Elektrik- Elektronik Mühendisliği
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Browsing Elektrik- Elektronik Mühendisliği by Subject "Alzheimer's disease."
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Item Multi-modal tensor representations of brain networks(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2021., 2021.) Durusoy, Göktekin.; Acar, Burak.Considering the economic, social and psychological burdens of Alzheimer’s disease (AD), the most common form of dementia, it is essential to gain insight into the process and underlying mechanisms of the disease. Using structural and functional brain connectomes obtained by in-vivo MRI techniques as biomarkers is a promising approach. In this thesis, the B-Tensor structure that allows the representation of brain connectomes defined in structurally and functionally with a uni-modal and multi-modal fashion is presented. With the projection of structural connectomes onto known func tional networks, patients with AD and healthy control group are distinguished in a 7-dimensional space with a separation performance of over 90%. In addition, with the uni-modal and multi-modal tensor factorization methods, 47 patients with different levels of AD, are diagnosed with an accuracy of 77% - 100% in a 5-dimensional space. The results show that the multi-modal factorization technique performs better than the uni-modal one by successfully fusing the structural and functional networks which offer complementary information. While the neurological evaluations of the obtained sub-networks are highly consistent with previous literature, new findings regarding the progression of the disease are also recommended.Item Structural brain connectome embedding for Alzheimer's disease(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2019., 2019.) Gamgam, Gurur.; Acar, Burak.Neurodegenerative diseases are known to alter brain connectivity. Alzheimer’s Disease (AD) is the most common one among these diseases. Although, many re searches have been made to understand AD, there are still more to explore about the complicated nature of AD. To solve these mysteries, features extracted from connec tomes are widely used. Following the poor specificity of global connectome features, more recently focus has been shifted towards substructures as potential biomarkers. A new model, inspired by the Deepwalk, is proposed to represent these substructures in this thesis. The model treats each individual connectome as a unique graph and learns nodal embeddings per connectome by means of a random walk and a neural network approach. The learned nodal embeddings are used as latent representations of local connectivity and their discriminative power is assessed in SVM based leave-one-out ex periments over a cohort of 91 individuals. Promising results were obtained for AD-SCI / AD-MCI / MCI-SCI / AD-MCI-SCI classification tasks. Apart from classification, such latent representations of local connectivity may serve as an appropriate space to define the continuum of neurodegenerative disease progression temporally and spatially which means nodal embeddings can be utilized for monitoring disease progression