Abstract
Huntington’s disease (HD) is genetically determined but with variability in symptom onset, leading to uncertainty as to when pharmacological intervention should be initiated. Here we take a computational approach based on neurocognitive phenotyping, computational modeling, and classification, in an effort to provide quantitative predictors of HD before symptom onset. A large sample of subjects—consisting of both pre-manifest individuals carrying the HD mutation (pre-HD), and early symptomatic—as well as healthy controls performed the antisaccade conflict task, which requires executive control and response inhibition. While symptomatic HD subjects differed substantially from controls in behavioral measures [reaction time (RT) and error rates], there was no such clear behavioral differences in pre-HD. RT distributions and error rates were fit with an accumulator-based model which summarizes the computational processes involved and which are related to identified mechanisms in more detailed neural models of prefrontal cortex and basal ganglia. Classification based on fitted model parameters revealed a key parameter related to executive control differentiated pre-HD from controls, whereas the response inhibition parameter declined only after symptom onset. These findings demonstrate the utility of computational approaches for classification and prediction of brain disorders, and provide clues as to the underlying neural mechanisms.
Highlights
ObjectivesThe aim of the current study was to apply quantitative computational modeling to the TRACK-Huntington’s disease (HD) behavioral data set to separate processes thought to relate to selective response inhibition and executive control
Huntington’s disease (HD) is a debilitating neurodegenerative disease with progressive degradation of motor and cognitive function
Pre-HD subjects were further subdivided into pre-manifest individuals carrying the HD mutation (pre-HD)-A and preHD-B, where pre-HD-B were estimated to be closer than pre-HD-A to progression to HD based on CAG repeat length and age [25]
Summary
The aim of the current study was to apply quantitative computational modeling to the TRACK-HD behavioral data set to separate processes thought to relate to selective response inhibition and executive control
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have