Dynamic and Static Functional Network Connectivity Analysis in Autism: A Resting State fMRI Analysis
In this article, we study both static Functional Connectivity (sFC) and dynamic Functional Connectivity (dFC) on Autism Spectrum Disorder (ASD) and a control group using resting state fMRI. We exploit group ICA (gICA) technique to determine Resting State Networks' (RSNs) spatial maps and time courses. Then, we calculate Pearson correlation among time courses as a measure of FC in both sFC and dFC. For dFC, Sliding Window technique was utilized to calculate FCs within the obtained windows. Subsequently, we perform K means clustering algorithm on the dFC matrices to obtain transient FC patterns (states) After that, we determine mean dwell time for each state. Finally, we obtain differences in dFC, sFC, and mean dwell time among the ASD and control group using two sample t test and permutation test. Our results demonstrated both overconnectivity and underconnectivity patterns (mostly underconnectivity) in ASDs' sFC, and also differences in mean dwell time of states 1 and 2 between the two groups. These states illustrated low absolute FC z values (weak over and underconnectivity) within Salience network and also between DMN and Sensorimotor.
2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME)