Evidence map›Paper›PMID 34598786›Full record

ArticleBiological psychiatry2022

Computational Modeling of Electroencephalography and Functional Magnetic Resonance Imaging Paradigms Indicates a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia.

Rick A Adams, Dimitris Pinotsis, Konstantinos Tsirlis, Leonhardt Unruh, Aashna Mahajan, Ana Montero Horas, Laura Convertino, Ann Summerfelt, Hemalatha Sampath, Xiaoming Michael Du and 7 more

Open access · hybridAbstract read
In one paragraph

Article in Biological psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 2 pooled it
7.8field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

58 citing papers in PubMed, 2 syntheses or guidelines pooled it, 96 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 6 institutions in 3 countries.

Rick A AdamsCentre for Medical Image Computing and Artificial Intelligence, University College London, London, United Kingdom; Institute of Cognitive Neuroscience, University College London, London, United Kingdom; Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut. Electronic address: rick.adams@ucl.ac.uk.
Dimitris PinotsisCentre for Mathematical Neuroscience and Psychology and Department of Psychology, City University of London, London, United Kingdom; Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Konstantinos TsirlisCentre for Medical Image Computing and Artificial Intelligence, University College London, London, United Kingdom.
Leonhardt UnruhInstitute of Cognitive Neuroscience, University College London, London, United Kingdom.
Aashna MahajanCentre for Medical Image Computing and Artificial Intelligence, University College London, London, United Kingdom.
Ana Montero HorasCentre for Medical Image Computing and Artificial Intelligence, University College London, London, United Kingdom.
Laura ConvertinoInstitute of Cognitive Neuroscience, University College London, London, United Kingdom.
Ann SummerfeltDepartment of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland.
Hemalatha SampathDepartment of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland.
Xiaoming Michael DuDepartment of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland.
Peter KochunovDepartment of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland.
Jie Lisa JiDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
Grega RepovsDepartment of Psychology, University of Ljubljana, Ljubljana, Slovenia.
John D MurrayDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
Karl J FristonWellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.
L Elliot HongDepartment of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland.
Alan AnticevicDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
University College London · GBUniversity of Maryland, Baltimore · USYale University · USCity, University of London · GBUniversity of Ljubljana · SIWellcome Centre for Human Neuroimaging · GB

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
SOLAR-Eclipse Computational Tools for Imaging GeneticsR01EB015611 · NIBIB · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KOCHUNOV, PETER V. · 2012 to 2024
$5.0M
Linking Large-Scale Dysconnectivity in Schizophrenia to Cortical Circuit Function at the Individual Level through Computational Modeling and Multimodal NeuroimagingR01MH112746 · NIMH · YALE UNIVERSITY · PI MURRAY, JOHN DAVID · 2017 to 2020
$1.7M
Redefine Trans-Neuropsychiatric Disorder Brain Patterns through Big-Data and Machine LearningRF1MH123163 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V., THOMPSON, PAUL M · 2021 to 2021
$1.2M
Hybrid GPU/CPU Computing Resource to Support Connectomic and GenomicsS10OD023696 · OD · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V. · 2018 to 2018
$593k
Medical Research Council MR/S007806/1NCATS NIH HHS UL1 TR001863NIBIB NIH HHS R01 EB015611NIH HHS S10 OD023696NIMH NIH HHS R01 MH112746NIMH NIH HHS RF1 MH123163Wellcome Trust
6 · The paper itself

Abstract

backgroundDiminished 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.

methodsPeople with schizophrenia diagnoses (PScz) (n = 108), their relatives (n = 57), and control subjects (n = 107) underwent 3 electroencephalography (EEG) paradigms-resting, mismatch negativity, and 40-Hz auditory steady-state response-and resting functional magnetic resonance imaging. Dynamic causal modeling was used to quantify synaptic connectivity in cortical microcircuits.

resultsClassic group differences in EEG features between PScz and control subjects were replicated, including increased theta and other spectral changes (resting EEG), reduced mismatch negativity, and reduced 40-Hz power. Across all 4 paradigms, characteristic PScz data features were all 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 PScz in 3 paradigms.

conclusionsFirst, characteristic EEG changes in PScz in 3 classic paradigms are all attributable to the same underlying parameter change: greater self-inhibition in pyramidal cells. Second, psychotic symptoms in PScz relate to disinhibition in neural circuits. These findings are more commensurate with the hypothesis that in PScz, a primary loss of synaptic gain on pyramidal cells is then compensated by interneuron downregulation (rather than the converse). They further suggest that psychotic symptoms relate to this secondary downregulation.

Indexed as

SchizophreniaComputer SimulationElectroencephalographyEvoked Potentials, AuditoryHumansMagnetic Resonance ImagingPyramidal CellsAuditory steady-stateDynamic causal modelMismatch negativityPsychosisResting stateSchizophrenia

Identifiers

PMID34598786
PMCPMC8654393
OpenAlexW3159456120

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.