Test-retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices

Abstract

Countless studies have advanced our understanding of the human brain and its organization by using functional magnetic resonance imaging (fMRI) to derive network representations of human brain function. However, we do not know to what extent these “functional connectomes” are reliable over time. In a large public sample of healthy participants ( N = 833) scanned on two consecutive days, we assessed the test-retest reliability of fMRI functional connectivity and the consequences on reliability of three common sources of variation in analysis workflows: atlas choice, global signal regression, and thresholding. By adopting the intraclass correlation coefficient as a metric, we demonstrate that only a small portion of the functional connectome is characterized by good (6–8%) to excellent (0.08–0.10%) reliability. Connectivity between prefrontal, parietal, and temporal areas is especially reliable, but also average connectivity within known networks has good reliability. In general, while unreliable edges are weak, reliable edges are not necessarily strong. Methodologically, reliability of edges varies between atlases, global signal regression decreases reliability for networks and most edges (but increases it for some), and thresholding based on connection strength reduces reliability. Focusing on the reliable portion of the connectome could help quantify brain trait-like features and investigate individual differences using functional neuroimaging.

Exploring intensity-dependent modulations in EEG resting-state network efficiency induced by exercise

“…Even if the absolute changes in brain network metrics are very small, functional connectomes rely on functional and structural connections (Shen et al 2015 ). Therefore, intra-individual modulations brain graphs should not be expected to be drastic within a given session as structural connections do not change extensively within short time periods (Tozzi et al 2020 ). Traditionally, theta oscillations are suggested to originate from the prefrontal cortex and reflect attentional involvement and executive function (Sauseng et al 2005 ).…”

EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks

“…As a brain graph is a result of both functional and structural connections which contribute to an existing connectome, both chronic and acute neuroplastic changes contribute to modulations of brain graphs. While structural connections are expected to remain stable over time, specific functional connections are more likely to rather reflect state than trait and therefore fluctuate more within smaller time periods 40 , 41 . Even if the analysis of brain graphs is majorly applied to distinguish healthy from diseased populations exposed to long-term neuroplastic changes 42 , first studies have shown that even acute circumstances like fatigue or vigilance correlate with modulations of functional graph outcomes 43 , 44 .…”

Replicability of brain-phenotype associations

“…We showed that structural brain measures generally showed poorer replicability than functional brain measures. Paradoxically, test-retest reliability for functional MRI is lower than for structural MRI [44][45][46] . This indicates that the additional noise from lower test-retest reliability of functional MRI is less important than the higher association strength for the replication of brainphenotype associations.…”