Common and Specific Intrinsic Functional Network Related to Episode Dynamics during Treatment in Bipolar Spectrum
Bibliographic record
Abstract
ABSTRACT A significant challenge in bipolar disorder (BD) is to understand the neural substrates of emotional fluctuations (i.e., episode phases) along the spectrum including manic (BipM), depressive (BipD), and remission states (rBD). Here, We constructed intrinsic functional connectome for 117 subjects with BD (BipM: 38, BipD: 42, and rBD: 37) and 35 healthy controls, then associated connectivities with emotional fluctuations to identify the common and specific patterns, and finally probed their biological underpinnings. We uncovered the common altered pattern in the salience-attention network and the specific pattern in the default mode-salience network specific for BipM and sensory-prefrontal network specific for BipD and rBD. These pathological patterns can accurately delineate the various episodes episodes types of bipolar disorder and forecast the corresponding clinical symptoms associated with each episodes type. Both common and specific patterns exhibited significant genetic stability and centered regions were enriched in multiple receptors such as MOR, NMDA, and H3 for specific pathology while A4B2, 5HTT, and 5HT1a for common pathology. Gene expression was enriched in PLEKHO1, SCN2A, POU3F2, and ANK3. Our study provides new insights into possible neurobiological interpretation for episode phases in the bipolar spectrum and holds promise for advancing personalized precision medicine approaches targeting various episodes of the condition.
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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) | 0.002 | 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".