Aberrant brain structural–functional connectivity coupling in euthymic bipolar disorder
Bibliographic record
Abstract
Aberrant structural (diffusion tensor imaging [DTI]) and resting-state functional magnetic resonance imagining connectivity are core features of bipolar disorder. However, few studies have explored the integrity agreement between structural and functional connectivity (SC-FC) in bipolar disorder. We examine SC connectivity coupling index whether could potentially provide additional clinical predictive value for bipolar disorder spectrum disorders besides the intramodality network measures. By examining the structural (DTI) and resting-state functional network properties, as well as their coupling index, among 57 euthymic bipolar disorder patients (age 13-28 years, 18 females) and 42 age- and gender-matched healthy controls (age 13-28 years, 16 females), we found that compared to controls, bipolar disorder patients showed increased structural rich-club connectivity as well as decreased functional modularity. Importantly, the coupling strength between structural and functional connectome was decreased in patients compared to controls, which emerged as the most powerful feature discriminating the two groups. Our findings suggest that structural-functional coupling strength could serve as a valuable biological trait-like feature for bipolar disorder over and above the intramodality network measures. Such measure can have important clinical implications for early identification of bipolar disorder individuals, and inform strategies for prevention of bipolar disorder onset and relapse.
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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".