U.S. Serial No. 15/987,794
`Office Action mailed March 14, 2019
`Response to Office Action filed April 30, 2019
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`AMENDMENTS TO THE CLAIMS
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`This listing of claims will replace all prior versions, and listings of claims in the
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`application.
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`1.
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`(Currently Amended) A computer-implemented method for determining a disorder state
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`of brain tissue in a brain of a subject, comprising:
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`(a) obtaining magnetic resonance imaging (MRI) data comprising at least one MRI
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`image of the brain, the MRI image comprising a plurality of voxels, a voxel of the
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`plurality of voxels being associated with the brain tissue of the brain of the subject
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`and comprising one or more measured MRI parameters in the MRI data,
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`(b) for the voxel of the plurality of voxels, using one or more computer processors to
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`process the one or more measured MRI parameters with one or more simulated
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`MRI parameters for the voxel, the one or more simulated MRI parameters being
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`generated from one or more microstructural models at the voxel, wherein the one
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`or more microstructural models comprise predicted values of at least one
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`parameter from at least one machine learning procedure, wherein the at least one
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`parameter is selected from the group consisting of: cell density, cell shape, cell
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`geometry, cell size, cell distribution, intercellular spacing, extracellular matrix
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`composition, extracellular matrix distribution, extracellular matrix homogeneity,
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`.1—
`and interstitial tortuosity within the voxel which one or more microstructural
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`models are not generated from the MRI data,
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`(c) for the voxel of the plurality of voxels, selecting a diagnostic model from the one
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`or more microstructural models, the diagnostic model meeting a threshold
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`congruence between the one or more measured MRI parameters and the one or
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`more simulated MRI parameters associated with the diagnostic model, and
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`(d) using the diagnostic model to generate an output indicative of the disorder state of
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`the brain tissue associated with at least the voxel.
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`2.
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`3.
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`(Canceled)
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`(Previously Presented) The method of claim 1, wherein the one or more measured MRI
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`parameters are a plurality of measured MRI parameters.
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`10708196_1.docx
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`U.S. Serial No. 15/987,794
`Office Action mailed March 14, 2019
`Response to Office Action filed April 30, 2019
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`(Previously Presented) The method of claim 3, wherein the one or more simulated MRI
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`parameters are a plurality of simulated MRI parameters.
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`(Canceled)
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`(Previously Presented) The method of claim 1, further comprising repeating (b)-(d) for all
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`other voxels of the plurality of voxels.
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`(Previously Presented) The method of claim 1, further comprising repeating (b)-(d) for all
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`voxels associated with a specified region of the brain to determine disorder states across
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`the brain tissue associated with the specified region of the brain of the subject.
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`(Previously Presented) The method of claim 1, further comprising repeating (b)-(d) for all
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`voxels associated with an entirety of the brain to determine disorder states of the brain
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`tissue associated with the entirety of the brain of the subject.
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`(Previously Presented) The method of claim 1, further comprising repeating (a)—(d) for a
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`plurality of MRI images, each MRI image of the plurality of MRI images associated with
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`a brain selected from a plurality of brains, each brain of the plurality of brains associated
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`with a subject selected from a plurality of subjects, to determine disorder states of the
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`brain tissue associated with the plurality of subjects.
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`(Previously Presented) The method of claim 1, wherein the MRI image is selected from
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`the group consisting of: a longitudinal relaxation time (Tl)-weighted MRI image, a
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`transverse relaxation time (T2)—weighted MRI image, and a diffusion-weighted MRI
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`image.
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`(Previously Presented) The method of claim 10, wherein the one or more measured MRI
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`parameters are selected from the group consisting of: a longitudinal relaxation time (T1),
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`a transverse relaxation time (T2), and a diffusion coefficient.
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`(Canceled)
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`(Previously Presented) The method of claim 1, wherein the one or more microstructural
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`models comprise information regarding at least one_parameter selected from the group
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`consisting of: intracellular content, extracellular content, distribution of extracellular
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`content within interstitial space, distribution of intracellular content within intracellular
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`10.
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`ll.
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`12.
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`13.
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`space, and tissue geometry.
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`14.
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`(Canceled)
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`10708196_1.docx
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`U.S. Serial No. 15/987,794
`Office Action mailed March 14, 2019
`Response to Office Action filed April 30, 2019
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`15.
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`l6.
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`17.
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`18.
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`19.
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`20.
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`21.
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`22.
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`23.
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`24.
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`25.
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`(Previously Presented) The method of claim 1, wherein the one or more microstructural
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`models are selected from a microstructural model library.
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`(Canceled)
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`(Previously Presented) The method of claim 15, wherein the microstructural model
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`library is constructed by:
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`(a)
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`creating a first microstructural model corresponding to a brain state that is not
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`associated with a disorder; and
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`(b)
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`iteratively subjecting the first microstructural model to a perturbation, each
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`iteration producing an additional perturbed microstructural model.
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`(Canceled)
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`(Canceled)
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`(Previously Presented) The method of claim 17, wherein the perturbation comprises an
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`operation selected from the group consisting of: depleting cells, altering cellular
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`morphology or distribution, altering intracellular or interstitial physico-chemical
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`composition or distribution, altering extracellular matrix composition or distribution, and
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`altering intercellular spacing.
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`(Previously Presented) The method of claim 20, wherein the perturbation comprises a
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`stochastic procedure.
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`(Previously Presented) The method of claim 21, wherein the threshold congruence is
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`determined by computing an objective function between the one or more measured MRI
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`parameters and the one or more simulated MRI parameters.
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`(Previously Presented) The method of claim 22, wherein the objective function comprises
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`an L1 norm or an L2 norm.
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`(Previously Presented) The method of claim 1, wherein determining the disorder state of
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`the brain tissue associated with the voxel is achieved at an accuracy of at least 90%.
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`(Previously Presented) The method of claim 7, wherein determining the disorder states
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`across the brain tissue associated with the specified region of the brain is achieved at an
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`accuracy of at least 90%.
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`U.S. Serial No. 15/987,794
`Office Action mailed March 14, 2019
`Response to Office Action filed April 30, 2019
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`26.
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`27.
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`28.
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`29.
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`30.
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`31.
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`32.
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`33.
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`34.
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`35.
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`(Previously Presented) The method of claim 8, wherein determining the disorder states of
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`the brain tissue associated with the entirety of the brain of the subject is achieved at an
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`accuracy of at least 90%.
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`(Previously Presented) The method of claim 9, wherein determining the disorder states of
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`the brain tissue associated with the plurality of subjects is achieved at an accuracy of at
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`least 90%.
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`(Previously Presented) The method of claim 1, wherein the disorder is a non-
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`neurodegenerative disorder.
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`(Previously Presented) The method of claim 28, wherein the disorder is selected from the
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`group consisting of: a primary neoplasm, a metastatic neoplasm, a motor neuron disease,
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`a seizure disorder, a seizure disorder with focal cortical dysplasia, multiple sclerosis, a
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`non-neurodegenerative encephalopathy, and a psychological disorder.
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`(Previously Presented) The method of claim 1, wherein the disorder is a
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`neurodegenerative disorder.
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`(Previously Presented) The method of claim 30, wherein the method enables diagnosis of
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`a neurodegenerative disorder more than 5 years prior to the development of symptoms
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`associated with the neurodegenerative disorder.
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`(Previously Presented) The method of claim 30, wherein the method enables monitoring
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`of the neurodegenerative disorder at a plurality of time points, the plurality of time points
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`separated by a plurality of time intervals.
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`(Previously Presented) The method of claim 30, wherein the neurodegenerative disorder
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`is selected from the group consisting of: Alzheimer’s disease, a non-Alzheimer’s
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`dementia disorder, Parkinson’s disease, a Parkinsonism disorder, a motor neuron disease,
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`Huntington’s disease, a Huntington’s disease-like syndrome, transmissible spongiform
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`encephalopathy, chronic traumatic encephalopathy, and a tauopathy.
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`(Previously Presented) The method of claim 1, further comprising constructing a brain
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`map that, for each voxel of the plurality of voxels, indicates the disorder state of the brain
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`tissue associated with the voxel.
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`(Previously Presented) The method of claim 34, further comprising displaying the brain
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`map on a graphical user interface of an electronic device of a user.
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`U.S. Serial No. 15/987,794
`Office Action mailed March 14, 2019
`Response to Office Action filed April 30, 2019
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`36.
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`37.
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`38.
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`39.
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`40.
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`41.
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`42.
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`(Previously Presented) The method of claim 34, wherein the brain map is selected from
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`the group consisting of: a qualitative abnormality map, a binary abnormality map, a
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`quantitative abnormality map, and a percent abnormality map.
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`(Canceled)
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`(Canceled)
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`(Canceled)
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`(Canceled)
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`(Canceled)
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`(New) The method of claim 1, further comprising, prior to (b), using the at least one
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`machine learning procedure to generate the predicted values of the at least one parameter.
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`10708196_1.d0cx
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`WSGR Docket No. 53242-701301
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