Coordinated Cortical Thickness Alterations across Psychiatric Conditions: A Transdiagnostic ENIGMA Study
Bibliographic record
Abstract
ABSTRACT Introduction Mental disorders are increasingly conceptualized as overlapping spectra with underlying polygenicity, neurodevelopmental etiology, and clinical comorbidity. They share multi-level neurobiological alterations, including network-like brain structural alterations. However, whether alteration patterns covary across mental disorders in a biologically meaningful way is currently unknown. Methods We accessed summary statistics on cortical thickness alterations from 12,024 patients with six mental disorders and 18,969 controls from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. First, we studied cortical thickness co-alteration networks as a form of pathological structural covariance. We identified regions exhibiting high inter-regional covariance across disorders (‘hubs’), and regions that strongly connect to these hubs facilitating network spreading of disease effects (‘epicenters’). Next, we applied manifold learning to reveal organizational gradients guiding transdiagnostic patterns of illness effects. Last, we tested whether these gradients capture differential cortical susceptibility with respect to normative cortical thickness covariance, cytoarchitectonic, transcriptomic, and meta-analytical task-based profiles. Results Co-alteration network hubs were linked to normative connectome hubs and anchored to prefrontal and temporal disease epicenters. The principal gradient derived from manifold learning captured maximally different embedding of prefrontal and temporal epicenters within co-alteration networks, followed a normative cortical thickness gradient, and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Conclusion Together, our findings indicate that disease impact occurs in a synchronized fashion and along multiple levels of hierarchical cortical organization. Such axes can help to disentangle the different neurobiological pathways underlying mental illness.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".