title: Engagement of large-scale networks is related to individual differences in inhibitory control.
abstract: Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examining the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of voxelwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent component analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research.
authors.affiliation: Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA. econgdon@ucla.edu
authors.forename: Eliza
authors.initials: E
authors.lastname: Congdon
authors.forename: Jeanette A
authors.initials: JA
authors.lastname: Mumford
authors.forename: Jessica R
authors.initials: JR
authors.lastname: Cohen
authors.forename: Adriana
authors.initials: A
authors.lastname: Galvan
authors.forename: Adam R
authors.initials: AR
authors.lastname: Aron
authors.forename: Gui
authors.initials: G
authors.lastname: Xue
authors.forename: Eric
authors.initials: E
authors.lastname: Miller
authors.forename: Russell A
authors.initials: RA
authors.lastname: Poldrack
mesh_terms: D001519:Behavior / Q000502:physiology; D001921:Brain / Q000502:physiology; D003627:Data Interpretation, Statistical; D005071:Evoked Potentials / Q000502:physiology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007206:Individuality; D007266:Inhibition, Psychological; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D010775:Photic Stimulation; D025341:Principal Component Analysis; D011597:Psychomotor Performance / Q000502:physiology; D011930:Reaction Time / Q000502:physiology; D055815:Young Adult