B-Tensor: Brain Connectome Tensor Factorization for Alzheimer's Disease
| Autor: | Burak Acar, Demet Yuksel Dal, Gunes Bayr, Alkan Kabakcoglu, Tamer Demiralp, Başar Bilgiç, Asl Demirtas-Tatldede, Hakan Gurvit, Cigdem Ulasoglu-Yildiz, Evren Özarslan, Erhan Ozacar, Elif Kurt, Zerrin Yldrm, Goktekin Durusoy |
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| Přispěvatelé: | Kabakçıoğlu, Alkan (ORCID 0000-0002-9831-3632 & YÖK ID 49854), Durusoy, Göktekin, Yıldırım, Zerrin, Dal, Demet Yüksel, Ulaşoğlu-Yıldız, Çiğdem, Kurt, Elif, Bayır, Güneş, Özacar, Erhan, Özarslan, Evren, Demirtaş-Tatlıdede, Aslı, Bilgiç, Başar, Demiralp, Tamer, Gürvit, Hakan, Acar, Burak, College of Sciences, Department of Physics |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Disease 03 medical and health sciences Neurological assessment 0302 clinical medicine Health Information Management Alzheimer Disease Tensor (intrinsic definition) Connectome medicine Humans Dementia Electrical and Electronic Engineering 030304 developmental biology Mathematical and computational biology Medical informatics 0303 health sciences Tensor factorization medicine.diagnostic_test Brain Cognition medicine.disease Magnetic Resonance Imaging Computer Science Applications Brain connectomes Structure and function Alzheimer’s disease fMRI DTI Functional magnetic resonance imaging Neuroscience 030217 neurology & neurosurgery Biotechnology |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2020.3023610 |
| Popis: | 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. Turkish Directorate of Strategy and Budget TAM Project; Scientific and Technological Research Council of Turkey (TÜBİTAK)-ARDEB 1003 Programme; Bogazici University Research Fund Grant |
| Databáze: | OpenAIRE |
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