NDLI: A Full-Brain, Bootstrapped Analysis of Diffusion Tensor Imaging Robustly Differentiates Parkinson Disease from Healthy Controls

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A Full-Brain, Bootstrapped Analysis of Diffusion Tensor Imaging Robustly Differentiates Parkinson Disease from Healthy Controls

Content Provider	Springer Nature Link
Author	Skidmore, F. M. Spetsieris, P. G. Anthony, T. Cutter, G. R. Deneen, K. M. Liu, Y. White, K. D. Heilman, K. M. Myers, J. Standaert, D. G. Lahti, A. C. Eidelberg, D. Ulug, A. M.
Copyright Year	2014
Abstract	There is a compelling need for early, accurate diagnosis of Parkinson’s disease (PD). Various magnetic resonance imaging modalities are being explored as an adjunct to diagnosis. A significant challenge in using MR imaging for diagnosis is developing appropriate algorithms for extracting diagnostically relevant information from brain images. In previous work, we have demonstrated that individual subject variability can have a substantial effect on identifying and determining the borders of regions of analysis, and that this variability may impact on prediction accuracy. In this paper we evaluate a new statistical algorithm to determine if we can improve accuracy of prediction using a subjects left-out validation of a DTI analysis. Twenty subjects with PD and 22 healthy controls were imaged to evaluate if a full brain diffusion tensor imaging-fractional anisotropy (DTI-FA) map might be capable of segregating PD from controls. In this paper, we present a new statistical algorithm based on bootstrapping. We compare the capacity of this algorithm to classify the identity of subjects left out of the analysis with the accuracy of other statistical techniques, including standard cluster-thresholding. The bootstrapped analysis approach was able to correctly discriminate the 20 subjects with PD from the 22 healthy controls (area under the receiver operator curve or AUROC 0.90); however the sensitivity and specificity of standard cluster-thresholding techniques at various voxel-specific thresholds were less effective (AUROC 0.72–0.75). Based on these results sufficient information to generate diagnostically relevant statistical maps may already be collected by current MRI scanners. We present one statistical technique that might be used to extract diagnostically relevant information from a full brain analysis.
Starting Page	7
Ending Page	18
Page Count	12
File Format	PDF
ISSN	15392791
Journal	Neuroinformatics
Volume Number	13
Issue Number	1
e-ISSN	15590089
Language	English
Publisher	Springer US
Publisher Date	2014-06-29
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Parkinson disease Diffusion tensor imaging Diagnosis Data analysis methods Neurosciences Bioinformatics Computational Biology/Bioinformatics Computer Application in Life Sciences Neurology
Content Type	Text
Resource Type	Article
Subject	Neuroscience Information Systems Software

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