Brain imaging

Abstract

To provide, generally, medical imaging and, more particularly, methods and systems for performing processing of magnetic resonance (MR) imaging of the brain which may be useful in the diagnosis of cognitive disorders.SOLUTION: The invention includes methods for processing cortical diffusion data from a region of a subject's brain, comprising determining values for the axial columnar refraction (ACR) using values for AngleR and axial diffusivity.SELECTED DRAWING: Figure 1B

Description

[Technical Field]

[0001] This disclosure generally relates to medical imaging, and more particularly to methods and systems for performing magnetic resonance (MR) imaging of the brain, which may be useful in the diagnosis of cognitive impairment. [Background technology]

[0002] The diagnosis and treatment of dementia are becoming increasingly important as the population ages. Currently, dementia affects more than 830,000 people in the UK. However, given the difficulty in accurately diagnosing these disorders, the actual proportion of people affected may be much higher. There are many recognized forms of dementia. These include Alzheimer's disease (AD), cerebrovascular disease (CVD), frontotemporal dementia (FTD), and dementia with Lewy Bodies (DLB). Mild cognitive impairment (MCI) is considered a precursor to dementia.

[0003] Current diagnostic methods typically rely on clinical screening tools, usually in the form of cognitive tests and assessments of behavioral symptoms. Currently, standard structural brain MRI may often be required to look for evidence of qualitative (i.e., visually apparent) reduction of hippocampal volume, ventricular enlargement, and the appearance of enlarged sulcus folding in the cerebral cortex. This assessment is subjective and nonspecific, and therefore, while it provides further evidence, it is not diagnostic in itself. Differential diagnosis of AD from CVD usually relies on a clinical assessment of the disease course; in AD, progressive cognitive decline is gradual, as opposed to "stepwise" (where a rapid decline is interrupted by a relatively stable "plateau"). Clearly, this too is subjective and subject to interpretation.

[0004] Current cognitive testing typically involves the MMSE (Mini-Mental State Examination), in which "healthy" is often considered to be a score >24, with MCI (Mild Cognitive Impairment) being between 21 and 24, and dementia being 20 or less. However, these boundaries are subject to change and further depend on interpretation. Some consider a score below 30 to correspond to MCI. As a further test, the MoCA (Montreal Cognitive Assessment) has recently been found to detect CVD-type cognitive changes that may be missed by the MMSE. However, it does not provide a differential diagnosis.

[0005] Currently, Alzheimer's disease and other forms of dementia may only be definitively diagnosed by post-mortem biopsy. The precise biochemical processes involved are not well understood as providing accurate alternatives to post-mortem examination. Furthermore, most existing measurements of neuropathology in dementia rely on the evaluation of plaques, entanglements, or individual cells and synapses, which are at the microscopic level and therefore cannot be detected using conventional non-invasive brain imaging.

[0006] Since neuropathological changes that occur in dementia are thought to begin much earlier than the onset of symptoms, early diagnosis of these symptoms is particularly important for effective clinical interventions to stop or slow the progression of the disease.

[0007] A further challenge in this medical field is that, despite several common risk factors, the clinical course and available treatment approaches differ between different types of dementia, such as Alzheimer's disease (AD) and cardiovascular disease (CVD). While cognitive tests provide indicators of mental function decline, it is difficult to differentiate between different types of dementia using currently available tools. Therefore, being able to distinguish between different types of dementia is particularly important from a clinical standpoint, so that an appropriate set of actions and treatments can be implemented.

[0008] Currently, biomarker detection is i) Invasive methods involving CSF or blood (which carry risks to the patient) ii) Invasive methods for imaging molecular markers in the brain (which often carry risks to the patient and cannot be repeated), iii) Non-invasive brain imaging methods based on statistical calculations of population samples using standard volumetric MRI or more recent texture analysis of structural MRI (e.g., T1 or T2). It depends on.

[0009] However, there is still a need for more accurate, non-invasive methods to assess the presence and / or severity of cognitive impairment, including dementia.

[0010] Ideal biological markers are sensitive to age-related microstructural changes and accessible through non-invasive methods such as neuroimaging. Instead of typical biochemical markers for AD, amyloid, and tau, microanatomical changes in cortical cell structure and neural networks remain untouched elements for tracking brain structural decline in later life (Esiri & Chance, 2006). Recent studies have found that cellular microcircuits called minicolumns, which constitute fundamental structural motifs across the cerebral cortex, are altered in a stepwise manner in healthy aging, mild cognitive impairment (MCI), and Alzheimer's disease (AD) (Chance et al., 2011). This microscopic erosion of columnar architecture correlates with cognitive decline and with conventional markers of AD pathology, such as plaque load (Chance et al., 2006). It is detectable independently of cortical volume, suggesting that locally specific changes in minicolumnar architecture may be associated with different pathologies (Opris & Casanova, 2014).

[0011] These changes can be non-invasively quantified using data derived from MRI scanning and imaging methods applied to the subject's brain, including but not limited to diffusion tensor imaging (DTI), Fine Structure Analysis (fineSA®; see www.acuitasmedical.com / technology.php), and other MRI acquisition methods. The data derived from these imaging studies can then be compared to predicted change patterns identified from patients with confirmed diagnoses of such conditions. Thus, this comparison of data acquired from subjects of interest with modeled data derived from patients with confirmed diagnoses is useful for evaluating and diagnosing different types of dementia or other cognitive impairments in living subjects, including early-stage dementia.

[0012] It has been previously found that signature patterns of minicolumnar changes within specific brain regions associated with neuropathological conditions actually exist and correlate with cognitive ability and decline, and that diffusion MRI measurements, particularly DTI, can be used to evaluate minicolumnar structures in the brain (WO2016 / 162682).

[0013] While DTI is sensitive to white matter microstructures, it has limited applications as a typical method for in vivo neuroimaging of cortical gray matter microstructures.

[0014] The rationale for this invention is the use of tissue biopsies and postmortem MRI to study novel non-invasive neuroimaging biomarkers and their correlation with specific elements of the ultrastructure of the cerebral cortex, with the intention that such methods may be used in living subjects to evaluate the progression of cellular structural changes in late-stage aging and dementia. [Overview of the project] [Problems that the invention aims to solve]

[0015] The present invention provides novel measurements derivable from MRI as shown herein that are sensitive to aging and changes associated with dementia in cortical microstructures using patients with confirmed postmortem diagnostic results. The present invention may be particularly useful in providing methods for diagnosing or staging Alzheimer's disease and other dementias using microstructural brain changes. The present invention may also be applicable to other cognitive impairments or neurological symptoms in which structural changes exist within the brain.

Means for Solving the Problems

[0016] In one embodiment, the present invention is a method of processing cortical diffusion data from a region of a subject's brain, the method comprising: (a) obtaining a value for the angle of deviation (AngleR) between the principal diffusion direction and the columnar direction (ColD) of the minicolumn in a first voxel in the gray-white matter region in the subject's brain; (b) obtaining a value for the axial diffusivity in a second voxel present in the white matter underlying the region of grey matter; and, (c) using the values for AngleR and axial diffusivity to specify a value for axial columnar refraction (ACR) for the voxel. A method is provided that includes the above steps.

[0017] Preferably, step (a) comprises: (a1) specifying the principal diffusion direction in a voxel in the gray-white matter region in the subject's brain; (a2) specifying the columnar direction (ColD) of the minicolumn in the voxel; (a3) Obtain a value for the deviation angle (AngleR) between the main diffusion direction and ColD, Includes.

[0018] In some embodiments, the present invention relates to a method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining ACR values ​​for multiple voxels in a region of the subject's brain using the method of the present invention. Includes, The magnitude of the ACR value for those voxels provides an indication of the level of cognitive impairment in the subject. Provide a method.

[0019] In another embodiment, the present invention relates to a method for obtaining an indication of the number of microsegment breaks in a region of a subject's brain, wherein the method is (a) A step of obtaining ACR values ​​for multiple voxels in a region of the subject's brain using the method of the present invention. Includes, The magnitude of the ACR value for those voxels indicates the number of microsegment breaks in that region. Provide a method.

[0020] The present invention relates to a method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining a value for the angle of deviation (AngleR) between the principal diffusion direction and the mean column direction (ColD) of the minicolumn in multiple voxels in a brain region of the subject, wherein the brain region is cortical area 9, PHG, or entorhinal cortex, Includes, The magnitude of the AngleR value from multiple voxels provides an indication of the level of cognitive impairment in the subject. Further methods will be provided.

[0021] The present invention relates to a method for obtaining a marking of the level of MS (multiple sclerosis) in a subject, wherein the method is (a) A step of obtaining values ​​for the angle of deviation (AngleR) between the principal diffusion direction and the mean column direction (ColD) of the minicolumn for multiple voxels in a region of the subject's brain. Includes, Preferably, the brain region is cortical area 9, area 41, or V1 (primary visual cortex). The magnitude of the AngleR value from multiple voxels provides an indication of the MS level in that subject. Further methods will be provided.

[0022] The present invention relates to a method for obtaining an indication of the number of microsegment breaks in a region of the brain of a subject, wherein the method is (a) A step of obtaining values ​​for the angle of deviation (AngleR) between the principal diffusion direction and the mean column direction (ColD) of the minicolumn for multiple voxels in a region of the subject's brain. Includes, The magnitude of the AngleR value from multiple voxels indicates the number of microsegment breaks in that region. Further methods will be provided.

[0023] Preferably, the brain region is cortical area 9, PHG, or entorhinal cortex. Preferably, step (a) is (a1) A step of identifying the principal diffusion direction in multiple voxels in a region of the subject's brain, (a2) A step of determining the column direction (ColD) of the minicolumn in the voxel, (a3) A step of determining the value for the deviation angle (AngleR) between the principal diffusion direction and ColD for each voxel, Includes.

[0024] In another embodiment, the present invention relates to a method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining values ​​for axial diffusion in multiple voxels in a region of the subject's brain. Includes, The brain region is cortical area 9, or PHG, or entorhinal cortex. The magnitude of the axial diffusion value from multiple voxels provides an indication of the level of cognitive impairment in the subject. Provide a method.

[0025] In another embodiment, the present invention relates to a method for obtaining an indication of the number of microsegment breaks in a region of a subject's brain, wherein the method is (a) The step includes obtaining values ​​for axial diffusion in multiple voxels in a region of the subject's brain, The magnitude of the axial diffusion value from multiple voxels provides an indication of the number of microsegment breaks in that region. Provide a method.

[0026] Preferably, the brain region is cortical area 9, or PHG, or the entorhinal cortex.

[0027] In another embodiment, the present invention relates to a method for processing cortical diffusion data from a region of the brain of a subject, wherein the method is (a) Obtain the value for perpendicular diffusion (Perp) in the first voxel in the gray matter region of the subject's brain, (b) Obtain values ​​for axial diffusion in the second voxel present in the white matter underlying the gray matter region, (c) Using the values ​​for Perp and axial diffusion, determine the value for Perpendicular Columnar Refraction (PerpCR) relative to the voxel, This provides a method that includes [something].

[0028] Preferably, step (a) is (a1) A step of obtaining measurement results for cortical diffusion in the first voxel, (a2) Principal diffusion vector in a voxel (D PDD ) is identified from the measurement results for cortical diffusion obtained in step (a1), (a3) A step of determining the column direction (ColD) of the minicolumn in the voxel, (a4) D on a plane perpendicular to ColD in that voxel PDD The steps involve projecting and determining the magnitude of the projection to determine the value for the vertical diffusion (Perp) relative to the voxel, Includes.

[0029] In another embodiment, the present invention relates to a method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining vertical column refraction (PerpCR) values ​​for multiple voxels in a region of the subject's brain using the method of the present invention. Includes, The magnitude of the vertical column refraction (PerpCR) value for those voxels provides an indication of the level of cognitive impairment in the subject. Provide a method.

[0030] In another embodiment, the present invention relates to a method for obtaining an indication of the number of microsegment breaks in a region of the brain of a subject, wherein the method is (a) A step of obtaining vertical column refraction (PerpCR) values ​​for multiple voxels in a region of the subject's brain using the method of the present invention. Includes, The magnitude of the vertical column refraction (PerpCR) value for those voxels indicates the number of microsegment breaks in that region. Provide a method.

[0031] In another embodiment, the present invention relates to a method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining values ​​for vertical diffusion (Perp) in multiple voxels in a brain region of the subject, wherein the brain region is cortical area 9, or PHG, or entorhinal cortex. Includes, The magnitude of Perp values ​​from multiple voxels provides an indication of the level of cognitive impairment in the subject. To provide a method.

[0032] In another embodiment, the present invention relates to a method for obtaining an indication of the number of microsegment breaks in a region of the brain of a subject, wherein the method is (a) A step of obtaining values ​​for vertical diffusion (Perp) in multiple voxels in a region of the subject's brain. Includes, The magnitude of the Perp value from multiple voxels indicates the number of microsegment breaks in that region. Provide a method.

[0033] Preferably, the brain region is cortical area 9, or PHG, or entorhinal cortex. Preferably, step (a) is performed for each voxel, (a1) A step of obtaining measurement results for cortical diffusion in voxels in a region of the subject's brain, (a2) Main diffusion vector in voxel (D PDD ) is identified from the measurement results for cortical diffusion obtained in step (a1), (a3) A step of determining the column direction (ColD) of the minicolumn in the voxel, (a4) D on a plane perpendicular to ColD in that voxel PDD The steps involve projecting and determining the magnitude of the projection to determine the value for the vertical diffusion (Perp) relative to the voxel, Includes.

[0034] The method of the present invention is preferably a computer-based method. Each step of the method of the present invention may be implemented by a computing device. Preferably, diffusion data is acquired using a medical imaging device, including a magnetic resonance (MR) scanner. The present invention is an ex vivo method.

[0035] In one embodiment, the present invention provides a method for processing cortical diffusion data from a region of a subject's brain. The cortical diffusion data may include one or more of AngleR, axial diffusion, Perp, and other optional parameters. The cortical diffusion data is preferably acquired by a neuroimaging method. Most preferably, the cortical diffusion data is magnetic resonance (MR) relaxation time measurement data. The diffusion data may be measured directly from an MRI scan of the subject's brain or from MRI data previously acquired from the subject's brain. In some embodiments, the cortical diffusion data may be values ​​derived from individual brain MRI measurement results using formulas, algorithms, databases, and / or reference tables. In a preferred embodiment, the cortical diffusion data is derived from MRI measurement results acquired from the brain or from brain images.

[0036] In all embodiments of the present invention, values, measurement results, etc., obtained from the subject's brain may be obtained from the subject's brain as part of the method of the present invention, or they may be values, measurement results, etc., previously obtained from the subject's brain.

[0037] In some preferred embodiments of the present invention, cortical diffusion data are obtained by diffusion MRI. As used herein, the term "diffusion MRI" refers to any magnetic resonance imaging (MRI) method that measures the diffusion process of molecules, preferably water molecules, in biological tissue. Diffusion MRI may also be called diffusion tensor imaging (DTI) or diffusion-weighted imaging.

[0038] Cortical diffusion data may be combined with T1, T2, or T2* mapping, or MRI measurements may be combined with T1, T2, or T2* localized to the brain.

[0039] In some embodiments, one or more measurement results are obtained using imaging methods described in WO2013 / 040086 (the contents of which are incorporated herein by reference), or Fine Structure Analysis® (fineSA®; Acuitas Medical), or other MRI acquisition methods.

[0040] Cortical diffusion is measured in at least one voxel. A voxel is a unit of volume that defines three-dimensional space. In this context, a voxel is preferably a unit in an MRI scan that has associated values ​​for several different parameters, depending on the scan acquisition (for example, a voxel may include gray matter intensity values ​​and the associated principal diffusion direction).

[0041] The method of the present invention is applied to one or more regions of the subject's brain. In preferred embodiments of the present invention, the measurement results or values ​​are obtained from one or more different regions of the brain, preferably two or more, three or more, four or more, five or more, six or more, seven or more, or eight or more different regions of the brain, most preferably five or more different regions of the brain. In some embodiments, the region of the subject's brain is the entire brain.

[0042] In a preferred embodiment of the present invention, the measurement result or value is obtained or derived from one or more regions of the cerebral cortex. Preferably, the region of the subject's brain is a cortical region including gray matter with underlying (subcortical) white matter.

[0043] In a preferred embodiment, the measurement results or values ​​are obtained from one or more specific layers of the cortex, preferably from cortical layer 3, cortical layer 5, or cortical layers 3-6. Upper layers 1 and 2 may also be useful. In a more preferred embodiment, the measurement results or values ​​are obtained from cortical layers 3-6, since cortical layers 3-6 also contain axonal bundles that may be useful for DTI signal analysis.

[0044] Preferably, the brain region is selected from the group consisting of the parahippocampal gyrus (PHG), fusiform gyrus (Fusi), dorsolateral prefrontal cortex area 9 (dlPFC), Heschl's gyrus (HG), planum temporale (PT), inferior parietal lobule (IPL), middle temporal gyrus (MTG), primary visual cortex (V1; area 17), and entorhinal cortex.

[0045] In some embodiments of the present invention, the brain region is preferably cortical gray matter. In some preferred embodiments, the brain region is cortical area 9, or PHG, or entorhinal cortex. In other preferred embodiments, the brain region is cortical area 9, area 41, or primary visual cortex, V1.

[0046] In some preferred embodiments, the measurement result or value is obtained or derived from one, two, three, four, five, six, seven, or eight of the regions described above.

[0047] In preferred embodiments of the present invention, where the method is used to distinguish between AD and CVD, the measurement results or values ​​are obtained or derived from one or more regions selected from the group consisting of the parahippocampal gyrus (PHG), fusiform gyrus (Fusi), dorsolateral prefrontal cortex (dlPFC), Heschl's gyrus (HG), and temporal plane (PT). In several preferred embodiments of the present invention, where the method is used to distinguish between AD and CVD, the measurement results or values ​​are obtained from all of these regions. The use of parameters obtained or derived from all five of these regions in the method of the present invention has achieved a predictive accuracy of more than 90% for distinguishing between CVD and AD. Further regions for measurement include the frontal lobe, parietal lobe, temporal lobe, and occipital lobe.

[0048] In a preferred embodiment of the present invention in which the method is used to obtain indication of the presence of mild cognitive impairment (MCI), the measurement result or value is obtained or derived from one or more regions of the cerebral cortex, preferably from one or more regions selected from the group consisting of the parahippocampal gyrus (PHG), fusiform gyrus (Fusi), dorsolateral prefrontal cortex (dlPFC), Heschl's gyrus (HG), temporal plane (PT), inferior parietal lobule (IPL), middle temporal gyrus (MTG), and primary visual cortex (V1). In some preferred embodiments of the present invention in which the method is used to obtain indication of the presence of mild cognitive impairment, the measurement result or value is obtained from all of these regions.

[0049] In a preferred embodiment of the present invention, in which the method is used to distinguish FTD from other dementias, the measurement results or values ​​are obtained or derived from one or more regions of the cerebral cortex, preferably from one or more regions selected from the group consisting of the parahippocampal gyrus (PHG), fusiform gyrus (Fusi), dorsolateral prefrontal cortex (dlPFC), Heschl's gyrus (HG), temporal plane (PT), inferior parietal lobule (IPL), middle temporal gyrus (MTG), caudal middle frontal cortex, and V1. In the most preferred embodiment of the present invention, in which the method is used to distinguish FTD from other dementias, the measurement results or values ​​are obtained from all of these regions.

[0050] The brain regions defined herein are preferably based on Brodmann's cellular structure of the human cortex (Brodmann, 1909). Equivalents can also be found in Von Economo and Koskinas (Von Economo C, Koskinas GN (1925) Die Cytoarchitektonik der Hirnrinde des Erwachsenen Menschen. Springer, Berlin (Germany) (translated by Dr. Lee Seldon)).

[0051] The methods of the present invention may be used to distinguish other cognitive or neuropsychiatric disorders as defined below. When the methods of the present invention are used to distinguish the disorders described below, the brain regions analyzed must include one or more, more preferably all, of the corresponding brain regions described below.

[0052] Autism: Fusiform cortex, superior temporal sulcus, orbitofrontal cortex, dlPFC, inferior parietal cortex, primary visual cortex, primary auditory cortex

[0053] Schizophrenia: dlPFC, dorsomedial PFC, cingulate gyrus, superior temporal gyrus, PHG

[0054] Bipolar disorder: PHG, subgenual PFC, dlPFC, shingles

[0055] Epilepsy: Entorhinal cortex, PHG

[0056] Dyslexia: inferior parietal cortex, superior temporal gyrus

[0057] Down syndrome: Superior temporal gyrus, PHG, dlPFC

[0058] Parkinson's disease: Entorhinal cortex, cingulate gyrus

[0059] Amyotrophic lateral sclerosis: motor cortex

[0060] Huntington's disease: motor cortex, cingulate gyrus

[0061] Multiple sclerosis: motor cortex, cortical area including MS lesions identified by MRI scan.

[0062] Prion disease: Primary visual cortex, cortical areas showing volumetric reduction compared to cortical areas without recognizable reduction.

[0063] Depression: dlPFC, dorsomedial PFC, cingulate gyrus

[0064] Obsessive-compulsive disorder: Cingulate gyrus, dlPFC, dorsomedial PFC

[0065] ADHD: Orbitofrontal cortex, dlPFC, cingulate cortex

[0066] As used herein, the term “minicolumn” refers to a vertical column passing through the cortical layers of the brain. A minicolumn may also be interchangeably called a cortical minicolumn, microcolumn, or cortical microcolumn. The term “minicolumn” may be understood as a combination of a cell-dense core and the surrounding cell-sparse peripheral neuropil space, or, in some contexts, simply as a cell-dense core (defined by the cell body). Typically, it is related to both the core and the periphery.

[0067] Microsegments are fragments of columnar microstructural tissue that arise from disrupted strings of aligned cells, connecting dendrites, or axons, and are not classified as minicolumns based on insufficient structural continuity, fewer cells, or longer intercellular distances. Microsegments are histopathologically described in Chance SA et al., "Micro-anatomical correlates of cognitive ability and decline: normal aging, MCI and Alzheimer's disease," Cerebral Cortex 21(8):1870-8 (2011).

[0068] The subjects may be any animal, preferably a mammal, and most preferably a human. In some embodiments, the subjects may be individuals with cognitive impairment, preferably dementia.

[0069] In some embodiments, the subjects are individuals suffering from Alzheimer's disease (AD), vascular dementia (CVD), mild cognitive impairment (MCI), frontotemporal dementia (FTD), or Lewy body dementia (DLB). Preferably, the subjects suffer from Alzheimer's disease, FTD, CVD, or MCI.

[0070] In other embodiments, the subjects may be individuals suffering from neurological disorders associated with changes in normal brain structure.

[0071] In some embodiments, the subjects are individuals suffering from autism, multiple sclerosis (MS), epilepsy, amyotrophic lateral sclerosis (ALS), Parkinson's disease, schizophrenia, bipolar disorder, dyslexia, Down syndrome, Huntington's disease, prion disease, depression, obsessive-compulsive disorder, or attention deficit hyperactivity disorder (ADHD).

[0072] In other embodiments, subjects are individuals suffering from subjective cognitive impairment, pre-MCI, and symptoms selected from the group consisting of prodromal AD, posterior cortical atrophy (a subset of AD), behavioral, semantic, progressive non-fluent aphasia (a subset of FTD), brain injury, hepatic encephalopathy, stroke, ischemia, ischemic hypoxia, neuroinflammation, traumatic brain injury (TBI), mild TBI, chronic traumatic encephalopathy, concussion, and mental confusion.

[0073] In some embodiments, the subjects are older than 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, or 90 years of age. In other embodiments, the subjects are between 5 and 100, 10 and 100, 20 and 100, 30 and 100, 40 and 100, 50 and 100, 60 and 100, 70 and 100, 80 and 100, or 90 and 100 years of age. In other embodiments, the subjects are between 1 and 5, 5 and 10, 10 and 20, 20 and 30, 30 and 40, 40 and 50, 50 and 60, 60 and 70, 70 and 80, 80 and 90, or 90 and 100 years of age.

[0074] In some embodiments, the subject is not a fetus.

[0075] Some embodiments of the present invention include the step of obtaining measurement results for cortical diffusion in voxels in a region of the subject's brain.

[0076] Cortical diffusion data can be obtained de novo, i.e., directly from the subjects. Alternatively, cortical diffusion data may be previously obtained from the subjects. In the latter case, the data may be obtained from graphs, reference tables, databases, or mathematical equations.

[0077] Some embodiments of the present invention relate to the principal diffusion direction or principal diffusion vector (D) in voxels in a region of the subject's brain. PDDThe process includes the step of identifying the principal diffusion vector (D). In this context, diffusion refers to the diffusion of water through microstructures in brain tissue (including minicolumns). The principal diffusion vector is a standard measurement result in DTI and is an eigenvector corresponding to the largest eigenvalue of the diffusion tensor. The tensor is essentially a covariance matrix of a 3D Gaussian distribution that models the Brownian motion of water molecules within a voxel. Preferably, the principal diffusion direction or vector (D PDD This is obtained from the measurement results for cortical diffusion.

[0078] Some embodiments of the present invention include the step of identifying or estimating the column direction (ColD) of a minicolumn in a voxel. As used herein, the terms "ColD" and "CRadial" may be used interchangeably. Furthermore, the terms "radial column direction," "radial direction," and "column direction" may also be used interchangeably.

[0079] This identification or estimation may be based on a cortical profile that extends across the cortex. It is a mathematical estimation of minicolumnar orientation derived from a typical healthy minicolumnar structure based on tissue structure. It is estimated for each region and each brain based on anatomical features. ColD may be de novo, i.e., obtained directly from the subject, or ColD may be previously obtained from the subject or from other subjects. In the latter case, data may be obtained from graphs, reference tables, databases, or mathematical equations, etc. Typical voxels (e.g., 1 mm) 3 This means that there will be many mini-columns within the voxel. Therefore, the ColD value for a voxel will be the average value.

[0080] Some embodiments of the present invention involve ColD and D for voxels. PDD The procedure includes determining the column misalignment angle (AngleR) between the direction and the direction. AngleR is measured at the individual voxel level. AngleR is determined at the gray matter voxel level. It is measured in radians.

[0081] In some embodiments of the present invention, the value for axial diffusion is obtained for a second voxel. The second voxel is present in the white matter of the brain.

[0082] As used herein, the term "axial diffusion" refers to diffusion along the principal diffusion axis (λ1). Axial diffusion is also known as longitudinal diffusion. Axial diffusion is measured in mm 2 per second.

[0083] The value for axial diffusion is obtained in a second voxel (or set of second voxels) present in the white matter underlying the gray matter region (including the first voxel or set of first voxels).

[0084] The voxels in the gray matter used to derive AngleR should generally overlap the corresponding set of voxels in the subcortical white matter used to derive axial diffusion. The first voxel and the second voxel must be in the same or connected brain regions.

[0085] The first voxel must form a set of continuous or semi - continuous gray matter voxels within a defined cortical region. The second voxel must form a set of continuous or semi - continuous white matter voxels within the same overall brain region or a brain region with known connections to the cortical region.

[0086] In some embodiments, the first voxel and / or the second voxel form a set of continuous or semi - continuous voxels that form a continuous or discontinuous arc across the cortex of the subject's brain.

[0087] The second voxel is located below the first voxel in the radial direction. It does not need to be adjacent, as boundary regions can be excluded. The second voxel (or set of voxels) is typically located in the white matter of the same brain region as the gray matter containing the first voxel (or set of voxels), based on anatomical knowledge information or on automatic region assignment using probabilistic brain maps, based on a previous database.

[0088] Some embodiments of the present invention include the step of using values ​​for AngleR and AxD to determine a value for axial column refraction (ACR) for one or more voxels.

[0089] Axial columnar refraction is a value derived from the values ​​for AngleR and axial diffusion. Axial columnar refraction is a value that represents the number and density of microsegments, the degree of abnormal cortical minicolumnar structures in the subject's brain, and consequently, the degree of cognitive impairment in the subject, and is directly (preferably positively) correlated with these.

[0090] In some embodiments, ACR is the sum or weighted sum of AngleR and axial diffusion, i.e., (k1 × AngleR) + (k2 × axial diffusion), where k1 and k2 are independent positive numbers, which may be the same or different. Most preferably, ACR is the result of multiplying AngleR and axial diffusion (i.e., AngleR × axial diffusion), or a multiple thereof. ACR can be assigned to a first voxel and / or a second voxel.

[0091] In some embodiments, gray matter voxels can "match" (i.e., combine) with more than one white matter voxel (or vice versa). In this case, the average is calculated.

[0092] In some embodiments, the method includes obtaining or specifying values ​​for both AngleR and axial diffusion in a plurality of voxels. For example, values ​​for AngleR and axial diffusion may be obtained or specified independently in 1 to 10,000, preferably 50 to 6,000, and more preferably 100 to 500 voxels. For whole-brain analysis, values ​​for AngleR may be obtained from 140,000 to 180,000, preferably about 160,000 gray matter voxels and combined with values ​​for axial diffusion in the same number of white matter voxels.

[0093] Optionally, the average value of the axial column refraction (ACR) for selected voxels is determined using the average values ​​of AngleR and axial diffusion for each of those voxels. Alternatively, some other mathematical function of AngleR and axial diffusion (e.g., a weighted sum of AngleR and axial diffusion) may be selected to provide a value for ACR.

[0094] In some embodiments, the present invention relates to a method for obtaining an indication of the level of cognitive impairment in a subject, (a) A step of obtaining ACR values ​​for multiple voxels in a region of the subject's brain using the method of the present invention. Includes, The magnitude of the ACR value for those voxels provides an indication of the level of cognitive impairment in the subject. Provide a method.

[0095] The ACR value may be used to distinguish between healthy controls and subjects with cognitive impairment, such as those with Alzheimer's disease.

[0096] As used herein, the term “cognitive impairment” refers to any mental health disorder affecting learning, memory, perception, and / or problem-solving. In preferred embodiments of the present invention, cognitive impairment may be any form of dementia.

[0097] Preferably, the cognitive impairment is selected from the group consisting of Alzheimer's disease (AD), vascular dementia (CVD), mild cognitive impairment (MCI), frontotemporal dementia (FTD), and Lewy body dementia (DLB).

[0098] In other embodiments, the cognitive impairment is a neurological disorder associated with changes in normal brain structure, and may preferably be a neurological disorder selected from the group consisting of autism, multiple sclerosis (MS), epilepsy, amyotrophic lateral sclerosis (ALS), and Parkinson's disease.

[0099] In other embodiments, the cognitive impairment is preferably a neuropsychiatric disorder, most preferably selected from the group consisting of autism, schizophrenia, bipolar disorder, epilepsy, dyslexia, Down syndrome, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease, multiple sclerosis, prion disease, depression, obsessive-compulsive disorder, and attention deficit hyperactivity disorder (ADHD).

[0100] In other embodiments, cognitive impairment includes subjective cognitive impairment, pre-MCI, and prodromal AD; posterior cortical atrophy (a subset of AD); behavioral, semantic, progressive non-fluent aphasia (a subset of FTD); brain injury, hepatic encephalopathy; stroke; ischemia, ischemic hypoxia; neuroinflammation; traumatic brain injury (TBI), mild TBI, chronic traumatic encephalopathy, concussion; and mental confusion.

[0101] The level of dementia can be quantified using the Braak stage (e.g., Braak H, Braak E. 1991. Neuropathological staging of Alzheimer-related changes. Acta Neuropathol. 82:239-259).

[0102] In another embodiment, the present invention relates to a method for obtaining an indication of the number of microsegment breaks in a region of the brain of a subject, wherein the method is (a) A step of obtaining ACR values ​​for multiple voxels in a region of the subject's brain using the method of the present invention. Includes, The magnitude of the ACR value for those voxels indicates the number of microsegment breaks in that region. Provide a method.

[0103] This specification shows that ACR positively correlates with the number of microsegment breaks in that brain region. The number of microsegment breaks in a given brain region indicates the density of microsegment breaks in that region.

[0104] In yet another embodiment, the present invention relates to a method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining a value for the angle of deviation (AngleR) between the principal diffusion direction and the mean column direction (ColD) of the minicolumn in multiple voxels in a brain region of the subject, wherein the brain region is cortical area 9, or PHG, or entorhinal cortex. Includes, The magnitude of the AngleR value from multiple voxels provides an indication of the level of cognitive impairment in the subject. Provide a method.

[0105] This specification shows that AngleR is positively correlated with the number of microsegment breaks in that brain region, and that this represents the level of cognitive impairment.

[0106] In yet another embodiment, the present invention relates to a method for obtaining an indication of the number of microsegment breaks in a region of the brain of a subject, wherein the method is (a) A step of obtaining values ​​for the angle of deviation (AngleR) between the principal diffusion direction and the mean column direction (ColD) of the minicolumn for multiple voxels in a region of the subject's brain. Includes, The magnitude of the AngleR value from multiple voxels indicates the number of microsegment breaks in that region. Provide a method.

[0107] Preferably, the brain region is cortical area 9, or PHG, or entorhinal cortex. It has been shown herein that AngleR is positively correlated with the number of microsegment breaks in cortical area 9 and PHG.

[0108] In yet another different embodiment, the present invention relates to a method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining values ​​for axial diffusion in multiple voxels in a brain region of the subject, wherein the brain region is cortical area 9, or PHG, or entorhinal cortex. Includes, The magnitude of the axial diffusion value from multiple voxels provides an indication of the level of cognitive impairment in the subject. Provide a method.

[0109] This specification shows that axial diffusion positively correlates with the number of microsegment breaks in cortical area 9 or PHG, and that this represents the level of cognitive impairment.

[0110] In yet another different embodiment, the present invention relates to a method for obtaining an indication of the number of microsegment breaks in a region of the brain of a subject, wherein the method is (a) A step of obtaining values ​​for axial diffusion in multiple voxels in a region of the subject's brain. Includes, The magnitude of the axial diffusion value from multiple voxels provides an indication of the number of microsegment breaks in that region. Provide a method.

[0111] This specification shows that axial diffusion is positively correlated with the number of microsegment breaks. Preferably, the brain region is cortical area 9, or PHG, or entorhinal cortex.

[0112] Some embodiments of the present invention include the step of determining a value for vertical column refraction (PerpCR) relative to a voxel using values ​​for axial diffusion and Perp. Vertical column refraction (PerpCR) is a value derived from the values ​​for axial diffusion and Perp. Vertical column refraction (PerpCR) is a value that represents and is positively correlated with the number and density of microsegments, the degree of abnormal cortical minicolumnar structures in the subject's brain, and consequently, the degree of cognitive impairment in the subject.

[0113] Axial diffusion is measured at the individual voxel level. Axial diffusion is identified in white matter voxels. Axial diffusion is measured in mm 2 It is measured in units of seconds.

[0114] Perp is measured at the individual voxel level. Perp is identified in gray matter voxels. Perp is measured in mm 2 It is measured in units of / second. In some embodiments, the vertical column refraction (PerpCR) is the sum or weighted sum of axial diffusion and Perp, i.e., (k1 × axial diffusion) + (k2 × Perp), where K1 and K2 are independent positive numbers, which may be the same or different. Most preferably, the vertical column refraction (PerpCR) is the result of multiplying axial diffusion and Perp (i.e., axial diffusion × Perp), or a multiple thereof. PerpCR may be assigned to a first voxel and / or a second voxel.

[0115] In some embodiments, gray matter voxels may "match" (i.e., combine) with more than one white matter voxel (or vice versa). In this case, the average is calculated.

[0116] In some embodiments, the method includes obtaining or specifying values ​​for both axial diffusion and Perp in a plurality of voxels.

[0117] For example, values ​​for axial diffusion and Perp may be obtained or specified in 1 to 10,000, preferably 50 to 6,000, and more preferably 100 to 500 voxels. For whole-brain analysis, values ​​for axial diffusion may be obtained from 140,000 to 180,000, preferably about 160,000 white matter voxels and may be combined with values ​​for Perp for overlapping gray matter voxels.

[0118] In some embodiments, the first voxel and / or the second voxel form a continuous or semi-continuous collection of voxels that form a continuous or discontinuous arc across the cortex of the subject's brain.

[0119] The second voxel is located below the first voxel in the radial direction. It does not need to be adjacent, as boundary regions can be excluded. The second voxel (or set of voxels) is typically located in the white matter of the same brain region as the gray matter containing the first voxel (or set of voxels), based on anatomical knowledge information or on automatic region assignment using probabilistic brain maps, based on a previous database.

[0120] Optionally, the average value of the vertical column refraction (PerpCR) for selected voxels is determined using the average values ​​of axial diffusion and Perp for each of those voxels. Alternatively, some other mathematical function of axial diffusion and Perp (e.g., a weighted sum of axial diffusion and Perp) may be selected to provide a value for the vertical column refraction (PerpCR).

[0121] Normal diffusion is the component of diffusion occurring in the principal diffusion direction perpendicular to ColD. This can be measured by multiplying the principal eigenvector (V1) by its corresponding eigenvalue (L1), and then decomposing it into its components. The value of the component perpendicular to ColD is the normal diffusion.

[0122] In particular, the value for vertical diffusion (Perp) relative to a voxel is calculated by placing D on a plane perpendicular to the ColD in that voxel. PDD This can be obtained by projecting and specifying the magnitude of the projection. Perp is the value of this magnitude.

[0123] The term "PerpPD" may also be used interchangeably with "Perp" in this specification.

[0124] When used herein, the expression "provides an indication of the level of cognitive impairment in the subject" means that there is a positive correlation between the value in question (e.g., AngleR, axial diffusion, Perp, PerpCR, or ACR) and the level of cognitive impairment. As a result, an increase in the value in question (compared to a value from a healthy subject as a control, or compared to a previous value from that subject) means a higher probability or statistically significant probability (a substantial increase) that the subject has cognitive impairment or a higher level of cognitive impairment. The same applies to a decrease in the value, with necessary modifications.

[0125] When used herein, the expression "provides an indication of the number of microsegment breaks in that region" means that there is a positive correlation between the value in question (e.g., AngleR, axial diffusion, Perp, PerpCR, or ACR) and the number of microsegment breaks in that region. As a result, an increase in the value in question (compared to a value from a healthy control subject, or compared to a previous value from that subject) means a higher or statistically significant probability (a substantial increase) that the subject has microsegment breaks in that region, or has an increase in the number of microsegment breaks in that region. The same applies to a decrease in the value, with necessary modifications.

[0126] In some embodiments, the method further includes the steps of positioning a subject in an MR scanner and acquiring MR diffusion data from the subject.

[0127] In other embodiments, the present invention relates to a method of treatment, which further comprises the following steps: if a subject is found to have a level of cognitive impairment that exceeds a certain reference level (above or below), or if a subject is found to have a value for ACR or PerpCR higher than a certain reference level, a cognitive impairment treatment agent is administered to the subject.

[0128] In other embodiments, the present invention relates to a method of treatment, which includes obtaining or receiving the results of a method for identifying an ACR or PerpCR as disclosed herein, and, if the ACR or PerpCR value is higher than a baseline level, providing indication of the presence of cognitive impairment in the subject, administering appropriate treatment to the subject to treat the cognitive impairment.

[0129] In another embodiment, the present invention relates to a computer-based method for obtaining indication of the duration of a disease in a subject, wherein the disease is MS, and the method is (a) Measurement results or values ​​derived therefrom of the width of axonal fiber bundles in one or more regions of the subject's brain, (b) Duration of the disease in the subjects and This is a step to correlate them, The measurement results of axonal fiber bundle width or the values ​​derived therefrom are negatively correlated with the duration of MS disease in the subject, or the steps to establish a correlation. The steps are as follows: to obtain indication of the duration of MS disease in the subject, Includes.

[0130] In another embodiment, the present invention relates to a computer-based method for obtaining indication of the duration of a disease in a subject, wherein the disease is MS, and the method is (i) Measurement results of axonal fiber bundle width from one or more regions of the subject's brain, or values ​​derived therefrom, (ii) A reference set of axonal fiber bundle width measurements or values ​​derived therefrom from corresponding brain regions of control subjects with a specific duration of MS disease. This is a comparison step, The steps involve comparing the measurement results of axonal fiber bundle width or the values ​​derived therefrom with the duration of MS disease, and determining whether they negatively correlate with the duration of the disease. The steps are as follows: to obtain indication of the duration of MS disease in the subject, Includes.

[0131] In another embodiment, the present invention relates to a computer-based method for obtaining indication of the duration of a disease in a subject, wherein the disease is MS, and the method is (a) A step to identify an indication of the duration of MS disease in the subject from the measurement results of axonal fiber bundle width obtained from one or more regions of the subject's brain, or values ​​derived therefrom. Includes.

[0132] Preferably, the axonal fiber bundle width measurement results are obtained by diffusion MRI or have been obtained previously. Preferably, the brain region is area 41.

[0133] If one or more measurement results are obtained, the average of the measurement results may be used in the method of the present invention.

[0134] In a further embodiment, the present invention provides a system or apparatus comprising at least one processing means configured to carry out the steps of the method of the present invention.

[0135] The processing means may be, for example, one or more computing devices and at least one application executable on one or more computing devices. The at least one application may include logic for carrying out the steps of the method of the present invention.

[0136] The present invention further provides the use of a system comprising at least one processing means configured to carry out the steps of the method of the present invention.

[0137] In a further embodiment, the present invention provides a medium storing software that includes instructions for configuring a processor to carry out steps of the method of the present invention.

[0138] The disclosures of each reference document included herein are expressly incorporated herein by reference in their entirety. [Brief explanation of the drawing]

[0139] [Figure 1A] This is a graphical example of a voxel for the derived diffusion-based scale. AngleR is averaged from a set of column voxels in the cortex. [Figure 1B] An example of cortical diffusion data for a representative region (right) is shown, including a graphical example of the derived diffusion-based measures in voxels (left). Lines with arrows pointing to DPDD indicate the principal diffusion vector in the voxel; on the right, only the direction is shown, while on the left, the diffusion tensor components along the PDD vector (DPDD) are shown. Lines with arrows pointing to CRadial indicate the radial direction perpendicular to the cortex (CRadial). The radiality angle in the voxel, AngleR (notation, θR), is the angle between the lines with arrows. The vertical diffusion PerpPD (notation, D1,⊥) is calculated by projecting DPDD onto a plane orthogonal to CRadial. The parallel diffusion PerlPD (notation, D1,∥) is calculated by projecting DPDD onto CRadial. The quantities are averaged along the radial cortical profile across the cortical layers and reflect the minicolumnar structure, as shown by the lines of light for the voxel set in Figure 1C. [Figure 1C] The quantity is averaged along the radial cortical profile across the cortical layer, reflecting a minicolumnar structure, as shown by the lines of light for voxel sets in Figure 1C. [Figure 2] This shows the relationship between AngleR, derived from postmortem DTI data, and standard neuropathological criteria (Black staging) for AD severity. The brains of controls (circles, dotted linear regression lines) and AD patients (squares, dashed linear regression lines) show a similar positive correspondence between AngleR and Black staging. [Figure 3A] Figure 3 shows the correlation between AngleR and the number of microsegments in two different brain regions. [Figure 3B] Figure 3 shows the correlation between AngleR and the number of microsegments in two different brain regions. [Figure 4A] Figure 4 shows the difference in AngleR between the control and AD in the trial in vivo data set (whole brain and subregion PHG). [Figure 4B] Figure 4 shows the difference in AngleR between the control and AD in the trial in vivo data set (whole brain and subregion PHG). [Figure 5A] Figure 5 shows the correlation between histological microsegments and axial diffusion and AngleR functions in two brain regions in postmortem Alzheimer's disease and control brains. Figure 5A is from the prefrontal cortex (area 9). Figure 5B is from the medial temporal lobe (parahippocampal gyrus, PHG). [Figure 5B] Figure 5 shows the correlation between histological microsegments and axial diffusion and AngleR functions in two brain regions in postmortem Alzheimer's disease and control brains. Figure 5A is from the prefrontal cortex (area 9). Figure 5B is from the medial temporal lobe (parahippocampal gyrus, PHG). [Figure 6A] Figure 6 shows the differences between postmortem Alzheimer's disease brains and control brains in axial diffusion and multiplication results with AngleR in two brain regions. Figure 6A is from the prefrontal cortex (area 9). Figure 6B is from the medial temporal lobe (parahippocampal gyrus, PHG). [Figure 6B]Figure 6 shows the differences between postmortem Alzheimer's disease brains and control brains in axial diffusion and multiplication results with AngleR in two brain regions. Figure 6A is from the prefrontal cortex (area 9). Figure 6B is from the medial temporal lobe (parahippocampal gyrus, PHG). [Figure 7] This graph shows the regional differences in a) AngleR, b) mini-column width, c) axon bundle spacing, and d) axon bundle width. Error bars indicate the standard deviation. [Figure 8] This shows the relationship between fasciculation width in the primary auditory cortex and disease duration in the brains of MS. [Modes for carrying out the invention]

[0140] Examples

[0141] The present invention is further illustrated by the following examples, in which proportions and percentages are by weight and degrees are in Celsius unless otherwise stated. These examples illustrate preferred embodiments of the present invention, but it should be understood that they are provided for illustrative purposes only. From the above description and these examples, those skilled in the art will be able to grasp the essential features of the present invention and make various modifications and alterations to adapt the present invention to various uses and conditions without departing from the spirit and scope of the present invention. Accordingly, in addition to those shown and described herein, various modifications of the present invention will be apparent to those skilled in the art from the above description. Such modifications are also intended to fall within the scope of the appended claims.

[0142] Example 1: Materials and methods for Example 2

[0143] Ex-vivo brains

[0144] Brains were MRI scanned from five patients diagnosed with Alzheimer's disease (AD) and six normally aged controls selected from the Oxford Brain Bank (OBB). The brains examined were provided by donors, and written informed consent for brain autopsy and the use of materials and clinical information for research purposes was obtained from these donors by the OBB. Dementia brains were obtained from the Brains for Dementia Research network in the UK. Brains were extracted from skulls fixed in 10% neutral buffered formalin and immersed. The post-mortem interval (PMI) was 49.2 ± 25.6 hours, and the period before scanning (scan interval) was 59.1 ± 39.9 weeks. Therefore, the brains were not fixed for many years, but for a period longer than 4 weeks, during which the major shrinkage associated with fixation occurs (Quester & Schroder, 1997). The diagnosis of AD brains was confirmed by a clinician neuropathologist. Samples from different brain regions were acquired for diagnostic confirmation according to the criteria of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) and assigned a Black score. Brains showing significant signs of other pathologies, including Creutzfeldt-Jakob disease, Parkinson's disease, Lewy body dementia, Huntington's disease, and cerebrovascular disease, were excluded. Comorbidities of alcohol or illegal drug abuse were not detected in our sample records. This project was carried out under the authorization of the UK National Research Ethics Service and provided to the Oxford Brain Bank, and informed consent was obtained from all subjects and / or family representatives. Brains were scanned using modified acquisition techniques suitable for postmortem tissue to derive structural and diffusion tensor imaging data.

[0145] Mini-column: Histological Analysis

[0146] Following scanning and neuropathological sampling, the brain was sectioned coronally. For each of the three regions from one hemisphere per brain, a 25mm × 25mm × 10mm block was sampled (representative samples from each hemisphere: 4 on the left, 6 on the right). Due to limitations in tissue availability from the brain bank, one dementia case was inaccessible for detailed histological sampling. For the remaining 10 cases, tissue blocks and surrounding anatomical structures were photographed using an Olympus C-5050 digital camera for reference. The dorsolateral prefrontal cortex (dlPFC, area 9) ROI included the paracingulate sulcus and the middle and superior frontal gyri, with the inferior frontal sulcus defining the boundary inferiorly. The dlPFC block was sampled at the same height as the cingulate gyrus. The inferior parietal lobe (area 40) was defined as the supramarginal gyrus, bordered superiorly by the interparietal sulcus, inferiorly by the Sylvian fissure, anteriorly by the postcentral sulcus, and posteriorly by the Jensen sulcus. The parahippocampal gyrus (PHG) was sampled within a range defined posteriorly by the posterior part of the hippocampus and anteriorly by the point where the hippocampus fuses with the amygdala, with the superior boundary being the fusion between the hippocampus and the hippocampal base. Furthermore, due to the high demand for medial temporal lobe samples for human brain research, two control cases were not available for the PHG region. ROI selection was confirmed cytostructically according to Von Economo and Koskinas (1925).

[0147] Tissue blocks were embedded in paraffin wax and continuously sectioned at 30 μm intervals. Two sections were systematically and randomly selected relative to the extent of the tissue block, and these were stained with cresyl violet nissl dye for minicolumn analysis (CV; ThermoFisher Scientific, Waltham, MA, USA).

[0148] The cell body-based minicolumn width was assessed using a semi-automated process previously described in detail (Buxhoeveden et al., 2001; Casanova and Switala, 2005). This process provides a value for the minicolumn width, consisting of the cell-density core region plus the surrounding associated neural network space. The number of microsegments was further measured as an index of disruption to the minicolumn tissue by counting "incomplete" minicolumn segments, as described by Chance et al. (2011). For each ROI, three photographs were taken from a single microscope slide, where possible, each approximately 1 mm. 2 The region included. Image locations were selected using a random number generator, excluding areas with high curvature that have been shown to affect cell distribution (Chance et al., 2004). Since the minicolumns are sharpest in layer III, the photographs were centered on that layer and acquired using an Olympus BX40 microscope with a 4x objective lens (further details can be found in Di Rosa et al. (2009) and Van Veluw et al. (2012)). Values ​​calculated from the three photographs were averaged to give one value for each region.

[0149] Measuring cortical disturbance using DTI.

[0150] Postmortem scan analysis

[0151] Novel analysis of MRI diffusion data has been applied as a potential biomarker for neurodegeneration. We hypothesized that it is sensitive to the cellular structure of the cerebral cortex associated with minicolumnar structures. Synaptic and neurite deletions, and subsequent cell death, lead to progressive damage to normal cortical neuronal tissue, generating altered cortical microstructures. We hypothesized that diffusion MRI may be sensitive to these effects, including cell deletion, minicolumnar thinning, and the generation of minicolumnar fragments (microsegments) through the disruption of axonal and dendritic bundles. Cortical diffusion analysis of postmortem brains consisted of three stages: masking of regions of interest (ROIs), calculation of diffusion metrics within the ROIs, and extraction of values ​​for comparison with tissue structure measurements.

[0152] Cortical ROIs corresponding to histologically sampled tissue for minicolumn measurements were represented using manually created masks against structural MRI images (areas 9, 40, and PHG were identified using labeling as described above). Careful comparisons were further performed using marked photographic positions on corresponding Nissl-stained slides. To account for the lower contrast between GM and WM tissue in postmortem brains due to fixation, structural scans were overlaid on previously colregistrated MD maps in structural space to include only gray matter voxels and to avoid contamination from WM or CSF.

[0153] To calculate diffusion scale values ​​for each ROI, cortical disarray values ​​were generated from lines in the cortex that modeled cortical profiles, i.e., columnar arrays of cells that move from the periventricular region along radial glia and emerge from the white matter overlapping to form the cortical gray matter of the brain. These cortical profiles were generated by calculating neuroanatomically based columnar directions that originate in the white matter below the cortex and extend through the cellular structure of the cortical laminae to the pia mater surface. The values ​​for the scale derived from the diffusion tensor were averaged along the cortical profiles to generate the mean values ​​for each cortical profile.

[0154] Next, mean values ​​for the tensor scale were generated by averaging the cortical profiles across each masked ROI, excluding the ROI edges (first and last slices). Measurements related to FA, MD, and the principal diffusion component, namely the angle between the columnar direction and the principal diffusion direction (AngleR), were averaged. It was hypothesized that these measurements were influenced by variations in the tissue and spacing of the radial barrier to diffusion provided by the cortical cell structure (it should be noted that cortical diffusion assessment is not the same as axial and radial diffusion; see Figure 1).

[0155] A single ROI mean value for each diffusion measure provides robustness against local noise / artifacts and further enhances consistency with tissue structure measurements where a single value is similarly calculated for each ROI. Previous work has shown that measurements of cell structure and myelin sheath construction are relatively stable within cortical regions (e.g., Von Economo and Koskinas, 1925), indicating that this is effective for deriving mean values ​​for those regions.

[0156] Exploratory in vivo dataset for diagnostic purposes.

[0157] In vivo data were based on a small pilot subset of scans previously collected from the ADNI data set, consisting of 15 controls and 14 AD patients (Weiner et al., 2013). Control scans were from five different centers, and AD scans were from eight different centers. Assignment to the diagnostic group was made possible by clinical diagnosis, MMSE, and Clinical Dementia Rating Scale (CDR) scores. Other conditions, including severe vascular disease, with a Hachinski score of less than 4 were excluded. Data were collected and provided with ethical approval in accordance with the ADNI Consortium guidelines.

[0158] For each subject, GM, WM, and CSF volumes were established using SPM8 segmentation including calculated GM fragments (GMf). For in vivo cortical diffusion validation, a whole cortical gray matter mask was initially used to apply cortical diffusion analysis. Next, region of interest analysis was applied to the same ROIs used for postmortem analysis: PHG, PFC, and area 40.

[0159] statistical analysis

[0160] All data was analyzed using SPSS v22 for Windows.

[0161] Due to the small sample size in the postmortem diagnosis group, postmortem data were used to compare the association between tissue structure and DTI values ​​within the subjects, and differences between the diagnosis groups were investigated in the in vivo data set. The significance threshold was modified for multiple comparisons.

[0162] The association between tissue structure (including within modality) and DTI measurement results was investigated using correlation analysis with Pearson's correlation or Spearman's rank correlation for smaller groups. Regarding neuropathological evaluation, only black staging of tau-positive neurofibrillary tangles underwent statistical analysis because it demonstrated intragroup and intergroup ranges.

[0163] Example 2: Results

[0164] Diagnostic Neuropathology

[0165] Black staging was higher in AD brains compared to controls (Mann-Whitney U 0.5, p<0.01) (see the distribution of values ​​in Figure 2). Other classification methods showed clear differences in diagnostic categories. The CERAD classification was "normal" for all control brains, but the CERAD classification for AD patients was "clearly AD" or "possibly AD." BNET amyloid B was median 5 for AD brains and 1 for control brains.

[0166] Correlation between DTI and neuropathology

[0167] Black staging was positively correlated with AngleR in PHG in control brains (Spearman's Rho 0.85, p<0.05) and at trend levels in AD (Spearman's Rho 0.82, p=0.09). Near agreement was given between the best-fitted regression lines (see Figure 2), suggesting a possible continuity between control and AD brains, as indicated by the overlap of black staging between AD and controls. To account for this continuity, controls and AD were grouped together, and a positive association between black staging and AngleR was found in all regions (PHG: Spearman's Rho 0.96, p<0.001; area 9: Spearman's Rho 0.74, p=0.009; area 40: Spearman's Rho 0.61, p=0.045). Figure 3.

[0168] Correlation between DTI and organizational structure

[0169] It was hypothesized that novel cortical diffusion values ​​are related to horizontal spacing and the completeness of minicolumns in cortical gray matter, i.e., the angle of deviation from the estimated minicolumn direction (AngleR). Correlation studies observed a relationship between AngleR and the number of histological minicolumn microsegments and minicolumn width.

[0170] A positive association was found between AngleR and the number of microsegments in all brain regions in AD, particularly in cortical area 9 and PHG (Spearman's Rhoads 1.0, p<0.01 for both).

[0171] An association was found across all subjects (see Figure 3), but the association was less clear in the control group.

[0172] The FA and MD measurement results did not correlate overall with the histological measurement results.

[0173] Demographic correlation

[0174] In general, demographic variables, age, postmortem interval, fixation period, and brain weight did not show statistically significant correlations with the measured histological or cortical diffusion variables. Only one trend was observed, and there was a non-significant indication of a positive association between age at death and AngleR in cortical area 9.

[0175] The CDR value was 0 for all control subjects and 3 for all AD patients except for one with a rating of 1.

[0176] In vivo cortical diffusion measurement results

[0177] For whole-brain analysis, ANOVA, when including age, subject movement, whole-brain gray matter fragments, mean diffusion, and fragment anisotropy as covariates, found that the AngleR was significantly higher in AD compared to the control group (F 8.9, df 1.22, p<0.01). None of the covariates were statistically significant (see Figure 4 for AngleR).

[0178] For ROI analysis, ANOVA using "brain regions" (PHG, PFC, area 40) as repeated measures found that novel cortical diffusion values ​​were significantly higher in AD compared to the control group (F 6.4, df 1.22, p<0.02). The effect was most pronounced for AngleR in the PHG region, which contributed to the trend against the x-region effect of the measured result (F 3.3, df 1.22, p=0.06). Age, subject movement, whole-brain gray matter fragments, mean diffusion, and fragment anisotropy were included as covariates.

[0179] Example 3: Materials and methods for Examples 4-5

[0180] Patient / Sample

[0181] Fixation whole brains (Table 1) from nine multiple sclerosis patients were obtained from the UK MS Tissue Bank (Imperial College, Hammersmith Hospital Campus, London). The brains were stored in 10% formalin before being transferred to a perfluorocarbon solution (Fomblin® LC08; Solvay Inc.; Bollate, Italy) for scanning. This does not contribute to the MRI signal and provides consistency with the tissue's susceptibility (reducing image artifacts).

[0182] Table 1. Brain characteristics provided for the study. Multiple sclerosis (MS) cases and healthy controls (HC). [Table 1] a MS clinical details: Data is not available for all cases.

[0183] MRI scan

[0184] Nine brains from multiple sclerosis patients and six control brains from an existing cohort in the Oxford Brain Bank were used for MRI comparison. Scanning was performed on a Siemens Trio 3T scanner using a 12-channel head coil. Scanning was performed at room temperature, and each scan session lasted approximately 24 hours. Diffusion-weighted data were acquired using a modified spin echo sequence with 3D segmented EPI (TE / TR = 122 / 530 ms, bandwidth = 789 Hz / pixel, matrix size: 168 × 192 × 120, resolution 0.94 × 0.94 × 0.94 mm). Diffusion-weighted images were isotropically distributed along 54 directions (b = 4500 s / mm²) with six b=0 images. This protocol took approximately 6 hours, and three averages were acquired over 18 hours of total diffusion imaging. Structural scans were acquired using a 3D balanced steady-state free precession (BSSFP) sequence (TE / TR = 3.7 / 7.4 ms, bandwidth = 302 Hz / pixel, matrix size: 352 × 330 × 416, resolution: 0.5 × 0.5 × 0.5 mm). Images were acquired with and without RF phase alternation to avoid banding artifacts. These were averaged over 8 iterations to improve the signal-to-noise ratio. For further details, see Miller et al. (2011).

[0185] The data were processed using the FMRIB software library (FSL) (Smith et al., 2004; Woolrich et al., 2009). The FSL diffusion toolbox, with an in-house processing pipeline incorporated to compensate for gradient-induced thermal drift and eddy current strain, was used to process the diffusion-enhanced data to generate maps of fragmental anisotropy (FA), mean diffusivity (MD), and diffusion tensor components (Miller et al., 2011).

[0186] Selection of brain regions

[0187] The results of cortical thickness measurements in the dorsolateral prefrontal cortex (area 9) and primary visual cortex (V1), as well as diffusion measurements of connected white matter tracts (FA and MD), correlated with histological myelin formation measurements in our previous study (Kolasinski et al., 2012), and since multiple sclerosis is a demyelinating disorder, these areas were selected for further investigation in this study. In addition, these areas were appropriately characterized and were found to represent a range of cortical cell structural constructs (i.e., wider minicolumns in area 9 and narrower minicolumns in V1). A further comparison area—the primary auditory cortex within Heschl's gyrus (area 41)—was included because its columnar architecture was appropriately characterized, but there were inconsistencies in previous reports regarding its PDD in healthy subjects (Kang et al., 2012; McNab et al., 2013). Investigating multiple cortical regions allows us to examine the sensitivity of diffusion scales to local distinctions, which is of interest for future research into neurological disorders.

[0188] Neurohistological sampling

[0189] The brain was sectioned coronally, and the diagnosis of multiple sclerosis was confirmed by a clinician neuropathologist. A 25mm × 25mm × 10mm block was sampled from each of three regions from one hemisphere per brain (representative random samples from the hemisphere: 7 on the left, 8 on the right). The blocks and surrounding tissues were photographed using an Olympus C-5050 digital camera for reference. Area 9 included the middle and superior frontal gyri, bordered inferiorly by the parasingulate sulcus and inferior frontal sulcus. The area 9 block was sampled at the same height as the anterior limit of the cingulate gyrus. The area 41 block included the Heschl gyrus, bordered medially by the insular cortex and laterally by the temporal plane. The V1 block was sampled along the calcaneal fissure at the same height as the medium transverse occipital gyrus. The selection of regions of interest (ROIs) was confirmed cellularly according to Von Economo and Koskinas (1925).

[0190] Tissue blocks were embedded in paraffin wax and continuously sectioned at 10 μm for mini-column analysis and myelin level quantification, and at 30 μm for axon bundle measurement. The sections were stained with cresyl violet (CV; ThermoFisher Scientific, Waltham, MA, USA) for mini-column analysis, with anti-proteolipide protein stain (AbD AbSerotec, Oxford, UK) (anti-PLP) for light transmittance myelin quantification, and with Sudan Black, a myelin-sensitive lipophilic dye, for axon bundle measurement.

[0191] Cortical diffusion analysis

[0192] This was a region of interest approach. Cortical ROIs corresponding to histologically sampled areas were represented using manually created masks against structural postmortem images. The nearest matching coronal slice of the structural MRI scan was identified by careful reference to photographic images of physically cut coronal brain slices before and after tissue block removal, and to corresponding Nissl-stained slides. The cortical ROI was masked across 15 coronal slices of MRI images centered on this slice, taking care to include only gray matter voxels to avoid contamination from white matter or CSF. To ensure that the masked area matched the histologically sampled area, the limits of the cortical ROI were identified by careful comparison of the photographic images and corresponding Nissl-stained slides. A novel software script (Mark Jenkinson, University of Oxford, 2018; WO2016 / 162682A1; U.S. Patent Application No. 15 / 564344) was used to generate cortical profiles for MRI scans, i.e., lines spanning the cortex in the radial direction, to reproduce columnar tissue within the cortex. Values ​​for diffusion tensor-derived measures were averaged along the cortical profile across the entire masked ROI, except for terminal slices at the anterior and posterior ends of the ROI. The calculated measures included MD, FA, and three measures related to the principal diffusion component (see WO2016162682(A1); U.S. Patent Application No. 15 / 564344 for further reference), namely, the displacement angle between the radial direction and the principal diffusion direction spanning the cortical layer (AngleR, θ). R These were the principal diffusion component projected onto a plane perpendicular to the radial direction across the cortex (and thus described as vertical diffusion, i.e., PerpPD, D1,⊥(×10⁻³ mm² / sec)) and the principal diffusion component projected radially across the cortex (and thus described as parallel to the radial direction, i.e., ParlPD, D1,∥(×10⁻³ mm² / sec)).

[0193] The mean value reduced the impact of noise in the DTI data, effectively smoothed the data, ensured that only directionality with some local coherence was dominant, and protected from the effects of random deviations from the radial direction. Averaging further provided consistency with histological measurements, where a single value was similarly calculated for each cortical region. Previous work has found that measurements of cellular structure and myelin architecture are relatively stable within cortical subregions (e.g., Von Economo and Koskinas (1925)), which indicates that it is effective for deriving a mean value for that region.

[0194] Mini-column analysis

[0195] The cell body-based minicolumn width was evaluated in histological tissue sections using a semi-automated process previously described in detail (Buxhoeveden et al., 2001; Casanova and Switala, 2005). This process provides a value for the minicolumn width, which consists of the cell-density core region plus the associated neural network space surrounding it. The neural network spacing is the width of the cell-sparse neural network region between the cores of adjacent minicolumns, while the core represents the width of the cell-density region at the center of the minicolumn. The microsegment number is the number of strands of cells that do not form a complete minicolumn, because they are discontinuous with the rest of the minicolumn, either due to them extending beyond the cross-section or due to minicolumn fragmentation as a result of pathology. Cell density represents the density of cells recognized by the automated tissue structure analysis program within each field of view of the evaluated digital micrographs (see Chance et al., 2011 for further explanation of microsegments and cell density). For each ROI, three digital micrographs were taken from one slide, where possible, each approximately 1 mm. 2The region included the following. Image locations were selected using a random number generator, except for areas with high curvature that have been shown to affect cell distribution (Chance et al., 2004). The minicolumns were sharpest in Layer III, and photographs were acquired using an Olympus BX40 microscope with a 4x objective lens, centered on that layer (further details can be found in Di Rosa et al. (2009) and Chance et al. (2004)). Values ​​calculated from the three photographs were averaged to give one value for each region.

[0196] Quantification of myelin levels

[0197] Cortical myelin content was assessed using light transmittance to quantify the intensity of myelin staining in anti-PLP stained tissue sections. Data were collected using Axiovision v4.7.2 software on a PC receiving signals from an Axiocam MRc (Carl-Zeiss, Jena, Germany) mounted on a BX40 microscope (Olympus, Japan) with a 10x objective lens. Settings were calibrated in RGB mode with fixed white balance and incident light, using standard slide / coverslip preparations and light filters (6%, 25%, and 100% transmittance). For each ROI, 58,240 μm of anti-PLP stained section was collected. 2 Using a virtual frame, three measurements of transmittance (T) were obtained at different locations across layers III to V, and the resulting values ​​were averaged.

[0198] Axon bundle analysis

[0199] Since the axon bundles are clearest in layer V, three images were taken for each region using an Olympus BX40 microscope with a 10x objective lens (resolution 1.10 μm), centering on layer V. As with the mini-column measurements, areas with extreme curvature were avoided where possible.

[0200] The axon bundle center spacing and the width of the bundles themselves were measured manually in Axiovision using the built-in measurement tools. The digital resolution of the analyzed images was 0.67 μm / pixel. A sample line of standard length (590 μm; determined by the size of the image view) was drawn perpendicular to the bundle direction and through the center of the photograph to identify the bundles to be measured. Only bundles that intersect this line were measured, and those that extended beyond the sectioning plane above or below the line were not included. A single axon or pair of axons intersecting the line was not considered to constitute an axon bundle for this analysis.

[0201] Bundles (more than two axons) were identified, and their centers were marked. Next, bundle spacing measurements were performed from the center of each marked bundle to the center of the nearest bundle. The width of each axon bundle was further measured. For width measurement, the edges of the bundles were marked at the points where they intersected a line, and the bundle width was identified as the distance between these two points. The edges of the axon bundles were distinguished by the change in staining intensity from the background, which identified the start of axon bundles that were stained darker. Pilot data revealed the high reliability of this method, deriving a high correlation (r=0.737, p<0.001) between measurement results from photographs taken in two different situations. Next, the values ​​from the three photographs were averaged to give one value for bundle spacing and one value for bundle width for each ROI.

[0202] This yielded the average of 28(±5), 22(±5), and 44(±5) bundles sampled for each subject in areas 9, 41, and V1, respectively. We were unable to evaluate the orientation of axonal bundles within the cortex using methods directly comparable to our DTI analysis, because such three-dimensional estimation is not possible in histological sections with limited depth, combined with z-direction compression in microscope slides. However, by considering a subset of cases involving relatively uncurved sections of the cortex, where a three-dimensional geometric longitudinal can be reasonably assumed to be close to a two-dimensional estimation from the histological section, we were able to measure the orientation of axonal bundles. This showed that the axonal bundles deviated from the radial direction across the cortex by an average of 3.50(±2.68) degrees.

[0203] statistical analysis

[0204] All data was analyzed using SPSS v22 for Windows and the R statistics package (version 3.3.3) (R Core Team, 2013).

[0205] The relationship between tissue structure and DTI was investigated by correlation analysis using Spearman's correlation coefficient. We performed correlation analysis for each of the three regions of interest (area 9, area 41, and V1), including five diffusion measures (FA, MD, Angle_R, PerpPD, ParlPD) and six tissue structure measurements (minicolumn width, core width, reticular spacing, microsegment count, axonal bundle width, bundle spacing). All p-values ​​were adjusted using false discovery rate correction (FDR < 0.05) (Benjamini and Yekutieli, 2001), with p and P for significant results. FDR This was reported using the approach of Preziosa et al. (2019) by providing [the necessary information].

[0206] Mean regional differences—regional differences in both intragroup tissue structure and DTI scale—were assessed using repeated measures ANOVA, and significant main effects were followed up using post-hoc t-tests. Regional differences in DTI between groups were assessed using repeated measures ANOVA.

[0207] Tissue structure measurement results - The relationships between six tissue structure measurement results (mini-column width, core width, neural network spacing, number of microsegments, axonal bundle width, and bundle spacing) were investigated using Spearman's correlation coefficient and adjusted for FDR correction (FDR < 0.05).

[0208] Clinical Correlations in Multiple Sclerosis – Our previous study has shown associations between the degree of white matter changes and cell tissue in area 9 and V1 (Kolasinski et al., 2012). Since disease duration was the only clinical measure available for all subjects (Table 1), this study investigated whether significant correlations existed between DTI-derived measures and disease duration in these cortical areas (area 9 and V1), and whether the correlations differed from those in the comparison area (area 41). Age was assumed to correlate with disease duration, and this was controlled, where appropriate, against the use of partial correlations using standard SPSS iterative algorithms.

[0209] Example 4: DTI differences between groups and brain regions

[0210] We investigated the results of diffusion measurements between groups using six existing control cohorts. Repeated measures ANOVA revealed the main significant effect of diagnosis on diffusion measures (Tables 2 and 3). Specifically, AngleR(F 1,13 =15.575, p=0.002), MD(F 1,13 =20.468, p=0.002), PerpPD(F 1,13 =39.177, p=0.000), and ParlPD(F 1,13 (F = 16.905, p = 0.001) The values ​​were higher in multiple sclerosis cases compared to controls in all domains, and FA did not differ between groups (F1,13 =0.928, p=0.353). Other regions (F 2,26 Compared to the control group (=5.512, p=0.026), the higher AngleR value in V1 resulted in a greater intra-subject region effect (Figure 7) (regional differences were slightly higher in the control group, but no region-diagnosis correlation was found). No significant differences were found between regions within the subjects for FA, MD, PerpPD, or ParlPD.

[0211] (ParlPD is a component of the principal diffusion vector parallel to the radial minicolumnar direction spanning the cortex.)

[0212] Table 2. Mean values ​​for histological variables in each region of the MS brain. Standard deviations are shown in parentheses. [Table 2]

[0213] Table 3. Mean values ​​for diffusion measurements for each region in MS brains and controls. Standard deviations are shown in parentheses. [Table 3] *=Value significantly larger than HC in intergroup comparisons; #=Value significantly larger than other ranges in intragroup comparisons

[0214] Example 5: Differences in tissue structure between brain regions

[0215] Repeated-measures ANOVA revealed significant primary effects of regions in all histological measurements (Tables 2 and 3; Figure 7). The primary visual cortex contained the narrowest minicolumns and the narrowest axonal bundles, while area 41 contained the widest axonal spacing and the widest bundles.

[0216] Example 6: Relationship with clinical variables

[0217] Due to the presence of a strong correlation between disease duration and age (r=0.883, p=0.002), partial correlation control for age was used to investigate the association with disease duration. A significant negative correlation between fascicle width and disease duration was observed in area 41 (r=-0.867, p=0.011) (Figure 8), but not in area 9 (r=-0.438, p=0.278) or V1 (r=-0.077, p=0.856).

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[0292] Woolrich, MW, Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., ... & Smith, SM (2009). Bayesian analysis of neuroimaging data in FSL. Neuroimage, 45(1), S173-S186. (Additional note 1) A method for processing cortical diffusion data from brain regions of a subject, wherein the method is (a) Obtain a value for the displacement angle (AngleR) between the principal diffusion direction of the first voxel in the gray matter region of the subject's brain and the column direction (ColD) of the minicolumn, (b) Obtaining a value for axial diffusion in a second voxel present in the white matter that forms the basis of the gray matter region, (c) Using the values ​​for AngleR and axial diffusion, determine the value for axial column refraction (ACR) for the voxel, The method, including the method described above. (Additional note 2) The above step (a) is, (a1) Identifying the principal diffusion direction in voxels in the gray matter region of the subject's brain, (a2) Specifying the column direction (ColD) of the mini-column in the voxel, (a3) Obtain the value for the displacement angle (AngleR) between the main diffusion direction and the ColD, including, The method described in Appendix 1. (Additional note 3) A method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining ACR values ​​for multiple voxels in a region of the subject's brain by the method described in Appendix 1 or Appendix 2, Includes, The magnitude of the ACR value for the voxel provides the indication of the level of cognitive impairment in the subject. method. (Additional note 4) A method for obtaining an indication of the number of microsegment breaks in a brain region of a subject, wherein the method is (a) A step of obtaining ACR values ​​for a plurality of voxels in the region of the subject's brain by the method described in Appendix 1 or Appendix 2, Includes, The magnitude of the ACR value for the voxel provides the indication of the number of microsegment breaks in the region. method. (Additional note 5) A method for processing cortical diffusion data from brain regions of a subject, wherein the method is (a) Obtaining a value for vertical diffusion (Perp) in the first voxel in the gray matter region of the subject's brain, (b) Obtaining a value for axial diffusion in a second voxel present in the white matter that forms the basis of the gray matter region, (c) Using the values ​​for Perp and axial diffusion, determine the value for the vertical column refraction (PerpCR) for the voxel, Methods that include... (Additional note 6) The above step (a) is, (a1) A step of obtaining measurement results for cortical diffusion in the first voxel, (a2) The main diffusion vector in the voxel (D PDD ) is identified from the measurement results for cortical diffusion obtained in step (a1), (a3) A step of determining the column direction (ColD) of the mini-column in the voxel, (a4) The D on a plane perpendicular to the ColD in the voxel PDDThe steps include: determining the value for the vertical diffusion (the Perp) relative to the voxel by projecting and determining the magnitude of the projection; The method described in Appendix 5. (Additional note 7) A method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining vertical column refraction (PerpCR) values ​​for multiple voxels in a region of the subject's brain by the method described in Appendix 5 or Appendix 6, Includes, The magnitude of the value of the vertical column refraction (PerpCR) relative to the voxel provides the indication of the level of cognitive impairment in the subject. method. (Additional note 8) A method for obtaining an indication of the number of microsegment breaks in a brain region of a subject, wherein the method is (a) A step of obtaining values ​​of vertical column refraction (PerpCR) for a plurality of voxels in the region of the subject's brain by the method described in Appendix 5 or Appendix 6, Includes, The magnitude of the value of the vertical column refraction (PerpCR) for the voxel provides the indication of the number of microsegment breaks in the region. method. (Additional note 9) A method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining a value for the angle of deviation (AngleR) between the principal diffusion direction and the mean column direction (ColD) of the minicolumn in multiple voxels in a region of the subject's brain, Includes, The region of the brain is cortical area 9, PHG, or entorhinal cortex. The magnitude of the AngleR value from a plurality of voxels provides the indication of the level of cognitive impairment in the subject. method. (Additional note 10) A method for obtaining an indication of the level of MS in a subject, wherein the method is (a) A step of obtaining a value for the angle of deviation (AngleR) between the principal diffusion direction and the mean column direction (ColD) of the minicolumn in multiple voxels in a region of the subject's brain, It includes, preferably, The region of the brain is cortical area 9, area 41, or V1 (primary visual cortex), The magnitude of the AngleR value from a plurality of voxels provides the indication of the MS level in the subject. method. (Additional note 11) A method for obtaining an indication of the number of microsegment breaks in a brain region of a subject, wherein the method is (a) A step of obtaining a value for the angle of deviation (AngleR) between the main diffusion direction and the mean column direction (ColD) of the minicolumn in multiple voxels in the region of the subject's brain, Includes, The brain region is preferably cortical area 9, PHG, or entorhinal cortex. The magnitude of the AngleR value from a plurality of voxels provides the indication of the number of microsegment breaks in the region. method. (Additional note 12) A method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining values ​​for axial diffusion in multiple voxels in a region of the subject's brain, Includes, The region of the brain is cortical area 9, PHG, or entorhinal cortex. The magnitude of the axial diffusion from the plurality of voxels provides the indication of the level of cognitive impairment in the subject. method. (Additional note 13) A method for obtaining an indication of the number of microsegment breaks in a brain region of a subject, wherein the method is (a) A step of obtaining values ​​for axial diffusion in a plurality of voxels in the region of the subject's brain, Includes, The brain region is preferably cortical area 9, PHG, or entorhinal cortex. The magnitude of the axial diffusion from the plurality of voxels provides the indication of the number of microsegment breaks in the region. method. (Additional note 14) A method for obtaining an indication of the level of cognitive impairment in a subject, wherein the method is (a) A step of obtaining values ​​for vertical diffusion (Perp) in multiple voxels in a region of the subject's brain, Includes, The region of the brain is cortical area 9, PHG, or entorhinal cortex. The magnitude of the Perp value from a plurality of voxels provides the indication of the level of cognitive impairment in the subject. method. (Additional note 15) A method for obtaining an indication of the number of microsegment breaks in a brain region of a subject, wherein the method is (a) A step of obtaining values ​​for vertical diffusion (Perp) in multiple voxels in the region of the subject's brain, Includes, The brain region is preferably cortical area 9, PHG, or entorhinal cortex. The magnitude of the Perp value from a plurality of voxels provides the indication of the number of microsegment breaks in the region. method. (Additional note 16) The values ​​for AngleR, axial diffusion, and / or Perp are obtained by a neuroimaging method, preferably by magnetic resonance imaging (MRI), most preferably by diffusion MRI. The method described in any one of the appendices 1 to 15. (Additional note 17) The values ​​for AngleR, axial diffusion, and / or Perp are obtained from or derived from one or more regions of the cerebral cortex, preferably regions of the cortex including gray matter with underlying (subcortical) white matter. The method described in any one of the appendices 1 to 16. (Additional note 18) The brain region is selected from the group consisting of the parahippocampal gyrus (PHG), fusiform gyrus (Fusi), dorsolateral prefrontal cortex area 9 (dlPFC), area 41, Heschl's gyrus (HG), temporal plane (PT), inferior parietal lobule (IPL), middle temporal gyrus (MTG), primary visual cortex (V1;17), and entorhinal cortex, preferably selected from cortical area 9, PHG, or entorhinal cortex. The method described in Appendix 17. (Additional note 19) The aforementioned subjects are those suffering from cognitive impairment selected from the group consisting of Alzheimer's disease (AD), vascular dementia (CVD), mild cognitive impairment (MCI), frontotemporal dementia (FTD), Lewy body dementia (DLB), autism, multiple sclerosis (MS), epilepsy, amyotrophic lateral sclerosis (ALS), Parkinson's disease, schizophrenia, bipolar disorder, dyslexia, Down syndrome, Huntington's disease, prion disease, depression, obsessive-compulsive disorder, or attention deficit hyperactivity disorder (ADHD), subjective cognitive impairment, preMCI, and prodromal AD, posterior cortical atrophy (a subset of AD), behavioral, semantic, or progressive non-fluent aphasia (a subset of FTD), brain injury, hepatic encephalopathy, stroke, ischemia, ischemic hypoxia, neuroinflammation, traumatic brain injury (TBI), mild TBI, chronic traumatic encephalopathy, concussion, and mental confusion. The method described in any one of the appendices 1 to 18. (Additional note 20) A method for treating a subject, wherein the method for treating a subject includes the method described in any one of the appendices 1 to 3, 5 to 7, 9, 12, or 14, and if the subject is observed to have a level of cognitive impairment exceeding a specific reference level (upward or downward), or if the subject is observed to have a value for ACR or PerpCR higher than a specific reference level, a cognitive impairment treatment drug is administered to the subject. Treatment method. (Additional note 21) A method for treating a subject, the method for treating a subject, comprising obtaining or receiving the results of a method for identifying an ACR or PerpCR as described in any one of the appendices 1 to 3, 5 to 7, 9, 12, or 14, and, if the value of the ACR or PerpCR is higher than a baseline level, indicating the presence of a cognitive impairment in the subject, then administering appropriate treatment to the subject to treat the cognitive impairment. Treatment method. (Additional note 22) The system comprises at least one processing means configured to carry out the steps of the method described in any one of the appendices 1 to 19, A system or device. (Additional note 23) Software is stored which includes instructions for configuring the processor to perform the steps of the method described in any one of the appendices 1 through 19. Medium.

Claims

1. A computer-based method for processing cortical diffusion data obtained from previously acquired magnetic resonance imaging of a subject's brain region, wherein the method is: (a) A step of obtaining a value for vertical diffusion (Perp) in at least one first voxel in the gray matter region of the subject's brain, (b) A step of obtaining a value for axial diffusion in at least one second voxel in the white matter region of the subject's brain that is related to and underlying the gray matter region, (c) Using the values ​​for Perp and the axial diffusion, a step of determining a value for vertical column refraction (PerpCR) for each of the at least one first voxel and the at least one second voxel, wherein the PerpCR value includes at least one of the sum of axial diffusion and Perp, the weighted sum of axial diffusion and Perp, or the result of the multiplication of axial diffusion and Perp or a multiple thereof. A method performed by a computer, including the above.

2. The above step (a) is, (a1) A step of obtaining measurement results for cortical diffusion in the first voxel, (a2) The main diffusion vector (D) in the first voxel ＰＤＤ ) is identified from the measurement results for cortical diffusion obtained in step (a1), (a3) A step of determining the column direction (ColD) of the minicolumn in the first voxel, (a4) The D in the first voxel is on a plane perpendicular to the ColD. ＰＤＤ The steps include determining the value for the vertical diffusion (the Perp) for the first voxel by projecting and determining the magnitude of the projection, A computer-based method according to claim 1, including the method described in claim 1.

3. (a) The step of obtaining a value of the vertical column refraction (PerpCR) for at least one first voxel and at least one second voxel, The magnitude of the value of the vertical column refraction (the PerpCR) for the at least one first voxel and the at least one second voxel provides an indication of the level of cognitive impairment in the subject. A computer-based method according to claim 1 or claim 2.

4. (a) The step of obtaining a value of the vertical column refraction (PerpCR) for at least one first voxel and at least one second voxel, The magnitude of the value of the vertical column refraction (the PerpCR) for at least one first voxel and at least one second voxel provides an indication of the number of microsegment breaks in the brain region of the subject. A computer-based method according to claim 1 or claim 2.

5. Values ​​for axial diffusion and / or Perp are obtained by a neuroimaging method. A computer-based method according to any one of claims 1 to 4.

6. The values ​​for axial diffusion and / or Perp are obtained by magnetic resonance imaging (MRI). The computer-based method described in claim 5.

7. The values ​​for axial diffusion and / or Perp are obtained by diffusion MRI. The computer-based method described in claim 5 or claim 6.

8. A value for axial diffusion and / or Perp is obtained from or derived from one or more regions of the cerebral cortex. A computer-based method according to any one of claims 1 to 7.

9. The aforementioned one or more regions of the cortex include gray matter accompanied by underlying (subcortical) white matter, The computer-based method described in claim 8.

10. The region of the brain is selected from the group consisting of the parahippocampal gyrus (PHG), fusiform gyrus (Fusi), dorsolateral prefrontal cortex area 9 (dlPFC), area 41, Heschl's gyrus (HG), temporal plane (PT), inferior parietal lobule (IPL), middle temporal gyrus (MTG), primary visual cortex (V1; area 17), and entorhinal cortex. The computer-based method according to claim 8 or claim 9.

11. The one or more regions of the cortex are selected from cortical area 9, PHG, or entorhinal cortex. The computer-based method described in claim 10.

12. The aforementioned individuals may have Alzheimer's disease (AD), vascular dementia (CVD), mild cognitive impairment (MCI), frontotemporal dementia (FTD), Lewy body dementia (DLB), autism, multiple sclerosis (MS), epilepsy, amyotrophic lateral sclerosis (ALS), Parkinson's disease, schizophrenia, bipolar disorder, dyslexia, Down syndrome, Huntington's disease, prion disease, depression, obsessive-compulsive disorder, or attention deficit hyperactivity disorder. Individuals suffering from cognitive impairment selected from the group consisting of: ADHD, subjective cognitive impairment, pre-MCI, and prodromal AD, posterior cortical atrophy (a subset of AD), behavioral, semantic, or progressive non-fluent aphasia (a subset of FTD), brain injury, hepatic encephalopathy, stroke, ischemia, ischemic hypoxia, neuroinflammation, traumatic brain injury (TBI), mild TBI, chronic traumatic encephalopathy, concussion, and mental confusion. A computer-based method according to any one of claims 1 to 11.

13. The invention comprises at least one processing means configured to carry out the steps of the method described in any one of claims 1 to 12, A system or device.

14. A system that stores software including instructions for configuring a processor to perform steps of the method according to any one of claims 1 to 12. Medium.

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