Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets.

ABSTRACT: Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-sca

SUBMITTER: Iraji A 

PROVIDER: S-EPMC10619392 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature