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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Multi-modal Brain Tensor Factorization: Preliminary Results with AD Patients
verfasst von : Göktekin Durusoy, Abdullah Karaaslanlı, Demet Yüksel Dal, Zerrin Yıldırım, Burak Acar
Erschienen in: Connectomics in NeuroImaging
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Abstract
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.