O1.5. CLUSTERING-BASED BEHAVIOURAL PHENOTYPES OF INTRINSIC MOTIVATION IN SCHIZOPHRENIA DETERMINED USING OBJECTIVELY QUANTIFIED REAL-WORLD PERFORMANCE
Bibliographic record
Abstract
Abstract Background Intrinsic motivation (IM) deficits are a prominent feature of schizophrenia that substantially impacts functional outcome. Previous investigations have primarily assessed IM as a general construct or towards specific activities, but multifaceted examination of IM using objective and ecologically valid means has been lacking. The current study aimed to determine whether IM-driven real-world behaviours of individuals with and without schizophrenia reveal distinct behavioural phenotypes that predict differential clinical and functional profiles. Methods Forty-five stable adult outpatients with schizophrenia or schizoaffective disorder (SZ) and 47 healthy controls (HC), group-matched for age and sex, completed the study. Wireless motion capture was used to quantify participants’ behaviour (during two previously validated 15-minute open-field tasks) aligning with distinct aspects of IM: exploratory behaviour (in an unfamiliar setting that contained common and uncommon objects) and unincentivized activity engagement (under provision of explicit choice between active versus passive engagement options). Sparse k-means clustering was applied to task-derived indices of locomotion and object exploration, and activity duration, intensity, and persistence to identify behaviourally differentiable subgroups. Clusters were compared across standard clinical measures of general amotivation, cognition, and community functioning. Diagnostic group by cluster assignment interaction effects were evaluated to identify potential discrepancies in within-cluster SZ versus HC differences across clusters. Results Three clusters emerged: medium activity and low exploration (20 SZ and 19 HC), low activity and medium exploration (15 SZ and 8 HC), and high activity and exploration (10 SZ and 20 HC). Cluster assignment did not significantly predict diagnostic group (Fisher’s exact p=0.067) but indicated increased odds of diminished exploration or activity in SZ (odds ratio=2.57, p=0.047). General amotivation differed across clusters (F(2,89)=6.31, p=0.003) due to significantly elevated scores in the low exploration and low activity clusters versus the high performance cluster. Evaluation of the diagnostic group by cluster assignment interaction effect for amotivation (F(2,86)=3.12, p=0.062) revealed significantly higher scores in SZ versus HC within the low exploration and high performance clusters, but not the low activity cluster. Evaluation of the interaction effect for community functioning (F(2,86)=4.86, p=0.007) revealed significantly lower scores in SZ versus HC within all clusters, with the discrepancy increasing from high performance to low activity to low exploration. Discussion Multidimensional characterization of IM stands to provide valuable insights into motivation and functioning deficits in schizophrenia that behaviourally manifest differently across individuals. Impairment in IM-driven behaviour may be substantially detrimental particularly for individuals with schizophrenia, as SZ participants demonstrated clinically observable deficits even compared to within-cluster HC counterparts. The current lack of objective standards to measure IM underscores the benefit of behaviour-based characterization. Further, as IM has been deemed a valuable psychosocial intervention target in schizophrenia, multidimensional behavioural phenotyping may help guide personalized therapy by differentiating between IM impairments demanding amelioration versus unimpaired tendencies that may be leveraged to facilitate remediation.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".