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Thickness NETwork (ThickNet) Features for the Detection of Prodromal AD

  • Pradeep Reddy Raamana
  • Lei Wang
  • Mirza Faisal Beg
  • for The Alzheimer’s Disease Neuroimaging Initiative
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)

Abstract

Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer’s disease (AD), but not its inter-regional covariation. We present novel features based on the inter-regional co-variation of cortical thickness. Initially the cortical labels of each patient is partitioned into small patches (graph nodes) by spatial k-means clustering. A graph is then constructed by establishing a link between two nodes if the difference in thickness between the nodes is below a certain threshold. From this binary graph, thickness network (ThickNet) features are computed using nodal degree, betweenness and clustering coefficient measures. Fusing them with multiple kernel learning, we demonstrate their potential for the detection of prodromal AD.

Keywords

thickness network fusion mild cognitive impairment alzheimer 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Pradeep Reddy Raamana
    • 1
  • Lei Wang
    • 2
  • Mirza Faisal Beg
    • 1
  • for The Alzheimer’s Disease Neuroimaging Initiative
    • 1
  1. 1.School of Engineering ScienceSimon Fraser UniversityCanada
  2. 2.Feinberg School of MedicineNorthwestern UniversityUSA

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