1 Ocak 2018 Konferans bildirisi Açık Erişim

Durusoy, Goktekin; Karaaslanli, Abdullah; Dal, Demet Yuksel; Yildirim, Zerrin; Acar, Burak

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        "affiliation": "Bogazici Univ, VAVlab, Dept Elect & Elect Engn, Istanbul, Turkey", 
        "name": "Durusoy, Goktekin"
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      {
        "affiliation": "Bogazici Univ, VAVlab, Dept Elect & Elect Engn, Istanbul, Turkey", 
        "name": "Karaaslanli, Abdullah"
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      {
        "affiliation": "Bogazici Univ, VAVlab, Dept Elect & Elect Engn, Istanbul, Turkey", 
        "name": "Dal, Demet Yuksel"
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      {
        "affiliation": "Istanbul Univ, Dept Neurosci, Aziz Sancar Expt Med Res Inst, Istanbul, Turkey", 
        "name": "Yildirim, Zerrin"
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      {
        "affiliation": "Bogazici Univ, VAVlab, Dept Elect & Elect Engn, Istanbul, Turkey", 
        "name": "Acar, Burak"
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    "description": "Global brain network parameters suffer from low classification performance and fail to provide an insight into the neurodegenerative diseases. Besides, the variability in connectivity definitions poses a challenge. We propose to represent multi-modal brain networks over a population with a single 4D brain tensor (B) and factorize B to get a lower dimensional representation per case and per modality. We used 7 known functional networks as the canonical network space to get a 7D representation. In a preliminary study over a group of 20 cases, we assessed this representation for classification. We used 6 different connectivity definitions (modalities). Linear discriminant analysis results in 90-95% accuracy in binary classification. The assessment of the canonical coordinates reveals Salience subnetwork to be the most powerful in classification consistently over all connectivity definitions. The method can be extended to include functional networks and further be used to search for discriminating subnetworks.", 
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      "title": "CONNECTOMICS IN NEUROIMAGING, CNI 2018"
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    "title": "Multi-modal Brain Tensor Factorization: Preliminary Results with AD Patients"
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