Predicting Mental and Neurological Illnesses Based on Cerebellar Normative Features
Why this work is in the frame
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Bibliographic record
Abstract
Background Mental and neurological conditions have been linked to structural brain variations. However, aside from dementia, the value of brain structural characteristics derived from brain scans for prediction is relatively low. One reason for this limitation is the clinical and biological heterogeneity inherent to such conditions. Recent studies have implicated aberrations in the cerebellum – a relatively understudied brain region – in these clinical conditions. Methods Here, we used machine learning to test the value of individual deviations from normative cerebellar development across the lifespan (based on trained data from >27k participants) for prediction of autism spectrum disorder (ASD) (n=317), bipolar disorder (BD) (n=238), schizophrenia (SZ) (n=195), mild cognitive impairment (MCI) (n=122), and Alzheimer's disease (AD) (n=116), with individuals without diagnoses were matched to the clinical cohorts. We applied several atlases and derived median, variance, and percentages of extreme deviations within each region of interest. Results Our results show that lobular and voxel-wise cerebellar data can be used to discriminate reference samples from ASD and SZ with moderate accuracy (the area under the receiver operating characteristic curves ranged from 0.56 to 0.65), The contributions to these predictive models originated from both anterior and posterior regions of the cerebellum. Conclusions Our study highlights the utility of cerebellar normative modelling in predicting ASD and SZ, aided by four cerebellar atlases that enhanced the interpretability of the findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it