Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

• Children with ASD demonstrated aberrant dynamic brain networks constructed by EEG and fNIRS measurements. • Multimodal covariance network analysis revealed distinct neurovascular characteristics in children with ASD. • The neurovascular characteristics at an early stage can predict the developmental progress of adaptive functioning in ASD. Autism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. Resting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. 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. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. The multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

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Publication Details

Journal
NeuroImage
Published
2024-10-18
DOI
https://doi.org/10.1016/j.neuroimage.2024.120895
Citations
12
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
4.11

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article

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

Fali Li, Rihui Li, Dalin Yang, Guang Yang et al.
12 citations
NeuroImage
Functional Brain Connectivity Studies
4.11
article

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

Fali Li, Rihui Li, Dalin Yang, Guang Yang, Lin Wan, Jian Chen, Yuhang Li, Gang Zhu, Wen Wang
article en
12 citations

Abstract

• Children with ASD demonstrated aberrant dynamic brain networks constructed by EEG and fNIRS measurements. • Multimodal covariance network analysis revealed distinct neurovascular characteristics in children with ASD. • The neurovascular characteristics at an early stage can predict the developmental progress of adaptive functioning in ASD. Autism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. Resting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. 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. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. The multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

NeuroImageVol. 302
University of Electronic Science and Technology of China (CN), University of Macau (MO), Chinese PLA General Hospital (CN), Mallinckrodt (United States) (US), Southern Medical University (CN)
National Natural Science Foundation of China, Universidade de Macau, Natural Science Foundation of Beijing Municipality, Equipment Development Department of the Central Military Commission
Openalex Percentile: Top 5%
Functional Brain Connectivity Studies
4.11
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