Effects of Altered Excitation-Inhibition Balance on Decision Making in a Cortical Circuit Model.

NMDAR hypofunction computational model decision making drift-diffusion model excitation-inhibition balance psychophysics

Journal

The Journal of neuroscience : the official journal of the Society for Neuroscience
ISSN: 1529-2401
Titre abrégé: J Neurosci
Pays: United States
ID NLM: 8102140

Informations de publication

Date de publication:
09 02 2022
Historique:
received: 25 05 2020
revised: 01 11 2021
accepted: 29 11 2021
pubmed: 11 12 2021
medline: 26 2 2022
entrez: 10 12 2021
Statut: ppublish

Résumé

The synaptic balance between excitation and inhibition (E/I balance) is a fundamental principle of cortical circuits, and disruptions in E/I balance are commonly linked to cognitive deficits such as impaired decision-making. Explanatory gaps remain in a mechanistic understanding of how E/I balance contributes to cognitive computations, and how E/I disruptions at the synaptic level can propagate to induce behavioral deficits. Here, we studied how E/I perturbations may impair perceptual decision-making in a biophysically-based association cortical circuit model. We found that both elevating and lowering E/I ratio, via NMDA receptor (NMDAR) hypofunction at inhibitory interneurons and excitatory pyramidal neurons, respectively, can similarly impair psychometric performance, following an inverted-U dependence. Nonetheless, these E/I perturbations differentially alter the process of evidence accumulation across time. Under elevated E/I ratio, decision-making is impulsive, overweighting early evidence and underweighting late evidence. Under lowered E/I ratio, decision-making is indecisive, with both evidence integration and winner-take-all competition weakened. The distinct time courses of evidence accumulation at the circuit level can be measured at the behavioral level, using multiple psychophysical task paradigms which provide dissociable predictions. These results are well captured by a generalized drift-diffusion model (DDM) with self-coupling, implementing leaky or unstable integration, which thereby links biophysical circuit modeling to algorithmic process modeling and facilitates model fitting to behavioral choice data. In general, our findings characterize critical roles of cortical E/I balance in cognitive function, bridging from biophysical to behavioral levels of analysis.

Identifiants

pubmed: 34887320
pii: JNEUROSCI.1371-20.2021
doi: 10.1523/JNEUROSCI.1371-20.2021
pmc: PMC8824494
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1035-1053

Subventions

Organisme : NIMH NIH HHS
ID : R01 MH062349
Pays : United States
Organisme : NCATS NIH HHS
ID : TL1 TR000141
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH112746
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : NIH HHS
ID : DP5 OD012109
Pays : United States

Informations de copyright

Copyright © 2022 the authors.

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Auteurs

Norman H Lam (NH)

Department of Physics, Yale University, New Haven, Connecticut 06511.

Thiago Borduqui (T)

Department of Neurosciences and Behavior, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil 14049-900.

Jaime Hallak (J)

Department of Neurosciences and Behavior, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil 14049-900.

Antonio Roque (A)

Physics Department, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil 14049-900.

Alan Anticevic (A)

Department of Psychiatry, Yale University, New Haven, Connecticut 06511.

John H Krystal (JH)

Department of Psychiatry, Yale University, New Haven, Connecticut 06511.

Xiao-Jing Wang (XJ)

Center for Neural Science, New York University, New York, New York 10003.

John D Murray (JD)

Department of Physics, Yale University, New Haven, Connecticut 06511 john.murray@yale.edu.
Department of Psychiatry, Yale University, New Haven, Connecticut 06511.

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