Infant Left Amygdala Volume Is Negatively Associated with Fecal Microbiota Diversity
Bibliographic record
Abstract
Abstract Introduction Rodent studies have addressed the importance of early life gut microbiota in the development of emotional and social functioning. Studies in human infants are still scarce, but associations with cognition and temperament have been reported. Neuroimaging studies have linked the amygdala with fecal microbiota diversity in infants, but crucially these studies have not covered the neonatal period, and the current study addressed this gap. Methods The study population included 65 infants drawn from the ongoing, general population based FinnBrain Birth Cohort Study. Brain MRI was performed around the age of one month (mean age 25 days). Fecal microbiota profiles (mean 68 days) were assessed by 16s rRNA amplicon sequencing at the age of 2.5 months. Results We found a negative association between infant left amygdala volume and alpha diversity (n=52, beta =-0.0043, p=0.034, adjusted for infant sex, breastfeeding, delivery mode, age during fecal sampling, age from conception during scan, and intracranial volume, Fig.1). Amygdala volumes were not associated with beta diversity (p=0.21), nor with the abundances of individual genera when adjusted for the same covariates and multiple testing. Conclusion Our results provide first evidence for associations between the brain and fecal microbiota in human neonates. Although the reported data do not allow investigation of underlying mechanisms, i.e. about the directionality of the hypothesized gut-brain connection, the reported connection encourages for future investigations of manifestations of gut-brain axis in early life.
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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) | 0.003 | 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".