Structural and metabolic differentiation between bipolar disorder with psychosis and substance-induced psychosis: An integrated MRI/PET study
Bibliographic record
Abstract
BACKGROUND: Bipolar disorder (BD) may be characterized by the presence of psychotic symptoms and comorbid substance abuse. In this context, structural and metabolic dysfunctions have been reported in both BD with psychosis and addiction, separately. In this study, we aimed at identifying neural substrates differentiating psychotic BD, with or without substance abuse, versus substance-induced psychosis (SIP) by coupling, for the first time, magnetic resonance imaging (MRI) and positron emission tomography (PET). METHODS: Twenty-seven BD type I psychotic patients with (n=10) or without (n=17) substance abuse, 16 SIP patients and 54 healthy controls were enrolled in this study. 3T MRI and 18-FDG-PET scanning were acquired. RESULTS: Gray matter (GM) volume and cerebral metabolism reductions in temporal cortices were observed in all patients compared to healthy controls. Moreover, a distinct pattern of fronto-limbic alterations were found in patients with substance abuse. Specifically, BD patients with substance abuse showed volume reductions in ventrolateral prefrontal cortex, anterior cingulate, insula and thalamus, whereas SIP patients in dorsolateral prefrontal cortex and posterior cingulate. Common alterations in cerebellum, parahippocampus and posterior cingulate were found in both BD with substance abuse and SIP. Finally, a unique pattern of GM volumes reduction, with concomitant increased of striatal metabolism, were observed in SIP patients. CONCLUSIONS: These findings contribute to shed light on the identification of common and distinct neural markers associated with bipolar psychosis and substance abuse. Future longitudinal studies should explore the effect of single substances of abuse in patients at the first-episode of BD and substance-induced psychosis.
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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".