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  5. Computational modelling of EEG and fMRI paradigms reveals a consistent loss of pyramidal cell synaptic gain in schizophrenia

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Preprint
en
2021

Computational modelling of EEG and fMRI paradigms reveals a consistent loss of pyramidal cell synaptic gain in schizophrenia

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en
2021
DOI: 10.1101/2021.01.07.21249389

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Karl Friston
Karl Friston

University College London

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Rick A. Adams
Dimitris A. Pinotsis
Konstantinos Tsirlis
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Abstract

Abstract Diminished synaptic gain – the sensitivity of postsynaptic responses to neural inputs – may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms. Participants with schizophrenia (Scz, n=108), their relatives (n=57), and controls (n=107) underwent three electroencephalography paradigms – resting, mismatch negativity, and 40 Hz auditory steady-state response – and resting functional magnetic resonance imaging. Dynamic causal modelling was used to quantify synaptic connectivity in cortical microcircuits. Across all four paradigms, characteristic Scz data features were best explained by models with greater self-inhibition (decreased synaptic gain), in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in Scz, in three paradigms. Thus, psychotic symptoms of Scz may result from a downregulation of inhibitory interneurons that may compensate for diminished postsynaptic gain in pyramidal cells.

How to cite this publication

Rick A. Adams, Dimitris A. Pinotsis, Konstantinos Tsirlis, Leonhardt Unruh, Aashna Mahajan, Ana Montero Horas, Laura Convertino, Ann Summerfelt, Hemalatha Sampath, Xiaoming Du, Peter Kochunov, Jie Lisa Ji, Grega Repovš, John D. Murray, Karl Friston, L. Elliot Hong, Alan Anticevic (2021). Computational modelling of EEG and fMRI paradigms reveals a consistent loss of pyramidal cell synaptic gain in schizophrenia. , DOI: https://doi.org/10.1101/2021.01.07.21249389.

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Publication Details

Type

Preprint

Year

2021

Authors

17

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1101/2021.01.07.21249389

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