Structural Covariance-Based Morphometric Connectivity in Psychosis: Investigating Dysconnectivity and Cognition Across Disease Stages with Ultra-High Field MRI
Bibliographic record
Abstract
Psychosis is among the most disabling disorders worldwide due to its broad spectrum of symptoms. Research has recognized brain dysconnectivity as a key feature of psychosis that is closely linked to cognitive impairments. Conventionally, brain connectivity is defined by structural and functional connectivity. This study employs a novel approach by using structural covariance, the correlation between morphometric changes across individuals, to define morphometric connectivity (MC). Although previous research has linked MC to developmental and cognitive processes, its role in psychosis and its relationship to cognition remain understudied, partly due to limitations associated with low-field magnetic resonance imaging (MRI) studies. This study investigates how MC and its correlation with cognitive functions is impacted by disease progression in psychosis. Data were collected using 7-Tesla MRI from 112 participants (Healthy Controls = 31, First-episode Psychotic Patients = 62, Clinical High-Risk Individuals = 10, Multiple-episode Psychotic Patients = 9). MC was computed using Graph Theory (strength and efficiency) from cortical thickness (62 regions) and hippocampal volume (18 subfields). Across disease stages, changes in MC were evaluated using a General Linear Model, while partial correlation matrices (correcting for age, gender, and total brain volume) compared shifts in the relationship between MC and cognitive performance. Preliminary analysis shows that, as disease progresses, MC is significantly impaired in certain regions (10/80 regions), and the correlation between MC and cognitive functions is weakened in specific regions (13/80 regions). These findings deepen our understanding of structural covariance-based MC as a hallmark of psychosis and its progression, offering insights into the cognitive decline in psychotic patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".