title: Independent component analysis of functional networks for response inhibition: Inter-subject variation in stop signal reaction time.
abstract: Cognitive control is a critical executive function. Many studies have combined general linear modeling and the stop signal task (SST) to delineate the component processes of cognitive control. For instance, by contrasting stop success (SS) and stop error (SE) trials in the SST, investigators examined regional responses to stop signal inhibition. In contrast to this parameterized approach, independent component analysis (ICA) elucidates brain networks subserving cognitive control. In our earlier work of 59 adults performing the SST during fMRI, we characterized six independent components (ICs). However, none of these ICs correlated with stop signal performance, raising questions about their behavioral validity. Here, in a larger sample (n = 100), we identified and explored 23 ICs for correlation with the stop signal reaction time (SSRT), a measure of the efficiency of response inhibition. At a corrected threshold (P < 0.0005), a paracentral lobule-midcingulate network and a left inferior parietal-supplementary motor-somatomotor network showed a positive correlation between SE beta weight and SSRT. In contrast, a midline cerebellum-thalamus-pallidum network showed a negative correlation between SE beta weight and SSRT. These findings suggest that motor preparation and execution prolongs the SSRT, likely via an interaction between the go and stop processes as suggested by the race model. Behaviorally, consistent with this hypothesis, the difference in G and SE reaction times is positively correlated with SSRT across subjects. These new results highlight the importance of cognitive motor regions in response inhibition and support the utility of ICA in uncovering functional networks for cognitive control in the SST.
authors.affiliation: Department of Psychiatry, Yale University, New Haven, Connecticut.
authors.forename: Sheng
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Department of Medicine, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Shang-Jui
authors.initials: SJ
authors.lastname: Tsai
authors.affiliation: Department of Psychiatry, Yale University, New Haven, Connecticut.
authors.forename: Sien
authors.initials: S
authors.lastname: Hu
authors.affiliation: Department of Psychiatry, Yale University, New Haven, Connecticut.
authors.forename: Jiansong
authors.initials: J
authors.lastname: Xu
authors.affiliation: Department of Internal Medicine, Yale University, New Haven, Connecticut.|Medical Service, VA Connecticut Health Care System, West Haven, Connecticut.
authors.forename: Herta H
authors.initials: HH
authors.lastname: Chao
authors.affiliation: Department of Psychiatry, Yale University, New Haven, Connecticut.|The Mind Research Network, Albuquerque, New Mexico.|Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico.
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
authors.affiliation: Department of Psychiatry, Yale University, New Haven, Connecticut.|Department of Neurobiology, Yale University, New Haven, Connecticut.|Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut.
authors.forename: Chiang-Shan R
authors.initials: CS
authors.lastname: Li
mesh_terms: D000328:Adult; D000368:Aged; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003627:Data Interpretation, Statistical; D056344:Executive Function / Q000502:physiology*; D005260:Female; D006801:Humans; D007266:Inhibition, Psychological; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000502:physiology; D009483:Neuropsychological Tests; D011597:Psychomotor Performance / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted
keywords: ICA; fMRI; inhibitory control; neural network; neuroimaging; nogo; stop signal