Functional and effective connectivity of stopping

René J. Huster, Sergey M. Plis, Christina F. Lavallee, Vince D. Calhoun, Christoph S. Herrmann

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Behavioral inhibition often is studied by comparing the electroencephalographic responses to stop and to go signals. Most studies simply assess amplitude differences of the N200 and P300 event-related potentials, which seem to best correspond to increased activity in the theta and delta frequency bands, respectively. However, neither have reliable indicators for successful behavioral inhibition been identified nor have the causal dependencies of stop-related neurocognitive processes been addressed yet. By studying functional and effective connectivity underlying stopping behavior, this study opens new directions for the investigation of behavioral inhibition. Group independent component analysis was used to infer functionally coherent networks from electroencephalographic data, which were recorded from healthy human participants during processing of a stop signal task. Then, the temporal dynamics of causal dependencies between independent components were identified by means of Bayesian network estimations. The mean clustering coefficient and the characteristic path length measure indicated time windows between 130 and 180. ms and between 420 and 500. ms to express significantly different connectivity profiles between conditions. Three components showed significant correlations between 120 and 260. ms with stop signal reaction times and the number of failed stops. Two of these components acted as sources of causal flow, one capturing P300/delta characteristics while the other was characterized by alpha power depletion putatively representing the evaluation or processing of stimulus features. Although results suggest that the P300 and associated delta activity seem to be statistically dependent on earlier processes associated with behavioral inhibition, the time window critical for inhibition coincides with early changes in causal patterns and largely precedes peak amplitude differences between go and stop trials. Altogether, utilizing the analysis of stopping-related connectivity, previously undetected patterns emerged that warrant further investigation.

Original languageEnglish (US)
Pages (from-to)120-128
Number of pages9
StatePublished - Jul 1 2014
Externally publishedYes


  • Connectivity
  • Delta
  • Inhibition
  • N200
  • P300
  • Stop signal task
  • Stopping
  • Theta

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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