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Hum Brain Mapp. 2015 Sep;36(9):3289-302. doi: 10.1002/hbm.22819. Epub 2015 Jun 18.

Independent component analysis of functional networks for response inhibition: Inter-subject variation in stop signal reaction time.

Zhang S1, Tsai SJ2, Hu S1, Xu J1, Chao HH3,4, Calhoun VD1,5,6, Li CS1,7,8.

Author information

1
Department of Psychiatry, Yale University, New Haven, Connecticut.
2
Department of Medicine, National Yang-Ming University, Taipei, Taiwan.
3
Department of Internal Medicine, Yale University, New Haven, Connecticut.
4
Medical Service, VA Connecticut Health Care System, West Haven, Connecticut.
5
The Mind Research Network, Albuquerque, New Mexico.
6
Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico.
7
Department of Neurobiology, Yale University, New Haven, Connecticut.
8
Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut.

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.

KEYWORDS:

ICA; fMRI; inhibitory control; neural network; neuroimaging; nogo; stop signal

PMID:
26089095
PMCID:
PMC4545723
DOI:
10.1002/hbm.22819
[Indexed for MEDLINE]
Free PMC Article

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