Inhibitory behavioral control

a stochastic dynamic causal modeling study using network discovery analysis

Liangsuo Ma, Joel L. Steinberg, Kathryn Cunningham, Scott D. Lane, Larry A. Kramer, Ponnada A. Narayana, Thomas R. Kosten, Antoine Bechara, F. Gerard Moeller

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This study employed functional magnetic resonance imaging (fMRI)-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity underlying inhibitory behavioral control. fMRI data were acquired from 15 healthy subjects while they performed a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard NoGo conditions) in distinguishing spatial patterns of lines. Based on the previous inhibitory control literature and the present fMRI activation results, 10 brain regions were postulated as nodes in the effective connectivity model. Due to the large number of potential interconnections among these nodes, the number of models for final analysis was reduced to a manageable level for the whole group by conducting DCM Network Discovery, which is a recently developed option within the Statistical Parametric Mapping software package. Given the optimum network model, the DCM Network Discovery analysis found that the locations of the driving input into the model from all the experimental stimuli in the Go/NoGo task were the amygdala and the hippocampus. The strengths of several cortico-subcortical connections were modulated (influenced) by the two NoGo conditions. Specifically, connectivity from the middle frontal gyrus (MFG) to hippocampus was enhanced by the Easy condition and further enhanced by the Hard NoGo condition, possibly suggesting that compared with the Easy NoGo condition, stronger control from MFG was needed for the hippocampus to discriminate/learn the spatial pattern in order to respond correctly (inhibit), during the Hard NoGo condition.

Original languageEnglish (US)
Pages (from-to)177-186
Number of pages10
JournalBrain Connectivity
Volume5
Issue number3
DOIs
StatePublished - Apr 1 2015

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Magnetic Resonance Imaging
Hippocampus
Parahippocampal Gyrus
Amygdala
Healthy Volunteers
Theoretical Models
Software
Brain

Keywords

  • dynamic causal modeling
  • Go/NoGo
  • impulsivity
  • inhibitory control
  • top-down

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Inhibitory behavioral control : a stochastic dynamic causal modeling study using network discovery analysis. / Ma, Liangsuo; Steinberg, Joel L.; Cunningham, Kathryn; Lane, Scott D.; Kramer, Larry A.; Narayana, Ponnada A.; Kosten, Thomas R.; Bechara, Antoine; Moeller, F. Gerard.

In: Brain Connectivity, Vol. 5, No. 3, 01.04.2015, p. 177-186.

Research output: Contribution to journalArticle

Ma, L, Steinberg, JL, Cunningham, K, Lane, SD, Kramer, LA, Narayana, PA, Kosten, TR, Bechara, A & Moeller, FG 2015, 'Inhibitory behavioral control: a stochastic dynamic causal modeling study using network discovery analysis', Brain Connectivity, vol. 5, no. 3, pp. 177-186. https://doi.org/10.1089/brain.2014.0275
Ma, Liangsuo ; Steinberg, Joel L. ; Cunningham, Kathryn ; Lane, Scott D. ; Kramer, Larry A. ; Narayana, Ponnada A. ; Kosten, Thomas R. ; Bechara, Antoine ; Moeller, F. Gerard. / Inhibitory behavioral control : a stochastic dynamic causal modeling study using network discovery analysis. In: Brain Connectivity. 2015 ; Vol. 5, No. 3. pp. 177-186.
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