Abstract
Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.
Highlights
Functional magnetic resonance imaging has proven invaluable for identifying the neural architecture of human behavior and cognition (Betti et al, 2013; Fox et al, 2007; Huth et al, 2016)
All confidence intervals for connectome-based predictive modeling (CPM) prediction estimates were generated with bootstrap resampling, using AFNI’s 1dCorrelate tool. 381 Results 382 What is the test-retest reliability of general functional connectivity (GFC)?
A paired sample t-test comparing ICCs across all edges revealed that GFC is significantly more reliable than resting-state functional connectivity when both are estimated with 40 minutes of data (t(34715) = 140.01, p < .001)
Summary
Functional magnetic resonance imaging (fMRI) has proven invaluable for identifying the neural architecture of human behavior and cognition (Betti et al, 2013; Fox et al, 2007; Huth et al, 2016). FMRI studies have expanded in both scale and scope, often collecting data in thousands of individuals in an effort to adequately power the search for neural correlates of complex human traits and predictive biomarkers for mental illness (Casey et al, 2018; Elliott et al, 2018; Miller et al, 2016; Swartz et al, 2015; Thompson et al, 2014) In this context, most studies have focused on the acquisition of resting-state functional MRI to map the intrinsic connectivity of neural networks. The field of individual122 differences neuroscience would benefit from the resulting increase in reliability, replicability, 123 and power that would follow the widespread adoption of combining task and resting-state fMRI
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