Mapping Longitudinal Psychiatric Signatures in Huntington’s Disease
Bibliographic record
Abstract
OBJECTIVE: Although Huntington's disease is characterized by motor onset, psychiatric disturbances may present years prior and affect functioning. However, there is inter-individual variability in psychiatric expression and progression. This study therefore strives to stratify longitudinal psychiatric signatures that may inform Huntington's disease prognosis, with potential clinical applications. METHODS: Forty-six Huntington's disease gene carriers (21 premanifest, 25 manifest; 31 female; age range 25-69) underwent short-Problem Behavior Assessment for depression, irritability, apathy, and dysexecutive behaviors for up to six longitudinal visits. The Disease Trajectories software, a machine-learning approach, was employed to perform unsupervised clustering of psychiatric trajectories. Linear fits were calculated for each cluster. Lastly, the main clusters of shared trajectories were assessed for group differences in demographic and clinical characteristics. RESULTS: The Disease Trajectories analysis software identified two main psychiatric patterns comprising premanifest and manifest patients that explained 54% of the sample. These two clusters evinced a dissociation in the development of depression and irritability; the first cluster was defined by increasing irritability with no depression and the second by a rise-and-fall in depression with no irritability. Both clusters showed a longitudinal increase in clinically relevant apathy and dysexecutive behaviors. CONCLUSIONS: Ultimately, through the detection of individual-level psychiatric trajectories with machine-learning, this exploratory study reveals that a dissociation of depression and irritability is apparent even in premanifest stages. These findings underscore individual differences in the severity of longitudinal multivariate clinical characteristics for real-world patient stratification, with implications for precision medicine.
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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 | 0.000 | 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".