BRAIN IMAGING

Abstract

The present disclosure relates generally to medical imaging and, more particularly, it relates to methods and systems for performing processing of magnetic resonance (MR) imaging of the brain which may be useful in the diagnosis of cognitive disorders. More specifically, 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.

Claims

1. A method for 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 minicolumns in a first voxel in a region of grey matter in a subject's brain; (b) obtaining a value for the Axial Diffusivity in a second voxel which is present in the white matter underlying the region of grey matter; and (c) determining a value for the Axial Columnar Refraction (ACR) for the voxels using values for AngleR and Axial Diffusivity.

2. A method as claimed in claim 1, wherein Step (a) comprises: (a1) determining the principal diffusion direction in a voxel in the region of grey matter in a subject's brain; (a2) determining the columnar direction (ColD) of the minicolumns in the voxel; and (a3) obtaining a value for the angle of deviation (AngleR) between the principal diffusion direction and ColD.

3. A method of obtaining an indication of the level of a cognitive disorder in a subject, the method comprising the steps: (a) obtaining values of ACR by a method as claimed in claim 1 or claim 2 for a plurality of voxels in a region of the subject's brain, wherein the magnitude of the values of ACR for those voxels provides an indication of the level of a cognitive disorder in that subject.

4. A method of obtaining an indication of the number of microsegment breaks in a region of a subject's brain, the method comprising the steps: (a) obtaining values of ACR by a method as claimed in claim 1 or claim 2 for a plurality of voxels in a region of the subject's brain, wherein the magnitude of the values of ACR for those voxels provides an indication of the number of microsegment breaks in that region.

5. A method for processing cortical diffusion data from a region of a subject's brain, the method comprising: (a) obtaining a value for the perpendicular diffusivity (Perp) in a first voxel in a region of grey matter in a subject's brain; (b) obtaining a value for the Axial Diffusivity in a second voxel which is present in the white matter underlying the region of grey matter; and (c) determining a value for the Perpendicular Columnar Refraction (PerpCR) for the voxels using values for Perp and Axial Diffusivity.

6. A method as claimed in claim 5, wherein Step (a) comprises the steps: (a1) obtaining a measurement for cortical diffusion in the first voxel; (a2) determining, from the measurement for cortical diffusion obtained in Step (a1), the principal diffusion vector (D.sub.PDD) in the voxel; (a3) determining the columnar direction (ColD) of the minicolumns in the voxel; and (a4) determining a value for the perpendicular diffusivity (Perp) for the voxel by projecting D.sub.PDD onto the plane which is orthogonal to ColD in that voxel and determining the magnitude of the projection.

7. A method of obtaining an indication of the level of a cognitive disorder in a subject, the method comprising the steps: (a) obtaining values of Perpendicular Columnar Refraction (PerpCR) by a method as claimed in claim 5 or claim 6 for a plurality of voxels in a region of a subject's brain, wherein the magnitude of the values of Perpendicular Columnar Refraction (PerpCR) for those voxels provides an indication of the level of a cognitive disorder in that subject.

8. A method of obtaining an indication of the number of microsegment breaks in a region of a subject's brain, the method comprising the steps: (a) obtaining values of Perpendicular Columnar Refraction (PerpCR) by a method as claimed in claim 5 or claim 6 for a plurality of voxels in a region of a subject's brain, wherein the magnitude of the values of Perpendicular Columnar Refraction (PerpCR) for those voxels provides an indication of the number of microsegment breaks in that region.

9. A method of obtaining an indication of the level of a cognitive disorder in a subject, the method comprising the steps: (a) obtaining, in a plurality of voxels in a region of a subject's brain, values for the angle of deviation (AngleR) between the principal diffusion direction and the average columnar direction (ColD) of the minicolumns, wherein the brain region is cortical area 9, the PHG or the entorhinal cortex, wherein the magnitude of the values of AngleR from the plurality of voxels provides an indication of the level of a cognitive disorder in that subject.

10. A method of obtaining an indication of the level of MS in a subject, the method comprising the steps: (a) obtaining, in a plurality of voxels in a region of a subject's brain, values for the angle of deviation (AngleR) between the principal diffusion direction and the average columnar direction (ColD) of the minicolumns, preferably wherein the brain region is cortical area 9, area 41, or V1 (primary visual cortex) wherein the magnitude of the values of AngleR from the plurality of voxels provides an indication of the level of MS in that subject.

11. A method of obtaining an indication of the number of microsegment breaks in a region of a subject's brain, the method comprising the steps: (a) obtaining, in a plurality of voxels in a region of a subject's brain, values for the angle of deviation (AngleR) between the principal diffusion direction and the average columnar direction (ColD) of the minicolumns, wherein the brain region is preferably cortical area 9, the PHG or the entorhinal cortex, wherein the magnitude of the values of AngleR from the plurality of voxels provides an indication of the number of microsegment breaks in that region.

12. A method of obtaining an indication of the level of a cognitive disorder in a subject, the method comprising the steps: (a) obtaining, in a plurality of voxels in a region of a subject's brain, values for the axial diffusivity, wherein the brain region is cortical area 9, the PHG or the entorhinal cortex, wherein the magnitude of the values of axial diffusivity from the plurality of voxels provides an indication of the level of a cognitive disorder in that subject.

13. A method of obtaining an indication of the number of microsegment breaks in a region of a subject's brain, the method comprising the steps: (a) obtaining, in a plurality of voxels in a region of a subject's brain, values for the axial diffusivity, wherein the brain region is preferably cortical area 9, the PHG or the entorhinal cortex, wherein the magnitude of the values of axial diffusivity from the plurality of voxels provides an indication of the number of microsegment breaks in that region.

14. A method of obtaining an indication of the level of a cognitive disorder in a subject, the method comprising the steps: (a) obtaining, in a plurality of voxels in a region of a subject's brain, a value for the perpendicular diffusivity (Perp), wherein the brain region is cortical area 9, the PHG or the entorhinal cortex, wherein the magnitude of the values of Perp from the plurality of voxels provides an indication of the level of a cognitive disorder in that subject.

15. A method of obtaining an indication of the number of microsegment breaks in a region of a subject's brain, the method comprising the steps: (a) obtaining, in a plurality of voxels in a region of a subject's brain, a value for the perpendicular diffusivity (Perp), wherein the brain region is preferably cortical area 9, the PHG or the entorhinal cortex, wherein the magnitude of the values of Perp from the plurality of voxels provides an indication of the number of microsegment breaks in that region.

16. A method as claimed in any one of the preceeding claims, wherein the value for AngleR, Axial Diffusivity and/or Perp is obtained by a neuro-imaging method, preferably by magnetic resonance imaging (MRI), most preferably by diffusion MRI.

17. A method as claimed in any one of the preceeding claims, wherein the value for AngleR, Axial Diffusivity and/or Perp is obtained from or derived from one or more regions of the cortex of the brain, preferably a region of the cortex comprising grey matter with underlying (subcortical) white matter.

18. A method as claimed in claim 17, wherein the region of the brain is selected from the group consisting of parahippocampal gyrus (PHG), fusiform gyrus (Fusi), dorsolateral prefrontal cortex area 9 (dIPFC), area 41, Heschl's gyrus (HG), planum temporale (PT), inferior parietal lobule (IPL), middle temporal gyrus (MTG), primary visual cortex (V1; area 17) and entorhinal cortex, and preferably selected from cortical area 9, the PHG or the entorhinal cortex.

19. A method as claimed in any one of the preceding claims, wherein the subject is one who has a cognitive disorder selected from the group consisting of Alzheimer's Disease (AD), cerebrovascular dementia (CVD), mild cognitive impairment (MCI), frontotemporal dementia (FTD), dementia with Lewy Bodies (DLB), autism, multiple sclerosis (MS), epilepsy, amyotrophic lateral sclerosis (ALS), Parkinson's disease, schizophrenia, bipolar disorder, dyslexia, Down's 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 (subset of AD), behavioural, semantic, or progressive non-fluent aphasia (subsets of FTD), encephalopathy, hepatic encephalopathy, stroke, ischaemia, ischaemic hypoxia, neuro-inflammation, traumatic brain injury (TBI), mild TBI, chronic traumatic encephalopathy, concussion and delirium.

20. A method of treatment of a subject, wherein the method comprises a method as claimed in any one of claims 1-3, 5-7, 9, 12 or 14, wherein, if the subject is found have a level of a cognitive disorder beyond (above or below) a specified reference level or is found to have a value for ACR or PerpCR above a specified reference level, a cognitive-disorder treating medicament is administered to the subject.

21. A method of treatment of a subject, wherein the method comprises obtaining or receiving results of a method for determining ACR or PerpCR as defined in any one of claims 1-3, 5-7, 9, 12 or 14, and if the ACR or PerpCR value is higher than a reference level, thereby providing an indication of the presence of a cognitive disorder in the subject, administering a treatment to the subject appropriate for treating the cognitive disorder.

22. A system or apparatus comprising at least one processing means arranged to carry out the steps of a method as claimed in any one of claims 1 to 19.

23. A carrier bearing software comprising instructions for configuring a processor to carry out the steps of a method as claimed in any one of claims 1 to 19.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0185] FIG. 1A. An illustrative voxel example of the derived diffusion-based measures. AngleR is averaged from the columnar set of voxels in the cortex.

[0186] FIG. 1B. Example of the cortical diffusion data for one representative region (right), including an illustrative voxel example of the derived diffusion-based measures (left). The arrowed line towards D.sub.PDD indicates the principal diffusion vector in a voxel: on the right, only the direction is indicated, while on the left the diffusion tensor component along the PDD vector (D.sub.PDD) is shown. The arrowed line towards CRadial indicates the radial direction normal to the cortex (CRadial). The angle of radiality, AngleR (notation θ.sub.R), in a voxel is the angle between the arrowed lines. The perpendicular diffusivity, PerpPD (notation D1,), was calculated by projecting D.sub.PDD onto the plane perpendicular to CRadial. The parallel diffusivity, ParlPD (notation D1,//), was calculated by projecting D.sub.PDD onto the CRadial. Quantities were averaged along the radial cortical profile across the cortical layers, reflecting the minicolumnar organisation, as indicated for a set of voxels by the light line in FIG. 1C.

[0187] FIG. 2. The relationship between AngleR derived from post-mortem DTI data and the neuropathological gold standard for AD severity, Braak staging. Controls (circles, dotted linear regression line) and AD patient brains (squares, dashed linear regression line) have similar positive correspondence between AngleR and Braak staging.

[0188] FIG. 3. The correlations between AngleR and microsegment number in two different brain regions.

[0189] FIG. 4. The difference in AngleR (whole brain and sub-region PHG) between controls and AD in the trial in vivo dataset.

[0190] FIG. 5: Correlations between histological Microsegments and a function of Axial Diffusivity and AngleR in two brain regions in post-mortem confirmed Alzheimer's disease and control brains. FIG. 5A—in prefrontal region (area 9). FIG. 5B—in medial temporal lobe (parahippocampal gyrus, PHG).

[0191] FIG. 6: The difference between post-mortem confirmed Alzheimer's disease and control brains in the product of Axial Diffusivity and AngleR in two brain regions. FIG. 6A—in prefrontal region (area 9). FIG. 6B—in medial temporal lobe (parahippocampal gyrus, PHG).

[0192] FIG. 7: Regional differences in a) AngleR, b) minicolumn width, c) axon bundle spacing, and d) axon bundle width. Error bars show standard deviations.

[0193] FIG. 8: Relationship between bundle width and disease duration in primary auditory cortex in MS brains.

EXAMPLES

[0194] The present invention is further illustrated by the following Examples, in which parts and percentages are by weight and degrees are Celsius, unless otherwise stated. It should be understood that these Examples, while indicating preferred embodiments of the invention, are given by way of illustration only. From the above discussion and these Examples, one skilled in the art can ascertain the essential characteristics of this invention, and without departing from the spirit and scope thereof, can make various changes and modifications of the invention to adapt it to various usages and conditions. Thus, various modifications of the invention in addition to those shown and described herein will be apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims.

Example 1: Materials and Methods for Example 2

[0195] Ex-Vivo Brains

[0196] Brains were MRI scanned from 5 patients with a diagnosis of AD and 6 normally aged controls selected from the Oxford Brain Bank (OBB). The brains studied had been provided by donors from whom written informed consent had been obtained by the OBB for brain autopsy and use of material and clinical information for research purposes. Dementia brains were drawn from the Brains for Dementia Research network in the UK. Brains were extracted from the cranium and immersion fixed in a 10% neutral buffered formalin. The post-mortem interval (PMI) was 49.2±25.6 hours and the time before scanning (scan interval) was 59.1±39.9 weeks. Therefore the brains were not fixed for years, but had undergone fixation for a period of more than four weeks when the main shrinkage associated with fixation occurs (Quester & Schroder, 1997). Diagnosis of AD brains was confirmed by a clinical neuropathologist. Samples from different brain regions were taken for confirmation of diagnosis according to the criteria of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) and assigned a Braak score. Brains that showed substantial signs of other pathology, including Creutzfeldt-Jacob disease, Parkinson's disease, Lewy body disease, Huntington's disease, and cerebrovascular disease were excluded. No comorbidity of alcohol or illicit drug misuse was detected in our sample's records. This project was carried out with approval of the UK National Research Ethics Service, provided to the Oxford brain bank, and informed consent was obtained from all subjects and/or family representatives. The brains were scanned using modified acquisition suited to post-mortem tissue in order to derive structural and diffusion tensor imaging data.

[0197] Minicolumn Histological Analysis

[0198] After scanning and neuropathological sampling, brains were sectioned coronally. Blocks of size 25 mm×25 mm×10 mm were sampled for each of three regions from one hemisphere per brain (a representative sample of hemispheres: 4 left, 6 right). Due to constraints on availability of tissue from the brain bank, one dementia case was not accessible for the detailed histological sampling. For the 10 remaining cases, tissue blocks and the surrounding anatomy were photographed using an Olympus C-5050 digital camera for reference. The dorsolateral prefrontal cortex (dIPFC, area 9) ROI included the middle and superior frontal gyri bounded inferiorly at the paracingulate sulcus and inferior frontal sulcus. dIPFC blocks were sampled level with the cingulate gyrus. The inferior parietal lobe (area 40) was defined as the supramarginal gyrus, which is bounded superiorly by the intraparietal sulcus, inferiorly by the Sylvian fissure, anteriorly by the postcentral sulcus and posteriorly by the Jensen sulcus. The parahippocampal gyrus (PHG) was sampled within the limits defined posteriorly by the most posterior part of the hippocampus, anteriorly by the point where the hippocampus merges with the amygdala, and the superior boundary was the fusion between the hippocampus and the subiculum. For the PHG region two control cases were also not available, due to the high demand for medial temporal lobe samples for human brain studies. ROI selection was confirmed cyto-architecturally in accordance with Von Economo and Koskinas (1925).

[0199] Tissue blocks were embedded in paraffin wax and serially sectioned at 30 μm. Two sections were selected systematically randomly with respect to the limits of the tissue block and these were stained for the minicolumn analysis using cresyl violet Nissl stain (CV; ThermoFisher Scientific, Waltham, Mass., USA).

[0200] Minicolumn width based on cell bodies was assessed using a semi-automated procedure that has been described in detail previously (Buxhoeveden et al., 2001; Casanova and Switala, 2005). This procedure gives a value for the minicolumn width consisting of the cell-dense core region plus the associated neuropil space surrounding it. Microsegment number was also measured as an index of disruption to minicolumn organisation by counting ‘incomplete’ minicolumn segments as described in Chance et al. (2011). For each ROI, three pictures were taken from a single microscope slide where possible, each containing a region of about 1 mm.sup.2. Image locations were selected using a random number generator, excluding areas of high curvature which have been shown to affect cell distribution (Chance et al., 2004). As minicolumns are clearest in layer III, photographs were centred on that layer and obtained through a 4× objective lens, with an Olympus BX40 microscope (more 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 a single value for each region.

[0201] Measuring Cortical Disruption Using DTI

[0202] Post-Mortem Scan Analysis

[0203] A novel analysis of MRI diffusion data was applied as a potential biomarker of neurodegeneration. We hypothesized that it would be sensitive to the cyto-architectural organisation of the cerebral cortex related to the minicolumn structure. Loss of synapses and neurites, followed by cell death, causes progressive damage to the normal organization of cortical neurons, producing altered cortical micro-geometry. We speculated that diffusion MRI may be sensitive to these effects, including the creation of minicolumn fragments (microsegments) through cell loss, minicolumn thinning, and disruption of axon and dendrite bundles. Cortical diffusivity analysis on post-mortem brains consisted of three stages: masking of the region of interest (ROI), calculation of the diffusion metrics within the ROI and extraction of the values for comparison with the histology measurements.

[0204] Cortical ROIs corresponding to those sampled histologically for minicolumn measurement were delineated using manually created masks on the structural MRI images (areas 9, 40 and PHG were identified using landmarks as described above). Careful comparison was also made with the photograph locations marked on the corresponding Nissl stained slide. Considering the reduced contrast between GM and WM tissue in post-mortem brains, due to fixation, the structural scan was overlaid with MD maps that had been co-registered previously in structural space, in order to include only grey matter voxels and avoid contamination from WM or CSF.

[0205] In order to calculate the diffusion metric values for each ROI, Cortical Disarray values were generated data from cortical profiles, i.e. lines in the cortex modelling the columnar arrays of cells that have migrated from the peri-ventricular region along radial glia and emerge from the white matter on top of each other to form the brain's cortical grey matter. These cortical profiles were generated by calculating the columnar direction based on neuroanatomy, with an origin in the white matter below the cortex, extending through the cyto-architecture of the cortical laminae to the pial surface. Values for the diffusion tensor-derived metrics were averaged along the cortical profiles, generating average values for each cortical profile.

[0206] A mean for the tensor metric was then created by averaging the cortical profiles across each masked ROI, excluding the ends of the ROI (the first and last slices). Averages were calculated for FA, MD and a measure relating to the principal diffusion component, namely: the angle between the columnar direction and the principal diffusion direction, (AngleR). It was hypothesized that these measures were affected by variations in the organization and spacing of the radial barriers to diffusion provided by the cortical cyto-architecture. (It should be noted that the cortical diffusion assessments are not the same as axial and radial diffusivity—see FIG. 1).

[0207] A single ROI-average value for each diffusion metric enabled robustness against local noise/artifacts and also allowed for consistency with the histology measurements, which similarly calculated a single value for each ROL. Previous work has found that measures of the cyto- and myelo-architecture are relatively stable within a cortical region (e.g. Von Economo and Koskinas, 1925)) indicating that it is valid to find an average value for the region.

[0208] Exploratory In Vivo Dataset

[0209] In vivo data was based on a small pilot subset of previously collected scans from the ADNI dataset (Weiner et al., 2013): 15 controls and 14 AD. Controls scans came from 5 different centres and AD from 8 different centres. Assignment to diagnostic groups was enabled by clinical diagnosis, MMSE, and Clinical Dementia Rating scale (CDR) scores. Other conditions, including significant vascular disease, were ruled out, with Hachinski score less than 4. Data had been collected and provided with ethical approval according to the ADNI consortium guidelines.

[0210] For each subject, GM, WM, and CSF volumes were established using SPM8 segmentation, including computed GM fraction (GMf). For in vivo cortical diffusion validation the whole brain cortical grey matter mask was used initially to apply the cortical diffusion analysis. Then a region of interest analysis was applied to the same ROIs as were used for the post-mortem analysis: PHG, PFC, and Area 40.

[0211] Statistical Analysis

[0212] All data were analyzed using SPSS v22 for Windows.

[0213] Due to the small sample size in the post-mortem diagnostic groups, the post-mortem data was used to compare the relationships between histology and DTI values within subjects while diagnostic group differences were investigated in the in vivo dataset. Significance thresholds were corrected for multiple comparisons.

[0214] The relationships between histology and DTI measures (including within-modality relationships) were investigated by correlation analysis using Pearson's correlation, or Spearman's Rank correlation for small groups. Of the neuropathological assessments, only Braak staging of tau positive neurofibrillary tangles was subjected to statistical analysis because it showed a range within and between groups.

Example 2: Results

[0215] Diagnostic Neuropathology

[0216] Braak staging was higher in AD brains compared with controls (Mann-Whitney U 0.5, p<0.01) (see distribution of values in FIG. 2). Other classifiers showed clear diagnostic category differences. CERAD classification was ‘normal’ for all control brains, whereas it was ‘definite AD’ or ‘probable AD’ for AD patients. BNET amyloid-B had a median value of 5 for AD brains and 1 for control brains.

[0217] Correlation Between DTI and Neuropathology

[0218] Braak stage was positively correlated with AngleR in the PHG in control brains (Spearman's rho 0.85, p<0.05) and at a trend level in AD (Spearman's rho 0.82, p=0.09). Given the close match between best fit regression lines (see FIG. 2) it was apparent that there may be continuity between control and AD brains, which was also indicated by the overlap in Braak staging between the AD and controls. To consider this continuity, controls and AD were grouped together and a positive relationship between Braak staging and AngleR was present 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). FIG. 3.

[0219] Correlation between DTI and Histology

[0220] A novel cortical diffusion value was hypothesized to relate to the horizontal spacing and integrity of minicolumns in the cortical grey matter: the angle of deviation from the estimated minicolumn direction (AngleR). The relationship between AngleR and histological minicolumn microsegment number and minicolumn width was examined by correlation testing.

[0221] There was a positive relationship between AngleR and microsegment number in all brain regions in AD, particularly in cortical areas 9 and PHG (Spearman's rho 1.0, p<0.01, for both).

[0222] The relationship was present across all subjects (see FIG. 3), although it was less clear in controls.

[0223] The measurements of FA and MD did not generally correlate with histological measures.

[0224] Demographic Correlates

[0225] In general, the demographic variables, age, post-mortem interval, fixation time and brain weight did not show statistically significant correlations with the measured histological or cortical diffusion variables. Only one trend was observed; there was a non-significant indication of a positive association between age at death and AngleR in cortical area 9.

[0226] The CDR value was 0 for all control subjects and a value of 3 for all AD patients except one who had a rating of 1.

[0227] In Vivo Cortical Diffusion Measurements

[0228] For the whole brain analysis, an ANOVA found that AngleR was significantly higher in AD compared to controls (F 8.9, df 1.22, p<0.01), with age, subject movement, whole brain grey matter fraction, mean diffusivity and fractional anisotropy included as covariates. None of the covariates were statistically significant. (see FIG. 4 for AngleR).

[0229] For the ROI analysis, an ANOVA with ‘brain region’ as a repeated measure (PHG, PFC, Area 40) found that novel cortical diffusion values were significantly higher in AD compared to controls (F 6.4, df 1.22, p<0.02). The effect was most pronounced for AngleR in region PHG contributing to a trend for a measure x region effect (F 3.3, df 1.22, p=0.06). Age, subject movement, whole brain grey matter fraction, mean diffusivity and fractional anisotropy were included as covariates.

Example 3: Materials and Methods for Examples 4-5

[0230] Patients/Samples

[0231] Fixed whole brains from nine multiple sclerosis patients (Table 1) were obtained from the UK MS Tissue Bank (Imperial College, Hammersmith Hospital Campus, London). Brains were stored in 10% formalin before being transferred to a perfluorocarbon solution (Fomblin® LC08; Solvay Inc.; Bollate, Italy) for scanning, which contributes no MRI signal and provides susceptibility matching to tissue (reducing image artefacts).

TABLE-US-00001 TABLE 1 Characteristics of brains provided for study. Multiple sclerosis cases (MS) and healthy controls (HC) Disease Time disease Time in a Post-mortem Scan Disease Duration was progressive wheelchair Interval Interval Cause of Subject Sex Age Hemisphere Progression (yrs) (yrs).sup.a (yrs).sup.a (hours) (days) Death MS 254 F 69 R Secondary 37 12 7 66 1198 MS MS 281 F 74 L Primary 33 17 40 929 Sepsis MS 314 F 78 R Secondary 45 24 17 60 435 Colonic carcinoma MS 316 F 79 R Secondary 55 40 36 26 1052 Pneumonia MS 322 M 72 L Secondary 28 4 59 1201 Pneumonia MS 332 F 50 R Secondary 22 10 2 69 1134 Breast cancer mets MS 334 M 66 R Secondary 15 1 37 1126 Prostate cancer MS 396 F 86 R Primary 54 54 578 Lymphoma MS 400 F 60 L Secondary 11 7 21 539 MS HC 1 M 72 R — — — — 24 693 HC 2 F 88 R — — — — 24 655 HC 3 M 68 R — — — — 48 1236 HC 4 F 82 L — — — — 48 1197 HC 5 F 68 L — — — — 48 1216 Pancreas carcinoma HC 6 F 48 R — — — — 48 1151 Pneumonia .sup.aMS clinical details where data was not available for all cases

[0232] MRI Scanning

[0233] Nine multiple sclerosis patients and six control brains from a pre-existing cohort in the Oxford Brain Bank, were used for the MRI comparison. Scanning was carried out on a Siemens Trio 3T scanner using a 12-channel head coil. Scanning was conducted 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 weighting was isotropically distributed along 54 directions (b=4500 s/mm2) with six b=0 images. This protocol takes approximately 6 hours, and three averages were acquired for 18 hours 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 artefacts. This was averaged over eight repeats to increase signal to noise ratio. For more details see Miller et al. (2011).

[0234] Data was processed using the FMRIB software library (FSL) (Smith et al., 2004; Woolrich et al., 2009). The FSL diffusion toolbox was used to process diffusion weighted data, which incorporates an in house processing pipeline to compensate for gradient-induced-heating drift and eddy-current distortions, to produce maps of fractional anisotropy (FA), mean diffusivity (MD) and the diffusion tensor components (Miller et al., 2011).

[0235] Selection of Brain Regions

[0236] Measures of cortical thickness in dorsolateral prefrontal cortex (Area 9) and primary visual cortex (V1) and diffusion measures of connected white matter tracts (FA and MD) were correlated with histological myelination measures in our previous study (Kolasinski et al., 2012) and, as multiple sclerosis is a demyelinating disorder, these areas were chosen for further investigation in the present study. In addition, these areas are well characterised and are known to represent a range of cortical cyto-architectural arrangements (i.e. wider minicolumns in Area 9 and narrower minicolumns in V1). An additional comparison region was included—the primary auditory cortex within Heschl's gyrus (Area 41)—because its columnar architecture is well characterised but there have been inconsistencies in previous reports on its PDD in healthy subjects (Kang et al., 2012; McNab et al., 2013). Investigation of multiple cortical regions allowed us to explore the sensitivity of diffusion metrics to regional differentiation, which would be of interest in future investigations of neurological disorders.

[0237] Neurohistological Sampling

[0238] Brains were sectioned coronally and the diagnosis of multiple sclerosis was confirmed by a clinical neuropathologist. Blocks of size 25 mm×25 mm×10 mm were sampled for each of the three regions from one hemisphere per brain (a representative random sample of hemispheres: 7 left, 8 right). Blocks and the surrounding tissue were photographed using an Olympus C-5050 digital camera for reference. Area 9 included the middle and superior frontal gyri bounded inferiorly at the paracingulate sulcus and inferior frontal sulcus. Area 9 blocks were sampled level with the anterior limit of the cingulate gyrus. Area 41 blocks incorporated Heschl's gyrus, bordered medially by the insula cortex and laterally by the planum temporale. V1 blocks were sampled along the calcarine fissure, level with the medium transverse occipital gyrus. Region of interest (ROI) selection was confirmed cyto-architecturally in accordance with Von Economo and Koskinas (1925).

[0239] Tissue blocks were embedded in paraffin wax and serially sectioned at 10 μm for the minicolumn analysis and quantification of myelin levels, and at 30 μm for the bundle measurements. Sections were stained with cresyl violet (CV; ThermoFisher Scientific, Waltham, Mass., USA) for minicolumn analysis, anti-proteolipid protein stain (AbD AbSerotec, Oxford, UK) (anti-PLP) for light transmittance myelin quantification, and Sudan black, a myelin sensitive lipophilic dye, for measurement of axonal bundles.

[0240] Cortical Diffusivity Analysis

[0241] This was a region-of-interest approach. Cortical ROIs corresponding to those sampled histologically were delineated using manually created masks on the structural post-mortem images. By careful reference to photographic images of the physically cut coronal brain slice before and after the tissue block was removed, and the corresponding Nissl stained slide, the closest matching coronal slice of the structural MRI scan was identified. Cortical ROIs were masked over 15 coronal slices of the MRI image centred around this slice, taking care to include only grey matter voxels to avoid contamination from white matter or CSF. The limits of the cortical ROIs were determined by careful comparison with the photographic images and corresponding Nissl stained slide in order to ensure the masked area matched the histologically sampled area. Novel software scripts (Mark Jenkinson, University of Oxford, 2018; WO2016/162682A1; U.S. patent application Ser. No. 15/564,344) were used to generate cortical profiles on the MRI scans, i.e. lines across the cortex in a radial direction, replicating a columnar organisation within the cortex. Values for the diffusion tensor derived metrics were averaged along the cortical profiles, across the entire masked ROI, excluding the terminal slices at the anterior and posterior ends of the RO. The metrics calculated were MD, FA and three metrics relating to the principal diffusion component (see also WO2016162682A1; U.S. patent application Ser. No. 15/564,344), namely: the angle of the deviation between the radial direction across the cortical layers and the principal diffusion direction (AngleR, θ.sub.R); the principal diffusion component projected onto the plane perpendicular to the radial direction across the cortex (described therefore as the perpendicular diffusivity, i.e. PerpPD, D1,⊥ (×10-3 mm2/sec)), and the principal diffusion component projected onto the radial direction across the cortex (described therefore as parallel to the radial direction, i.e. ParlPD, D1,∥ (×10-3 mm2/sec).

[0242] Averaging values reduced the influence of noise in the DTI data, effectively smoothing the data, and ensuring only directionality with some local coherence would dominate, guarding against the influence of random deflections from the radial direction. Averaging also provided consistency with the histological measurements, which similarly calculated a single value for each cortical region. Previous work has found that measures of the cyto- and myelo-architecture are relatively stable within a cortical subregion (e.g. Von Economo and Koskinas (1925)) indicating that it is valid to find an average value for that region.

[0243] Minicolumn Analysis

[0244] Minicolumn width, based on cell bodies, was assessed in the histological tissue sections using a semi-automated procedure that has been described in detail previously (Buxhoeveden et al., 2001; Casanova and Switala, 2005). This procedure gives a value for the minicolumn width consisting of the cell dense core region plus the associated neuropil space surrounding it. The neuropil spacing is the width of the cell sparse neuropil region between the cores of neighbouring minicolumns, while the core refers to the width of the cell dense region at the centre of the minicolumn. The microsegment number is the number of strings of cells that do not form a complete minicolumn because they are discontinuous with the rest of a minicolumn due to it passing out of the plane of section or due to minicolumn fragmentation as a result of pathology. Cell density refers to the density of cells recognised by the automated histology analysis programme within the field of view of each assessed digital photomicrograph. (See Chance et al 2011 for further discussion of microsegments and cell density) For each ROI, three digital photomicrographs were taken from a single slide where possible, each containing a region of about 1 mm.sup.2. Image locations were selected using a random number generator, excluding areas of high curvature which have been shown to affect cell distribution (Chance et al., 2004). As minicolumns are clearest in layer III, photographs were centred on that layer and obtained through a 4× objective lens, with an Olympus BX40 microscope (more 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 a single value for each region.

[0245] Quantification of Myelin Levels

[0246] Cortical myelin content was assessed using light transmittance to quantify the intensity of myelin stain in anti-PLP stained tissue sections. Data were collected using Axiovision v4.7.2 software on a PC receiving a signal from an Axiocam MRc (Carl-Zeiss, Jena, Germany) mounted on a BX40 microscope (Olympus. Japan) with a 10× objective lens. The set up was calibrated in RGB mode with fixed white balance and incident light, using a standard slide/coverslip preparation and light filters (6%, 25% and 100% transmittance). For each ROI three measures of transmittance (T) were taken in different locations across layers III to V using a 58,240 μm.sup.2 virtual frame on anti-PLP stained sections and the resulting values averaged.

[0247] Axon Bundle Analysis

[0248] For each region three photographs were obtained through a 10× objective lens (resolution 1.10 μm) with an Olympus BX40 microscope, centred around layer V as the axon bundles are clearest there. Areas of extreme curvature were avoided where possible, as was done for the minicolumn measurements.

[0249] Measurements of axon bundle centre-to-centre spacing, and the width of the bundles themselves were made manually in Axiovision, using the in-built measurement tools. The digital resolution of the analysed images was 0.67 μm/pixel. A sample line of standard length (590 μm; determined by the size of the image view) was drawn across the centre of the photograph, perpendicular to the bundle direction in order to identify the bundles to be measured. Only bundles intersecting this line were measured, those that passed out of the plane of sectioning above or below the line were not included. Single axons or pairs of axons crossing the line were not considered to constitute axon bundles for the purposes of this analysis.

[0250] Bundles (>2 axons) were identified and their centres marked. Bundle spacing measurements were then made from the centre of each bundle marked in this way to the centre of the adjacent bundle. The width of each axon bundle was also measured. For the width measurements, the edges of the bundles were marked at the point where they intersected the line, and the bundle width was determined as the distance between these two points. Edges of axon bundles were distinguished by the change in intensity of staining from the background, which identified the start of the more darkly stained axon bundle. Pilot data revealed high reliability of this method, finding a high correlation (r=0.737, p<0.001) between measurements of photos taken on two different occasions. The values from the three photographs were then averaged to give a single value for bundle spacing and a single value for bundle width for each ROI.

[0251] This resulted in an average of 28 (±5), 22 (±5), and 44 (±5) bundles being sampled for Area 9, Area 41 and V1 respectively for each subject. It was not possible to assess the orientation of the axon bundles within the cortex in a manner directly comparable to our DTI analysis because such a three-dimensional estimate is not possible in histological sections that have a limited depth, compounded by z-direction compression on the microscope slide. However, taking a subset of cases with a relatively un-curved section of cortex where it may be assumed that the three-dimensional geometric vertical is reasonably close to the two-dimensional estimate from the histological section, we were able to measure the orientation of the axon bundles relative to this. This indicated that the axon bundles deviate from the radial direction across the cortex by an average of 3.50 (±2.68) degrees.

[0252] Statistical Analysis

[0253] All data were analysed using SPSS v22 for Windows and the R statistical package (version 3.3.3) (R Core Team, 2013).

[0254] Relationship between histology and DTI—The relationship between the microanatomy and MRI diffusion metrics across the full data set was investigated by correlation analysis using Spearman's Correlation Coefficient. We carried out a correlation analysis for each of the 3 regions of interest (Area 9, Area 41 and V1) including the 5 diffusion metrics (FA, MD, Angle_R, PerpPD, ParlPD) and the 6 histology measures (Minicolumn width, Core width, Neuropil Spacing, Microsegment number, Axon bundle width, Bundle spacing). All p-values were adjusted with false discovery rate correction (FDR<0.05) (Benjamini & Yekutieli., 2001) and were reported using the approach of Preziosa et al. (2019) by providing p and P.sub.FDR for significant results.

[0255] Mean regional differences—Regional differences in both histology and DTI metrics within groups were assessed using repeated measures ANOVAs and significant main effects were followed up with post-hoc t-tests. Regional differences in DTI between groups were assessed using repeated measures ANOVA.

[0256] Histology measures—Relationships between the 6 histology measures (Minicolumn width, Core width, Neuropil Spacing, Microsegment number, Axon bundle width, Bundle spacing) were investigated using Spearman's Correlation Coefficient and adjusted by FDR correction (FDR<0.05) Multiple sclerosis clinical correlates—Our previous study indicated a relationship between the degree of change in white matter and cellular organisation in Area 9 and V1 (Kolasinski et al., 2012). As disease duration was the only clinical measure available for all subjects (Table 1), the present study investigated whether there was a significant correlation between DTI derived metrics and disease duration in these cortical regions (Area 9 and V1), and whether the correlations were different to that in the comparison region (Area 41). As age was expected to correlate with disease duration this was controlled for where appropriate using partial correlations using the standard SPSS recursive algorithm.

Example 4: DTI Differences Between Groups and Brain Regions

[0257] We used a pre-existent cohort of six controls to investigate the diffusivity measures between groups. Repeated measures ANOVAs revealed a significant main effect of diagnosis on diffusion metrics, (Tables 2, 3): AngleR (F.sub.1,13=15.575, p=0.002), MD (F.sub.1,13=20.468, p=0.002), PerpPD (F.sub.1,13=39.177, p=0.000), and ParlPD (F.sub.1,13=16.905, p=0.001) values were higher in multiple sclerosis cases compared to controls in all regions, while FA was not different between groups (F.sub.1,13=0.928, p=0.353). There was also a within subjects effect of region (FIG. 7) due to higher AngleR values in V1 compared to other regions (F.sub.2,26=5.512, p=0.026) (the region difference was slightly greater in controls but there was no region x diagnosis interaction). No significant differences between regions were found within subjects for FA, MD, PerpPD or ParlPD.

[0258] (ParlPD is the component of the principal diffusion vector that was parallel to the radial minicolumn direction across the cortex.)

TABLE-US-00002 TABLE 2 Mean values for histological variables for each region in MS brains. Standard deviations are given in brackets. Minicolumn Minicolumn Minicolumn Minicolumn Axon Bundle Axon Bundle Width Spacing Core width Microsegment Cell Spacing Width Regions (μm) (μm) (μm) number/mm.sup.2 Density (μm) (μm) Area 9 37.7 15.5 28.9 203.5 123.1 45.3 8.2 (2.50) (2.07) (4.16) (103.66) (42.53) (3.74) (1.33) Area 41 33.7 16.1 27.8 203.4 146.3 48.3 9.6 (4.14) (1.10) (2.61) (40.56) (49.82) (6.82) (0.70) V1 27.1 15.5 26.7 247.7 95.8 28.6 7.3 (3.38) (1.59) (3.33) (64.39) (48.06) (3.94) (0.84)

TABLE-US-00003 TABLE 3 Mean values for diffusion measures for each region in MS brains and controls. Standard deviations are given in brackets. Regions FA MB AngleR PerpPD ParlPD MS Area 9 0.0712 0.320* 0.898* 0.163* 0.334* cohort (0.02) (0.09) (0.09) (0.05) (0.24) Area 41 0.0909 0.435* 0.868* 0.179* 0.101* (0.02) (0.07) (0.15) (0.20) (0.03) V1 0.0875 0.251* 0.911* 0.139* 0.113* (0.03) (0.05) (0.13) (0.04) (0.04) HC Area 9 0.0925 0.190 0.679 0.075 0.138 cohort (0.02) (0.03) (0.02) (0.02) (0.02) Area 41 0.1018 0.120 0.677 0.057 0.081 (0.02) (0.01) (0.03) (0.01) (0.02) VI (0.0916) 0.164 0.821# 0.073 0.085 (0.03) (0.03) (0.06) (0.01) (0.03) *= value significantly higher than HC in between group comparison; #= value significantly higher than other regions in within group comparison.

Example 5: Histology Differences Between Brain Regions

[0259] Repeated measures ANOVA revealed a significant main effect of region on all histological measures (Tables 2, 3; FIG. 7). Primary visual cortex had the narrowest minicolumns and narrowest axon bundles, with Area 41 having the widest spacing of axon bundles and the widest bundles.

Example 6: Relationships with Clinical Variables

[0260] Due to the presence of a strong correlation between disease duration and age (r=0.883, p=0.002) partial correlations controlling for age were used to investigate the relationships with disease duration. A significant negative correlation was observed between bundle width and disease duration in Area 41 (r=−0.867, p=0.011) (FIG. 8) but not Area 9 (r=−0.438, p=0.278) or V1 (r=−0.077, p=0.856).

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