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Multilayer Network Approach to Brain Connectivity Analysis in Cognitive Disorder

Talezade Lari, Emran | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51688 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Rabiee, Hamid Reza; Manzori, Mohammad Taghi
  7. Abstract:
  8. Brain is the most complex part of the human body. This three pound organ acting as seed of intelligence, database of memories, interpreter of the senses, and managing our movement. Network neuroscience plays an important role in revealing hidden aspects of brain functions. Recently, multilayer network models have been proposed to achieve a deeper analysis on the brain networks. Multilayer network is a framework that can represent multiple relations between nodes. In a single layer brain network, different shared information methods can be used to find connection between Regions of Interests (ROIs), but in a multilayer approach, ROIs can have multiple connections in different domains such as frequency bands. Therefore, studying multilayer brain network in frequency domain enables us to efficiently explore the brain network relation in different frequency bands.We used preprocessed RS-fMRI data in this study. By considering the effects of age, sex and range of IQ on Autism Spectrum Disorder (ASD), we selected 74 control, 46 Autistic, and 16 Asperger subjects, male only, age and IQ matched from the open access dataset ABIDE. For each subject, we build up a multilayer brain network in frequency domain, with each layer representing the correlations on band-pass filtered signals of a specific frequency range. Multilayer network prepares extra information versus a single layer one, which can be recognized using graph entropy concepts. We used Von Neumann Entropy to measure those added information we gain in a multilayer framework. In each step by using the Quantum Jensen-Shannon divergence, we detected layer which added the least extra information according to other layers, merged it into the remaining ones and calculated the entropy for new multilayer network, until we reached to a single layer network. Variation in extra information per number of layers (which we call it signal) gave us the insight about how different frequencies interact in the brain.We constructed a multilayer brain network for each subject for different number of layers and calculated signal for each subject in each corresponding multilayer network.We observed that the averaged signal for Asperger group was above Autism and Control groups in the range of different number of layers. Moreover, at provided Cumulative Distribution Function(CDF) of maximum extra information for Asperger, Autism, and Control groups, the Asperger’s CDF has a tendency to the right, which means its Probability Density Function (PDF), has more concentration on higher values. Higher signal magnitude means more information in the multilayer network. Hence, with fixed number of layers, higher magnitude means different network layers are independent of each other. Therefore, our result suggests that ROIs connection in different frequency bands have less correlation with each other in Asperger disorder versus Autism or Control.We also quantify signal variation and difference in order of merge on different groups.The proposed method can be extensively applied to different datasets and domains
  9. Keywords:
  10. Multilayer Network ; Von Neumann Entropy ; Brain Modeling ; Autism Spectrum Disorders (ASD) ; Brain Functional Network

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