Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/103347
Title: Classification of Huntington's Disease Stage with Features Derived from Structural and Diffusion-Weighted Imaging
Authors: Lavrador, Rui Filipe David 
Júlio, Filipa 
Januário, Cristina 
Castelo Branco, Miguel 
Caetano, Gina 
Keywords: Huntington’s disease; grey matter density; fractional anisotropy; classification; support vector machine; basal ganglia
Issue Date: 28-Apr-2022
Project: PTDC/SAU-ENB/112306/2009 
POCI-01-0145-FEDER-007440 
UIDP/50009/2020 
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP/04950/2020 
Serial title, monograph or event: Journal of Personalized Medicine
Volume: 12
Issue: 5
Abstract: The purpose of this study was to classify Huntington's disease (HD) stage using support vector machines and measures derived from T1- and diffusion-weighted imaging. The effects of feature selection approach and combination of imaging modalities are assessed. Fourteen premanifest-HD individuals (Pre-HD; on average > 20 years from estimated disease onset), eleven early-manifest HD (Early-HD) patients, and eighteen healthy controls (HC) participated in the study. We compared three feature selection approaches: (i) whole-brain segmented grey matter (GM; voxel-based measure) or fractional anisotropy (FA) values; (ii) GM or FA values from subcortical regions-of-interest (caudate, putamen, pallidum); and (iii) automated selection of GM or FA values with the algorithm Relief-F. We assessed single- and multi-kernel approaches to classify combined GM and FA measures. Significant classifications were achieved between Early-HD and Pre-HD or HC individuals (accuracy: generally, 85% to 95%), and between Pre-HD and controls for the feature FA of the caudate ROI (74% accuracy). The combination of GM and FA measures did not result in higher performances. We demonstrate evidence on the high sensitivity of FA for the classification of the earliest Pre-HD stages, and successful distinction between HD stages.
URI: https://hdl.handle.net/10316/103347
ISSN: 2075-4426
DOI: 10.3390/jpm12050704
Rights: openAccess
Appears in Collections:I&D CIBIT - Artigos em Revistas Internacionais
FPCEUC - Artigos em Revistas Internacionais
I&D ICNAS - Artigos em Revistas Internacionais

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