • GMLSS generalized additive model for location, scale, and shape
  • the reference population of individuals having the disorder includes a population of individuals having a neurological disorder.
  • the neurological disorder is MS, AD, PD, a traumatic central nervous system (CNS) injury, DS, glaucoma, ALS, FTD, or HD.
  • the reference population of individuals having the disorder includes a population of individuals having MS.
  • Examples of MRI images include images captured with a 1.5 Tesla (T) or higher MRI scanner, a 3T or higher MRI scanner, or a 7T or higher MRI scanner.
  • T 1.5 Tesla
  • the brain images are captured with a 1.5T or higher MRI scanner.
  • the brain images are captured with a 3T or higher MRI scanner.
  • the brain images are captured with a 7T or higher MRI scanner.
  • Examples of MRI images also include images captured with a protocol including T1-weighting, T2- weighting, or T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) imaging.
  • T1-weighting T2- weighting
  • FLAIR T2-weighted Fluid Attenuated Inversion Recovery
  • Imaging may take place in a scanner that is obtained from any suitable manufacturer.
  • the manufacturer of the MRI is obtained by DICOM tags within the MRI.
  • the one or more sets of images includes combination images.
  • combination images represent a combination between different image acquisitions, different acquisition protocols, and/or different manufacturers.
  • Imaging may take place at one or more time points.
  • obtaining a set of brain images includes obtaining a set of brain images captured at one time point.
  • obtaining a set of brain images includes obtaining a set of brain images captured at two, three, four, or five or more (e.g., six, seven, eight, nine, or ten or more) time points.
  • different image acquisitions can refer to sets of images acquired at different timepoints e.g., a first set of images acquired at a first timepoint and a second set of images acquired at a second timepoint.
  • the set of images acquired at a first timepoint represent pre-contrast images.
  • the set of images acquired at a second timepoint represent post-contrast images.
  • systems for plotting volumetric graphs For example, also described herein, in some embodiments, are methods of generating a representation of a modeled volumetric measurement. In some embodiments, the method further includes plotting the representation of the modeled volumetric measurement.
  • the steps described here for modeling a volumetric measurement of one or more brain regions can be performed using any suitable quality metric.
  • the quality metric is one of CNR, T1_summary_gm_05, T1_fber, T2_inu_med, T2_summary_bg_p95, or T1_summary_gm_stdv.
  • the quality metric is CNR.
  • modeling using the quality metric includes generating a CNR, which includes generating a CNR between the gray and white matter of the brain. 25 ACTIVE/124820102.8 Attorney Docket No.
  • gray matter can include a portion of adjacent matter (e.g., white matter) of about + 0.2 mm (e.g., about + 0.1 mm) or mean signal in regions of the image marked as gray matter using an automated or manual annotation.
  • white matter can include a portion of adjacent matter (e.g., gray matter) of about + 0.2 mm (e.g., about + 0.1 mm) or mean signal in regions of the image marked as white matter using an automated or manual annotation.
  • exemplary Methods for Modeling Volumetric Measurements using a Quality Metric [00119]
  • the model is a machine learning model.
  • the model is any one of a regression model (e.g., linear regression, logistic regression, or polynomial regression), decision tree, random forest, support vector machine, Na ⁇ ve Bayes model, k-means cluster, or neural network (e.g., feed-forward networks, convolutional neural networks (CNN), deep neural networks (DNN), autoencoder neural networks, generative adversarial networks, or recurrent networks (e.g., long short-term memory networks (LSTM), bi-directional recurrent networks, or deep bi-directional recurrent networks).
  • a regression model e.g., linear regression, logistic regression, or polynomial regression
  • decision tree e.g., logistic regression, or polynomial regression
  • random forest e.g., support vector machine
  • Na ⁇ ve Bayes model e.g., k-means cluster
  • neural network e.g., feed-forward networks, convolutional neural networks (CNN), deep neural networks (DNN), autoencoder neural networks, generative
  • the model can be trained using a machine learning implemented method, such as any one of a linear regression algorithm, logistic regression algorithm, decision tree algorithm, support vector machine classification, Na ⁇ ve Bayes classification, K-Nearest Neighbor classification, random forest algorithm, deep learning algorithm, or gradient boosting algorithm, and dimensionality reduction techniques such as manifold learning, principal component analysis, factor analysis, autoencoder regularization, and independent component analysis, or combinations thereof.
  • the machine learning model is trained using supervised learning algorithms, unsupervised learning algorithms, semi- supervised learning algorithms (e.g., partial supervision), weak supervision, transfer, multi-task learning, or any combination thereof. 26 ACTIVE/124820102.8 Attorney Docket No.
  • the model has one or more parameters, such as hyperparameters or model parameters.
  • Hyperparameters are generally established prior to training. Examples of hyperparameters include the learning rate, depth or leaves of a decision tree, number of hidden layers in a deep neural network, number of clusters in a k-means cluster, penalty in a regression model, and a regularization parameter associated with a cost function.
  • Model parameters are generally adjusted during training. Examples of model parameters include weights associated with nodes in layers of neural network, support vectors in a support vector machine, and coefficients in a regression model. The model parameters of the machine learning model are trained (e.g., adjusted) using the training data to improve the predictive power of the machine learning model.
  • the model is a generalized additive model.
  • the model is a generalized additive model for location, scale, and shape (GAMLSS) model.
  • the inputs to the model 220 include a quality metric 205.
  • the inputs to the model 220 further include one or more of age 210, volumetric measurement 212, and additional variable(s) 215.
  • the inputs to the model 220 include a quality metric 205.
  • Quality metrics can include one of CNR, T1_summary_gm_05, T1_fber, T2_inu_med, T2_summary_bg_p95, or T1_summary_gm_stdv.
  • the inputs to the model 220 include age 210, such as the age of a patient having MS or the age of a control subject. In various embodiments, the age of a patient having MS or the age of a control subject is any age.
  • the inputs to the model 220 include volumetric measurements 212.
  • the volumetric measurement is a measurement of the volume of a brain region (e.g., the ventricles) from an individual (e.g., an individual with MS).
  • the volumetric measurement is a measurement of the volume of the ventricles, in e.g., mL, from an individual with MS.
  • the inputs to the model 220 include additional variable(s) 215, such as subject height, race, or ethnicity; scanner information (e.g., manufacture, model, software used); imaging sequence (e.g., T1-weighted); scanner receive coil (e.g., 8 channel, 32 channel); time of day; consumption of a substance, such as alcohol or caffeine, by subject in about past 24 hours; hydration level of subject; pharmacological 27 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO treatment of subject (e.g., subject is receiving a pharmacological treatment); and methods used to obtain the data.
  • the model 220 may include one or more model parameters 225.
  • the model is a GAMLSS model and includes one or more parameters.
  • parameters of a GAMLSS model can include one or more of location (e.g., mean), scale (e.g., variance), and shape (e.g., skewness and kurtosis).
  • location e.g., mean
  • scale e.g., variance
  • shape e.g., skewness and kurtosis
  • parameters of a GAMLSS model include each of location (e.g., mean), scale (e.g., variance), and shape (e.g., skewness and kurtosis).
  • the output of the model 220 is a modeled volumetric measurement 230 and a determined percentile of volume 240.
  • the modeled volumetric measurement 230 is a modeled measurement of the volume of a brain region (e.g., the ventricles) from an individual (e.g., an individual with MS).
  • the modeled volumetric measurement is a modeled measurement of the volume of the ventricles, in e.g., mL, from an individual with MS.
  • the determined percentile of volume 240 is a percentile of the volume of a brain region (e.g., the ventricles) from an individual (e.g., an individual with MS).
  • the determined percentile of volume is a percentile of the modeled measurement of the volume of the ventricles from an individual with MS.
  • FIG. 3 is a flow process 305 for generating modeled percentile(s) of the volume of one or more brain regions from an individual, in accordance with an embodiment.
  • Step 305 involves obtaining a set of images from an individual at one or more time points with MRI.
  • Step 310 involves generating a volumetric measurement of one or more brain regions in the set of brain images at each of the one or more time points.
  • step 315 involves generating a quality metric of the set of brain images, followed by steps 325 modeling the volumetric measurement of the one or more brain regions using at least the quality metric, 330 comparing the modeled volumetric measurement of the one or more brain regions to one or more reference percentiles of volumes of the one or more brain regions at each of the one or 28 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO more time points to 335 determine a percentile for the individual, and 340 determining a state of disorder for the individual using the determined percentile. Characterizing a State of Disorder Using the Brain Images [00135] Embodiments disclosed herein involving identifying a state of disorder of an individual from which brain images have been obtained.
  • a state of disorder includes a worsened state of disorder, which is determined in an individual having a brain region volume having a greater or lower percentile as compared to a reference.
  • the determined state of disorder includes a worsened state of disorder for a higher determined (e.g., estimated) percentile for the individual in comparison to a determined state of disorder for a lower determined (e.g., estimated) percentile for the individual.
  • the determined state of disorder includes a worsened state of disorder for a lower determined (e.g., estimated) percentile for the individual in comparison to a determined state of disorder for a higher determined (e.g., estimated) percentile for the individual.
  • methods including generating a slope of the determined (e.g., estimated) percentile of the one or more brain regions of the individual across two or more (e.g., three, four, five, six, seven, eight, nine, or ten or more) time points. Such a slope can also be obtained from the estimated percentile of a reference population.
  • determining the state of disorder for the individual includes using the determined (e.g., estimated) percentile, which further includes: generating a slope of the determined (e.g., estimated) percentile of the one or more brain regions of the individual across the two, three, four, five, six, seven, eight, nine, or ten or more time points; and determining the state of disorder using the generated slope for the individual.
  • determining the state of disorder for the individual further includes obtaining a slope of the estimated percentile of the one or more brain regions from a reference population of individuals having the disorder across the two, three, four, five, six, seven, eight, nine, or ten or more time points; and comparing the generated slope for the individual to the generated slope for the reference population of individuals.
  • the slope of the modeled volumetric measurements can be used to characterize a state of disorder in the individual.
  • the determined state of disorder includes a worsened state of disorder responsive 29 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO to a comparison that the generated slope for the individual is greater than the generated slope for the reference population of individuals.
  • the slope of the modeled volumetric measurements can be used to characterize a state of disorder in the individual.
  • the determined state of disorder includes an improved state of disorder responsive to a comparison that the generated slope for the individual is less than the generated slope for the reference population of individuals.
  • Disorders and State of Disorders [00141] Methods described herein involve generating a volumetric brain model for individuals, which are useful for characterizing disorders. Example disorders can include, but are not limited to, any neurological disorder or neurodegenerative disease.
  • Volumetric brain models may be generated for an individual at risk of developing or is diagnosed as having a disorder (e.g., a neurological disorder).
  • the disease is a neurological disease.
  • a neurological disease can be characterized by anatomical abnormalities, such as one or more lesions or atrophy.
  • the neurological disorder or neurological disease is any one of MS, AD, PD, a traumatic CNS injury, DS, glaucoma, ALS, FTD, and HD.
  • the neurological or neurological disease is any one of Absence of the Septum Pellucidum, Acid Lipase disease, Acid Maltase Deficiency, Acquired Epileptiform Aphasia, Acute Disseminated Encephalomyelitis, ADHD, Adie’s Pupil, Adie’s syndrome, Adrenoleukodystrophy, Agenesis of the corpus callosum, Agnosia, Aicardi syndrome, AIDS, Alexander disease, Alper’s disease, Alternating Hemiplegia, Anencephaly, Aneurysm, Angelman syndrome, Angiomatosis, Anoxia, Antiphosphipid syndrome, Aphasia, Apraxia, Arachnoid Cysts, Arachnoiditis, Arnold-Chiari Malformation, Arteriovenous Malformation, Asperger syndrome, Ataxia, Ataxia Telangiectasia, Ataxias and Cerebellar or Spinocerebellar Degeneration, Autism, Autonomic Dysfunction,
  • OVB-006WO syndrome Klippel-Feil syndrome, KTS, Kluver-Bucy syndrome, Korsakoff’s Amnesic syndrome, Krabbe disease, Kugelberg-Welander disease, Kuru, Lambert-Eaton Myasthenic syndrome, Landau-Kleffner syndrome, Lateral Medullary syndrome, a learning disability, Leigh’s disease, Lennox-Gastaut syndrome, Lesch-Nyhan syndrome, Leukodystrophy, Levine- Critchley syndrome, Lewy Body Dementia, Lipid Storage diseases, Lipoid Proteinosis, Lissencephaly, Locked-In syndrome, Lou Gehrig’s disease, Lupus, Lyme disease, Machado- Joseph disease, Macrencephaly, Melkersson-Rosenthal syndrome, Meningitis, Menkes disease, Meralgia Paresthetica, Metachromatic Leukodystrophy, Microcephaly, Migraine, Miller Fisher syndrome, Mini-Strokes, a
  • OVB-006WO Headache Syncope, Syphilitic Spinal Sclerosis, Syringomyelia, Tabes Dorsalis, Tardive Dyskinesia, Tarlov Cysts, Tay-Sachs disease, Temporal Arteritis, Tethered Spinal Cord syndrome, Thomsen’s Myotonia, Thoracic Outlet syndrome, Thyrotoxic Myopathy, Tinnitus, Todd’s Paralysis, Tourette syndrome, Transient Ischemic Attack, a transmissible spongiform encephalopathy, Transverse Myelitis, a traumatic brain injury, Tremor, Trigeminal Neuralgia, Tropical Spastic Paraparesis, Troyer syndrome, Tuberous Sclerosis, Vasculitis including Temporal Arteritis, Von Economo’s disease, VHL, Von Recklinghausen’s disease, Wallenberg’s syndrome, Werdnig-Hoffman disease, Wernicke-Korsakoff syndrome, West syndrome, Whiplash, Whipple’s disease, Williams syndrome,
  • the methods of the disclosure can be used to determine a state of a disorder (e.g., a neurological disorder).
  • the state of disorder includes a rate of relapse, expression of symptoms, appearance or disappearance of active lesions, severity of atrophy, a response of an individual to a therapy, a degree of disorder- related disability, and a risk of developing a disorder.
  • the state of disorder is a rate of relapse.
  • the state of disorder is an expression of symptoms.
  • the state of disorder is an appearance of active lesions.
  • the state of disorder is a disappearance of active lesions.
  • the state of disorder is a severity of atrophy.
  • the state of disorder is a response of an individual to a therapy. In some embodiments, the state of disorder is a degree of disorder-related disability. In some embodiments, the state of disorder is a risk of developing a disorder [00146] In some embodiments, the neurological disorder is MS, AD, PD, a traumatic CNS injury, DS, glaucoma, ALS, FTD, or HD. Multiple Sclerosis: an Exemplary Disorder [00147] Methods described herein involve generating a volumetric brain model for individuals, which are useful for characterizing the state of a disorder, such as MS. MS is an inflammatory and secondary degenerative disease of the human CNS. MS is a global disease with 70%-80% of the incidence occurring in individuals of between 20 and 40 years of age.
  • MS is a heterogeneous disease based upon clinical course, MRI scan assessment, and pathological analysis of biopsy and autopsy materials.
  • the disease manifests in many possible combinations of defects including spinal cord, brain stem, cranial nerve, cerebellar, cerebral, 33 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO and cognitive syndromes.
  • Progressive disability is the fate of most patients with MS, and MS is a leading cause of neurological disability in young- and middle-aged adults.
  • MS is categorized as having four disease patterns: relapsing-remitting MS (RRMS; 80%-85% of onset cases), primary progressive MS (PPMS; 10%-15% of onset), progressive relapse Type MS (PRMS; 5% of onset); and secondary progressive (SPMS).
  • the methods of the disclosure can be used to determine a state of a disorder (e.g., a neurological disorder).
  • the state of neurological disorder is the state of MS.
  • the state of MS includes MS atrophy, MS disease activity, MS disease progression, MS severity, MS relapse, and MS remission.
  • Guided Decision Making using the Volumetric Brain Model [00149] Embodiments described herein involve determining a state of a disorder for an individual by using a volumetric brain model described herein. In some embodiments, the state of disorder characterization is useful for performing a differential diagnosis of the disorder.
  • the disorder characterization can reveal the presence of one or more anatomical abnormalities that are indicative of the presence of the disorder.
  • the state of disorder characterization can be used to diagnose the individual with the disorder.
  • the state of disorder characterization is useful for determining an efficacy of a therapy previously administered to the individual.
  • the individual may already be administered a therapy.
  • the state of disorder characterization can reveal whether the therapy is effective in treating the disorder (e.g., reversing the disorder or eliminating the disorder) based on the volumetric changes of one or more brain regions that are indicative of the disorder.
  • the state of disorder characterization is useful for selecting a therapy (e.g., a candidate therapy) for the individual.
  • a therapy e.g., a candidate therapy
  • the state of disorder characterization may reveal that the disorder has progressed or is continuing to 34 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO progress, as evidenced by the volumetric changes over time of one or more brain regions.
  • a therapy that is approved to treat the disorder in the progressed state can be selected.
  • the method further includes responsive to the determination of a worsened state of disorder, providing a therapy to the individual.
  • the method further includes responsive to the determination of an improved state of disorder, continuing a therapy for the individual.
  • a selected therapy can include one or more of a biologic, e.g. a cytokine, antibody, soluble cytokine receptor, anti-sense oligonucleotide, small interfering RNA (siRNA), etc.
  • a biologic e.g. a cytokine, antibody, soluble cytokine receptor, anti-sense oligonucleotide, small interfering RNA (siRNA), etc.
  • Such biologic agents encompass muteins and derivatives of the biological agent, which derivatives can include, for example, fusion proteins, PEGylated derivatives, cholesterol conjugated derivatives, and the like as known in the art. Also included are antagonists of cytokines and cytokine receptors, e.g., traps and monoclonal antagonists, e.g. IL-1Ra, IL-1 Trap, sIL-4Ra, etc. Also included are biosimilar or bioequivalent drugs to the active agents set forth herein.
  • Example therapies for multiple sclerosis include corticosteroids, plasma exchange, ocrelizumab (Ocrevus®), IFN- ⁇ (e.g., Avonex®, Betaseron®, Rebif®, Extavia®, and Plegridy®), Glatiramer acetate (e.g., Copaxone® and Glatopa®), anti-VLA4 (e.g., Tysabri and natalizumab), dimethyl fumarate (e.g., Tecfidera® and Vumerity®), teriflunomide (Aubagio®), monomethyl fumarate (BafiertamTM), ozanimod (Zeposia®), siponimod (Mayzent®), fingolimod (Gilenya®), anti-CD52 antibody (e.g., alemtuzumab (Lemtrada®)), mitoxantrone (Novantrone®), methotrexate, cladribine (
  • a pharmaceutical composition can be selected and/or administered to the individual based on the characterization of the state of the disorder, with the expectation that the selected therapeutic agent is likely to exhibit efficacy against the disorder.
  • a pharmaceutical composition administered to an individual includes an active agent.
  • the active ingredient may be present in a therapeutically effective amount, i.e., an amount sufficient when administered to treat a disorder or medical condition mediated thereby.
  • compositions can also include various other agents to enhance delivery and efficacy, e.g. to 35 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO enhance delivery and stability of the active ingredients.
  • a therapeutic composition can also include, depending on the formulation desired, pharmaceutically acceptable, non-toxic carriers or diluents, which are defined as vehicles commonly used to formulate pharmaceutical compositions for animal or human administration.
  • the diluent is selected so as not to affect the biological activity of the combination. Examples of such diluents are distilled water, buffered water, physiological saline, PBS, Ringer’s solution, dextrose solution, and Hank’s solution.
  • a pharmaceutical composition or formulation can include other carriers, adjuvants, or non-toxic, nontherapeutic, nonimmunogenic stabilizers, excipients and the like.
  • a composition can also include additional substances to approximate physiological conditions, such as pH adjusting and buffering agents, toxicity adjusting agents, wetting agents and detergents.
  • a composition can also include any of a variety of stabilizing agents, such as an antioxidant.
  • a pharmaceutical composition or therapeutic agent described herein can be administered in a variety of different ways.
  • Methods of the invention which include methods of generating and implementing a volumetric brain model, are, in some embodiments, performed on one or more computers.
  • non-transitory computer readable mediums including instructions that, when executed by a processor, cause the processor to: obtain a set of brain images captured with MRI from an individual at one or more time points; generate a volumetric measurement of the one or more brain regions in the set of brain images at each of the one or more time points; generate a quality metric (e.g., CNR) of the set of brain images; obtain one or more reference percentiles of volumes of the one or more brain regions; model the volumetric measurement of the one or more brain regions using at least the quality metric; and compare the modeled volumetric measurement of the one or more brain regions to the reference percentiles of volumes at each of the one or more time points to determine a percentile for the individual to determine a state of disorder for the individual.
  • a quality metric e.g., CNR
  • the building and deployment of a volumetric brain model can, for example, be implemented in hardware or software, or a combination of both.
  • a 36 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO machine-readable storage medium is provided, the medium including a data storage material encoded with machine readable data which, when using a machine programmed with instructions for using said data, is capable of building and implementing a volumetric brain model and/or displaying any of the datasets or results described herein.
  • the invention can be implemented in computer programs executing on programmable computers, including a processor, a data storage system (including volatile and non-volatile memory and/or storage elements), a graphics adapter, a pointing device, a network adapter, at least one input device, and at least one output device.
  • a display is coupled to the graphics adapter.
  • Program code is applied to input data to perform the functions described above and generate output information.
  • the output information is applied to one or more output devices, in known fashion.
  • the computer can be, for example, a personal computer, microcomputer, or workstation of conventional design. [00161]
  • Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired.
  • the language can be a compiled or interpreted language.
  • Each such computer program is preferably stored on a storage media or device (e.g., ROM or magnetic diskette) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein.
  • the system can also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein.
  • the signature patterns and databases thereof can be provided in a variety of media to facilitate their use. “Media” refers to a manufacture that contains the signature pattern information of the present invention.
  • the databases of the present invention can be recorded on computer readable media, e.g. any medium that can be read and accessed directly by a computer.
  • Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; and hybrids of these categories such as magnetic/optical storage media.
  • magnetic storage media such as floppy discs, hard disc storage medium, and magnetic tape
  • optical storage media such as CD-ROM
  • electrical storage media such as RAM and ROM
  • hybrids of these categories such as magnetic/optical storage media.
  • the methods of the invention are performed on one or more computers in a distributed computing system environment (e.g., in a cloud computing environment).
  • a distributed computing system environment e.g., in a cloud computing environment.
  • cloud computing is defined as a model for enabling on-demand network access to a shared set of configurable computing resources. Cloud computing can be employed to offer on-demand access to the shared set of configurable computing resources.
  • a cloud-computing model can be composed of various characteristics such as, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, and so forth.
  • a cloud-computing model can also expose various service models, such as, for example, Software as a Service (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure as a Service (“IaaS”).
  • SaaS Software as a Service
  • PaaS Platform as a Service
  • IaaS Infrastructure as a Service
  • a cloud-computing model can also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, and so forth.
  • a “cloud-computing environment” is an environment in which cloud computing is employed.
  • EXAMPLES [00164] Below are examples of specific embodiments for carrying out the present invention. The examples are offered for illustrative purposes only and are not intended to limit the scope of the present invention in any way. Efforts have been made to ensure accuracy with respect to numbers used, but some experimental error and deviation should be allowed for.
  • MRI magnetic resonance imaging
  • CNR contrast-to-noise ratio
  • T1_summary_gm_05 is the 5 th centile of the gray matter voxels in an image as calculated by MRIqc. This is calculated by identifying gray matter in the image and estimating the 5 th centile of the distribution of these voxels: this corresponds to voxels that are dark but still considered gray matter. With images of increasing quality, there is greater differentiation 39 ACTIVE/124820102.8 Attorney Docket No.
  • T1_summary_gm_05 reflects the differentiation between gray matter voxels and other tissues.
  • white matter lesions are also hypointense on T1-weighted imaging, and thus T1_summary_gm_05 is likely to also be capturing these lesions.
  • T1_fber is the foreground-to-background energy ratio (FBER) calculated using a T1-weighted image, which consists of the image energy in the foreground of the image (e.g., the head) compared to that outside the head.
  • FBER foreground-to-background energy ratio
  • T1_summary_gm_stdv is the standard deviation of the gray matter voxels in the image as calculated by MRI quality control (MRIQC). This is calculated by identifying gray matter in the image and estimating the standard deviation of the intensity values of these voxels. From a technical standpoint, this measures the degree of variation in gray matter. In images of low quality, T1_summary_gm_stdv is likely to reflect mis-segmentation of the gray matter due to confusion with other tissues, though it may also reflect artifact in the gray matter due to acquisition-related factors (e.g., image intensity inhomogeneity e.g., motion artifact). In MS, white matter lesions will likely be included in the gray matter segmentation.
  • MRIQC MRI quality control
  • T2_inu_med is the median of the intensity non-uniformity (INU) correction as estimated by the N4 algorithm. Briefly, MRI images are acquired assuming that the magnetic field inside the scanner is uniform, and can be perturbed uniformly. In MRI, the same matter appears slightly differently depending on where in the scanner bore it is sitting. To address this so-called INU, the N4 algorithm estimates the degree of bias or difference between the true image intensities and the observed image intensities. This INU correction is represented as an image indicating the degree to which each location should be adjusted due to non-uniformity. The variable T2_inu_med represents the median of this field as estimated for the T2-weighted FLAIR images in this study.
  • INU intensity non-uniformity
  • T2_summary_bg_p95 is the 95 th centile of the voxels in the image that are outside of the head as calculated by MRIQC. This is calculated by identifying locations outside of the head in the image and estimating the 95 th centile of the distribution of these voxels, which corresponds to voxels that are very bright relative to other background voxels.
  • the first, mean-only model consisted of the model for only, and referred to: This model allowed for the variance in to be estimated as and the skewness to be estimated as .
  • the kurtosis was fixed as per the normal distribution assumption.
  • Additive models were subsequently employed based on worm plots for an additive variance component model, followed by re-inspection of worm plots.
  • the additive model employed was expressed as: and This process continued for skewness and kurtosis, and if model fit worsened with an increase in model complexity (e.g., when an additive model was employed), then the less complex model was selected.
  • a kurtosis term was employed using a Box-Cox-T distribution (4-parameter).
  • Example 3 Brain Charts for People Living with Multiple Sclerosis [00181] This Example demonstrates the establishment of brain charts for ventricle volumes, based upon MRI for people living with MS. These brain charts accounted for demographics and differences in acquisition and image quality. Methods 42 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO [00182] Data were acquired at 5 MS centers using 13 MRI scanner models from 2 scanner manufacturers employing a variety of protocols that included T1-weighted and T2- weighted FLAIR imaging.
  • T1 CNR differed in images across the three timepoints, as shown in the bottom panel.
  • the incorporation of T1 CNR significantly changed the resulting estimated cross-sectional ventricle volume percentile 43 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO for the patient across the three timepoints (middle panel).
  • the estimated percentile of the patient’s ventricle volume was 68th percentile at a first timepoint, 73rd percentile at a second timepoint, and 74th percentile at a third timepoint.
  • the patient’s estimated cross-sectional ventricle volume percentile was 66th percentile at a first timepoint, 74th percentile at a second timepoint, and 69th percentile at a third timepoint.
  • T1 CNR differed in the structural MRI images across the three timepoints, as shown in the bottom panel.
  • the determined (e.g., estimated) percentile of the patient’s ventricle volume was 89th percentile at a first timepoint, 89th percentile at a second timepoint, and 89th percentile at a third timepoint.
  • the patient’s estimated cross- sectional ventricle volume percentile was 85th percentile at a first timepoint, 85th percentile at a second timepoint, and 88th percentile at a third timepoint.
  • FIG. 6 is a graph of the volumetric quantification of structural MRI images taken of a patient having MS at three different time points (59 years old, about 6 months later, and then about another 12 months later; top panel).
  • T1 CNR differed in images across the three timepoints, as shown in the bottom panel.
  • T1 CNR significantly changed the resulting estimated cross-sectional ventricle volume percentile for the patient across the three timepoints (middle panel).
  • the determined (e.g., estimated) percentile of the patient’s ventricle volume was 97th percentile at a first timepoint, 97th percentile at a second timepoint, and 97th percentile at a third timepoint.
  • the patient’s ventricle volume was 93rd percentile at a first timepoint, 99th percentile at a second timepoint, and 95th percentile at a third timepoint.
  • GAMLSS allow for the modeling of data whose distribution does not follow an exponential family. This approach also allows for modeling the mean structure as well as the variance, skewness, and kurtosis in terms of flexible nonlinear associations with covariates of interest. All calculations were conducted in the R statistical environment, using code modified from the GAMLSS package.
  • FIGs. 8A-8C show example volumetric measurements of the ventricles from individuals with multiple sclerosis.
  • the determined percentile of the patient’s ventricle volume was 33rd percentile at a first timepoint and down to 32nd percentile at a second timepoint.
  • the patient’s determined longitudinal ventricle volume was 35th percentile at a first timepoint and up to 36th percentile at a second timepoint.
  • the model without T1 CNR indicated that this patient’s 47 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO disease state at age 26 was well controlled from a standpoint of atrophy, with ventricle volume in the bottom 20 th percentile.
  • taking T1 CNR into account altered this conclusion and indicated that this patient should be monitored closely using MRI.
  • the determined percentile of the patient’s ventricle volume was 21st percentile at a first timepoint and up to 23rd percentile at a second timepoint.
  • T1 CNR the patient’s determined longitudinal ventricle volume was 26th percentile at a first timepoint and 26th percentile at a second timepoint. While the model without T1 CNR was consistent with the conclusion of a worsening disease state, the model with T1 CNR indicated disease stability. Based upon this, this patient would merit less frequent MRI follow-ups and is less likely to require a change in disease-modifying therapy. [00202] In these scenarios, T1 CNR differed in the images and, accordingly, the determined (e.g., estimated) percentile of the patient’s ventricle volume using such a quality metric also different. Therefore, the incorporation of the T1 CNR metric may change the guided treatment provided to the patient.
  • the patient may be exhibiting stable or a reversal in disease state.
  • the patient need not undergo a change in the treatment.
  • the T1 CNR metric is not incorporated, based on the slopes of the determined (e.g., estimated) percentile from the first to second timepoint, which, for example, may be a positive slope, and from the second to third timepoint, which, for example, may be a positive slope, the patient may be believed to be exhibiting stable or disease progression.
  • Predictors of ventricle volume distribution included patient age (p ⁇ 0.001), previous ventricle volume (p ⁇ 0.001), and the time interval of observation (p ⁇ 0.01). Higher image quality was associated with less variance in volumes (p ⁇ 0.02).
  • Visual inspection of worm plots indicated good model fit across all variables of interest.
  • Assessment of fitted conditional centiles indicated that they provide intuitive visualizations of longitudinal changes in ventricle volume.
  • ICV-adjusted and ICV-normalized modeling resulted in similar conclusions. Together, these results demonstrate the establishment of individualized 48 ACTIVE/124820102.8 Attorney Docket No.
  • Example 6. Tracking Longitudinal Change in Brain Volumes through Conditional Quantiles with Alternate Methods [00205] Longitudinal analysis of growth curves is more complex than standard cross- sectional modeling. This is due to documented biases as well as conditional interpretation. After observing a ventricular volume on a previous MRI, for example, the interpretation of each subsequent measurement changes. This Example demonstrates the establishment of longitudinal brain charts for ventricle volumes, based upon MRI for people living with MS. These brain charts accounted for demographics and heterogeneity in acquisition and image quality over time using a quality metric.
  • FIG. 9, FIG. 10, and FIG. 11 show example volumetric measurements of the ventricles from individuals with MS. For example, as depicted in FIG. 9, without considering T1 CNR, the determined percentile of the patient’s ventricle volume was 40th percentile at a first timepoint and 84th percentile at a second timepoint.
  • the patient’s determined longitudinal ventricle volume was 36th percentile at a first timepoint and 97th percentile at a second timepoint. While both models indicated unusual ventricle growth at age 52.5, the T1 CNR model leveraged the high-quality scan to further emphasize this conclusion. Based on the T1 CNR model, this patient would merit immediate consideration for change in therapy and more frequent MRI monitoring. [00212] As depicted in FIG. 10, without considering T1 CNR, the determined percentile of the patient’s ventricle volume was 47th percentile at a first timepoint and 37th percentile at a second timepoint.
  • the patient’s determined longitudinal ventricle volume was 74th percentile at a first timepoint and 42nd percentile at a second timepoint. While the model without T1 CNR indicated relative stability in ventricle volume, the model accounting for T1 CNR detected an unusual change in brain volume that would merit a change in disease-modifying therapy. [00213] As depicted in FIG. 11, without considering T1 CNR, the determined percentile of the patient’s ventricle volume was 95th percentile at a first timepoint and 88th percentile at a second timepoint.
  • T1 CNR the patient’s determined longitudinal ventricle volume was 82nd percentile at a first timepoint and 69th percentile at a second timepoint. While the model without T1 CNR indicated an extreme change in ventricle volume at the second time point (age 70.6), taking T1 CNR into account attenuated this conclusion. Based upon this, more conservative decisions about treatment for the patient were warranted. [00214] In these scenarios, T1 CNR differed in the images and, accordingly, the determined percentile of the patient’s ventricle volume using such a quality metric also different. Therefore, the incorporation of the T1 CNR metric may change the guided treatment 50 ACTIVE/124820102.8 Attorney Docket No.
  • OVB-006WO provided to the patient.
  • the patient may be exhibiting stable or a reversal in disease state.
  • the patient need not undergo a change in the treatment.
  • the T1 CNR metric is not incorporated, based on the slopes of the determined percentile from the first to second timepoint, which, for example, may be a positive slope, and from the second to third timepoint, which, for example, may be a positive slope, the patient may be believed to be exhibiting stable or disease progression.
  • Predictors of ventricle volume distribution included patient age (p ⁇ 0.001), previous ventricle volume (p ⁇ 0.001), and the time interval of observation (p ⁇ 0.01). Higher image quality was associated with less variance in volumes (p ⁇ 0.02).
  • Visual inspection of worm plots indicated good model fit across all variables of interest.
  • Assessment of fitted conditional centiles indicated that they provide intuitive visualizations of longitudinal changes in ventricle volume.
  • ICV-adjusted and ICV-normalized modeling resulted in similar conclusions. Together, these results demonstrate the establishment of individualized longitudinal volumetric brain charts for patients having a neurological disorder that account for image quality with the CNR quality metric.
  • volumetric Modeling Developed from MRI Images of Patients Having a Neurological Disorder
  • the goal is to create a visualization of an individual patient’s volumetric measurement of one or more brain regions (e.g., a MS patient’s brain with ventricle volume and/or MS lesions), in a way that is intuitive and familiar for physicians and patients.
  • a MS patient e.g., a MS patient’s brain with ventricle volume and/or MS lesions
  • displaying MRI images in a plot of the volumetric measurement physicians and patients can absorb the information faster and trust the metrics.
  • plotted representations of modeled volumetric measurements (e.g., using a quality metric) of MRI images provides utility for care and management of patients with a neurological disorder.
  • plotted representations of modeled volumetric measurements (e.g., using a quality metric) of MRI images by providing volumetric and temporal analysis of a patient’s state of disorder, can be useful for differential diagnosis of the patient’s disorder, can be useful for selecting candidate therapies 51 ACTIVE/124820102.8 Attorney Docket No. OVB-006WO for the patient, and/or for determining an efficacy of therapies previously administered to the patient.
  • Plotting such a representation enables understanding of the volumetric and temporal characteristics of a patient’s MS, such as the state of their MS over time. Thus, this understanding can guide the treatment care provided to the patient.
  • an individual with a higher rate of atrophy will be determined to have more progressive MS and may therefore benefit from a higher efficacy treatment. If the individual’s longitudinal slope of atrophy change is increasing at a rate faster than a reference control, such as faster than other individuals with MS, it may indicate that their current treatment plan is not working. 52 ACTIVE/124820102.8

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Abstract

Selon l'invention, une métrique de qualité est générée à partir d'images capturées chez des individus. La métrique de qualité est utilisée pour comparer une mesure volumétrique du cerveau provenant des images avec un témoin de référence. Les mesures volumétriques sont utilisées pour déterminer un percentile du volume d'une région cérébrale de l'individu, qui révèle l'état de trouble d'une maladie neurologique.
PCT/US2023/076710 2022-10-12 2023-10-12 Mesure de volumes cérébraux pour modélisation d'état de maladie neurologique WO2024081800A1 (fr)

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US20130243348A1 (en) * 2010-12-01 2013-09-19 Koninklijke Philips Electronics N.V. Contrast to noise ratio (cnr) enhancer
US20140155730A1 (en) * 2010-12-17 2014-06-05 The Trustees Of Columbia University In The City Of New York Apparatus, method and computer-accessible medium for diagnosing and subtyping psychiatric diseases
US20210228144A1 (en) * 2018-10-10 2021-07-29 The Trustees Of Columbia University In The City Of New York System, method and computer-accessible medium for neuromelanin-sensitive magnetic resonance imaging as a non-invasive proxy measure of dopamine function in the human brain
US20220273184A1 (en) * 2019-08-20 2022-09-01 Terran Biosciences, Inc. Neuromelanin-sensitive mri for assessing parkinson's disease

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Publication number Priority date Publication date Assignee Title
US20130243348A1 (en) * 2010-12-01 2013-09-19 Koninklijke Philips Electronics N.V. Contrast to noise ratio (cnr) enhancer
US20140155730A1 (en) * 2010-12-17 2014-06-05 The Trustees Of Columbia University In The City Of New York Apparatus, method and computer-accessible medium for diagnosing and subtyping psychiatric diseases
US20210228144A1 (en) * 2018-10-10 2021-07-29 The Trustees Of Columbia University In The City Of New York System, method and computer-accessible medium for neuromelanin-sensitive magnetic resonance imaging as a non-invasive proxy measure of dopamine function in the human brain
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