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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
 

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Article DOI: 10.3791/50319-v 14:27 min June 26th, 2013
June 26th, 2013

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Summary

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

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Keywords: Spatial Covariance Patterns Neuroimaging Data Scaled Subprofile Model (SSM) Group Invariant Subprofile (GIS) PCA FDG PET Parkinson's Disease Disease-related Patterns Network-specific Contributions Differential Diagnosis Disease Progression Treatment Effects
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