Babies, bugs and brains: How the early microbiome associates with infant brain and behavior development
Bibliographic record
Abstract
Growing evidence is demonstrating the connection between the microbiota gut-brain axis and neurodevelopment. Microbiota colonization occurs before the maturation of many neural systems and is linked to brain health. Because of this it has been hypothesized that the early microbiome interactions along the gut-brain axis evolved to promote advanced cognitive functions and behaviors. Here, we performed a pilot study with a multidisciplinary approach to test if the microbiota composition of infants is associated with measures of early cognitive development, in particular neural rhythm tracking; language (forward speech) versus non-language (backwards speech) discrimination; and social joint attention. Fecal samples were collected from 56 infants between four and six months of age and sequenced by shotgun metagenomic sequencing. Of these, 44 performed the behavioral Point and Gaze test to measure joint attention. Infants were tested on either language discrimination using functional near-infrared spectroscopy (fNIRS; 25 infants had usable data) or neural rhythm tracking using electroencephalogram (EEG; 15 had usable data). Infants who succeeded at the Point and Gaze test tended to have increased Actinobacteria and reduced Firmicutes at the phylum level; and an increase in Bifidobacterium and Eggerthella along with a reduction in Hungatella and Streptococcus at the genus level. Measurements of neural rhythm tracking associated negatively to the abundance of Bifidobacterium and positively to the abundance of Clostridium and Enterococcus for the bacterial abundances, and associated positively to metabolic pathways that can influence neurodevelopment, including branched chain amino acid biosynthesis and pentose phosphate pathways. No associations were found for the fNIRS language discrimination measurements. Although the tests were underpowered due to the small pilot sample sizes, potential associations were identified between the microbiome and measurements of early cognitive development that are worth exploring further.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".