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Dynamic Functional Connectivity in Autism Spectrum Disorder Using Resting-State fMRI
Jalil Piran, Fardin | 2021
406
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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 54599 (05)
- University: Sharif University of Technology
- Department: Electrical Engineering
- Advisor(s): Fatemizadeh, Emadeddin
- Abstract:
- Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disorders that cause repetitive behaviors and social and communication skills abnormalities. Autistic Disorder(AD) is one of the disorders in ASD that is being investigated in this study. There has been an increase in research about AD in recent years due to the increasing AD prevalence and the high autistic living costs. The dynamic functional connectivity between healthy and autistic groups has been analyzed by using graph theory. The brain is modeled as a dynamic graph using resting-state fMRI. The graph theory metric is calculated in the dynamic graph of each subject, and the distinction of the two groups is checked using statistical hypothesis tests. In this study, the distinction of two groups in 10 data sets is investigated by graph theory metrics: Connectivity Number Entropy, Density, Graph Index Complexity, Diversity Coefficient, Temporal Flexibility, Spatiotemporal Diversity, Characteristic Path Length, Clustering Coefficient, Eccentricity, Stress, and Transitivity. The data sets come from ABIDE 1 Preprocessed database. The subjects are between the ages of 8 and 20. Dosenbach atlas is used for parcellation of the brain. DynamicBC toolbox implemented in MATLAB is used for calculating correlation connectivity matrices.Finally, Graph Index Complexity distinguishes two groups in 9 data sets, and other metrics distinguish two groups in all data sets. So, metrics that are used in this study distinguish two groups very well
- Keywords:
- Autism Spectrum Disorders (ASD) ; Resting State Brain ; Functional Magnetic Resonance Imaging (FMRI) ; Graph Theory Metric ; Dynamic Graph Theory ; Dynamic Functional Connectivity
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