Back

Differentiating Hyperkinetic and Hypokinetic Motor Features in the Progression of Huntingtons Disease

Halabi, N. M.; Schultz, J. L.; Nopoulos, P.; Killoran, A.

2025-04-21 neurology
10.1101/2025.04.17.25325819 medRxiv
Show abstract

BackgroundHuntingtons disease (HD) is a monogenic neurodegenerative disorder typically characterized by chorea, a hyperkinetic motor feature. Historical data suggest that hypokinetic features, like rigidity and bradykinesia, become more prominent in later stages of HD. No evidence-based analysis has confirmed this observation. Additionally, several motor features of the disease are not clearly defined as hypokinetic or hyperkinetic. ObjectivesThis study aimed to 1) elucidate the trajectory of hyperkinetic and hypokinetic features across the disease course and 2) to classify vague motor features as following a hyperkinetic or hypokinetic trajectory. MethodsData from 13,475 motor-manifest HD patients from the Enroll-HD platform were analyzed. Linear mixed-effects models were constructed for each of the 31 Unified Huntingtons Disease Rating Scale (UHDRS) motor subscales, with disease burden as the primary predictor. The models were used to generate the trajectories of features known to represent hyperkinesis and hypokinesis, with the same being done for vague subscales. Dynamic time warping (DTW) was then used to classify said subscales as having a hyperkinetic or hypokinetic trajectory. ResultsHyperkinetic features rise initially and diminish in middle disease, while hypokinetic features continually increase across the disease course. All non-choreiform features demonstrated a hypokinetic-like trajectory. ConclusionsHD is generally considered a hyperkinetic movement disorder, but the middle and late stages of the disease are predominated by hypokinesis. These findings suggest that hypokinetic features may be a larger contributor to the overall motor burden of HD. This has significant implications for clinical trial design, motor phenotype clustering, and pharmacotherapy.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.