Introducing a novel approach of network oriented analysis of ERPs, demonstrated on adult attention deficit hyperactivity disorder

G. Shahaf, A. Reches, N. Pinchuk, T. Fisher, G. Ben Bashat, A. Kanter, I. Tauber, D. Kerem, I. Laufer, J. Aharon-Peretz, H. Pratt, A. B. Geva

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Objective: Introducing a network-oriented analysis method (brain network activation [BNA]) of event related potential (ERP) activities and evaluating its value in the identification and severity-grading of adult ADHD patients. Methods: Spatio-temporal interrelations and synchronicity of multi-sited ERP activity peaks were extracted in a group of 13 ADHD patients and 13 control subjects for the No-go stimulus in a Go/No-go task. Participants were scored by cross-validation against the most discriminative ensuing group patterns and scores were correlated to neuropsychological evaluation scores. Results: A distinct frontal-central-parietal pattern in the delta frequency range, dominant at the P3 latency, was unraveled in controls, while central activity in the theta and alpha frequency ranges predominated in the ADHD pattern, involving early ERP components (P1-N1-P2-N2). Cross-validation based on this analysis yielded 92% specificity and 84% sensitivity and individual scores correlated well with behavioral assessments. Conclusions: These results suggest that the ADHD group was more characterized by the process of exerting attention in the early monitoring stages of the No-go signal while the controls were more characterized by the process of inhibiting the response to that signal. Significance: The BNA method may provide both diagnostic and drug development tools for use in diverse neurological disorders.

Original languageEnglish
Pages (from-to)1568-1580
Number of pages13
JournalClinical Neurophysiology
Volume123
Issue number8
DOIs
StatePublished - 1 Aug 2012

Keywords

  • Attention deficit hyperactivity disorder
  • BNA (brain network activation)
  • Event-related potentials
  • Go/No-go
  • Pattern recognition
  • Unsupervised clustering

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