Campabadal Delgado, AnnaAbós, AlexandraSegura i Fàbregas, BàrbaraMonté Rubio, Gemma C.Pérez Soriano, AlexandraGiraldo, Darly M.Muñoz, EstebanCompta, YaroslauJunqué i Plaja, Carme, 1955-Martí Domènech, Ma. Josep2021-12-212021-12-212021-09-101051-2284https://hdl.handle.net/2445/181941Background 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. Keywords: cognition; cortical thickness; machine learning; multiple system atrophy; neuroimaging.10 p.application/pdfengcc by-nc-nd (c) Campabadal, Anna et al., 2021http://creativecommons.org/licenses/by-nc-nd/3.0/es/CognicióAtròfia muscularCognitionMuscular atrophyinfo:eu-repo/semantics/article7148702021-12-21info:eu-repo/semantics/openAccess