| INTRODUC TI ONSpinocerebellar ataxia (SCA) type 1 and 2 are dominantly inherited neurodegenerative disorders. These SCA subtypes are two of the most widely prevalent SCAs worldwide with a similar pattern found in India as well. SCA1 and SCA2 are caused by the abnormal expansion of CAG trinucleotide repeats in ATXN1 and ATXN2 genes, respectively. This results in progressive neuronal loss in the cerebellum along with certain cortical and subcortical brain regions in SCA subtypes 1,2 which subsequently leads to widespread clinical manifestations. Motor symptoms of SCA comprise cerebellar ataxia, peripheral neuropathy, ophthalmoplegia, pyramidal and extrapyramidal signs. 3 Furthermore, symptoms of autonomic dysfunction have also been observed in SCA patients. Thus, the evaluation of autonomic profile in SCA subtypes has an extensive clinical value. Objectives: To assess the time and frequency domain measures of cardiac autonomic activity/tone in patients of genetically defined spinocerebellar ataxia (SCA) types 1 and 2, as well as to decipher the probable associations among the cardiovascular autonomic parameters and genetic and clinical characteristics. Materials and methods:Simultaneous 5-min recording of RR interval (RRI) and blood pressure (BP) for the calculation of heart rate variability (HRV), blood pressure variability (BPV) and baroreflex sensitivity (BRS) were performed in genotypically confirmed SCA1 (n = 31) and SCA2 (n = 40) patients and healthy controls (n = 40).Additionally, the International Cooperative Ataxia Rating Scale (ICARS) was used for scoring of clinical severity in SCA patients.Results: Time and frequency domain parameters of HRV, BPV and BRS were depressed in SCA1 and SCA2 subtypes as compared to controls, although there was no statistically significant difference in autonomic tone between the two SCA subtypes.On correlation analysis, autonomic tone parameters were found to be associated with the clinical and genetic features of the SCA subtypes. Also, ICARS was associated with the genotype (CAG repeat length) in SCA2 patents. Conclusions:Cardiac autonomic tone is depressed in both SCA1 and 2 as compared to healthy controls while the two SCA subtypes do not differ in terms of autonomic tone. Also, a typical association exists between disease characteristics and autonomic indices. K E Y W O R D Sbaroreflex sensitivity, blood pressure variability, cardiac autonomic activity/tone, heart rate variability, spinocerebellar ataxia

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More than a century ago, C. Bernard stated the connection between the brain and the heart. Here, we have emphasized on the central autonomic network (CAN) based on our previous pioneering work of structure-function connectivity in spinocerebellar ataxia (SCA) patients. In order to study the correlation, SCA was the suitable disease model as being neurodegenerative disorder with fequent autonomic manifestations. The prefrontal cortex, bilateral middle temporal, left cuneus, left lingual and left caudate were the key brain areas of CAN for top down regulation of autonomic function assessed as heart rate variability and severity scoring. We have evaluated CAN to know parasympathetic and sympathic autonomic brain areas vis-a-vis parasympathetic and sympathetic autonomic function in SCA which may shed light on putative pathophysiology.

Introduction: Genetically defined spinocerebellar ataxia (SCA) type 1 and 2 patients have differential clinical profile along with probable distinctive cortical and subcortical neurodegeneration. We compared the degree of brain atrophy in the two subtypes with their phenotypic and genotypic parameters. Methods: MRI was performed using a 3T scanner (Philips, Achieva) to obtain 3D T1-weighted scans of the whole brain and analyzed by FreeSurfer (version 5.3 and 6 dev.) software. Genetically proven SCA1 (n = 18) and SCA2 (n = 25) patients with age-matched healthy controls (n = 8) were recruited. Clinical severity was assessed by the International Cooperative Ataxia Rating Scale (ICARS). To know the differential pattern of atrophy, the groups were compared using ANOVA/Kruskal-Wallis test and followed by correlation analysis with multiple corrections. Further, machine learning-based classification of SCA subtypes was carried out. Result: We found (i) bilateral frontal, parietal, temporal, and occipital atrophy in SCA1 and SCA2 patients; (ii) reduced volume of cerebellum, regions of brain stem, basal ganglia along with the certain subcortical areas such as hippocampus, amygdala, thalamus, diencephalon, and corpus callosum in SCA1 and SCA2 subtypes; (iii) higher subcortical atrophy SCA2 than SCA1 (iv) correlation between brain atrophy and disease attributes; (v) differential predictive pattern of two SCA subtypes using machine learning approach. Conclusion: The present study suggests that SCA1 and SCA2 do not differ in cortical thinning while a characteristic pattern of subcortical atrophy SCA2 > SCA1 is observed along with correlation of brain atrophy and disease attributes. This may provide the diagnostic guidance of MRI to SCA subtypes and differential therapies.