Information-Theoretic Clustering of Neuroimaging Metrics Related to Cognitive Decline in the Elderly

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• 关键词：Machine learning ; Diffusion weighted imaging ; Brain connectivity ; Spectral graph theory ; Gray matter
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9601
• 期：1
• 页码：13-23
• 全文大小：2,399 KB
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• 作者单位：Madelaine Daianu (21) (22)
  Greg Ver Steeg (23)
  Adam Mezher (21)
  Neda Jahanshad (21)
  Talia M. Nir (21)
  Xiaoran Yan (22)
  Gautam Prasad (21)
  Kristina Lerman (23)
  Aram Galstyan (23)
  Paul M. Thompson (21) (22) (24)
  
  21. Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA
  22. Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
  23. USC Information Sciences Institute, Marina del Rey, CA, USA
  24. Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, University of Southern California, Los Angeles, CA, USA
  
• 丛书名：Medical Computer Vision: Algorithms for Big Data
• ISBN：978-3-319-42016-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9601

文摘

As Alzheimer’s disease progresses, there are changes in metrics of brain atrophy and network breakdown derived from anatomical or diffusion MRI. Neuroimaging biomarkers of cognitive decline are crucial to identify, but few studies have investigated how sets of biomarkers cluster in terms of the information they provide. Here, we evaluated more than 700 frequently studied diffusion and anatomical measures in 247 elderly participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used a novel unsupervised machine learning technique - CorEx - to identify groups of measures with high multivariate mutual information; we computed latent factors to explain correlations among them. We visualized groups of measures discovered by CorEx in a hierarchical structure and determined how well they predict cognitive decline. Clusters of variables significantly predicted cognitive decline, including measures of cortical gray matter, and correlated measures of brain networks derived from graph theory and spectral graph theory.