Waking to Brain Health: How Chronotype Relates to Brain Structure in Young Adults
Bibliographic record
Abstract
Abstract Introduction Sleep patterns are critical for brain health and functioning, influencing mental health, learning, memory, and emotional regulation, particularly in young adults. However, the effects of chronotype, sleep quality, and daytime sleepiness on brain structure remain poorly understood. This study aimed to examine anatomical brain changes in individuals with early chronotype (EC) compared to those with late chronotype (LC). Early and late chronotypes, which refer to preferences for early or late sleep-wake times, represent distinct biological rhythms that may impact brain structure and function. Methods We used anatomical magnetic resonance imaging (MRI) to identify morphological and macroscopic brain differences between 68 young, healthy EC individuals and 68 age– and sex-matched LC participants. Two widely used atrophy estimation pipelines, voxel-based morphometry (VBM) and cortical thickness analysis, were employed to assess regional brain structure differences between the two groups. Results Whole-brain VBM analysis revealed decreased gray matter volume in LC individuals, specifically in the left cerebellum posterior lobe, left declive, and left cerebellum crus I. Additionally, linear regression models showed significant cortical thinning in the left caudal anterior cingulate, right caudal anterior cingulate, and right lateral occipital regions in the LC group compared to the EC group. However, there was no significant difference between the two groups in terms of white matter morphology or brain aging. Discussion Our findings suggest that chronotype-related differences in sleep patterns are associated with significant structural alterations in gray matter volume and cortical thickness. These changes may reflect the impact of chronic circadian misalignment on neurobiological integrity, even in young, healthy individuals. Findings emphasize the importance of considering sleep timing as a critical factor in brain health and lay the groundwork for future longitudinal studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".