Sex-Related Variations in the Brain Motor-Network Connectivity at Rest during Puberty
Bibliographic record
Abstract
The development of functional neuroimaging technologies has resulted in a flood of mathematical models that investigate functional brain connections in health and disease. Motor network activity in the resting state and its response to puberty remains to be investigated. Variations between sexes in puberty may appear not just in brain regions involved in sexual and emotional activities, but also in cognitive and motor abilities that are active even when the individual is resting. The aim of this study was to investigate the interactions of the motor subnetwork in the resting state of healthy males and females aged 12 and 16. This study used the OpenNeuro Dataset ds004169:1.0.7, Queensland Twin IMaging. The MRI signals were preprocessed to get adjacency matrices from the sensory/somatomotor and cerebellar networks in the Power atlas. Network topology was analyzed using the centrality measures of strength, hubness, and leverage. The strength of the nodes increases with age in both sex groups. Both sexes had right hemisphere dominance in the cerebellar-mouth subnetwork and left dominance in the cerebellar-hand subnetwork. Eleven leverage centrality regions were common to all groups, the most relevant were the Precuneus, the cingulum postcentral and the supplementary motor area. In both sexes, hubs at age 12 were detected only in the right hemisphere. This dominance was reduced at age 16. Understanding connectivity changes in the brain during rest may enable the identification of neurophysiological mechanisms of cognitive and behavioral development that may contribute to long-term psychological well-being in adolescents.
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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 | 0.002 |
| 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".