Common-specific edge-centric connectome across Four Episodes in Bipolar Disorder
Bibliographic record
Abstract
Abstract Background Bipolar disorder (BD) is a heterogeneous psychiatric illness marked by dynamic mood states, including manic (BipM), depressive (BipD), mixed (mBD), and euthymic/remitted (rBD) episodes. These clinical fluctuations are accompanied by widespread functional disruptions in the brain. However, the shared and individual-specific neural mechanisms across distinct episodes of BD remain poorly understood. Methods We analyzed resting-state fMRI data from 190 participants (BD patients in four episodes and healthy controls) using edge-centric functional connectomes (eFC), which capture time-resolved co-fluctuations between brain regions. A common-orthogonal-basis extraction (COBE) algorithm was applied to decompose individual eFC matrices into shared and individual-specific subspaces. We characterized the spatial topology, genetic relevance, and circuit-level correlates of the shared component. Dynamic properties (entropy, identifiability) and symptom prediction models were assessed using entropy metrics, intraclass correlation coefficients, and support vector regression. Results The shared eFC pattern was stable across participants, aligned with the sensory–association gradient ( p < 0.0001), and exhibited significant heritability and test–retest reliability ( p < 0.05). Entropy of individual loadings increased with illness duration and was significantly elevated in BD, particularly in mBD. Microcircuit modeling revealed that this shared pattern was inversely related to external input strength ( r = – 0.34, p spin < 0.05), indicating intrinsic network dominance. mBD was associated with globally elevated eFC entropy and markedly reduced fingerprint stability. Symptom severity (HDRS, YMRS, HAMA) was significantly predicted from individual network topographies across BD phases, highlighting clinically meaningful dynamic signatures. Conclusion Our findings demonstrate that BD episodes are underpinned by a conserved functional scaffold and distinct individual-specific neural fingerprints. Edge-centric dynamics—especially those derived from individual-specific decompositions—offer robust biomarkers for mood state characterization and symptom severity, and may facilitate future personalized interventions 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 | 0.001 |
| 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".