Orbitofrontal-striatal structural alterations linked to negative symptoms at different stages of the schizophrenia spectrum
Bibliographic record
Abstract
Abstract Among the most debilitating manifestations of schizophrenia are negative symptoms such as anhedonia and apathy. Imaging studies have linked these symptoms to morphometric abnormalities in two brain regions implicated in reward and motivation: the orbitofrontal cortex (OFC) and ventral striatum. Negative symptoms generally are associated with reduced OFC thickness, while apathy specifically maps to reduced striatal volume. However, it remains unclear whether these tissue losses are a consequence of chronic illness and its treatment, or an underlying phenotypic trait. Here we use multicentre MRI data to investigate orbitofrontal-striatal abnormalities across the schizophrenia-spectrum from healthy populations with schizotypy, to unmedicated and medicated first-episode psychosis patients, and patients with chronic schizophrenia. Striatal volumes and OFC thickness were estimated from T1-weighted images acquired in all three diagnostic groups and controls from four sites (n=337). Results were first established in one test cohort (“Zurich sample”) and replicated in three independent samples. There was a correlation between apathy and striatal volume only in healthy individuals with schizotypy; however, medicated patients exhibited larger striatal volumes, which appears to be a consequence of antipsychotic medications. The association between reduced OFC thickness and negative symptoms generally also appeared to vary along the disease course, being significant only in first-episode psychosis patients. In schizotypy there was increased OFC relative to controls. Our findings suggest that negative symptoms associate with a temporal continuum of orbitofrontal-striatal abnormalities that may predate the occurrence of schizophrenia. Thicker OFC in schizotypy may represent either compensatory or pathological mechanisms prior to disease-onset.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".