Fatigue is one of the most frequent symptoms in multiple sclerosis (MS), and recent studies have described a relationship between the sensorimotor cortex and its afferent and efferent pathways as a substrate of fatigue. The objectives of this study were to assess the neural correlates of fatigue in MS through gray matter (GM) and white matter (WM) atrophy, and resting state functional connectivity (rs-FC) of the sensorimotor network (SMN). Eighteen healthy controls (HCs) and 60 relapsing-remitting patients were assessed with the Fatigue Severity Scale (FSS). Patients were classified as fatigued (F) or nonfatigued (NF). We investigated GM and WM atrophy using voxel-based morphometry, and rs-FC changes with a seed-based method and independent component analysis (ICA). F patients showed extended GM and WM atrophy focused on areas related to the SMN. High FSS scores were associated with reductions of WM in the supplementary motor area. Seed analysis of GM atrophy in the SMN showed that HCs presented increased rs-FC between the primary motor and somatosensory cortices while patients with high FSS scores were associated with decreased rs-FC between the supplementary motor area and associative somatosensory cortex. ICA results showed that NF patients presented higher rs-FC in the primary motor cortex compared to HCs and in the premotor cortex compared to F patients. Atrophy reduced functional connectivity in SMN pathways and MS patients consequently experienced high levels of fatigue. On the contrary, NF patients experienced high synchronization in this network that could be interpreted as a compensatory mechanism to reduce fatigue sensation.

Objective: The objective of this paper is to explore differences in resting-state functional connectivity between cognitively impaired and preserved multiple sclerosis (MS) patients. Methods: Sixty MS patients and 18 controls were assessed with the Brief Repeatable Battery of Neuropsychological Tests (BRB-N). A global Z score of the BRB-N was obtained and allowed us to classify MS patients as cognitively impaired and cognitively preserved ( n = 30 per group). Functional connectivity was assessed by independent component analysis of resting-state networks (RSNs) related to cognition: the default mode network, left and right frontoparietal and salience network. Between-group differences were evaluated and a regression analysis was performed to describe relationships among cognitive status, functional connectivity and radiological variables. Results: Compared to cognitively preserved patients and healthy controls, cognitively impaired patients showed a lesser degree of functional connectivity in all RSNs explored. Cognitively preserved patients presented less connectivity than the control group in the left frontoparietal network. Global Z scores were positively and negatively correlated with brain parenchymal fraction and lesion volume, respectively. Conclusion: Decreased cognitive performance is accompanied by reduced resting state functional connectivity and directly related to brain damage. These results support the use of connectivity as a powerful tool to monitor and predict cognitive impairment in MS patients.

Sex differences in 116 local gray matter volumes (GMVOL) were assessed in 444 males and 444 females without correcting for total intracranial volume (TIV) or after adjusting the data with the scaling, proportions, power-corrected proportions (PCP), and residuals methods. The results confirmed that only the residuals and PCP methods completely eliminate TIV-variation and result in sex-differences that are “small” (∣d∣ < 0.3). Moreover, as assessed using a totally independent sample, sex differences in PCP and residuals adjusted-data showed higher replicability ($$\approx $$ ≈ 93%) than scaling and proportions adjusted-data $$( \approx $$ ( ≈ 68%) or raw data ($$\approx $$ ≈ 45%). The replicated effects were meta-analyzed together and confirmed that, when TIV-variation is adequately controlled, volumetric sex differences become “small” (∣d∣ < 0.3 in all cases). Finally, we assessed the utility of TIV-corrected/ TIV-uncorrected GMVOL features in predicting individuals’ sex with 12 different machine learning classifiers. Sex could be reliably predicted (> 80%) when using raw local GMVOL, but also when using scaling or proportions adjusted-data or TIV as a single predictor. Conversely, after properly controlling TIV variation with the PCP and residuals’ methods, prediction accuracy dropped to $$\approx $$ ≈ 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GMVOL

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