Variance decomposition for single-subject task-based fMRI activity estimates across many sessions.

ABSTRACT: Here we report an exploratory within-subject variance decomposition analysis conducted on a task-based fMRI dataset with an unusually large number of repeated measures (i.e., 500 trials in each of three different subjects) distributed across 100 functional scans and 9 to 10 different sessions. Within-subject variance was segregated into four primary components: variance across-sessions, variance across-runs within a session, variance across-blocks within a run, and residual measurement/modeling error. Our results reveal inhomogeneous and distinct spatial distributions of these variance components across significantly active voxels in grey matter. Measurement error is dominant across the whole brain. Detailed evaluation of the remaining three components shows that across-session variance is

SUBMITTER: Gonzalez-Castillo J 

PROVIDER: S-EPMC5398961 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature