Elevated body mass index in youth is associated with dysregulated surrogate markers of neural inhibition & excitation, and internetwork functional dysconnectivity
Bibliographic record
Abstract
Abstract The developing child and adolescent brain is thought to have an increased vulnerability to the negative impact of obesity and excessive consumption of hyperpalatable and energy-rich foods. In this study, we investigated the neurophysiological effects of overweight and obesity in 30 participants spanning childhood and adolescence (8-19 years), using a naturalistic viewing paradigm with Inscapes, in a pseudo-resting-state protocol scan with magnetoencephalography (MEG). Subjects were median split on body mass indices (BMI), categorised into two groups comprising: lower <25 kg/m 2 (n=15) and higher ≥25 kg/m 2 (n=15). We assessed spontaneous, regional neural function indexed by oscillatory activity, and functional connectivity within and between intrinsic resting brain networks, including the default mode network, dorsal and ventral attention, somatomotor, visual, language, central executive and salience networks. Elevated BMI was associated with significant reductions in activity of the posterior dominant rhythm, and gamma hyperactivity across widespread cortical areas, suggesting intrinsic neuronal hyperexcitability and disinhibition in children and adolescents. Additionally, we observed low-frequency theta hypoconnectivity between resting state networks including the salience, visual, and default mode networks, and overall reduced global efficiency in brain network structure, suggesting reduced effectiveness in neural communication. These findings underscore the neural impact of body composition on the developing brain, suggesting deleterious alterations in excitation and inhibition from surrogate neural markers associated with neurochemistry and brain networks linked with cognitive and behavioural functioning. These alterations may contribute to the persistent behavioural rigidity and difficulties in adopting healthier eating behaviours into adulthood.
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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".