Supplementary Materials Appendix S1. pattern analysis. Linear soft\threshold support vector machine models were used to separate responders from nonresponders. The permutation tests Polygalaxanthone III demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a considerably bigger component allegiance of ACC was within treatment responders in comparison to nonresponders also, recommending high interactivities of ACC to additional regions could be good for the recovery after treatment. Constant outcomes across multicenters verified that ACC could serve as a predictor of escitalopram monotherapy treatment result, implying strong probability of replication in the foreseeable future. = 15/4/11), 15?mg/day time (= 15/20/19), and 10 mg/day time (= 4/10/6). The common dose (shows the times a node adjustments its modules between two consecutive period home windows in personal level. In this real way, the representative versatility resulted in a complete of 95 features (one for every area) for predicting specific treatment result. 2.7. Multivariate pattern analysis Multivariate pattern analysis (MVPA) was a well\appropriate method and was carried out to discern minor differences root the ideals (Mohr, Wolfensteller, & Ruge, 2017). The insight features for the MVPA had been constructed by determining subsets of data that are most highly relevant to the treatment result using minimal redundancy optimum relevance (mRMR) (Peng, Very long, & Ding, 2005). As a favorite strategy over neuroimaging research, the linear smooth\threshold support vector machine (SVM) model (linear kernel, smooth margin = 1) (Schrouff et al., 2013) was utilized to split up responders from non-responders. The model precision was examined using departing\value of accuracy/weight was calculated from the permutation procedure that had a larger accuracy/weight compared to the null model. The value and significance of the weights provided an Polygalaxanthone III indication of the relative importance of the respective features for classification and prediction. It should be noted, however, that these values must be interpreted with care. The weights Polygalaxanthone III indicated the direction and quantified the contribution of each node to classifier decision rather than unfolded the difference between classes. 2.9. Statistical analysis The interaction among meaningful discriminative regions was compared between responders and nonresponders according to two\sample two\tailed tests. In order to account for multiple comparisons and counteract the likelihood of false positives, false\discovery rate (FDR) correction was applied (Storey, 2003). The statistically significant difference was set at test; bPearson Chi\square test. 3.2. Dynamic network module Dynamic modules, in this case, represent the groups of mutually correlated brain regions, which are weakly connected to the rest of network along time. We investigated modular organization across the brain networks in both responders and nonresponders using multilayer modularity framework. The multilayer community assignment of one randomly selected responder and nonresponder were showed in Physique ?Physique1a.1a. Six networks were transiently integrated and segmented and thereafter, rearranged into special communities across the intrinsic networks. The results of averaged FC matrices and averaged MA matrices within each group were compared. The intrinsic functional architecture was better delineated via MA matrices. Regions within the DMN were more likely to be assigned into the same modules while the CCN PTCRA integrated with other intrinsic networks (Physique ?(Physique1b,c).1b,c). Comparable results for the samples in second and third sites were showed in Figures S2 and S3. Open in a separate window Physique 1 Dynamic modular structure and functional connectivity. (a).