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      GABA-ergic Dynamics in Human Frontotemporal Networks Confirmed by Pharmaco-Magnetoencephalography

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          Abstract

          To bridge the gap between preclinical cellular models of disease and in vivo imaging of human cognitive network dynamics, there is a pressing need for informative biophysical models. Here we assess dynamic causal models (DCM) of cortical network responses, as generative models of magnetoencephalographic observations during an auditory oddball roving paradigm in healthy adults.

          Abstract

          To bridge the gap between preclinical cellular models of disease and in vivo imaging of human cognitive network dynamics, there is a pressing need for informative biophysical models. Here we assess dynamic causal models (DCM) of cortical network responses, as generative models of magnetoencephalographic observations during an auditory oddball roving paradigm in healthy adults. This paradigm induces robust perturbations that permeate frontotemporal networks, including an evoked 'mismatch negativity' response and transiently induced oscillations. Here, we probe GABAergic influences in the networks using double-blind placebo-controlled randomized-crossover administration of the GABA reuptake inhibitor, tiagabine (oral, 10 mg) in healthy older adults. We demonstrate the facility of conductance-based neural mass mean-field models, incorporating local synaptic connectivity, to investigate laminar-specific and GABAergic mechanisms of the auditory response. The neuronal model accurately recapitulated the observed magnetoencephalographic data. Using parametric empirical Bayes for optimal model inversion across both drug sessions, we identify the effect of tiagabine on GABAergic modulation of deep pyramidal and interneuronal cell populations. We found a transition of the main GABAergic drug effects from auditory cortex in standard trials to prefrontal cortex in deviant trials. The successful integration of pharmaco- magnetoencephalography with dynamic causal models of frontotemporal networks provides a potential platform on which to evaluate the effects of disease and pharmacological interventions.

          SIGNIFICANCE STATEMENT Understanding human brain function and developing new treatments require good models of brain function. We tested a detailed generative model of cortical microcircuits that accurately reproduced human magnetoencephalography, to quantify network dynamics and connectivity in frontotemporal cortex. This approach identified the effect of a test drug (GABA-reuptake inhibitor, tiagabine) on neuronal function (GABA-ergic dynamics), opening the way for psychopharmacological studies in health and disease with the mechanistic precision afforded by generative models of the brain.

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          Author and article information

          Journal
          J Neurosci
          J. Neurosci
          jneuro
          jneurosci
          J. Neurosci
          The Journal of Neuroscience
          Society for Neuroscience
          0270-6474
          1529-2401
          19 February 2020
          19 August 2020
          : 40
          : 8
          : 1640-1649
          Affiliations
          [1] 1Cambridge University Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, CB2 0SZ, United Kingdom,
          [2] 2Medical Research Council Cognition and Brain Sciences Unit, Cambridge CB2 7EF, United Kingdom, and
          [3] 3Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff CF24 4HQ, United Kingdom
          Author notes
          Correspondence should be addressed to James B. Rowe at james.rowe@ 123456mrc-cbu.cam.ac.uk

          Author contributions: N.E.A., L.E.H., H.N.P., A.D.S., A.G.M., T.E.C., W.R.B.-J., L.P., and D.N. performed research; N.E.A. and A.D.S. contributed unpublished reagents/analytic tools; N.E.A. analyzed data; N.E.A. wrote the first draft of the paper; N.E.A., L.E.H., H.N.P., A.D.S., and J.B.R. edited the paper; N.E.A. wrote the paper; L.E.H. and J.B.R. designed research.

          Author information
          https://orcid.org/0000-0002-1065-7175
          https://orcid.org/0000-0003-1172-391X
          https://orcid.org/0000-0001-7216-8679
          Article
          PMC7046325 PMC7046325 7046325 1689-19
          10.1523/JNEUROSCI.1689-19.2019
          7046325
          31915255
          d9abec9f-d16f-43f3-8d5d-6e86184fc5e8
          Copyright © 2020 the authors
          History
          : 16 July 2019
          : 25 November 2019
          : 25 December 2019
          Categories
          Research Articles
          Systems/Circuits

          generative modeling,tonic inhibition,pharmaco-MEG,frontotemporal,conductance-based model,GABA

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