B-Tensor: Brain Connectome Tensor Factorization for Alzheimer's Disease

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Durusoy G., Yildirim Z., Yüksel Dal D., Ulasoglu-Yildiz Ç., Kurt E., Bayir G., ...More

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol.25, no.5, pp.1591-1600, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 25 Issue: 5
  • Publication Date: 2021
  • Doi Number: 10.1109/jbhi.2020.3023610
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1591-1600
  • Keywords: Tensile stress, Dementia, Informatics, Functional magnetic resonance imaging, Sociology, Statistics, Brain Connectomes, Structure and Function, Tensor Factorization, Dementia, Alzheimer&apos, s Disease, fMRI, DTI, ASSOCIATION WORKGROUPS, DIAGNOSTIC GUIDELINES, NATIONAL INSTITUTE, CONNECTIVITY, RECOMMENDATIONS, APPROXIMATIONS
  • Istanbul University Affiliated: Yes


AD is the highly severe part of the dementia spectrum and impairs cognitive abilities of individuals, bringing economic, societal and psychological burdens beyond the diseased. A promising approach in AD research is the analysis of structural and functional brain connectomes, i.e., sNETs and fNETs, respectively. We propose to use tensor representation (B-tensor) of uni-modal and multi-modal brain connectomes to define a low-dimensional space via tensor factorization. We show on a cohort of 47 subjects, spanning the spectrum of dementia, that diagnosis with an accuracy of 77% to 100% is achievable in a 5D connectome space using different structural and functional connectome constructions in a uni-modal and multi-modal fashion. We further show that multi-modal tensor factorization improves the results suggesting complementary information in structure and function. A neurological assessment of the connectivity patterns identified largely agrees with prior knowledge, yet also suggests new associations that may play a role in the disease progress.