Region-Level Functional and Effective Network Analysis of Human Brain During Cognitive Task Engagement

Sandeep Avvaru, Noam Peled, Nicole R. Provenza, Alik S. Widge, Keshab K. Parhi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Mental disorders are a major source of disability, with few effective treatments. It has recently been argued that these diseases might be effectively treated by focusing on decision-making, and specifically remediating decision-making deficits that act as "ingredients" in these disorders. Prior work showed that direct electrical brain stimulation can enhance human cognitive control, and consequently decision-making. This raises a challenge of detecting cognitive control lapses directly from electrical brain activity. Here, we demonstrate approaches to overcome that challenge. We propose a novel method, referred to as maximal variance node merging (MVNM), that merges nodes within a brain region to construct informative inter-region brain networks. We employ this method to estimate functional (correlational) and effective (causal) networks using local field potentials (LFP) during a cognitive behavioral task. The effective networks computed using convergent cross mapping differentiate task engagement from background neural activity with 85% median classification accuracy. We also derive task engagement networks (TENs): networks that constitute the most discriminative inter-region connections. Subsequent graph analysis illustrates the crucial role of the dorsolateral prefrontal cortex (dlPFC) in task engagement, consistent with a widely accepted model for cognition. We also show that task engagement is linked to prefrontal cortex theta (4-8 Hz) oscillations. We, therefore, identify objective biomarkers associated with task engagement. These approaches may generalize to other cognitive functions, forming the basis of a network-based approach to detecting and rectifying decision deficits.

Original languageEnglish (US)
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
StatePublished - Aug 17 2021

Bibliographical note

Publisher Copyright:


  • Decision making
  • Electric potential
  • Electrodes
  • Interference
  • Merging
  • Monitoring
  • Multi-Source Interference task
  • Task analysis
  • cognitive control
  • effective connectivity
  • functional connectivity
  • local field potential
  • maximal variance node merging
  • task engagement network
  • Brain
  • Humans
  • Cognition
  • Prefrontal Cortex
  • Magnetic Resonance Imaging
  • Brain Mapping

PubMed: MeSH publication types

  • Journal Article


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