Neural mass model-based study of frontal-temporal theta oscillations in human subjects during the performance of a cognitive control task

Alexander Ross, Angelique C. Paulk, Sydney S. Cash, Alik S. Widge, Ishita Basu

Research output: Contribution to journalArticlepeer-review

Abstract

Cognitive control, the ability to rapidly shift one's attention and behavioral strategy in response to environmental changes, is often compromised across psychiatric disorders. One of the well-validated behavioral paradigms for tapping into the cognitive control circuits is a cognitive interference task, where subjects must suppress a natural response to follow a less intuitive rule. Slower response times on these tasks indicate difficulty exerting control to overcome response conflict. Conflict evokes robust electrophysiological signatures, such as theta (4-8 Hz) oscillations in the prefrontal cortex (PFC). However, the underlying neural mechanisms of conflict-evoked theta oscillations in the PFC are not clear. The objective of this work is to use a neural mass model (NMM) to find feasible cortical networks generating theta oscillations during conflict processing in human subjects. We used intracranial EEG (iEEG) recorded from dorsolateral PFC (dIPFC) and lateral temporal lobe (LTL) of human subjects with intractable epilepsy undergoing invasive monitoring, while they performed a multi-source interference task (MSIT). We used a dynamic causal modeling (DCM) framework to simulate dIPFC-LTL theta using a Jansen-Rit NMM. We found significant evidence for an LTL input into the dlPFC during the initial 500 ms of conflict processing compared to a bidirectional connection between the dlPFC and LTL. We conclude that a neural mass modeling framework can be used to elucidate candidate mechanisms of neural oscillations underlying conflict resolution in human subjects. Clinical Relevance - This can be used to find feasible target mechanisms for designing therapy in patients with compromised cognitive control.

Bibliographical note

Funding Information:
We gratefully acknowledge technical assistance with data collection from Afsana Afzal, Gavin Belok, Kara Farnes, Julia Felicione, Rachel Franklin, Anna Gilmour, Aishwarya Gosai, Mark Moran, Madeleine Robertson, Christopher Salthouse, Deborah Vallejo-Lopez, and Samuel Zorowitz. We also thank the research participants, without whose generous help none of this would have been possible. This work was supported by grants from the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number W911NF-14-2-0045 issued by the Army Research Organization (ARO) contracting office in support of DARPA’s SUBNETS Program and the University of Cincinnati CCTST Pilot and Innovative Core Grant Program. The views, opinions, and findings expressed are those of the authors. They should not be interpreted as representing the official views or policies of the Department of Defense, Department of Health & Human Services, any other branch of the U.S. Government, or any other funding entity.

Publisher Copyright:
© 2022 IEEE.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, Non-U.S. Gov't

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