0254 Fronto-Temporo-Occipital Cortical Thickness Measures Predict Poor Sleep Quality in At-Risk Youth
Bibliographic record
Abstract
Poor subjective sleep quality (SQ) is a prominent risk factor for most forms of psychiatric illness, yet objective biomarkers of SQ have remained elusive. Our goal was to identify neural markers of SQ using a combination of structural and functional neuroimaging assessments in youth expressing a range of psychopathology. A transdiagnostic sample of 40 youth (8-17yr) completed an MRI assessment and rated past-week SQ with a modified Pittsburgh Sleep Quality Index (N=22 good SQ [PSQI≤5]; N=18 poor SQ [PSQI>5]). Group-lasso logistic regression identified non-zero predictors of SQ from cortical thickness measures; BOLD response to reward and emotion fMRI tasks; sleep history; and demographic/clinical features. Poor SQ was associated with higher depression severity and cortical thickness in sensory regions (thinner right superior temporal sulcus and left temporal pole, thicker right lateral occipital cortex). Age interacted with right superior frontal (SFC) cortical thickness to predict SQ, such that SFC thickness and age were positively associated in youth with good SQ and negatively related in those with poor SQ. Anxiety severity interacted with right rostral anterior cingulate (rACC) cortical thickness to predict SQ, such that rACC thickness and anxiety were negatively related in youth with good SQ and positively related among youth with poor SQ. Predictors explained 51.2% of the variance in SQ and correctly classified 85% of cases. Age, internalizing symptoms, and cortical thickness in sensory and frontal midline regions, were useful classifiers of SQ. A combination of measures may be necessary to understand the neural basis of poor SQ and its role in psychiatric illness. R01MH060952; K01MH111953.
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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".