[en] Using machine learning based models in clinical applications has become current practice and can prove useful to provide information at the subject’s level, such as predicting an (early) diagnosis or monitoring the evolution of a disease. However, the performance of these models
depends on the choice of a biomarker to detect the presence or absence of a disease. Choosing a biomarker is not straightforward, especially in the case of Parkinson’s disease when compared to healthy subjects. In the present work, we investigated the mental imagery of gait as a biomarker of Parkinson’s disease and showed that the signal in the mesencephalic locomotor region during the mental imagery of gait at a comfortable pace can discriminate significantly between idiopathic Parkinson’s disease patients and healthy subjects. Although there is room for improvement, the results of this preliminary study are promising.
Research Center/Unit :
GIGA CRC (Cyclotron Research Center) In vivo Imaging-Aging & Memory - ULiège
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others Human health sciences: Multidisciplinary, general & others
Author, co-author :
Schrouff, Jessica ; Université de Liège - ULiège > Centre de recherches du cyclotron
Cremers, Julien ; Université de Liège - ULiège > Département des sciences cliniques > Neurologie
D'Ostilio, Kevin ; Université de Liège - ULiège > Centre de recherches du cyclotron
Delvaux, Valérie ; Centre Hospitalier Universitaire de Liège - CHU > Neurologie Sart Tilman
Garraux, Gaëtan ; Université de Liège - ULiège > Département des sciences cliniques > Neurologie
Phillips, Christophe ; Université de Liège - ULiège > Centre de recherches du cyclotron
Language :
English
Title :
Discriminant BOLD Activation Patterns during Mental Imagery in Parkinson’s Disease
Publication date :
07 December 2012
Event name :
Machine Learning and Interpretation in NeuroImaging workshop at NIPS
F.R.S.-FNRS - Fonds de la Recherche Scientifique FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture ULiège - Université de Liège