Journal
JOURNAL OF NEUROIMAGING
Volume 32, Issue 1, Pages 80-89Publisher
WILEY
DOI: 10.1111/jon.12927
Keywords
cognition; cortical thickness; multiple system atrophy; machine learning; neuroimaging
Funding
- Fondo de Investigaciones Sanitarias of Spain [FISPI17/00096]
- Michael J. Fox Foundation for Parkinson Disease (MJFF) [018_0130, 11, MJF_PPMI_10_001, PI044024]
- Spanish Ministry of Economy and Competitiveness [PSI2017-86930-P]
- CIBERNED [CB06/05/0018-ISCIII]
- Departament d'Innovacio, Universitats i Empresa, Generalitat de Catalunya [2017SGR1502, 2017SGR748]
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Multiple system atrophy (MSA) can be classified into two subtypes based on symptoms, and this study found differential gray matter atrophy distribution between MSA-C and MSA-P using MRI. Cerebellar atrophy is a significant differentiator between the two subtypes.
Background and Purpose Multiple system atrophy(MSA) is a rare adult-onset synucleinopathy that can be divided in two subtypes depending on whether the prevalence of its symptoms is more parkinsonian or cerebellar (MSA-P and MSA-C, respectively). The aim of this work is to investigate the structural MRI changes able to discriminate MSA phenotypes. Methods The sample includes 31 MSA patients (15 MSA-C and 16 MSA-P) and 39 healthy controls. Participants underwent a comprehensive motor and neuropsychological battery. MRI data were acquired with a 3T scanner (MAGNETOM Trio, Siemens, Germany). FreeSurfer was used to obtain volumetric and cortical thickness measures. A Support Vector Machine (SVM) algorithm was used to assess the classification between patients' group using cortical and subcortical structural data. Results After correction for multiple comparisons, MSA-C patients had greater atrophy than MSA-P in the left cerebellum, whereas MSA-P showed reduced volume bilaterally in the pallidum and putamen. Using deep gray matter volume ratios and mean cortical thickness as features, the SVM algorithm provided a consistent classification between MSA-C and MSA-P patients (balanced accuracy 74.2%, specificity 75.0%, and sensitivity 73.3%). The cerebellum, putamen, thalamus, ventral diencephalon, pallidum, and caudate were the most contributing features to the classification decision (z > 3.28; p < .05 [false discovery rate]). Conclusions MSA-C and MSA-P with similar disease severity and duration have a differential distribution of gray matter atrophy. Although cerebellar atrophy is a clear differentiator between groups, thalamic and basal ganglia structures are also relevant contributors to distinguishing MSA subtypes.
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