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鉴定的使用神经影像学数据的与疾病-相关的的空间协方差模式

JoVE Journal
Medicine

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JoVE Journal Medicine

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Please note that all translations are automatically generated. Click here for the English version.

鉴定的使用神经影像学数据的与疾病-相关的的空间协方差模式

DOI:

10.3791/50319-v

•

14:27 min

•

June 26, 2013

•

Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg

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

Automatic Translation

多元技术，包括主成分分析（PCA）已经脑功能图像用于识别签名区域变化模式。我们已经开发了一种算法来识别重复的网络神经退行性疾病的诊断，评估病情恶化，患者人群的治疗效果和客观评价的生物标志物。

Tags

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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