Publication

Multimodal imaging signatures of Parkinson's disease

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Last modified
  • 02/20/2025
Type of Material
Authors
    FDB Bowman, Columbia University Medical CenterDF Drake, Columbia University Medical CenterDaniel Huddleston, Emory University
Language
  • English
Date
  • 2016-04-18
Publisher
  • Frontiers Media
Publication Version
Copyright Statement
  • © 2016 Bowman, Drake and Huddleston.
License
Final Published Version (URL)
Title of Journal or Parent Work
ISSN
  • 1662-4548
Volume
  • 10
Issue
  • APR
Start Page
  • 131
End Page
  • 131
Grant/Funding Information
  • This research was funded by a grant from the NINDS (U18 NS082143) at NIH as part of the Parkinson's Disease Biomarker Program. Funding support leading to the generation of the dataset came from the William N. and Bernice E. Bumpus Foundation Early Career Investigator Innovation Award (BFIA 2011.3, Huddleston), the Emory University Morris K. Udall Center for Parkinson's Disease Research (P50-NS071669), and the Emory Alzheimer's Disease Research Center (P50-AG025688).
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.
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