中国科学院心理健康重点实验室知识库

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterized by restricted interests and repetitive behaviors. Non-invasive measurements of brain activity with functional magnetic resonance imaging (fMRI) have demonstrated that the abnormality in the default mode network (DMN) is a crucial neural basis of ASD, but the time-frequency feature of the DMN has not yet been revealed. Hilbert-Huang transform (HHT) is conducive to feature extraction of biomedical signals and has recently been suggested as an effective way to explore the time-frequency feature of the brain mechanism. In this study, the resting-state fMRI dataset of 105 subjects including 59 ASD participants and 46 healthy control (HC) participants were involved in the time-frequency clustering analysis based on improved HHT and modified k-means clustering with label-replacement. Compared with HC, ASD selectively showed enhanced Hilbert weight frequency (HWF) in high frequency bands in crucial regions of the DMN, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC) and anterior cingulate cortex (ACC). Time-frequency clustering analysis revealed altered DMN organization in ASD. In the posterior DMN, the PCC and bilateral precuneus were separated for HC but clustered for ASD; in the anterior DMN, the clusters of ACC, dorsal MPFC, and ventral MPFC were relatively scattered for ASD. This study paves a promising way to uncover the alteration in the DMN and identifies a potential neuroimaging biomarker of diagnostic reference for ASD.

This work was supported by fund for building world-class universities (disciplines) of Renmin University of China under Grant RUCPSY0005. (Corresponding author: Xia Wu ) Han Zhang and Qing Li are with the School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China (e-mail: hanzh@mail. bnu.edu.cn; [email protected]).