通过大规模的动态功能网络连接来识别和分析自闭症谱系障碍
Wenwen Zhuang1, Hai Jia1, Yunhong Liu1
1Mental Health Education Center and School of Science, Xihua University, Chengdu, China.
概括
动态功能网络连接 (dFNC) 有效地识别了个人的自闭症谱系障碍 (ASD). 这种神经成像方法揭示了大脑的变化,与社会分数相关,为ASD检测提供了潜在的生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 神经成像是一种神经成像.
- 生物标志物 生物标志物
背景情况:
- 自闭症谱系障碍 (ASD) 是一种具有重大认知影响的神经发育状况.
- 大脑功能网络连接 (FNC) 在识别自闭症和理解其行为联系方面表现有前途.
- 动态FNC (dFNC) 作为ASD的诊断特征尚未得到充分研究.
研究的目的:
- 调查动态大规模FNC作为生物标志物的实用性,用于识别患有自闭症的个体.
- 为了确定在静态fMRI数据中分析dFNC的最佳窗口长度.
- 探索dFNC模式与ASD中的社会赤字之间的关系.
主要方法:
- 静态fMRI数据使用时滑窗方法分析,以捕获动态功能网络连接 (dFNC).
- 窗户长度从10-75 TRs (每个为2s) 不同,以避免任意选择.
- 线性支向量机分类器和嵌套的10倍交叉验证框架被用于分类.
主要成果:
- 在各种窗口长度中实现了94.88%的总平均分类准确度,用于识别ASD.
- 最优的窗口长度产生了97.77%的最高分类准确率.
- 背部和腹部注意力网络 (DAN,VAN) 中的dFNC在分类上最具影响力;DAN和轨道正面网络 (TOFN) 之间的dFNC与ASD的社会分数负相关.
结论:
- 动态功能网络连接 (dFNC) 作为识别自闭症谱系障碍 (ASD) 的潜在生物标志物.
- 这种方法为与ASD相关的认知变化提供了新的见解.
- dFNC分析可以帮助预测临床分数并了解与ASD相关的行为变化.
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