Late‐Breaking Abstracts
Bibliographic record
Abstract
Introduction: Neuroimaging studies are widely used to identify aberrant brain activation and functional connectivity associated with bipolar disorder (BD).The results of these experiments, however, vary considerably across samples, imaging method, and design.We present the first meta-analysis that aims to investigate neural differences distinguishing BD from healthy controls (HC) across taskbased and resting-state functional neuroimaging studies.Method: Appropriate neuroimaging studies, using fMRI or PET, reporting whole-brain activation or seed-to-whole-brain functional connectivity (n = 166) comparing adults with BD (n = 4,655) to HC (n = 5,269) were obtained by a PubMed literature search.Activation likelihood estimation, a method of voxel-based quantitative metaanalysis estimating convergence across experiments by modeling the spatial uncertainty of neuroimaging data using anatomical coordinates of activation, was used to identify consistent regions of activation.Results are reported using a statistical threshold of pFWE < 0.05, cluster-corrected (P < .001cluster forming threshold).Results: Across resting state (n = 49) and task studies (n = 118) comprised of 248 individual neuroimaging experiments, patients with BD significantly differed from HC in two regions: a significant cluster located in the right ventrolateral prefrontal cortex (peak MNI coordinates: xyz = 38,36,-10; Z = 5.04, k = 271 voxels, cluster pFWE < 0.05), and a significant cluster located in the right putamen (xyz = 32,2,-12; Z = 4.93, k = 264, cluster pFWE < 0.05). Conclusions:Our results show abnormal fronto-striatal activation in BD compared to HC across all studies, which substantiates reproducible localization of functional abnormalities in BD despite heterogeneous data.
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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 | 0.013 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.374 | 0.161 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".