194. Neuromelanin-Sensitive MRI as an Early Indicator of Dopamine Dysfunction in Individuals at Risk for Psychosis
Bibliographic record
Abstract
Background: The current study uses neuromelanin-sensitive MRI (NM-MRI), a brief, noninvasive, imaging technique that has widely been used in Parkinson’s disease, to investigate signal changes in the substantial nigra of subjects at risk for psychosis which may reflect excess dopamine activity prior to the full onset of psychosis. Methods: We performed NM-MRI scans on 20 healthy controls, 10 individuals at risk of schizophrenia, 20 patients with schizophrenia, and 3 postmortem sections of human midbrain. To validate NM-MRI as a neuromelanin-sensitive measure (not just a marker of dopamine cell loss as it has been used in Parkinson’s disease), tissue concentrations of neuromelanin were estimated in postmortem samples using spectrophotometry (ex vivo study). We compared the signal intensity changes of NM-MRI across clinical groups using voxelwise analysis (in vivo study). Results: NM-MRI signal variation in different regions of a post-mortem midbrain sample was highly correlated to neuromelanin concentration in the same regions (Pearson r = .81, P = .0004). Voxelwise analysis within the SN examining clinical groups, identified an SN cluster, where patients with schizophrenia had higher NM-MRI signal than matched controls (P < .05, uncorrected) and a partially overlapping cluster was observed to have higher signal in individuals at risk of schizophrenia compared to controls (P < .05, uncorrected). Conclusion: These preliminary data indicate that NM-MRI indeed appears to be sensitive to neuromelanin content, even in the absence of neurodegeneration. The method shows promise as an imaging tool in neuropsychiatric illness, since it may be able to capture interindividual variability in dopamine system function and psychopathology. Indeed, we found that the signal may be altered in individuals at risk of psychosis. Further work is needed to confirm these results.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".