Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation - Gratton, Caterina; Laumann, Timothy O.; Nielsen, Ashley N.; Greene, Deanna J.; Gordon, Evan M.; Gilmore, Adrian W.; Nelson, Steven M.; Coalson, Rebecca S.; Snyder, Abraham Z.; Schlaggar, Bradley L.; Dosenbach, Nico U.F.; Petersen, Steven E. (Neuron (Cambridge, Mass.), 2018)
The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine. [Display omitted] •Functional networks are dominated by common group and stable individual features•Task state only modestly influences brain networks, largely varying by individual•With substantial data, day-to-day variability is minimal•Variance sources show distinct topography and links to intrinsic and evoked factors Gratton et al. comprehensively measure individual, day-to-day, and task variance in functional brain networks, revealing that networks are dominated by stable individual factors, not cognitive content. These findings suggest utility of functional network measurements in personalized medicine.