(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT)
`
`(10) International Publication Number
`
`WO 2018/106713 A1
`
`h a
`
`WlPOl PCT
`
`(19) World Intellectual Property
`Organization
`International Bureau
`
`(43) International Publication Date
`14 June 2018 (14.06.2018)
`
`(51)
`
`International Patent Classification:
`
`A613 5/00 (2006.01)
`A613 5/055 (2006.01)
`G01R 33/20 (2006.01)
`
`G01R 33/48 (2006.01)
`G06K 9/20 (2006.01)
`G06T 7/00 (2017.01)
`
`(21)
`
`International Application Number:
`
`PCT/US2017/064745
`
`(22)
`
`International Filing Date:
`05 December 2017 (05.12.2017)
`
`(25) mung Language:
`(26) Publication Language:
`_
`_
`(30) “WW Data:
`
`EnghSh
`English
`
`'
`'
`’
`(71) Applicant: DARMIYAN, INC. [US/US]; 1900 Powell
`Street, Suite 600, Emeryville, California 94608 (US).
`
`(72)
`
`1900 Powell
`Inventors: KAMALI-ZARE, Padideh;
`Street, Suite 600, Emeryville, California 94608 (US). VEJ-
`DANI, Kaveh; 1900 Powell Street, Suite 600, Emeryville,
`California 94608 (US). LIEBMANN, Thomas; 1900 Pow-
`ell Street, Suite 600, Emeryville, California 94608 (US).
`
`ESFANDYARPOUR, Hesaam; 1900 Powell Street, Suite
`600, Emeryville, California 94608 (US).
`
`(74)
`
`Agent: KENNEDY, Daniel J.; Wilson Sonsini Goodrich
`& Rosati, 650 Page Mill Road, Palo Alto, California 94304
`(US).
`
`(81)
`
`Designated States (unless otherwise indicated, for every
`kind ofnational protection available): AE, AG, AL, AM,
`A0, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ,
`CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO,
`DZ, EC, EE, EG, Es, FI, GB, GD, GE, GH, GM, GT, HN,
`HR, HU, ID, IL, IN, IR, IS, Jo, JP, KE, KG, KH, KN, KP,
`KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME,
`MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ,
`mm
`SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN,
`TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW~
`(84) Designated States (unless otherwise indicated, for every
`kind of regional protection available): ARIPO (BW, GH,
`GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ,
`UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ,
`TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK,
`EE, ES, FI, FR, GB, GR, IIR, IIU, IE, IS, IT, LT, LU, LV,
`MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, S1, SK, SM,
`
`(54) Title: METHODS AND SYSTEMS FOR IDENTIFYING BRAIN DISORDERS
`
`wo\
`
`more measured MR3 parameters.
`
`Obtain magnetic resonance imaging (MRE) data comprising one or
`
`)i‘e nomputer nroeeesore to process: the one or more
`Uee one orr
`n‘ MR3 pei‘emeiers with one or more simulated MRE
`mees
`parame
`generated from one or mere n‘Iier'oetrueturai modete.
`
`
`iii)
`
`”320
`
`Select a diagnostic model from the one or more microetruciurei modele
`based on a ihreshoid congruence between the one or more measured — 130
`MRE parameters and the one or more eiiniiieied WERE parameters.
`
`in a brain at a ambient,
`
`Use the diegnoetie modei to determine the riieorder state of brain tissue
`
`”340
`
`(57) Abstract: Methods and systems for determining whether brain tissue is indicative of a disorder, such as a neurodegenerative
`disorder, are provided. The methods and systems generally utilize data processing techniques to assess a level of congruence between
`measured parameters obtained from magnetic resonance imaging (MRI) data and simulated parameters obtained from computational
`modeling of brain tissues.
`
`[Continued on nextpage]
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`
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`TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW,
`KM, ML, MR, NE, SN, TD, TG).
`
`Declarations under Rule 4.17:
`
`— as to applicant’s entitlement to applyfor and be granted a
`patent (Rule 4.17(ii))
`— as to the applicant’s entitlement to claim the priority ofthe
`earlier application (Rule 4.17(iii))
`Published:
`
`— with international search report (Art. 21(3))
`
`

`

`WO 2018/106713
`
`PCT/USZOl7/064745
`
`METHODS AND SYSTEMS FOR IDENTIFYING BRAIN DISORDERS
`
`CROSS-REFERENCE
`
`[0001]
`
`This application claims the benefit of US. Provisional Patent Application Serial
`
`Number 62/430,351, filed December 6, 2016, and US. Provisional Patent Application Serial
`
`Number 62/481,839, filed April 5, 2017, each of which is entirely incorporated herein by reference
`
`for all purposes.
`
`BACKGROUND
`
`[0002]
`
`Neurodegenerative diseases leading to dementia are a tremendous societal burden,
`
`currently devastating 9 million people domestically and 47 million people worldwide. Current
`
`inability to effectively prevent, diagnose and combat neurodegeneration results in staggering direct
`
`and indirect costs. Alzheimer's disease (AD), the most common cause of dementia, alone afflicts
`
`over 5 million Americans and accounts for the 6th leading cause of death in the USA. AD requires
`
`an estimated 18 billion hours of unpaid caretaking and well over $250 billion of medical costs
`
`annually. Prevalence of the disease is projected to escalate to nearly 14 million people domestically
`
`and 135 million worldwide by 2050, with no potential cure in immediate sight. There is a dire need
`
`for technological advancements toward diagnostics, prevention, therapeutics and eventual cures
`
`that will each have profound beneficial impacts on the population.
`
`[0003]
`
`Current clinical evaluation typically includes non-invasive brain imaging with magnetic
`
`resonance imaging (MRI), positron emission tomography (PET), or other advanced imaging
`
`strategies which provide insight into tissue volume changes, chemical composition, cortical
`
`metabolic rate, alterations associated with tissue cellularity and disease biomarkers, and structural
`
`abnormalities attributed to neurodegenerative disease. To aid in the diagnosis of AD and
`
`differential diagnosis from non-Alzheimer dementias, fiuorodeoxyglucose (FDG) PET and amyloid
`
`PET reveal AD—associated patterns of cerebral cortical metabolism and beta amyloid deposits in the
`
`gray matter, respectively. Similarly, tau PET reveals neurofibrillary tangles in the brain. However,
`
`due to a lack of advancement in analysis technologies, meaningful use of these imaging techniques
`
`for neurodegenerative disease is restricted to late stages when considerable tissue damage and
`
`cognitive or other clinical abnormalities are present. As we deepen our understanding of the
`
`multiplicity of abnormalities associated with AD, there is increasing evidence that the continual
`
`targeting of these amyloid plaques and neurofibrillary tangles may merely be treating late stage
`
`symptoms rather than the underlying causes. The inability to effectively detect early stages of AD
`
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`precludes pre-symptomatic intervention and conceals the potential beneficial effects of drug
`
`candidates.
`
`[0004]
`
`Implicit to the neurodegenerative process is the death of the signaling nerve cells in the
`
`brain, though this can merely be the ultimate consequence in a cascade of degeneration within brain
`
`tissue. The structural integrity of tissue is necessary for neuron support and survival and clearance
`
`of molecular waste that must be removed from the brain for maintenance of neural tissue
`
`homeostasis and efficient function. Alterations in non—cellular components of the brain are
`
`complicit in the degenerative process and may be a precursor of lost nerve cell function. It has been
`
`shown that proper regulation of neural tissue homeostasis is necessary for eliminating toxic residue
`
`buildup, a process that can be altered in the AD brain. Yet, there remains limited understanding of
`
`brain structural content and its impact on transport of molecules in the brain interstitium. Currently,
`
`the clinical use and the diagnostic capacity of brain MRI remains limited to differential diagnosis,
`
`only after symptomatic presentation, principally due to the inherently low spatial resolution — MRI
`
`image voxels are in mm dimensions, whereas structural changes contributing to tissue degeneration
`
`originate at the sub—micron scale. FDG, amyloid, and tau PET scans suffer from similar limitations.
`
`SUMMARY
`
`[0005]
`
`Recognized herein is a need for tools that allow early detection of Alzheimer’s disease
`
`and other neurodegenerative disorders, including tools that may utilize approaches that detect
`
`microscopic changes in brain tissue from low-resolution magnetic resonance imaging (MRI) scans.
`
`Such approaches may leverage a deeper understanding of brain tissue microstructure to more
`
`reliably predict and interpret the health of the brain from MRI scans well before severe tissue
`
`damage irreversibly impedes healthy cognitive function.
`
`[0006]
`
`Provided herein is an image analysis platform that can detect and quantify brain tissue
`
`abnormality (such as neurodegeneration) in every voxel of standard clinical brain MRI. The
`
`platform may provide detailed information about the brain tissue health at the microscopic level
`
`and the resulting observed patterns of pathologic involvement that is currently missing in the
`
`neuroimaging/ brain diagnostics field. As a result, complex brain diseases, such as Alzheimer's
`
`disease, which are currently diagnosed very late (i.e., at the late symptomatic stages), may be
`
`diagnosed or otherwise identified prior to the onset of advanced symptoms. The platform may
`
`allow early-stage testing of novel drug candidates for clinical trials, which have previously failed
`
`due to poor patient selection, late intervention, and very high trial costs. All of these factors may be
`
`significantly improved using the platform.
`
`[0007]
`
`Provided herein are methods and systems for determining whether brain tissue is
`
`indicative of a disorder, such as a neurodegenerative disorder. The methods and systems may allow
`2
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`the early diagnosis of a brain disorder much earlier than would be possible using prior methods and
`
`systems, such as many years before the development of symptoms associated with the disorder that
`
`are detectable using prior methods and systems. The methods and systems may provide high
`
`accuracy in diagnosing a brain disorder (such as greater than 90% accuracy), as measured by a
`
`variety of criteria described herein.
`
`[0008]
`
`The methods and systems of the present disclosure may utilize data processing
`
`techniques to assess a level of congruence between measured parameters obtained from magnetic
`
`resonance imaging (MRI) data and simulated parameters obtained from computational modeling of
`
`brain tissues. The methods and systems generally operate by determining a level of congruence
`
`between the one or more measured parameters and the one or more simulated parameters for one or
`
`more voxels of the MRI data. The simulated parameters are obtained from a plurality of
`
`microstructural models. Each mi crostructural model of the plurality of microstructural models is
`
`obtained by subjecting a microstructural model that is not indicative of a disorder to a series of
`
`microstructural perturbations. After assessing the level of congruence between the one or more
`
`measured parameters and the one or more simulated parameters for a number of microstructural
`
`models of the plurality of microstructural models, a diagnostic microstructural model that meets a
`
`threshold congruence is selected. The diagnostic microstructural model is used to determine the
`
`disorder state of the brain tissue associated with the voxel.
`
`[0009]
`
`The methods and systems may be applied to a plurality of voxels of the MRI data, such
`
`that a level of congruence is determined for each voxel of the plurality of voxels. In this manner, a
`
`diagnostic model and a disorder state may be determined for each voxel of the plurality of voxels.
`
`The methods and systems may be applied to determine a diagnostic model and a disorder state for a
`
`plurality of voxels located within a particular region of a brain, within a whole brain, or across a
`
`plurality of brains from a plurality of subjects.
`
`[0010]
`
`In an aspect, a method for determining a disorder state of brain tissue in a brain of a
`
`subject may comprise: (a) obtaining magnetic resonance imaging (MRI) data comprising at least
`
`one IVERI image of the brain, the WI image comprising a plurality of voxels, a voxel of the
`
`plurality of voxels being associated with the brain tissue of the brain of the subject and comprising
`
`one or more measured MRI parameters in the MRI data, (b) for the voxel of the plurality of voxels,
`
`using one or more computer processors to process the one or more measured MRI parameters with
`
`one or more simulated MRI parameters for the voxel, the one or more simulated MRI parameters
`
`being generated from one or more microstructural models at the voxel, (c) for the voxel of the
`
`plurality of voxels, selecting a diagnostic model from the one or more microstructural models, the
`
`diagnostic model meeting a threshold congruence between the one or more measured MRI
`
`3
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`parameters and the one or more simulated MRI parameters associated with the diagnostic model,
`
`and (d) for the voxel of the plurality of voxels, using the diagnostic model to determine the disorder
`
`state of the brain tissue associated with the voxel.
`
`[0011]
`
`Each voxel may comprise a plurality of measured MRI parameters. The one or more
`
`measured MRI parameters may be a plurality of measured MRI parameters. The one or more
`
`simulated MRI parameter may be a plurality of simulated MRI parameters.
`
`[0012]
`
`The method may further comprise repeating (b)—(d) one or more times for additional
`
`voxels of the plurality of voxels. The method may further comprise repeating (b)—(d) for all other
`
`voxels of the plurality of voxels. The method may further comprise repeating (b)-(d) for all voxels
`
`associated with a specified region of the brain. The method may further comprise repeating (b)-(d)
`
`for all voxels associated with an entirety of the brain. The method may further comprise repeating
`
`(a)-(d) for a plurality of MRI images, each MRI image of the plurality of MRI images associated
`
`with a brain selected from a plurality of brains, each brain of the plurality of brains associated with
`
`a subject selected from a plurality of subjects.
`
`[0013]
`
`The MRI image may be selected from the group consisting of: a longitudinal relaxation
`
`time (TU—weighted MRI image, a transverse relaxation time (T2)—Weighted MRI image, and a
`
`diffusion-weighted MR1 image. The measured MRI parameter may be selected from the group
`
`consisting of: a longitudinal relaxation time (Tl), a transverse relaxation time (T2), and a diffusion
`
`coefficient. The simulated NERI parameter may be selected from the group consisting of: a
`
`longitudinal relaxation time (T1), a transverse relaxation time (T2), and a diffusion coefficient.
`
`[0014]
`
`The one or more microstructural models may comprise information regarding a
`
`parameter selected from the group consisting of: intracellular content, extracellular content,
`
`distribution of extracellular content Within interstitial space, distribution of intracellular content
`
`Within intracellular space, and tissue geometry. The one or more microstructural models may
`
`comprise measured or predicted values of a parameter selected from the group consisting of: cell
`
`density, cell shape, cell geometry, cell size, cell distribution, intercellular spacing, extracellular
`
`matrix homogeneity, interstitial tortuosity, water to protein ratio, water to lipid ratio, water to
`
`carbohydrate ratio, protein to lipid ratio, protein to carbohydrate ratio, and lipid to carbohydrate
`
`ratio. The one or more microstructural models may be selected from a microstructural model
`
`library. The microstructural model library may comprise at least 100 microstructural models.
`
`[0015]
`
`The microstructural model library may be constructed by: (a) creating a first
`
`microstructural model corresponding to a brain state that is not associated with a disorder, and (b)
`
`iteratively subjecting the first microstructural model to a perturbation, each iteration producing an
`
`additional perturbed microstructural model. (b) may comprise subjecting the first microstructural
`
`4
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`model to at least lOO iterations to generate at least lOO perturbed microstructural models. The first
`
`microstructural model may be selected based on knowledge of the brain region associated with the
`
`voxel. The perturbation may comprise an operation selected from the group consisting of: depleting
`
`cells, altering cellular morphology or distribution, altering intracellular or interstitial physico-
`
`chemical composition or distribution, altering extracellular matrix composition or distribution, and
`
`altering intercellular spacing. The perturbation may comprise a stochastic procedure.
`
`[0016]
`
`The threshold congruence may be determined by computing an objective function
`
`between the one or more measured MRI parameters and the one or more simulated MRI
`
`parameters. The objective function may comprise an L1 norm or an L2 norm.
`
`[0017]
`
`Determining the disorder state of the brain tissue associated with the voxel may be
`
`achieved at an accuracy of at least 90%. Determining the disorder state across the brain tissue
`
`associated with the specified region of the brain may be achieved at an accuracy of at least 90%.
`
`Determining the disorder state of the brain tissue associated with the whole brain of the subject may
`
`be achieved at an accuracy of at least 90%. Determining the disorder state of the brain tissue
`
`associated with the plurality of subjects may be achieved at an accuracy of at least 90%.
`
`[0018]
`
`The disorder may be a non—neurodegenerative disorder. The disorder may be selected
`
`from the group consisting of: a primary neoplasm, a metastatic neoplasm, a seizure disorder, a
`
`seizure disorder with focal cortical dysplasia, a demyelinating disorder, a non-neurodegenerative
`
`encephalopathy, a cerebrovascular disease, and a psychological disorder. The disorder may be a
`
`neurodegenerative disorder. The disorder may be selected from the group consisting of:
`
`Alzheimer’s disease, a non-Alzheimer’s dementia disorder, Parkinson’s disease, a Parkinsonism
`
`disorder, a motor neuron disease, Huntington’s disease, a Huntington’s disease-like syndrome,
`
`transmissible spongiform encephalopathy, chronic traumatic encephalopathy, and a tauopathy.
`
`[0019]
`
`The method may enable diagnosis of a neurodegenerative disorder more than 5 years
`
`prior to the development of symptoms associated with the neurodegenerative disorder. The method
`
`may enable monitoring of the neurodegenerative disorder at a plurality of time points, the plurality
`
`of time points separated by a plurality of time intervals.
`
`[0020]
`
`The method may further comprise constructing a brain map that, for each voxel of the
`
`plurality of voxels, indicates the disorder state of the brain tissue associated with the voxel. The
`
`method may further comprise displaying the brain map on a graphical user interface of an
`
`electronic device of a user. The brain map may comprise a qualitative abnormality map. The brain
`
`map may comprise a binary abnormality map. The brain map may comprise a quantitative
`
`abnormality map. The brain map may comprise a percent abnormality map.
`
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`WO 2018/106713
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`PCT/USZOl7/064745
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`[0021]
`
`In an aspect, a method for determining a disorder state ofa tissue in a portion of a body
`
`of a subject may comprise: obtaining magnetic resonance imaging (lVIRI) data comprising at least
`
`one MRI image of the tissue, the MRI image comprising a plurality of voxels, a voxel of the
`
`plurality of voxels being associated With the tissue of the subject and comprising one or more
`
`measured MRI parameters in the MRI data; (b) for the voxel of the plurality of voxels, using one or
`
`more computer processors to process the one or more measured MRI parameters with one or more
`
`simulated MRI parameters for the voxel, the one or more simulated MRI parameters being
`
`generated from one or more microstructural models at the voxel; (c) for the voxel of the plurality of
`
`voxels, selecting a diagnostic model from the one or more microstructural models, the diagnostic
`
`model meeting a threshold congruence between the one or more measured lVERI parameters and the
`
`one or more simulated MRI parameters associated with the diagnostic model, and (d) for the voxel
`
`of the plurality of voxels, using the diagnostic model to determine the disorder state of the tissue
`
`associated with the voxel.
`
`[0022]
`
`The tissue may be selected from the group consisting of: spinal cord tissue, heart tissue,
`
`vascular tissue, lung tissue, liver tissue, kidney tissue, esophageal tissue, stomach tissue, intestinal
`
`tissue, pancreatic tissue, thyroid tissue, adrenal tissue, spleen tissue, lymphatic tissue, appendix
`
`tissue, breast tissue, bladder tissue, vaginal tissue, ovarian tissue, uterine tissue, penile tissue,
`
`testicular tissue, prostatic tissue, skeletal muscle tissue, skin, and non-brain tissue of the head and
`
`neck.
`
`[0023]
`
`In an aspect, a non-transitory computer-readable medium may comprise machine-
`
`executable code that, upon execution by one or more computer processors, implements a method
`
`for detecting a disorder state of brain tissue in a brain of a subject, the method comprising: (a)
`
`obtaining magnetic resonance imaging (MRI) data comprising at least one MRI image of the brain,
`
`the MRI image comprising a plurality of voxels, a voxel of the plurality of voxels being associated
`
`with the brain tissue of the brain of the subject and comprising one or more measured NERI
`
`parameters in the MRI data, (b) for the voxel of the plurality of voxels, using one or more computer
`
`processors to process the one or more measured MRI parameters with one or more simulated lV[RI
`
`parameters for the voxel, the one or more simulated MRI parameters being generated from one or
`
`more microstructural models at the voxel, (c) for the voxel of the plurality of voxels, selecting a
`
`diagnostic model from the one or more microstructural models, the diagnostic model meeting a
`
`threshold congruence between the one or more measured MRI parameters and the one or more
`
`simulated MRI parameters associated with the diagnostic model; and (d) for the voxel of the
`
`plurality of voxels, using the diagnostic model to determine the disorder state of the brain tissue
`
`associated with the voxel.
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`[0024]
`
`Each voxel may comprise a plurality of measured MRI parameters. The one or more
`
`measured MRI parameters may be a plurality of measured IVIRI parameters. The one or more
`
`simulated MRI parameter may be a plurality of simulated MRI parameters.
`
`[0025]
`
`The method may further comprise repeating (b)-(d) one or more times for additional
`
`voxels of the plurality of voxels. The method may further comprise repeating (b)-(d) for all other
`
`voxels of the plurality of voxels. The method may further comprise repeating (b)—(d) for all voxels
`
`associated with a specified region of the brain. The method may further comprise repeating (b)—(d)
`
`for all voxels associated with an entirety of the brain. The method may further comprise repeating
`
`(a)-(d) for a plurality of MRI images, each MRI image of the plurality of MRI images associated
`
`With a brain selected from a plurality of brains, each brain of the plurality of brains associated with
`
`a subject selected from a plurality of subjects.
`
`[0026]
`
`The MRI image may be selected from the group consisting of: a longitudinal relaxation
`
`time (TD-weighted MRI image, a transverse relaxation time (T2)-Weighted MRI image, and a
`
`diffusion—weighted MRI image. The measured MRI parameter may be selected from the group
`
`consisting of: a longitudinal relaxation time (T1), a transverse relaxation time (T2), and a diffusion
`
`coefficient. The simulated MRI parameter may be selected from the group consisting of: a
`
`longitudinal relaxation time (T1), a transverse relaxation time (T2), and a diffusion coefficient.
`
`[0027]
`
`The one or more microstructural models may comprise information regarding a
`
`parameter selected from the group consisting of: intracellular content, extracellular content,
`
`distribution of extracellular content within interstitial space, distribution of intracellular content
`
`Within intracellular space, and tissue geometry. The one or more microstructural models may
`
`comprise measured or predicted values of a parameter selected from the group consisting of: cell
`
`density, cell shape, cell geometry, cell size, cell distribution, intercellular spacing, extracellular
`
`matrix homogeneity, interstitial tortuosity, water to protein ratio, water to lipid ratio, water to
`
`carbohydrate ratio, protein to lipid ratio, protein to carbohydrate ratio, and lipid to carbohydrate
`
`ratio. The one or more microstructural models may be selected from a microstructural model
`
`library. The microstructural model library may comprise at least 100 microstructural models.
`
`[0028]
`
`The microstructural model library may be constructed by: (a) creating a first
`
`microstructural model corresponding to a brain state that is not associated With a disorder, and (b)
`
`iteratively subjecting the first microstructural model to a perturbation, each iteration producing an
`
`additional perturbed microstructural model. (b) may comprise subjecting the first microstructural
`
`model to at least 100 iterations to generate at least 100 perturbed microstructural models. The first
`
`microstructural model may be selected based on knowledge of the brain region associated with the
`
`voxel. The perturbation may comprise an operation selected from the group consisting of: depleting
`
`7
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`cells, altering cellular morphology or distribution, altering intracellular or interstitial physico-
`
`chemical composition or distribution, altering extracellular matrix composition or distribution, and
`
`altering intercellular spacing. The perturbation may comprise a stochastic procedure.
`
`[0029]
`
`The threshold congruence may be determined by computing an objective function
`
`between the one or more measured MRI parameters and the one or more simulated MRI
`
`parameters. The objective function may comprise an L1 norm or an L2 norm.
`
`[0030]
`
`Determining the disorder state of the brain tissue associated with the voxel may be
`
`achieved at an accuracy of at least 90%. Determining the disorder state across the brain tissue
`
`associated with the specified region of the brain may be achieved at an accuracy of at least 90%.
`
`Determining the disorder state of the brain tissue associated with the Whole brain of the subject may
`
`be achieved at an accuracy of at least 90%. Determining the disorder state of the brain tissue
`
`associated the plurality of subjects may be achieved at an accuracy of at least 90%.
`
`[0031]
`
`The disorder may be a non-neurodegenerative disorder. The disorder may be selected
`
`from the group consisting of: a primary neoplasm, a metastatic neoplasm, a seizure disorder, a
`
`seizure disorder with focal cortical dysplasia, a demyelinating disorder, a non—neurodegenerative
`
`encephalopathy, a cerebrovascular disease, and a psychological disorder. The disorder may be a
`
`neurodegenerative disorder. The disorder may be selected from the group consisting of:
`
`Alzheimer’s disease, a non-Alzheimer’s dementia disorder, Parkinson’s disease, a Parkinsonism
`
`disorder, a motor neuron disease, Huntington’s disease, a Huntington’s disease-like syndrome,
`
`transmissible spongiform encephalopathy, chronic traumatic encephalopathy, and a tauopathy.
`
`[0032]
`
`The method may enable diagnosis of a neurodegenerative disorder more than 5 years
`
`prior to the development of symptoms associated with the neurodegenerative disorder. The method
`
`may enable monitoring of the neurodegenerative disorder at a plurality of time points, the plurality
`
`of time points separated by a plurality of time intervals.
`
`[0033]
`
`The method may further comprise constructing a brain map that, for each voxel of the
`
`plurality of voxels, indicates the disorder state of the brain tissue associated with the voxel. The
`
`method may further comprise displaying the brain map on a graphical user interface of an
`
`electronic device of a user. The brain map may comprise a qualitative abnormality map. The brain
`
`map may comprise a binary abnormality map. The brain map may comprise a quantitative
`
`abnormality map. The brain map may comprise a percent abnormality map.
`
`[0034]
`
`In an aspect, a non-transitory computer-readable medium may comprise machine-
`
`executable code that, upon execution by one or more computer processors, implements a method
`
`for detecting a disorder state of brain tissue in a brain of a subject, the method comprising:
`
`obtaining magnetic resonance imaging (MRI) data comprising at least one MRI image of the tissue,
`
`8
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`WO 2018/106713
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`PCT/USZOl7/064745
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`the MRI image comprising a plurality of voxels, a voxel of the plurality of voxels being associated
`
`with the tissue of the subject and comprising one or more measured 1V[RI parameters in the IVERI
`
`data, (b) for the voxel of the plurality of voxels, using one or more computer processors to process
`
`the one or more measured MRI parameters with one or more simulated MRI parameters for the
`
`voxel, the one or more simulated MRI parameters being generated from one or more
`
`microstructural models at the voxel; (c) for the voxel of the plurality of voxels, selecting a
`
`diagnostic model from the one or more microstructural models, the diagnostic model meeting a
`
`threshold congruence between the one or more measured MRI parameters and the one or more
`
`simulated MRI parameters associated with the diagnostic model; and (d) for the voxel of the
`
`plurality of voxels, using the diagnostic model to determine the disorder state of the tissue
`
`associated with the voxel.
`
`[0035]
`
`The tissue may be selected from the group consisting of: spinal cord tissue, heart tissue,
`
`vascular tissue, lung tissue, liver tissue, kidney tissue, esophageal tissue, stomach tissue, intestinal
`
`tissue, pancreatic tissue, thyroid tissue, adrenal tissue, spleen tissue, lymphatic tissue, appendix
`
`tissue, breast tissue, bladder tissue, vaginal tissue, ovarian tissue, uterine tissue, penile tissue,
`
`testicular tissue, prostatic tissue, skeletal muscle tissue, skin, and non—brain tissue of the head and
`
`neck.
`
`[0036]
`
`In an aspect, a system for determining a disorder state ofbrain tissue in a brain of a
`
`subject may comprise: (a) a database comprising magnetic resonance imaging (MRI) data
`
`comprising at least one MRI image of the brain, the MRI image comprising a plurality of voxels, a
`
`voxel of the plurality of voxels being associated with the brain tissue of the brain of the subject and
`
`comprising a measured MRI parameter in the MRI data; and (b) one or more computer processors
`
`operatively coupled to the database, wherein the one or more computer processors are individually
`
`or collectively programmed to: (i) for the voxel of the plurality of voxels, use one or more
`
`computer processors to process the one or more measured MRI parameters with one or more
`
`simulated MRI parameters for the voxel, the one or more simulated MRI parameters being
`
`generated from one or more microstructural models at the voxel; (ii) for the voxel of the plurality of
`
`voxels, select a diagnostic model from the one or more microstructural models, the diagnostic
`
`model meeting a threshold congruence between the one or more measured MRI parameters and the
`
`one or more simulated MRI parameters associated with the diagnostic model, and (iii) for the voxel
`
`of the plurality of voxels, use the diagnostic model to determine the disorder state of the brain
`
`tissue associated with the voxel.
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`

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`WO 2018/106713
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`PCT/USZOl7/064745
`
`[0037]
`
`Each voxel may comprise a plurality of measured MRI parameters. The one or more
`
`measured MRI parameters may be a plurality of measured lVIRI parameters. The one or more
`
`simulated MRI parameter may be a plurality of simulated MRI parameters.
`
`[0038]
`
`The one or more computer processors may be further individually or collectively
`
`programmed to repeat (i)-(iii) one or more times for additional voxels of the plurality of voxels.
`
`The one or more computer processors may be further individually or collectively programmed to
`
`repeat (i)—(iii) for all other voxels of the plurality of voxels. The one or more computer processors
`
`may be further individually or collectively programmed to repeat (i)-(iii) for all voxels associated
`
`with a specified region of the brain. The one or more computer processors may be further
`
`individually or collectively programmed to repeat (i)-(iii) for all voxels associated with an entirety
`
`of the brain. The one or more computer processors may be further individually or collectively
`
`program