White matter lesion loads associated with dynamic functional connectivity within attention network in patients with relapsing-remitting multiple sclerosis. (July 2019) - Huang, Muhua; Zhou, Fuqing; Wu, Lin; Wang, Bo; Guo, Linghong; Zhao, Yanlin; Wan, Hui; Li, Fangjun; Zeng, Xianjun; Gong, Honghan
Highlights: dFC reveals the time-varying characteristic of attention networks better than sFC. Alterations dFC pattern of attention network in RRMS are linked to lesions damage. Adaptive mechanism that maintain stable interactions occur in remission RRMS patients. Abstract: Using time-variant of blood oxygenation level dependent (BOLD) signal to investigate the temporal changes in functional connectivity (FC) between key nodes may shed light on the dynamic characteristics of network. Twenty-two relapsing-remitting multiple sclerosis (RRMS) and 22 well-matched healthy control subjects (HCs) participated in this study. Previously validated key nodes of attention network seeds were defined as spherical regions of interests (ROIs); then, we captured the pattern of dFC using sliding window correlation of ROIs in the RRMS and HCs during rest. Furthermore, correlation analysis between altered dFC of paired-ROIs with clinical measures in RRMS were performed. Compared with the HCs, the RRMS showed: a certain specificity transient pattern of FC of attention network at time window levels, including decreased dFC within dorsal attention network [connections of left intraparietal sulcus (LIPS)-right intraparietal sulcus (RIPS), LIPS-right frontal eye field (RFEF) and left frontal eye field (LFEF)-RIPS] and ventral attention network [connection of right ventral frontal cortex (RVFC)-right temporal parietal junction (RTPJ)], increased dFC between dorsal and ventral attention network (connections of LIPS-RTPJ and LIPS-RVFC). Secondary analysis indicated that the dFC coefficients of the connections of LIPS-RIPS (r = −0.467, P = 0.023) and RVFC-RTPJ (r = −0.452, P = 0.043) were significant negative correlated with the total white matter lesion load. In conclusion, we found that the instantaneous configuration pattern of FC in attention network of RRMS are relate to lesions loads.