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Author Notes:

Correspondence: F. DuBois Bowman, dubois.bowman@columbia.edu.

FB conceptualized methodology and guided evolution of the research. FB also wrote the majority of the paper.

DD processed the imaging data and performed statistical analyses on the results. DD contributed to the data and methods section and generated tables and figures for the paper.

DH managed data collection and provided expertise in Parkinson's disease research and imaging. DH provided thoughtful comments and corrections throughout the paper.

Additional significant contributions to the acquisition of the data were made by Stewart A. Factor and Rebecca McMurray of the Emory Department of Neurology, and by Jason Langley and Xiaoping Hu of the Emory Department of Biomedical Engineering.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Research Funding:

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).

Keywords:

  • MRI
  • Parkinson's disease
  • biomarker
  • classification
  • multimodal imaging
  • penalized regression
  • prediction

Multimodal imaging signatures of Parkinson's disease

Tools:

Journal Title:

Frontiers in Neuroscience

Volume:

Volume 10, Number APR

Publisher:

, Pages 131-131

Type of Work:

Article | Final Publisher PDF

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.

Copyright information:

© 2016 Bowman, Drake and Huddleston.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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