Distributed causality in resting-state network connectivity in the acute and remitting phases of RRMS
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BMC Neuroscience Open Access
RESEARCH ARTICLE
Distributed causality in resting‑state network connectivity in the acute and remitting phases of RRMS Lin Wu1,2, Muhua Huang1,2, Fuqing Zhou1,2*, Xianjun Zeng1,2 and Honghan Gong1,2
Abstract Background: Although previous studies have shown that intra-network abnormalities in brain functional networks are correlated with clinical/cognitive impairment in multiple sclerosis (MS), there is little information regarding the pattern of causal interactions among cognition-related resting-state networks (RSNs) in different disease stages of relapsing–remitting MS (RRMS) patients. We hypothesized that abnormalities of causal interactions among RSNs occurred in RRMS patients in the acute and remitting phases. Methods: Seventeen patients in the acute phases of RRMS, 24 patients in the remitting phases of RRMS, and 23 appropriately matched healthy controls participated in this study. First, we used group independent component analysis to extract the time courses of the spatially independent components from all the subjects. Then, the Granger causality analysis was used to investigate the causal relationships among RSNs in the spectral domain and to identify correlations with clinical indices. Results: Compared with the patients in the acute phase of RRMS, patients in the remitting phase of RRMS showed a significantly lower expanded disability status scale, modified fatigue impact scale scores, and significantly higher paced auditory serial addition test (PASAT) scores. Compared with healthy subjects, during the acute phase, RRMS patients had significantly increased driving connectivity from the right executive control network (rECN) to the anterior salience network (aSN), and the causal coefficient was negatively correlated with the PASAT score. During the remitting phase, RRMS patients had significantly increased driving connectivity from the rECN to the aSN and from the rECN to the visuospatial network. Conclusions: Together with the disease duration (mean disease duration 2 mm and a maximum rotation (x, y, z) > 2° were excluded. Group ICA
We performed a group spatial ICA on the preprocessed data of the patients with RRMS and normal controls using the Group ICA of fMRI Toolbox (GIFT, https:// icatb.sourceforge.net/groupica.htm). We chose a relatively high model order ICA (number of components, C = 75), as previous studies have demonstrated that such models yield refined components [21] and a highly stable ICA decomposition [22]. In the group ICA, the mean independent components of all the subjects, the corresponding mean time courses and the independent components for each subject were obtained from the group ICA separation and back reconstruction to ensure that all the subjects had the same components [23]. After standard preprocessing of the group ICA results, from 75 components, we identified fourteen RSNs via a templatematching algorithm based on the maximum spatial correlation value. These functional templates were provided by Shirer et al. [19]. Then, one-sampl
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