Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders
Bibliographic record
Abstract
ABSTRACT BACKGROUND Psychiatric diseases are increasingly conceptualized as brain network disorders. Hundreds of resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed patterns of functional brain dysconnectivity in disorders such as major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ). Although these disorders have been mostly studied in isolation, there is mounting evidence of shared neurobiological alterations across disorders. METHODS To uncover the nature of the relatedness between these psychiatric disorders, we conducted an innovative meta-analysis of past functional brain dysconnectivity findings obtained separately in MDD, BD and SZ. Rather than relying on a classical coordinate-based approach at the voxel level, our procedure extracted relevant neuroanatomical labels from text data and reported findings at the whole brain network level. Data were drawn from 428 rsfMRI studies investigating MDD (158 studies, 7429 patients / 7414 controls), BD (81 studies, 3330 patients / 4096 patients) and/or SZ (223 studies, 11168 patients / 11754 controls). Permutation testing revealed commonalities and specificities in hypoconnectivity and hyperconnectivity patterns across disorders. RESULTS Among 78 connections within or between 12 cortico-subcortical networks, hypoconnectivity and hyperconnectivity patterns of higher-order cognitive (default-mode, fronto-parietal, cingulo-opercular) networks were similarly observed across the 3 disorders. By contrast, dysconnectivity of lower-order (somatomotor, visual, auditory) networks in some cases differed between disorders, notably dissociating SZ from BD and MDD. CONCLUSIONS Our label-based meta-analytic approach allowed a comprehensive inclusion of prior studies. Findings suggest that functional brain dysconnectivity of higher-order cognitive networks is largely transdiagnostic in nature while that of lower-order networks may best discriminate mood and psychotic disorders, thus emphasizing the relevance of motor and sensory networks to psychiatric neuroscience.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".