Wang Junling, Zhang Ludan, Chen Sitong, Xue Huiqin, Du Minghao, Xu Yunuo, Liu Shuang, Ming Dong
School of Medicine, Tianjin University, Tianjin, China.
Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China.
Cogn Neurodyn. 2025 Dec;19(1):9. doi: 10.1007/s11571-024-10213-x. Epub 2025 Jan 9.
Individuals with high autistic traits (AT) encounter challenges in social interaction, similar to autistic persons. Precise screening and focused interventions positively contribute to improving this situation. Functional connectivity analyses can measure information transmission and integration between brain regions, providing neurophysiological insights into these challenges. This study aimed to investigate the patterns of brain networks in high AT individuals to offer theoretical support for screening and intervention decisions. EEG data were collected during a 4-min resting state session with eyes open and closed from 48 participants. Using the Autism Spectrum Quotient (AQ) scale, participants were categorized into the high AT group (HAT, n = 15) and low AT groups (LAT, n = 15). We computed the interhemispheric and intrahemispheric alpha coherence in two groups. The correlation between physiological indices and AQ scores was also examined. Results revealed that HAT exhibited significantly lower alpha coherence in the homologous hemispheres of the occipital cortex compared to LAT during the eyes-closed resting state. Additionally, significant negative correlations were observed between the degree of AT (AQ scores) and the alpha coherence in the occipital cortex, as well as in the right frontal and left occipital regions. The findings indicated that high AT individuals exhibit decreased connectivity in the occipital region, potentially resulting in diminished ability to process social information from visual inputs. Our discovery contributes to a deeper comprehension of the neural underpinnings of social challenges in high AT individuals, providing neurophysiological signatures for screening and intervention strategies for this population.
具有高自闭症特质(AT)的个体在社交互动中会遇到挑战,这与自闭症患者相似。精确的筛查和针对性的干预措施对改善这种情况有积极作用。功能连接分析可以测量大脑区域之间的信息传递和整合,为理解这些挑战提供神经生理学见解。本研究旨在调查高AT个体的脑网络模式,为筛查和干预决策提供理论支持。在48名参与者睁眼和闭眼的4分钟静息状态下收集脑电图数据。使用自闭症谱系商数(AQ)量表,将参与者分为高AT组(HAT,n = 15)和低AT组(LAT,n = 15)。我们计算了两组的半球间和半球内α相干性。还检查了生理指标与AQ分数之间的相关性。结果显示,在闭眼静息状态下,与LAT组相比,HAT组枕叶皮质同源半球的α相干性显著降低。此外,在AT程度(AQ分数)与枕叶皮质以及右额叶和左枕叶区域的α相干性之间观察到显著的负相关。研究结果表明,高AT个体枕叶区域的连接性降低,可能导致处理来自视觉输入的社会信息的能力下降。我们的发现有助于更深入地理解高AT个体社交挑战的神经基础,为该人群的筛查和干预策略提供神经生理学特征。