Neural Signatures of Bipolar Disorder and Psychotropic Medication Effects: A Multimodal Positron Emission Tomography–Magnetic Resonance Imaging Study
Bibliographic record
Abstract
BACKGROUND: C]UCB-J positron emission tomography (PET). METHODS: Nineteen individuals with BD, including BDI (n = 13) and BDII (n = 6), and healthy control (HC) participants (n = 26) completed SV2A PET imaging in parallel with structural and functional magnetic resonance imaging to assess frontolimbic synaptic density, gray matter volume (GMV), and intrinsic connectivity, respectively. RESULTS: Individuals with BD showed lower frontolimbic SV2A density, which exploratory analyses revealed to be seemingly driven by individuals taking psychotropic medications (BD-med). Conversely, group differences in GMV were observed only when accounting for medication status, with higher GMV in BD-med individuals relative to unmedicated individuals with BD (BD-none). Connectivity was higher in the BD group overall, but lower in the BD-med group relative to the BD-none group. SV2A and GMV were positively correlated in HC participants. However, this relationship was absent in individuals with BD, with lower SV2A/GMV ratios being associated with greater impulsivity in BD. CONCLUSIONS: These findings provide the first in vivo evidence of synaptic deficits in BD and offer preliminary evidence suggesting that psychotropic medications may differentially influence brain structure and function. These results underscore the importance of accounting for medication effects when interpreting neuroimaging findings in BD and the need to disentangle illness-related changes from medication effects on brain and behavior in BD.
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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 | 0.000 | 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) | 0.001 | 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".