Book Description

Resting-state functional magnetic resonance imaging has the ability to provide information about brain functioning. However, it is difficult to interpret conclusions about rsfMRI data due to questions about the reliability of resting-state functional connectivity (RSFC). This study investigated the test-retest reliability of resting-state networks using a "mini" multiverse approach for individuals who have sustained traumatic brain injuries (TBIs) using back-to-back rsfMRI scans. This is an understudied area that can improve our understanding of RSFC and its potential use in clinical populations. 45 individuals with TBI and 41 healthy controls received back-to-back rsfMRI scans. 25 individuals with TBI and 15 healthy controls received another scanning session approximately 2 years after the first. The data were preprocessed with fMRIPrep. XCP_D was used to create functional connectivity matrices using 8 different brain atlases. Several graph theory metrics were calculated. Intraclass correlation coefficients (ICCs) were utilized to examine the reliability of all graph metrics for each brain atlas across each participant's back-to-back rsfMRI scans for both scanning sessions. Results suggest that within-network connectivity, segregation, and modularity are the most reliable graph metrics, even after significant neurological compromise. The default mode network is one of the most reliable networks, whereas the limbic network is one of the least reliable networks. These results persist across the TBI and HC groups, brain atlases, and over time between the two scanning sessions, though there are some inconsistencies. This study underscores the importance of investigating the variability of ICCs. This will aid in the identification of resting-state biomarkers and will allow us to gain a better understanding of how subject characteristics and fMRI workflows impact RSFC reliability.