Identifying psychosis spectrum youth using support vector machines and cerebral blood perfusion as measured by arterial spin labeled fMRI
Bibliographic record
Abstract
Altered cerebral blood flow (CBF), as measured by arterial spin labelling (ASL), has been observed in several psychiatric conditions, but is a generally underutilized MRI technique, especially in the study of psychosis spectrum (PS) symptoms. We aimed to determine group differences in ASL resting state functional connectivity (rsFC) between PS and non-PS youth, and the reliability of a support vector machine (SVM) classifier trained on ASL rsFC features to differentiate PS and non-PS youth, especially compared to blood oxygen level dependent (BOLD) fMRI. 1146 youth aged 8–22 with ASL and BOLD data from the Philadelphia Neurodevelopmental Cohort were analyzed. Widespread ASL hyperconnectivity was found in the left cuneus, precuneus, and dorsolateral prefrontal cortex, and hypoconnectivity was found in the left cingulate cortex and orbitofrontal area (multiple linear regression, FDR corrected). An SVM trained on ASL and BOLD features outperformed either modality alone (AUC BOTH = 0.72 versus AUC ASL = 0.68 and AUC BOLD = 0.67). Classification performance and precision improved when the non-PS group had no psychiatric comorbidities. The relative success of the classifier suggests ASL rsFC changes exist in PS individuals that differ from BOLD rsFC changes, and extends previous findings of CBF dysregulation in PS.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".