Episode-specific and common intrinsic functional network patterns in bipolar
Bibliographic record
Abstract
Understanding the alterations in brain function across different episodes of bipolar disorder (BD), including manic (BipM), depressive (BipD), and remission states (rBD), poses a significant challenge. In our cross-sectional study, we collected resting-state functional magnetic resonance imaging data from 117 BD patients (BipM: 38, BipD: 42, rBD: 37) and 35 healthy controls. Our aim was to delineate functional connections associated with episode dynamics, delineate common and specific patterns, validate them as biomarkers, and elucidate their biological underpinnings. Initially, we identified a common altered pattern within the sub-regions of the ventral-attention network, alongside specific patterns observed in the default mode network for BipM, the prefrontal network for BipD, and the limbic network for rBD. Using large-sample data from the Human Connectome Project, we further identified that these connectivity patterns exhibit relatively high reliability and heritability. Also, these distinct patterns accurately characterized the diverse episodes of BD and effectively predicted the cor responding clinical symptoms linked with each episode type. Importantly, using out of sample data to decode possible neurobiological mechanisms underlying these patterns, we found that regions of particular interest were enriched in multiple receptors, including MOR, NMDA, and H3 for specific alterations, and A4B2, 5HTT, and 5HT1a for common alterations. Moreover, both episode-specific and common patterns demonstrated a high enrichment for cell types such as L5ET, Micro/PVM,oligodendrites and Chandelier. Our study offers novel insights concerning episode dynamics in BD, paving the way for personalized medicine approaches tailored to address the various episodes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".