M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA
Bibliographic record
Abstract
Abstract Background Although widespread structural brain abnormalities have been consistently reported in schizophrenia, their relation to the heterogeneous clinical manifestations is not well understood. Multivariate methods are needed to uncover covariance patterns between multiple symptom dimensions and system-wide brain imaging data. Methods This cross-sectional study used structural magnetic resonance imaging and neuropsychological data from 133 patients with chronic schizophrenia (48 female, 34.8±13.2 years) from the Northwestern University Schizophrenia Data and Software Tool (NUSDAST). We estimate disease-related voxel-wise tissue volume loss using deformation-based morphometry (DBM) of T1 weighted images. In patients with schizophrenia, multiple clinical dimensions including positive/negative symptoms and cognitive deficits, demographic data as well as individual tissue volume loss (DBM) were included in the multivariate model. Clinical-anatomical phenotypes were identified using partial least squares analysis. Results Multivariate analysis revealed three distinct clinical-anatomical phenotypes accounting for 27.5%, 15%, and 13% of the shared covariance between clinical-behavioural data and tissue volume loss (total of 55.5%). The first clinical-anatomical phenotype encompassed cognitive impairments, severity of negative symptoms and tissue volume loss within the default mode network and visual network. The second clinical-anatomical phenotype was associated with additional cognitive impairments and tissue volume loss within the frontoparietal and ventral attention network, while the third clinical-anatomical phenotype encompassed a mixed positive and negative symptoms phenotype and tissue volume loss within the dorsal attention network. Critically, the pattern of volume loss within the first most prevalent clinical-anatomical phenotype mediated (a*b) the effect of socioeconomic status on clinical outcome (cognitive performance and negative symptoms) (a*b=-0.033(0.008); P<1.0×〖10〗^(-4); 95% CI [-0.049, -0.018]). Finally, we partly replicated the first clinical-anatomical phenotype in an independent sample of patients with schizophrenia (n=108). Discussion The heterogeneous clinical manifestation of schizophrenia can be significantly explained by three clinical-anatomical phenotypes. Despite their distributed topography, each phenotype is centered on a specific, well-defined set of intrinsic networks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".