Computational protocol: Synchronization within, and interactions between, the default mode and dorsal attention networks in relapsing-remitting multiple sclerosis

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[…] Individual visible WM lesion masks were manually delineated based on their T2WI by an experienced neuroradiologist (FZ or XZ, who have >10 years of experience in neuroradiology). The total WM lesion loads (TWMLLs) were calculated as the volumes within the masks using MRIcron software. After co-registration with the T1WI, the T2WI was normalized into Montreal Neurological Institute (MNI) space, and this information was used to warp the lesion mask into the standard MNI dimensions. The lesion load calculated from the spatially normalized lesion mask (as the normalized TWMLL) reflected the lesion loads relative to the standard MNI brain volume to determine the controlled effects of differences in brain volume. To test the reproducibility, the manually delineated TWMLLs were measured on two separate occasions (at least 3 months apart) in the patients, and the inter-rater reliability was 92.8%.The high-resolution T1WI data were segmented into GM, WM, and cerebrospinal fluid using the new segmentation algorithm provided in Statistical Parametric Mapping (SPM12). The GM and WM probability images were then registered and warped into MNI space using the aforementioned DARTEL process. The brain parenchymal fraction (BPF) was then calculated as the ratio of the brain parenchymal volume (GM and WM) to the intracranial volume (ICV). [...] The FNC was analyzed using the FNC toolbox (v2.3) to determine the temporal correlation between the components (subnetworks) obtained by the ICA. The direct possibility was calculated using the constrained maximal lagged correlation coefficient (δXY) between each pair of subnetworks. Assume that X¯ occurs at the initial reference point i0(X¯i0) and that Y¯ circularly shifted Δi units from reference point of i0(Y¯i0+Δi); using this approach, individual correlations and lag values of components (subnetworks) were calculated as follows: δXY=max−t≤Δi≤t(X¯i0TY¯i0+ΔiX¯i0TX¯i0Y¯i0+ΔiT+Y¯i0+Δi)(1)where X¯ and Y¯ represent the corresponding time courses of two subnetworks, i0 represents the starting reference point of the two original time courses, T represents the number of time points in the time course, and Δi represents the noninteger change in time (lag time, maximal t = 2TR). The maximal correlation value and corresponding lag, δXY, were saved for time courses X¯ and Y¯. The lag values represent the amount of delay between two correlated component time courses averaged across the patients and controls.We calculated all 5 × (5 – 1)/2 = 10 pair-wise combinations and then conducted one-sample t-tests to determine the significance of the combination (p < 0.05, false discovery rate corrected). Two-sample t-tests were subsequently applied to detect the abnormal connections within the sub-networks and between the subnetworks, with age, gender, and TWMLL as covariates. […]

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