EFFECTS OF RIVASTIGMINE ON MOTOR AND COGNITIVE IMPAIRMENT IN HUNTINGTON'S DISEASE

Abstract. To evaluate the efficacy of rivastigmine on motor and cognitive impairment in Huntington’s disease (HD), we carried out a prospective, open-label, randomized, controlled study. Twenty-one HD patients were enrolled: 14 were randomly sorted into medication and 7 to no-treatment groups. Clinical and demographic features were similar between groups. After 8 months, an efficacy evaluation was carried out to compare the two groups. The improvement of cholinergic transmission in HD patients seemed to have a slight effect in ameliorating cognitive performance and slowing motor deterioration

Early modifications of auditory event-related potentials in carriers of the Huntington's disease gene

The P3 wave is one cognitive component of event-related potentials (ERP) used to investigate various types of dementia. The aim of this study was to use the odd-ball paradigm to evaluate the P3 in Huntington's Disease (HD) gene carriers who showed no symptoms of chorea, compared to a group of mildly affected HD patients. We selected 14 HD patients and six individuals who, despite testing positive for the HD gene, did not show any clinical evidence of the disease. Thirty-six normal subjects were also selected as controls. Statistical evaluation of N1, P2, N2 and P3 latencies and amplitudes was performed in each group. Both the N2 latency and the P3 latency corrected for age (cP3) were significantly correlated with the duration of illness in pooled symptomatic and presymptomatic gene carriers. However, these latencies did not correlate with any clinical scale or psychometric test, including WAIS subtests. As the individual P3 latency of the majority of HD patients and all presymptomatic gene carriers was distributed within normal confidence intervals, and no correlation existed between ERP parameters and the signs of illness progression, the data appear to provide preliminary evidence against the valence of P3 in detecting the early cognitive impairment of HD

ANN for electrophysiological analysis of neurological disease

The aim of this study was to develop a discriminant analysis based both on classical linear methods, as Fisher's Linear Discriminant (FLD) and Likelihood Ratio Method (LRM), and non-linear Artificial Neural Network (ANN) classifier in order to distinguish between patients affected by Huntington's disease (HD) and normal subjects. R.O.C. curve analysis revealed ANN to be the best classifier. Moreover the network classified gene-carrier relatives as normal thus suggesting the EEG to be a marker of the evolution of the HD