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Record W4229024134 · doi:10.1016/j.ynirp.2022.100097

Brain structure and function changes in inflammatory bowel disease

2022· article· en· W4229024134 on OpenAlexafffund
Jennifer Kornelsen, Kelcie Witges, Jennifer S. Labus, Emeran A. Mayer, Çharles N. Bernstein

Bibliographic record

VenueNeuroimage Reports · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Manitoba
FundersUniversity of California, Los AngelesNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of Manitoba
KeywordsInflammatory bowel diseaseUlcerative colitisBrain Structure and FunctionFunctional magnetic resonance imagingMagnetic resonance imagingMedicineDiseaseVoxelVoxel-based morphometryBrain functionNeurosciencePathologyPsychologyNeuroimagingWhite matterRadiology

Abstract

fetched live from OpenAlex

As the importance of the brain-gut axis in the pathobiology of inflammatory bowel disease (IBD) continues to evolve, a greater understanding of brain structure and functional connectivity (FC) is necessary. In this magnetic resonance imaging (MRI) study, we investigated differences in brain structure and in FC of brain regions in 111 participants with IBD (76 ulcerative colitis (UC) and 35 Crohn's disease (CD)) and 74 healthy controls (HC). Significant differences between IBD and HC were observed in the three analyses used (voxel based morphometry, region-of-interest, and independent component analysis) in brain regions of the default mode, cerebellar, and visual networks. Significant differences between IBD subtypes (UC, CD) were found. The results of the current study establish that a relationship between brain functional connectivity and the brain-gut axis exists in IBD.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.230
Teacher spread0.223 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2022
Admission routes2
Has abstractyes

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