NDLI: Parkinsonian differentiation using PCA image correlation scores

Content Provider	IEEE Xplore Digital Library
Author	Spetsieris, P.G. Dhawan, V. Eidelberg, D.
Copyright Year	2015
Description	Author affiliation: Center for Neurosciences, Feinstein Inst. for Med. Res., Manhasset, NY, USA (Spetsieris, P.G.; Dhawan, V.; Eidelberg, D.)
Abstract	Atypical parkinsonian syndromes are often difficult to diagnose because they present common clinical symptoms and differences in diagnostic images are subtle. Multivariate covariance analysis has been previously used in PET group data to identify neurodegenerative disease patterns. In particular, using SSM-PCA analysis, individual subject's pattern expression of characteristic disease patterns have been shown to correlate with independent measures of disease status. These scalar subject scores, evaluated as the inner product of the unitized pattern vector and the mean centered subject data vector, can be utilized in classification algorithms to differentiate patients requiring disease specific treatment. However, diagnostic accuracy is often compromised stemming from topographic pattern similarity resulting in overlapping disease score expression. Here, we show that some improvement in classification may be achieved by utilizing the inner product of standardized pattern/patient vectors equivalent to the Pearson's correlation coefficient to evaluate subject class scores.
Starting Page	118
Ending Page	121
File Size	645487
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163830
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diseases Sensitivity Correlation Positron emission tomography Principal component analysis Covariance matrices brain networks PCA Parkinson's disease FDG PET differential diagnosis
Content Type	Text
Resource Type	Article

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