Current approaches to studying human resting-state function in inflammatory bowel disease
Bibliographic record
Abstract
Crohn's disease and ulcerative colitis are 2 subtypes of Inflammatory Bowel Disease (IBD). The chronic, alternating periods of relapsing, and remitting inflammation of the gastrointestinal tract that underlie these diseases trigger a range of gut-related symptoms, in addition to being related to burdensome psychological and cognitive comorbidities. With advancing knowledge of the brain-gut axis and its dysregulation in diseases such as IBD, understanding IBD-related brain changes is an important focus for current research in this area. "Resting state" function refers to the spontaneous fluctuations in neural activity when a person is awake and resting-not focussing attention on a task or stimulus. The recent surge in human resting-state functional magnetic resonance imaging (rs-fMRI) studies suggest that resting function is altered in IBD, representing a potential neural biomarker to target in the development of novel interventions. There are, however, multiple factors that contribute to the approach of these studies, including factors related to participant sample characteristics (IBD subtype and incorporation of disease activity in group definition and comparison), application of different resting-state metrics to assess resting brain activity (via regional homogeneity or amplitude of low-frequency fluctuations) or functional connectivity (via independent component analysis, region-of-interest, seed-to-voxel, or graph theory analyses) and incorporation of additional, multimodal variables of interest. The present review provides a summary of current approaches to studying resting-state brain function in IBD, the most commonly identified brain regions/networks to exhibit aberrant function, and avenues for advancement that forthcoming research in this field can strive to address.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".