与自闭症谱系障碍儿童的临床症状相关的闭环路径:复杂的网络分析
Yonglu Wang1, Yuxin Qian1, Luyang Guan1
1Child Mental Health Research Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
Quantitative imaging in medicine and surgery
|October 13, 2025
概括
复杂网络分析在患有自闭症谱系障碍 (ASD) 的儿童中发现了异常的大脑路径,可能有助于早期诊断. 这些发现为ASD提供了洞察力.
科学领域:
- 神经科学是一个神经科学.
- 复杂系统分析 复杂系统分析
- 发育障碍 发育障碍 发展障碍
背景情况:
- 自闭症谱系障碍 (ASD) 是一种神经发育状况,其特征是社会沟通缺陷和重复性行为.
- 缺乏可靠的神经生物标志物阻碍了ASD早期诊断和个性化治疗策略.
- 识别功能性大脑网络的改变对于理解ASD病理生理学至关重要.
研究的目的:
- 通过复杂的网络分析来识别ASD潜在的神经生物标志物.
- 研究ASD儿童的因果性大脑网络和功能性闭环路径.
- 为了将网络发现与社会沟通缺陷和刻板印象行为相关联.
主要方法:
- 在58名自闭症患者和57名典型发育 (TD) 儿童 (6-12岁) 中构建了因果大脑网络和时间滞后网络.
- 采用深度学习模型推断因果关系和大脑区域之间的信号传输滞后.
- 进行统计分析来比较TD和ASD组,并将网络特征与ASD症状联系起来.
主要成果:
- 与TD对照组相比,在ASD儿童中确定了异常的功能途径,主要是在前额外和后额叶接口,与TD对照组相比.
- 三个特定的闭环路径与社会通信领域得分 (ADOS-2) 显示出显著的负相关性.
- 观察到这些途径之间的微弱积极相互作用,表明社会障碍和刻板印象行为之间的相互关系.
结论:
- 复杂的网络分析可以揭示异常的闭环路径,有助于早期的ASD诊断和治疗开发.
- 研究结果提供了神经生物学的洞察力,了解自闭症特征的社会障碍和刻板印象行为.
- 这些结果可能有助于制定针对ASD的个性化干预措施和治疗目标.
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