A neurocomputational model of posttraumatic stress disorder

Gregory P. Davis, Garrett E. Katz, Daniel Soranzo, Nathaniel Allen, Matthew J. Reinhard, Rodolphe J. Gentili, Michelle E. Costanzo, James A. Reggia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Despite the well-defined behavioral criteria for posttraumatic stress disorder (PTSD), clinical care is complicated by the heterogeneity of biological factors underlying impairment. Eye movement tasks provide an opportunity to assess the relationships between aberrant neurobiological function and non-volitional performance metrics that are not dependent on self-report. A recent study using an emotional variant of the antisaccade task demonstrated attentional control biases that interfered with task performance in Veterans with PTSD. Here we present a neuroanatomically-inspired computational model based on gated attractor networks that is designed to replicate oculomotor behavior on an affective anti-saccade task. The model includes the putative neural circuitry underlying fear response (amygdala) and top-down inhibitory control (prefrontal cortex), and is capable of generating testable predictions about the causal implications of changes in this circuitry on task performance and neural activation associated with PTSD. Calibrating the model with the results of behavioral and neuroimaging studies on patient populations yields a pattern of connectivity changes characterized by increased amygdala sensitivity and reduced top-down prefrontal control that is consistent with the fear conditioning model of PTSD. In addition, the model makes experimentally verifiable predictions about the consequences of increased prefrontal connectivity associated with cognitive reappraisal training.

Original languageEnglish (US)
Title of host publication2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
PublisherIEEE Computer Society
Pages107-110
Number of pages4
ISBN (Electronic)9781728143378
DOIs
StatePublished - May 4 2021
Event10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 - Virtual, Online, Italy
Duration: May 4 2021May 6 2021

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2021-May
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Country/TerritoryItaly
CityVirtual, Online
Period5/4/215/6/21

Keywords

  • Antisaccade task
  • Attentional bias
  • Cognitive control
  • Inhibitory control deficits
  • Posttraumatic stress disorder

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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