Song, Inuk2023-02-092023-02-092022-10-26http://hdl.handle.net/10919/113746The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions such as autism spectrum disorder, as well as for predicting psychosocial characteristics such as age. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) than temporal features of connectivity changes (connectome variability). The primary goal of the current study was to investigate the effectiveness of using the connectome variability in classifying an individual’s pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the connectome variability are reliable across various analysis procedures. To this end, three open public large rs-fMRI datasets including ABIDE, COBRE, and NKI were used. The static-FC and the connectome variability metrics were calculated with various brain parcellations and parameters and then utilized for subsequent machine learning (ML) classification and prediction. The results demonstrated that including the connectome variability increased the ML performances significantly in most cases of analytical variations. In addition, including the connectome variability prevented ML performance deterioration when excessive components were used. In conclusion, the current finding proved the usefulness of the connectome variability and its reliability.ETDapplication/pdfenCreative Commons Attribution 4.0 InternationalDynamic functional connectivityConnectome variabilityResting-state fMRIMachine learningTemporal variabilityNetwork connectivityThesis