BACKGROUND AND OBJECTIVE: Biomarkers to monitor neurological dysfunction in autosomal dominant inherited spinocerebellar ataxias (SCA) are lacking. We therefore aimed to visualize, quantify and correlate localized brain atrophy with clinical symptoms in SCA1, SCA3, and SCA6. METHODS: We compared patients suffering from SCA1 (n=48), SCA3 (n=24), and SCA6 (n=10) with 32 controls using magnetic resonance imaging (MRI) on four different scanners in eight centers followed by voxel-based morphometry (VBM) and quantitative three-dimensional (3D) volumetry. RESULTS: SCA1 and SCA3 patients presented with severe atrophy in total brainstem (consisting of midbrain, pons, and medulla), pons, medulla, total cerebellum, cerebellar hemispheres and cerebellar vermis, putamen and caudate nucleus. Atrophy in the cerebellar hemispheres was less severe in SCA3 than in SCA1 and SCA6. Atrophy in SCA6 was restricted to the grey matter of the cerebellum (VBM and volumetry), total brainstem and pons (volumetry only). Overall, we did not observe substantial atrophy in the cerebral cortex. A discriminant analysis taking into account data from pons, cerebellar hemispheres, medulla, midbrain and putamen achieved a reclassification probability of 81.7% for SCA1, SCA3, and SCA6. The repeat length of the expanded allele showed a weak negative correlation with the volume of the brainstem, pons, caudate nucleus and putamen in SCA3, and a weak correlation with the pons in SCA1, whereas no such correlation was found in SCA6. Clinical dysfunction as measured by the Scale for the Assessment and Rating of Ataxia (SARA) and the Unified Huntington's Disease Rating Scale functional assessment correlated best with the atrophy of pons in SCA1, with total brainstem atrophy in SCA3 and atrophy of total cerebellum in SCA6. CONCLUSIONS: Our data provide strong evidence that MRI is an attractive surrogate marker for clinical studies of SCA. In each SCA genotype clinical dysfunction may be caused by different patho-anatomical processes.
BACKGROUND AND OBJECTIVE: Biomarkers to monitor neurological dysfunction in autosomal dominant inherited spinocerebellar ataxias (SCA) are lacking. We therefore aimed to visualize, quantify and correlate localized brain atrophy with clinical symptoms in SCA1, SCA3, and SCA6. METHODS: We compared patients suffering from SCA1 (n=48), SCA3 (n=24), and SCA6 (n=10) with 32 controls using magnetic resonance imaging (MRI) on four different scanners in eight centers followed by voxel-based morphometry (VBM) and quantitative three-dimensional (3D) volumetry. RESULTS:SCA1 and SCA3patients presented with severe atrophy in total brainstem (consisting of midbrain, pons, and medulla), pons, medulla, total cerebellum, cerebellar hemispheres and cerebellar vermis, putamen and caudate nucleus. Atrophy in the cerebellar hemispheres was less severe in SCA3 than in SCA1 and SCA6. Atrophy in SCA6 was restricted to the grey matter of the cerebellum (VBM and volumetry), total brainstem and pons (volumetry only). Overall, we did not observe substantial atrophy in the cerebral cortex. A discriminant analysis taking into account data from pons, cerebellar hemispheres, medulla, midbrain and putamen achieved a reclassification probability of 81.7% for SCA1, SCA3, and SCA6. The repeat length of the expanded allele showed a weak negative correlation with the volume of the brainstem, pons, caudate nucleus and putamen in SCA3, and a weak correlation with the pons in SCA1, whereas no such correlation was found in SCA6. Clinical dysfunction as measured by the Scale for the Assessment and Rating of Ataxia (SARA) and the Unified Huntington's Disease Rating Scale functional assessment correlated best with the atrophy of pons in SCA1, with total brainstem atrophy in SCA3 and atrophy of total cerebellum in SCA6. CONCLUSIONS: Our data provide strong evidence that MRI is an attractive surrogate marker for clinical studies of SCA. In each SCA genotype clinical dysfunction may be caused by different patho-anatomical processes.
Authors: Annie X Du; Jennifer L Cuzzocreo; Bennett A Landman; David S Zee; Jerry L Prince; Sarah H Ying Journal: J Neurol Date: 2010-03-31 Impact factor: 4.849
Authors: Leonardo Baldarçara; Stuart Currie; M Hadjivassiliou; Nigel Hoggard; Allison Jack; Andrea P Jackowski; Mario Mascalchi; Cecilia Parazzini; Kathrin Reetz; Andrea Righini; Jörg B Schulz; Alessandra Vella; Sara Jane Webb; Christophe Habas Journal: Cerebellum Date: 2015-04 Impact factor: 3.847
Authors: Louisa P Selvadurai; Ian H Harding; Louise A Corben; Monique R Stagnitti; Elsdon Storey; Gary F Egan; Martin B Delatycki; Nellie Georgiou-Karistianis Journal: J Neurol Date: 2016-08-13 Impact factor: 4.849