Dataset Information

Multivariate prediction of motor diagnosis in Huntington's disease: 12 years of PREDICT-HD.

ABSTRACT:

Background

It is well known in Huntington's disease that cytosine-adenine-guanine expansion and age at study entry are predictive of the timing of motor diagnosis. The goal of this study was to assess whether additional motor, imaging, cognitive, functional, psychiatric, and demographic variables measured at study entry increased the ability to predict the risk of motor diagnosis over 12 years.

Methods

One thousand seventy-eight Huntington's disease gene-expanded carriers (64% female) from the Neurobiological Predictors of Huntington's Disease study were followed up for up to 12 y (mean = 5, standard deviation = 3.3) covering 2002 to 2014. No one had a motor diagnosis at study entry, but 225 (21%) carriers prospectively received a motor diagnosis. Analysis was performed with random survival forests, which is a machine learning method for right-censored data.

Results

Adding 34 variables along with cytosine-adenine-guanine and age substantially increased predictive accuracy relative to cytosine-adenine-guanine and age alone. Adding six of the common motor and cognitive variables (total motor score, diagnostic confidence level, Symbol Digit Modalities Test, three Stroop tests) resulted in lower predictive accuracy than the full set, but still had twice the 5-y predictive accuracy than when using cytosine-adenine-guanine and age alone. Additional analysis suggested interactions and nonlinear effects that were characterized in a post hoc Cox regression model.

Conclusions

Measurement of clinical variables can substantially increase the accuracy of predicting motor diagnosis over and above cytosine-adenine-guanine and age (and their interaction). Estimated probabilities can be used to characterize progression level and aid in future studies' sample selection.

SUBMITTER: Long JD

PROVIDER: S-EPMC4795466 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Huntington's disease (HD) causes progressive atrophy to the striatum, a critical node in frontostriatal circuitry. Maintenance of motor function is dependent on functional connectivity of these premotor, motor, and dorsolateral frontostriatal circuits, and structural integrity of the striatum itself. We aimed to investigate whether size and shape of the striatum as a measure of frontostriatal circuit structural integrity was correlated with functional frontostriatal electrophysiological neural premotor processing (contingent negative variation, CNV), to better understand motoric structure-function relationships in early HD. METHODS:Magnetic resonance imaging (MRI) scans and electrophysiological (EEG) measures of premotor processing were obtained from a combined HD group (12 presymptomatic, 7 symptomatic). Manual segmentation of caudate and putamen was conducted with subsequent shape analysis. Separate correlational analyses (volume and shape) included covariates of age, gender, intracranial volume, and time between EEG and MRI. RESULTS:Right caudate volume correlated with early CNV latency over frontocentral regions and late CNV frontally, whereas right caudate shape correlated with early CNV latency centrally. Left caudate volume correlated with early CNV latency over centroparietal regions and late CNV frontally. Right and left putamen volumes correlated with early CNV latency frontally, and right and left putamen shape/volume correlated with parietal CNV slope. CONCLUSIONS:Timing (latency) and pattern (slope) of frontostriatal circuit-mediated premotor functional activation across scalp regions were correlated with abnormalities in structural integrity of the key frontostriatal circuit component, the striatum (size and shape). This was accompanied by normal reaction times, suggesting it may be undetected in regular tasks due to preserved motor "performance." Such differences in functional activation may reflect atrophy-based frontostriatal circuitry despecialization and/or compensatory recruitment of additional brain regions.