Investigating the relationship between gut microbiota and electrocortical signatures of feedback processing: an ERP study
Bibliographic record
Abstract
RATIONALE: Evaluative processing of action outcome is considered crucial for learning and adaptive adjustments of behaviour. Feedback-related negativity (FRN) is an event-related potential elicited by feedback presentation, with implicated neural sources in the anterior cingulate cortex. Bidirectional communications within the brain-gut-microbiota axis modulate cognition and behaviour, and microbial composition has been associated with medial prefrontal cortex function and clinical risk for depression. OBJECTIVES: The present study aimed to investigate associations between specific gut microbiota and the FRN. METHODS: Twenty-nine healthy participants completed self-report measures of depression and a Faces and Feedback task during electroencephalography recording. Select implicated microbiota genera were enumerated from stool samples (Clostridium, Lactobacillus), along with plasma C-reactive protein (CRP) as an index of systemic inflammation. RESULTS: FRN amplitude for positive feedback was positively correlated with microbiota abundance. The relationship between Clostridium and FRN was confirmed by multilevel modelling analysis, controlling for depression and CRP. The latter was positively associated with FRN amplitude. CONCLUSIONS: Findings suggest that the brain-gut-microbiota-axis may modulate or be modulated by self-monitoring processes. The current work provides insights into neurophysiological mechanisms underlying reward processing and indicates novel directions for therapeutic interventions, such as those that modulate the gut microbiome.
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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) | 0.002 | 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".