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Quantitative brain volume differences between COVID-19 patients and non-COVID-19 volunteers: A systematic review

2025· review· en· W4408459778 on OpenAlexafffund
Breanna Nelson, Lea N Farah, Sidney A Saint, Catie Song, Thalia S. Field, Vesna Sossi, A. Jon Stoessl, Cheryl L. Wellington, William G. Honer, Donna J. Lang, Noah D. Silverberg, William J. Panenka

Bibliographic record

VenueNeuroImage · 2025
Typereview
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia HospitalBC Children's HospitalUniversity of British ColumbiaBC Mental Health & Substance Use Services
FundersWeston Brain Institute
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BetacoronavirusVolume (thermodynamics)MedicineCoronavirus InfectionsVirologyInternal medicinePhysics

Abstract

fetched live from OpenAlex

BACKGROUND: The majority of COVID-19 neuroimaging literature focuses on the acute period after infection and clinically evident lesions. The chronic effects of COVID-19 on brain structure are less well defined. There are inconsistencies in the existing structural neuroimaging studies regarding differences in brain volumes after COVID-19 infection. It was thus our aim to systematically evaluate the structural neuroimaging literature focusing on volumetric differences between patients with COVID-19, and volunteers without COVID-19, at greater than 6 weeks post-infection. METHODS: PubMed, Embase, and Scopus were searched in May 2023 with an updated search in September 2024, for studies with a quantitative comparison of brain volumes between COVID-19 patients and non-COVID-19 volunteers with MRI acquired more than 6-weeks after COVID-19. Exclusion criteria included COVID-19 patients selected for the presence of specific symptoms, case reports and case studies, and post-mortem studies. Forwards and backwards citation chasing were performed. RESULTS: Sixteen studies met inclusion criteria. The majority of studies reported smaller grey matter volumes amongst COVID-19 patients compared to healthy volunteers. However, there were inconsistent findings, with 3 studies reporting larger grey matter volumes in the COVID-19 groups. Additionally, studies with COVID-19 cohorts with more severe presentations, characterized by admission to the hospital or the ICU, were more likely to report smaller grey matter volumes compared to healthy volunteers, than studies that were focused on patients who recovered at home. CONCLUSION: A systematic review of the literature indicates that COVID-19 illness severity may explain some of the heterogeneity in brain volume differences between COVID-19 patients and healthy volunteers. More longitudinal follow-up studies are needed to assess the longitudinal course of COVID-19's effects on brain volumes.

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)0.006
metaresearch head score (Gemma)0.041
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 score0.017
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.041
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.008
Bibliometrics0.0170.015
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
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 head0.055
GPT teacher head0.392
Teacher spread0.337 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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