Functional alteration due to structural damage is network dependent: insight from multiple sclerosis.

Little is known about how the brain's functional organization changes over time with respect to structural damage. Using multiple sclerosis as a model of structural damage, we assessed how much functional connectivity (FC) changed within and between preselected resting-state networks (RSNs) in 122 subjects (72 with multiple sclerosis and 50 healthy controls). We acquired the structural, diffusion, and functional MRI to compute functional connectomes and structural disconnectivity profiles. Change in FC was calculated by comparing each multiple sclerosis participant's pairwise FC to controls, while structural disruption (SD) was computed from abnormalities in diffusion MRI via the Network Modification tool. We used an ordinary least squares regression to predict the change in FC from SD for 9 common RSNs. We found clear differences in how RSNs functionally respond to structural damage, namely that higher-order networks were more likely to experience changes in FC in response to structural damage (default mode R2 = 0.160-0.207, P < 0.001) than lower-order sensory networks (visual network 1 R2 = 0.001-0.007, P = 0.157-0.387). Our findings suggest that functional adaptability to structural damage depends on how involved the affected network is in higher-order processing.

Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial.

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA has recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. An example implementation of time-resolved MVPA based on linear SVM classification is described, with accompanying code in Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above-chance accuracy for classifying stimuli images. Extensions of the classification analysis are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.

Diagnosis of depression in multiple sclerosis is predicted by frontal-parietal white matter tract disruption.

BACKGROUND: Persons with multiple sclerosis (PwMS) are at an elevated risk of depression. Decreased Conscientiousness may affect patient outcomes in PwMS. Low Conscientiousness has a strong correlation with depression. Previous work has also reported that white matter (WM) tract disruption in frontal-parietal networks explains reduced Conscientiousness in PwMS. OBJECTIVE: We hypothesized that Conscientiousness-associated WM tract disruption predicts new-onset depression over 5 years in PwMS and evaluated this by assessing the predictive power of mean Conscientiousness associated frontal-parietal network (CFPN) disruption in PwMS for clinically diagnosed depression over 5 years. METHODS: This longitudinal retrospective analysis included 53 PwMS who were not previously diagnosed as depressed. All participants underwent structural MRI. Medical records were reviewed to evaluate diagnosis of depression for these patients over 5 years. WM tract damage between pairs of gray matter regions in the CFPN was measured using diffusion imaging. The relationship between CFPN disruption and depression was analyzed using logistic regression. RESULTS: Participants with MS had a mean age of 46.0 years (SD = 11.2). 22.6% (n = 12) acquired a diagnosis of clinical depression over the 5-year period. Baseline disruption in the CFPN was a significant predictor (ROC AUC = 61.8%). of new-onset clinical depression, accounting for age, sex, lateral ventricular volume, disease modifying treatment, and lesion volume. CONCLUSION: Baseline CFPN disruption is associated with progression to clinical depression over 5 years in PwMS. Development of new WM pathology within this network may be a risk factor for depression.

Preserved network functional connectivity underlies cognitive reserve in multiple sclerosis.

Cognitive reserve is one's mental resilience or resistance to the effects of structural brain damage. Reserve effects are well established in people with multiple sclerosis (PwMS) and Alzheimer's disease, but the neural basis of this phenomenon is unclear. We aimed to investigate whether preservation of functional connectivity explains cognitive reserve. Seventy-four PwMS and 29 HCs underwent neuropsychological assessment and 3 T MRI. Structural damage measures included gray matter (GM) atrophy and network white matter (WM) tract disruption between pairs of GM regions. Resting-state functional connectivity was also assessed. PwMS exhibited significantly impaired cognitive processing speed (t = 2.14, p = .037) and visual/spatial memory (t = 2.72, p = .008), and had significantly greater variance in functional connectivity relative to HCs within relevant networks (p < .001, p < .001, p = .016). Higher premorbid verbal intelligence, a proxy for cognitive reserve, predicted relative preservation of functional connectivity despite accumulation of GM atrophy (standardized-ß = .301, p = .021). Furthermore, preservation of functional connectivity attenuated the impact of structural network WM tract disruption on cognition (ß = -.513, p = .001, for cognitive processing speed; ß = -.209, p = .066, for visual/spatial memory). The data suggests that preserved functional connectivity explains cognitive reserve in PwMS, helping to maintain cognitive capacity despite structural damage.

Lower self-report fatigue in multiple sclerosis is associated with localized white matter tract disruption between amygdala, temporal pole, insula, and other connected structures.

BACKGROUND: Fatigue, a frequent and disabling symptom for people with multiple sclerosis (PwMS), inconsistently correlates with white matter (WM) pathology. Network-based analysis, accounting for the manner in which lesions disrupt networks of structurally connected gray matter (GM) regions, may provide additional insight. OBJECTIVE: To identify patterns of WM tract disruption which explain self-reported fatigue severity in PwMS. METHODS: 137 PwMS and 50 age- and sex-matched healthy controls (HC) underwent fatigue assessment and brain MRI. Lesion maps were applied to determine the severity of WM tract disruption between pairs of GM regions. Then, the Network-Based-Statistics tool was applied to identify structural networks whose disruption explained fatigue severity. To determine whether these networks explain unique variance above conventional MRI measures and depression, regressions were applied controlling for age, sex, brain volume, T2-lesion volume, and depression. RESULTS: Patient-perceived fatigue in PwMS was positively associated with overall lesion burden (ß = 0.563, p-value < 0.001). In contrast, localized disruptions in WM tracts between regions including the amygdala, insula, hippocampus, putamen, temporal pole, caudal-middle-frontal gyrus, rostral-middle-frontal gyrus, inferior-parietal gyrus, and banks of the superior temporal sulcus were significantly negatively correlated with fatigue in PwMS (ß = -0.586, p-value < 0.001). Average disruption within this specific, localized network explained significant additional variance in fatigue above what was otherwise explained by depression and conventional MRI measures of neuropathology (ΔR2 = 0.078, p-value < 0.001). CONCLUSION: Although overall lesion burden correlates positively with fatigue in PwMS, localized WM damage between the amygdala, temporal pole, and other connected structures is associated with lower severity of patient-perceived fatigue.