Dynamic functional connectivity in autism spectrum disorders: applications and research advances
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder resulting from impaired information flow in human brain systems, highly heritable and associated with impaired dynamic functional connectivity (DFC). Individuals with ASD are one of the more far-reaching child psychiatric disorders, and the families of diagnosed children are faced with multiple stressors and challenges from financial, emotional, and social perspectives. Previous research on ASD has been based on resting-state functional connectivity (SFC), but SFC has largely failed to take into account the presence and potential of temporal variability to impact brain function. In recent years, DFC has been widely used in ASD studies because it can accurately capture the fluctuations of functional connectivity (FC) over time and reveal the transitions between different FC states. In this paper, we review some common and newer methods of DFC, such as the sliding-window (SW) , the hidden Markov model (HMM), and the leading eigenvector dynamics analysis (LEiDA), as well as the applications and recent research progress of these methods in ASD, and summarize and compare the advantages and shortcomings of these methods. This review expects to provide a new way for early diagnosis and personalized treatment of ASD by summarizing the DFC methods and their applications. By analyzing DFC patterns, researchers are able to identify specific connective features associated with ASD, and are expected to develop DFC-based biomarkers to improve the diagnostic accuracy and reliability of ASD.
国家科技期刊平台
NSTL
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