title: Multimodal Imaging Signatures of Parkinson's Disease.
abstract: Parkinson's disease (PD) is a complex neurodegenerative disorder that manifests through hallmark motor symptoms, often accompanied by a range of non-motor symptoms. There is a putative delay between the onset of the neurodegenerative process, marked by the death of dopamine-producing cells, and the onset of motor symptoms, creating an urgent need to develop biomarkers that may yield early PD detection. Neuroimaging offers a non-invasive approach to examining the potential utility of a vast number of functional and structural brain characteristics as biomarkers. We present a statistical framework for analyzing neuroimaging data from multiple modalities to determine features that reliably distinguish PD patients from healthy control (HC) subjects. Our approach builds on elastic net, performing regularization and variable selection, while introducing additional criteria centering on parsimony and reproducibility. We apply our method to data from 42 subjects (28 PD patients and 14 HC). Our approach demonstrates extremely high accuracy, assessed via cross-validation, and isolates brain regions that are implicated in the neurodegenerative PD process.
authors.affiliation: Department of Biostatistics, The Mailman School of Public Health, Columbia University New York, NY, USA.
authors.forename: F DuBois
authors.initials: FD
authors.lastname: Bowman
authors.affiliation: Department of Biostatistics, The Mailman School of Public Health, Columbia University New York, NY, USA.
authors.forename: Daniel F
authors.initials: DF
authors.lastname: Drake
authors.affiliation: Department of Neurology, Emory University Atlanta, GA, USA.
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Huddleston
keywords: MRI; Parkinson's disease; biomarker; classification; multimodal imaging; penalized regression; prediction