Orbitofrontal Thickness and Network Associations as Transdiagnostic Signature of Amotivation Along the Bipolar-Schizophrenia Spectrum
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Negative symptoms of schizophrenia (SCZ), particularly amotivation, are prominent across both SCZ and bipolar disorder (BD). While orbitofrontal cortex (OFC) alterations have been implicated in the development of negative symptoms, their contributions across disorders remain to be established. Here, we examined how OFC thickness and network associations relate to amotivation compared to diminished expression across the BD-SCZ spectrum. STUDY DESIGN: We included 50 individuals with SCZ, 49 with BD, and 122 controls. We assessed amotivation and diminished expression and estimated thickness in the medial and lateral OFC as regions of interest as well as 64 other cortical regions. STUDY RESULTS: Across BD and SCZ, reduced right lateral and bilateral medial OFC thickness were specifically associated with amotivation, but not diminished expression or other clinical factors. We then generated intra-individual OFC structural covariance networks to evaluate how the system-level embedding of the OFC would link to brain-wide cortical maps of negative symptoms. We found that medial OFC covariance networks spatially correlated with the brain-wide cortical alterations of both negative symptom dimensions. Further analyses in independent SCZ data from the ENIGMA consortium (n = 4474) revealed associations with lateral OFC covariance networks. Finally, the brain-wide cortical alterations of amotivation were significantly correlated with normative functional and structural white-matter connectivity profiles of the right medial and left lateral OFC as well as adjacent prefrontal and limbic regions. CONCLUSIONS: Our work identifies OFC alterations as a possible transdiagnostic signature of amotivation and provides insights into network associations underlying the system-wide cortical alterations of negative symptoms across SCZ and BD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".