Abnormalities of brain dynamics based on large-scale cortical network modeling in autism spectrum disorder

Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiological activity of ASD remains unclear. To address this, we propose a large-scale cortical network model based on empirical structural connectivity data using the Wendling model, which successfully simulates both pathological and physiological electroencephalography (EEG) signals. Building on this, the EEG functional network is constructed using the phase lag index, effectively characterizing the functional connectivity. Our modeling results indicate that EEG activity and functional network properties undergo significant changes by globally increasing synaptic coupling strength. Specifically, it leads to abnormal neural oscillations clinically reported in ASD, including the decreased dominant frequency, the decreased relative power in the α band and the increased relative power in the δ+θ band, particularly in the frontal lobe. At the same time, the clustering coefficient and global efficiency of the functional network decrease, while the characteristic path length increases, suggesting that the functional network of ASD is inefficient and poorly integrated. Additionally, we find insufficient functional connectivity across multiple brain regions in ASD, along with decreased wavelet coherence in the α band within the frontal lobe and between the frontal and temporal lobes. Considering that most of the synaptic increases in ASD are limited, brain regions are further randomly selected to increase the local synaptic coupling strength. The results show that disturbances in local brain regions can also facilitate the development of ASD. This study reveals the intrinsic link between synapse increase and abnormal brain activity in ASD, and inspires treatments related to synapse pruning.