Gut unclassified Ruminococcaceae Reweights Cortical Functional Gradients and Small-World Topology with Links to Mood and Diet
Bibliographic record
Abstract
Abstract The microbiota-gut-brain axis is a key conduit linking metabolism, mood, and cognition; however, its position within the continuous functional hierarchy of the cortex and the underlying mechanisms are unclear. In this study, a cross-modal brain-gut dataset from 88 healthy male participants was utilized. By integrating functional magnetic resonance imaging (fMRI) gradient analysis, microbiome sequencing, and dietary behavioral information, this study systematically evaluated the relationships between the abundance of unclassified Ruminococcaceae ( Ruminococcaceae_unc ), cerebral functional hierarchy, network topology, and emotional symptoms. The results demonstrated that increased abundance of this bacterial group drives a functional shift in the brain from unimodal to transmodal hubs, accompanied by a drift of the small-world network toward randomization. Functional gradient values were significantly negatively correlated with depression and anxiety scores and were tightly coupled with latent components in the dietary behavioral dimension, including education, physical activity, and nutrient intake. Transcriptomic analysis further revealed that the GPCR-Rho/integrin-vesicular trafficking pathway may serve as the key molecular mechanism. In conclusion, this study proposes a multiscale coupling framework encompassing the gut microbiota, functional gradients, and emotional health, thereby providing a theoretical basis for the development of microbiota-targeted intervention strategies for modulating transmodal emotions and cognition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".