A team led by Professor Rihui Li from the Centre for Cognitive and Brain Sciences has made significant strides in understanding the developmental trajectory of Autism Spectrum Disorder (ASD) in children. Their groundbreaking research, published in the prestigious journal NeuroImage (5-year IF=6.1, JCR Q1), introduced a multimodal brain imaging approach that revealed the connection between neurovascular coupling and the progression of symptoms in children with ASD.

ASD, one of the most prevalent neurodevelopmental disorders, affects 1-2% of the global population. Children with ASD often face long-term challenges in social interaction and exhibit repetitive and stereotyped sensory-motor behaviors. Understanding the underlying neural mechanism of ASD symptoms is crucial for the development of effective therapeutic interventions. However, due to the high complexity and heterogeneity of ASD etiology and neurodevelopmental mechanisms, neural markers associated with ASD developmental trajectories remain largely unexplored.

To address the above challenges, Prof. Rihui Li’s research team collected cross-sectional and longitudinal resting-state EEG and fNIRS data from 58 children with ASD and 63 typically developing (TD) children. Through dynamic brain functional network clustering analysis of both modalities, they identified dynamic characteristics of brain functional networks in children with ASD compared to TD children. Specifically, EEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group.

By constructing a multimodal covariance network based on the neurovascular coupling characteristics of EEG and fNIRS, the team further identified significantly suppressed functional covariance between right superior temporal and left Broca’s areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, they found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD.

This study highlights the potential of using neuroimaging-based diagnostic tools to improve diagnostic accuracy and develop personalized intervention methods for ASD. Future research aims to increase sample size and explore the integration of high spatial resolution neuroimaging methods like fMRI for further insights.

Original article link: https://doi.org/10.1016/j.neuroimage.2024.120895

Prof. Rihui Li from the CCBS of the University of Macau and Prof. Guang Yang from the General Hospital of the People’s Liberation Army (PLA) are the co-corresponding authors of the article. PhD student Yuhang Li from the CCBS of the University of Macau and Dr. Lin Wan from the General Hospital of the PLA are the co-first authors of the article. This research is supported by the NSFC (82171540 and 82301743), the FDCT(0010/2023/ITP1 and 0016/2024/RIB1), the National Key R&D Project (2023YFC2706405 and 2022YFC2705301), and the University of Macau (SRG2023–00015-ICI).

Figure 1. The identified dFC states and characteristics of each EEG frequency band.

Figure 2. The identified dFC states and characteristics of fNIRS HbO response.

Figure 3. The pipeline of the proposed Multimodal Covariance Network (MCN) analysis.