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Record W3201220267 · doi:10.1503/jpn.210006

A systematic review of TMS and neurophysiological biometrics in patients with schizophrenia

2021· review· en· W3201220267 on OpenAlexvenueno aff
Meng di Hou, Viviana Santoro, Andrea Biondi, Sukhwinder S. Shergill, Isabella Premoli

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2021
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsnot available
FundersSouth London and Maudsley NHS Foundation TrustNIHR Maudsley Biomedical Research CentreMedical Research CouncilInstitute of Psychiatry, Psychology and Neuroscience, King’s College LondonKing's College LondonNational Institute for Health and Care Research
KeywordsTranscranial magnetic stimulationNeuroscienceSchizophrenia (object-oriented programming)PsychologySilent periodMagnetoencephalographyElectroencephalographyPhysical medicine and rehabilitationMedicinePsychiatryStimulation

Abstract

fetched live from OpenAlex

BACKGROUND: Transcranial magnetic stimulation can be combined with electromyography (TMS-EMG) and electroencephalography (TMS-EEG) to evaluate the excitatory and inhibitory functions of the cerebral cortex in a standardized manner. It has been postulated that schizophrenia is a disorder of functional neural connectivity underpinned by a relative imbalance of excitation and inhibition. The aim of this review was to provide a comprehensive overview of TMS-EMG and TMS-EEG research in schizophrenia, focused on excitation or inhibition, connectivity, motor cortical plasticity and the effect of antipsychotic medications, symptom severity and illness duration on TMS-EMG and TMS-EEG indices. METHODS: We searched PsycINFO, Embase and Medline, from database inception to April 2020, for studies that included TMS outcomes in patients with schizophrenia. We used the following combination of search terms: transcranial magnetic stimulation OR tms AND interneurons OR glutamic acid OR gamma aminobutyric acid OR neural inhibition OR pyramidal neurons OR excita* OR inhibit* OR GABA* OR glutam* OR E-I balance OR excitation-inhibition balance AND schizoaffective disorder* OR Schizophrenia OR schizophreni*. RESULTS: TMS-EMG and TMS-EEG measurements revealed deficits in excitation or inhibition, functional connectivity and motor cortical plasticity in patients with schizophrenia. Increased duration of the cortical silent period (a TMS-EMG marker of γ-aminobutyric acid B receptor activity) with clozapine was a relatively consistent finding. LIMITATIONS: Most of the studies used patients with chronic schizophrenia and medicated patients, employed cross-sectional group comparisons and had small sample sizes. CONCLUSION: TMS-EMG and TMS-EEG offer an opportunity to develop a novel and improved understanding of the physiologic processes that underlie schizophrenia and to assess the therapeutic effect of antipsychotic medications. In the future, these techniques may also help predict disease progression and further our understanding of the excitatory/inhibitory balance and its implications for mechanisms that underlie treatment-resistant schizophrenia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0130.017
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.312
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2021
Admission routes1
Has abstractyes

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