Impact of Independent Component Analysis Dimensionality on the Test-Retest Reliability of Resting-State Functional
Yizhou Ma1,2, Angus W MacDonald Iii2,3
1Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Abstract:
As resting-state functional connectivity (rsFC) research moves toward the study of individual differences, test-retest reliability is increasingly important to understand. Previous literature supports the test-retest reliability of rsFC derived with independent component analysis (ICA) and dual regression, yet the impact of dimensionality (i.e., the number of components to extract from group-ICA) remained obscure in the current context of large-scale data sets. To provide principled guidelines on this issue, ICA at dimensionalities varying from 25 to 350 was applied to the cortical surface with resting-state functional magnetic resonance imaging data from 1003 participants in the Human Connectome Project. The reliability of two rsFC measures, (within-component) coherence and (between-component) connectivity, was estimated. Reliability and its change with dimensionality varied by network: the cognitive (frontoparietal, cingulo-opercular, dorsal attention, and default) networks were measured with the highest reliability, which improved with increased dimensionality until at least 150; the visual and somatomotor networks were measured with lower reliability, which benefited mildly from increased dimensionality; and the temporal pole/orbitofrontal cortex (TP/OFC) network was measured with the lowest reliability. Overall, ICA reliability was optimized at dimensionalities of 150 or above. Compared with two popular binary, nonoverlapping cortical atlases, ICA and dual regression resulted in higher reliability for the cognitive networks, lower reliability for the somatomotor network, and similar reliability for the visual and TP/OFC networks. These findings highlight analytical decisions that maximize the reliability of rsFC measures and how they depend on one's networks of interest. Impact statement Independent component analysis (ICA) and dual regression is a popular approach to resting-state functional connectivity (rsFC) analysis. Yet there is little consensus around the optimal ICA dimensionality, i.e., how many brain components to extract from group-ICA. We proposed that rsFC test-retest reliability is an important criterion when choosing dimensionality. The present study compares rsFC reliability across various dimensionalities in the state-of-the-art Human Connectome Project data. We also compared reliability based on ICA versus two popular brain atlases. The findings are of interest to both researchers who study brain parcellation and those who use rsFC to examine brain-behavior relationship.
