DATA-DRIVEN DISEASE PROGRESSION PATTERNS OF BRAIN MORPHOLOGY IN SCHIZOPHRENIA: MORE PROGRESSED STAGES IN TREATMENT- RESISTANCE
Bibliographic record
Abstract
Abstract Background Given the heterogeneity, particularly in terms of treatment response, and possible disease progression in schizophrenia, identifying the neurobiological subtypes and progression patterns in each patient may lead to novel biomarkers. Treatment-resistant schizophrenia (TRS) is a distinct subpopulation showing poor response to conventional pharmacological treatment and, as a result, a form of the illness associated with serious social and economic burden. The neurobiological basis of TRS remains to be elucidated, despite numerous strategies including neuroimaging studies. Aims & Objectives Here, we applied a data-driven machine-learning technique to classify disease progression patterns and staging of brain morphology in schizophrenia, with the goal of identifying distinct biological subtypes in the context of illness progression and associations with clinical measures. We hypothesized that TRS may be associated with more progressed disease staging; in addition, we investigated relationship with other clinical characteristics. Method In this cross-sectional multi-center study, we included 177 patients with schizophrenia, characterized by treatment response or resistance, with 3D T1-weighted magnetic resonance imaging. Cortical thickness and subcortical volumes calculated by FreeSurfer were converted into Z-scores using 73 healthy controls data. The Subtype and Stage Inference (SuStaIn) algorithm was used for unsupervised machine-learning analysis. Results SuStaIn identified three different subtypes: 1) subcortical volume reduction (SC) type (73 patients), in which volume reduction of subcortical structures occurs first and moderate cortical thinning follows, 2) globus pallidus hypertrophy and cortical thinning (GP-CX) type (42 patients), in which globus pallidus hypertrophy initially occurs followed by progressive cortical thinning, 3) cortical thinning (pure CX) type (39 patients), in which thinning of the insular and lateral temporal lobe cortices primarily happens. The remaining 23 patients were assigned to baseline stage of progression (no change). SuStaIn also found 84 stages of progression, and treatment-resistant schizophrenia showed significantly more progressed stages than treatment-responsive cases (p=0.001). The GP-CX type presented earlier stages than the pure CX type (p=0.009), but otherwise the subtypes did not show any relationship with clinical characteristics. Discussion & Conclusions The brain morphological progressions in schizophrenia can be classified into three subtypes, and treatment-resistance was associated with more progressed stages. These brain morphological subtypes and staging may, in turn, lead to the development of clinically useful individualized biomarkers. Schizophrenia is a syndrome with heterogeneity, and to date, no clinically available biomarkers have been established from the symptom-based approaches. Thus, individual-level classification and staging based solely on the biological features obtained in this study may lead to more biologically accurate classification, which would allow us to clarify pathophysiology and select personalized treatment. We clarified the relationship between stage progression and TRS, suggesting the possibility of individual-level disease monitoring for treatment resistance in clinical practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".