E11 Compensation in huntington’s disease
Bibliographic record
Abstract
Background Compensation accounts for the dissociation between pathology and absence of behavioural change during premanifest stages of Huntington’s disease (HD). Despite neuronal loss, individuals with premanifest HD function at a level similar to that of a healthy population. Neural mechanisms underlying compensation, however, are generally poorly understood due to the lack of an operational definition of compensation. Here, we describe the first example of the modelling and empirical testing of compensation in HD. Aims We hypothesise that compensation occurs where increased brain activation is required to maintain normal levels of behaviour until pathology becomes too severe. A compensatory relationship is thus characterised by non-linear longitudinal trajectories of brain activity and behaviour as disease load increases linearly across sequential phases of disease progression. Methods We tested our model in a large cohort of premanifest and early HD gene-carriers from the TrackOn-HD study. Focusing on both cognitive and motor networks, brain activity was measured using task and resting-state fMRI, volumetric loss by structural MRI and behaviour by task performance. Compensation was tested for across three sequential phases of disease progression. Results Maintained global cognition was associated with increased effective connectivity between the left and right dorsolateral prefrontal cortex, an important region for cognitive processing, while maintained motor performance was associated with increased connectivity between bilateral premotor cortex. Conclusions Our empirical findings demonstrate theoretically-defined compensation in HD in networks central to the HD phenotype and can now be used to test both cross-sectional and longitudinal compensation in other neurodegenerative disease with similar patterns to HD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".