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 ORIGINAL ARTICLE
Year : 2021  |  Volume : 69  |  Issue : 5  |  Page : 1318--1325

Cortical and Subcortical Brain Area Atrophy in SCA1 and SCA2 Patients in India: The Structural MRI Underpinnings and Correlative Insight Among the Atrophy and Disease Attributes


1 Department of Zoology, Nalbari College, Assam, India
2 Department of Physiology, VMMC & Safdarjung Hospital, New Delhi, India
3 Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, India
4 Department of Biomedical Engineering, All India Institute of Medical Sciences; Center for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
5 Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology (CSIR -IGIB), New Delhi, India
6 Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
7 Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
8 Department of NMR, All India Institute of Medical Sciences, New Delhi, India

Correspondence Address:
Kishore K Deepak
Head, Department of Physiology Room No. 2009, Teaching Block, Second Floor, New Delhi-110 029
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0028-3886.329596

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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.






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