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

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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 the second largest contributor to total variance in occipital cortex, while across-runs variance is the second dominant source for the rest of the brain. Network-specific analysis revealed that across-block variance contributes more to total variance in higher-order cognitive networks than in somatosensory cortex. Moreover, in some higher-order cognitive networks across-block variance can exceed across-session variance. These results help us better understand the temporal (i.e., across blocks, runs and sessions) and spatial distributions (i.e., across different networks) of within-subject natural variability in estimates of task responses in fMRI. They also suggest that different brain regions will show different natural levels of test-retest reliability even in the absence of residual artifacts and sufficiently high contrast-to-noise measurements. Further confirmation with a larger sample of subjects and other tasks is necessary to ensure generality of these results.

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