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Background and ObjectivesCognitive impairment in multiple sclerosis (MS) is associated with functional connectivity abnormalities. While there have been calls to use functional connectivity measures as biomarkers, there remains to be a full understanding of why they are affected in MS. In this cross-sectional study, we tested the hypothesis that functional network regions may be susceptible to disease-related “wear and tear” and that this can be observable on co-occurring abnormalities on other magnetic resonance metrics. We tested whether functional connectivity abnormalities in cognitively impaired patients with MS co-occur with (1) overlapping, (2) local, or (3) distal changes in anatomic connectivity and cerebral blood flow abnormalities.MethodsMultimodal 3T MRI and assessment with the Brief Repeatable Battery of Neuropsychological tests were performed in 102 patients with relapsing-remitting MS and 27 healthy controls. Patients with MS were classified as cognitively impaired if they scored ≥1.5 SDs below the control mean on ≥2 tests (n = 55) or as cognitively preserved (n = 47). Functional connectivity was assessed with Independent Component Analysis and dual regression of resting-state fMRI images. Cerebral blood flow maps were estimated, and anatomic connectivity was assessed with anatomic connectivity mapping and fractional anisotropy of diffusion-weighted MRI. Changes in cerebral blood flow and anatomic connectivity were assessed within resting-state networks that showed functional connectivity abnormalities in cognitively impaired patients with MS.ResultsFunctional connectivity was significantly decreased in the anterior and posterior default mode networks and significantly increased in the right and left frontoparietal networks in cognitively impaired relative to cognitively preserved patients with MS (threshold-free cluster enhancement corrected at p ≤ 0.05, 2 sided). Networks showing functional abnormalities showed altered cerebral blood flow and anatomic connectivity locally and distally but not in overlapping locations.DiscussionWe provide the first evidence that functional connectivity abnormalities are accompanied by local cerebral blood flow and structural connectivity abnormalities but also demonstrate that these effects do not occur in exactly the same location. Our findings suggest a possibly shared pathologic mechanism for altered functional connectivity in brain networks in MS.

Cognitive impairment in multiple sclerosis is associated with functional connectivity abnormalities, but the pathological substrates of these abnormalities are not well understood. It has been proposed that resting-state network nodes that integrate information from disparate regions are susceptible to metabolic stress, which may impact functional connectivity. In multiple sclerosis, pathology could increase metabolic stress within axons, damaging the anatomical connections of network regions, and leading to functional connectivity changes. We tested this hypothesis by assessing whether resting state network regions that show functional connectivity abnormalities in people with cognitive impairment also show anatomical connectivity abnormalities.Multimodal MRI and neuropsychological assessments were performed in 102 relapsing remitting multiple sclerosis patients and 27 healthy controls. Patients were considered cognitively impaired if they obtained a z-score of ≤1.5 on ≥2 tests of the Brief Repeatable Battery of Neuropsychological Tests (n=55). Functional connectivity was assessed with Independent Component Analysis of resting state fMRI images, and anatomical connectivity with Anatomical Connectivity Mapping of diffusion-weighted MRI. Exploratory analyses of fractional anisotropy and cerebral blood flow changes were conducted to assess local tissue characteristics.We found significantly decreased functional connectivity in the anterior and posterior default mode networks and significant increases in the right and left frontoparietal networks in cognitively impaired relative to cognitively preserved patients. Networks showing functional abnormalities also showed reduced anatomical connectivity and white matter microstructure integrity as well as reduced local tissue cerebral blood flow.Our results identify key pathological correlates of functional connectivity abnormalities associated with impaired cognitive function in multiple sclerosis, consistent with metabolic dysfunction in functional network regions.

Background: The development of tailored recovery-oriented strategies in multiple sclerosis requires early identification of an individual’s potential for functional recovery. Objective: To identify predictors of visuomotor performance improvements, a proxy of functional recovery, using a predictive statistical model that combines demographic, clinical and magnetic resonance imaging (MRI) data. Methods: Right-handed multiple sclerosis patients underwent baseline disability assessment and MRI of the brain structure, function and vascular health. They subsequently undertook 4 weeks of right upper limb visuomotor practice. Changes in performance with practice were our outcome measure. We identified predictors of improvement in a training set of patients using lasso regression; we calculated the best performing model in a validation set and applied this model to a test set. Results: Patients improved their visuomotor performance with practice. Younger age, better visuomotor abilities, less severe disease burden and concurrent use of preventive treatments predicted improvements. Neuroimaging localised outcome-relevant sensory motor regions, the microstructure and activity of which correlated with performance improvements. Conclusion: Initial characteristics, including age, disease duration, visuo-spatial abilities, hand dexterity, self-evaluated disease impact and the presence of disease-modifying treatments, can predict functional recovery in individual patients, potentially improving their clinical management and stratification in clinical trials. MRI is a correlate of outcome, potentially supporting individual prognosis.

Huntington's disease is a genetic neurodegenerative disorder. White matter alterations have recently been identified as a relevant pathophysiological feature of Huntington's disease, but their etiology and role in disease pathogenesis and progression remain unclear. Increasing evidence suggests that white matter changes in this disorder are attributed to alterations in myelin-associated biological processes. This review first discusses evidence from neurochemical studies lending support to the demyelination hypothesis of Huntington's disease, demonstrating aberrant myelination and changes in oligodendrocytes in the Huntington's brain. Next, evidence from neuroimaging studies is reviewed, the limitations of the described methodologies are discussed, and suggested interpretations of findings from published studies are challenged. Although our understanding of Huntington's associated pathological changes in the brain will increasingly rely on neuroimaging techniques, the shortcomings of these methodologies must not be forgotten. Advances in magnetic resonance imaging techniques and tissue modeling will enable a better in vivo, longitudinal characterization of the biological properties of white matter microstructure. This in turn will facilitate identification of disease-related biomarkers and the specification of outcome measures in clinical trials. © 2020 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Accurate anatomical localisation of specific white matter tracts and the quantification of their tract-specific microstructural damage in conditions such as multiple sclerosis (MS) can contribute to a better understanding of symptomatology, disease evolution and intervention effects. Diffusion MRI-based tractography is being used increasingly to segment white matter tracts as regions-of-interest for subsequent quantitative analysis. Since MS lesions can interrupt the tractography algorithm’s tract reconstruction, clinical studies frequently resort to atlas-based approaches, which are convenient but ignorant to individual variability in tract size and shape. Here, we revisit the problem of individual tractography in MS, comparing tractography algorithms using: (i) The diffusion tensor framework; (ii) constrained spherical deconvolution (CSD); and (iii) damped Richardson-Lucy (dRL) deconvolution. Firstly, using simulated and in vivo data from 29 MS patients and 19 healthy controls, we show that the three tracking algorithms respond differentially to MS pathology. While the tensor-based approach is unable to deal with crossing fibres, CSD produces spurious streamlines, in particular in tissue with high fibre loss and low diffusion anisotropy. With dRL, streamlines are increasingly interrupted in pathological tissue. Secondly, we demonstrate that despite the effects of lesions on the fibre orientation reconstruction algorithms, fibre tracking algorithms are still able to segment tracts that pass through areas with a high prevalence of lesions. Combining dRL-based tractography with an automated tract segmentation tool on data from 131 MS patients, the cortico-spinal tracts and arcuate fasciculi could be reconstructed in more than 90% of individuals. Comparing tract-specific microstructural parameters (fractional anisotropy, radial diffusivity and magnetisation transfer ratio) in individually segmented tracts to those from a tract probability map, we show that there is no systematic disease-related bias in the individually reconstructed tracts, suggesting that lesions and otherwise damaged parts are not systematically omitted during tractography. Thirdly, we demonstrate modest anatomical correspondence between the individual and tract probability-based approach, with a spatial overlap between 35 and 55%. Correlations between tract-averaged microstructural parameters in individually segmented tracts and the probability-map approach ranged between \ud \ud \ud \ud \ud \ud r=.53\ud (\ud \ud \ud \ud \ud \ud \ud p

In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. The usage of deep neural networks for novelty detection remains an open challenge. Here we propose novelty detection methods based on training variational autoencoders (VAEs) on normal data. We apply these methods to magnetic resonance imaging, namely to the detection of diffusion-space (q-space) abnormalities in diffusion MRI scans of multiple sclerosis patients. q-Space novelty detection can reduce scan time duration and does not require any disease-specific prior knowledge, thus overcoming the disadvantages of other diffusion MRI processing methods. The methods proposed herein outperform the state of the art on q-space data in terms of quality and inference time. Our methods also outperform the state of the art on a standard novelty detection benchmark, and hence are also promising for non-MRI novelty detection.

Huntington’s disease (HD) is a genetic neurodegenerative disorder, characterised by atrophy of the neostriatum, and cortical grey matter abnormalities. White matter (WM) alterations have recently been identified as a relevant pathophysiological feature of HD, but the etiology of WM degeneration, and its role in disease pathogenesis and progression remain unclear. An increasing body of research suggests that WM changes in HD are due to alterations in myelin-associated biological processes at the cellular and molecular level. This review first discusses evidence from neurochemical studies lending support to the ‘De-myelination hypothesis’ of HD, and pointing towards a role for aberrant myelination and changes in oligodendrocytes in HD WM. Next, evidence from neuroimaging studies is reviewed, the limitations of the described methodologies are discussed and suggested interpretations of findings from published studies are challenged. Although our understanding of HD-associated pathological changes in the brain will increasingly rely on neuroimaging techniques, the shortcomings of these methodologies must not be forgotten. Advances in MRI techniques and tissue modeling will enable a better characterization of the biological properties of WM microstructure, and will allow more specific monitoring of longitudinal changes noninvasively. This, in turn, will provide insight into disease pathogenesis and progression and facilitate the identification of disease-related biomarkers and the specification of outcome measures in clinical trials.

Accurate anatomical localisation of specific white matter tracts and the quantification of their tractspecific microstructural damage in multiple sclerosis (MS) can contribute to a better understanding of symptomatology, disease progression and intervention effects. Diffusion MRI-based tractography is being used increasingly to segment white matter tracts as regions-of-interest for subsequent quantitative analysis. Since MS lesions can interrupt the tractography algorithms tract reconstruction, clinical studies frequently resort to atlas-based approaches, which are convenient but ignorant to individual variability in tract size and shape. Here, we revisit the problem of individual tractography in MS, comparing tractography algorithms using: (i) The diffusion tensor framework; (ii) constrained spherical deconvoution (CSD); and (iii) damped Richardson-Lucy (dRL) deconvolution. Firstly, using simulated and in vivo data from 29 MS patients and 19 healthy controls, we show that the three tracking algorithms respond differentially to MS pathology. While the tensor-based approach is unable to deal with crossing fibres, CSD produces spurious stream-lines, in particular in tissue with high fibre loss and low diffusion anisotropy. With dRL, streamlines are increasingly interrupted in pathological tissue. Secondly, we demonstrate that despite the effects of lesion on the fibre orientation reconstruction algorithms, fibre tracking algorithms are still able to segment tracts that pass areas with high prevalence of lesions. Combining dRL-based tractography with an automated tract segmentation tool on data from 131 MS patients, the corticospinal tracts and arcuate fasciculi were successfully reconstructed in more than 90% of individuals. Comparing tractspecific microstructural parameters (fractional anisotropy, radial diffusivity and magnetisation transfer ratio) in individually segmented tracts to those from a tract probability map, we showed that there is no systematic disease-related bias in the individually reconstructed tracts, suggesting that lesions and otherwise damaged parts are not systematically omitted during tractography. Thirdly, we demonstrate modest anatomical correspondence between the individual and tract probability-based approach, with a spatial overlap between 35 and 55%. Correlations between tract-averaged microstructural parameters in individually segmented tracts and the probability-map approach ranged between r = .52 (p < .001) for radial diffusivity in the right cortico-spinal tract and r = .97 (p < .001) for magnetization transfer ratio in the arcuate fasciculi. Our results show that MS white matter lesions impact fibre orientation reconstructions but this does not appear to hinder the ability to anatomically localise white matter tracts in MS. Individual tract segmentation in MS is feasible on a large scale and could prove a powerful tool for investigating diagnostic and prognostic markers.

Changes in the volume of brain gray matter in people with multiple sclerosis (MS) are a core MRI feature of the disease. Alterations in gray matter volume have been found to reflect the severity of the disease but also the emergence of cognitive impairment in people with MS. These findings have led to increasing research into the neuropathological factors affecting gray matter volume and outcomes associated with gray matter volume changes. In this chapter, we discuss the methods used to assess gray matter volume on MRI, links to brain lesions, and other MS-related MRI features and the potential underlying mechanisms that may lead to gray matter atrophy. We consider the relationships between altered regional gray matter volume and both physical and cognitive disability. Last, we discuss how a better understanding of brain volume changes and their implications in MS may help us to improve the use of this metric in clinical trials and in estimating prognosis in individual patients.

FMRI BOLD responses to changes in neural activity are influenced by the reactivity of the vasculature. By complementing a task-related BOLD acquisition with a vascular reactivity measure obtained through breath-holding or hypercapnia, this unwanted variance can be statistically reduced in the BOLD responses of interest. Recently, it has been suggested that vascular reactivity can also be estimated using a resting state scan. This study aimed to compare three breath-hold based analysis approaches (block design, sine–cosine regressor and CO2 regressor) and a resting state approach (CO2 regressor) to measure vascular reactivity. We tested BOLD variance explained by the model and repeatability of the measures. Fifteen healthy participants underwent a breath-hold task and a resting state scan with end-tidal CO2 being recorded during both. Vascular reactivity was defined as CO2-related BOLD percent signal change/mm Hg change in CO2. Maps and regional vascular reactivity estimates showed high repeatability when the breath-hold task was used. Repeatability and variance explained by the CO2 trace regressor were lower for the resting state data based approach, which resulted in highly variable measures of vascular reactivity. We conclude that breath-hold based vascular reactivity estimations are more repeatable than resting-based estimates, and that there are limitations with replacing breath-hold scans by resting state scans for vascular reactivity assessment.