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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

DOI:
10.3791/50319-v

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14:27 min

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June 26, 2013

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, , , , , ,
1Center for Neurosciences,The Feinstein Institute for Medical Research

Chapters

  • 00:05Title
  • 03:06Data Collection and Preprocessing
  • 04:34Perform Multivariate SSM/PCA
  • 08:47Pattern Biomarker Derivation
  • 10:36Results: Identification of Disease-related Spatial Covariance Patterns
  • 13:08Conclusion

Summary

자동 번역

자동 번역

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.

Tags

Spatial Covariance PatternsNeuroimaging DataScaled Subprofile Model (SSM)Group Invariant Subprofile (GIS)PCAFDG PETParkinson’s DiseaseDisease-related PatternsNetwork-specific ContributionsDifferential DiagnosisDisease ProgressionTreatment Effects

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