Khue Tran and Ian Murray, PhD

Introduction: Multiple sclerosis (MS) is a chronic autoimmune, inflammatory disease of the central nervous system, characterized by acute demyelinating attacks and progressive neurodegeneration.^1-4 MS can manifest into a wide range of symptoms, including physical disability, cognitive impairment, and decreased quality of life.^3-5 Although the mechanism of axonal attacks and disrupted signal transduction have been studied, the supraspinal control of the body affected by MS is complex and requires further investigation.^5,6 Functional connectivity (FC) allows for the study of specific coordinated patterns of activation between gray matter brain regions that are functionally connected.⁷ Using FC, we investigated the underlying neural basis for impaired cognition in MS patients. Methods: Two main methods to assess FC in brain imaging are seed-to-voxel and seed-to-seed.⁷ From functional magnetic resonance imaging (fMRI) images, seed-to-voxel starts with a seed region, whose activity (measured by blood-oxygen-level-dependent (BOLD) signal) during a time-course is correlated with those from other voxels, resulting in a heat map. Then a statistical threshold can be applied to reveal regions whose time-course is the most similar to the initial seed region’s.⁷ In seed-to-seed analysis, activity during a time-course of predefined atlas regions was first extracted, from which pairwise correlations between all regions can be computed, which can be visualized as a FC matrix.⁷ FC of different brain networks can be used to compare between MS patients and healthy controls, assess response to cognitive training, or predict MS progression. Results: MS patients show an altered FC network compared to controls, which can be explained by the disruption of signals caused by disease burdens, as well as the cross-network neuroplasticity as patients’ brain attempts to create new connections.^8-10 Following computer-based cognitive training, in both controls and MS patients, stronger FC was observed in fronto-parietal networks compared to untrained individuals.¹¹ Finally, when baseline resting-state FC, conventional MRI variables (gray matter volume and atrophy), and clinical variables were used in disease progression prediction, patients with abnormal FC within and between sensorimotor network and default-mode network showed worsened MS disability progression after 6.4 years.¹² Conclusions: Brain networks important for cognition in MS include the fronto-parietal, visual/spatial memory, and thalamic networks. MS patients showed greater variance in FC of cognitive networks compared to controls due to the cerebral reorganization processes of the brain in response to structural damage caused by MS. FC, in addition to clinical and conventional MRI variables, can be used to predict worsening disease progression in MS patients.

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