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基于脑功能网络分析的孤独症儿童辅助干预效果研究

李昕 王欣 安占周 蔡二娟 康健楠

李昕, 王欣, 安占周, 蔡二娟, 康健楠. 基于脑功能网络分析的孤独症儿童辅助干预效果研究. 自动化学报, 2021, 47(11): 2654-2663 doi: 10.16383/j.aas.c180707
引用本文: 李昕, 王欣, 安占周, 蔡二娟, 康健楠. 基于脑功能网络分析的孤独症儿童辅助干预效果研究. 自动化学报, 2021, 47(11): 2654-2663 doi: 10.16383/j.aas.c180707
Li Xin, Wang Xin, An Zhan-Zhou, Cai Er-Juan, Kang Jian-Nan. Study on the effect of autistic children's auxiliary intervention based on brain functional network analysis. Acta Automatica Sinica, 2021, 47(11): 2654-2663 doi: 10.16383/j.aas.c180707
Citation: Li Xin, Wang Xin, An Zhan-Zhou, Cai Er-Juan, Kang Jian-Nan. Study on the effect of autistic children's auxiliary intervention based on brain functional network analysis. Acta Automatica Sinica, 2021, 47(11): 2654-2663 doi: 10.16383/j.aas.c180707

基于脑功能网络分析的孤独症儿童辅助干预效果研究

doi: 10.16383/j.aas.c180707
基金项目: 

中国博士后科学基金 2014M550582

河北省自然科学基金 F2014203244

河北省自然科学基金 F2019203515

详细信息
    作者简介:

    王欣 燕山大学博士研究生. 2020年获得燕山大学硕士学位. 主要研究方向为医学信息处理和情感计算. E-mail: yddywangxin@163.com

    安占周 2019年获得燕山大学硕士学位. 主要研究方向为医学信息处理和情感计算. E-mail: 18332553763@163.com

    蔡二娟 2018年获得燕山大学硕士学位. 主要研究方向为为孤独症儿童脑电信息的处理和计算. E-mail: 18233587424@163.com

    康健楠 河北大学讲师. 2006年获得燕山大学硕士学位. 主要研究方向为孤独症儿童脑电信息处理. E-mail: kangjiannan81@163.com

    通讯作者:

    李昕 燕山大学教授. 2008年获得燕山大学博士学位. 主要研究方向为医学信息处理, 情感计算. 本文通信作者. E-mail: yddylixin@ysu.edu.cn

Study on the Effect of Autistic Children's Auxiliary Intervention Based on Brain Functional Network Analysis

Funds: 

China Postdoctoral Science Foundation 2014M550582

Hebei Provincial Natural Science Foundation F2014203244

Hebei Provincial Natural Science Foundation F2019203515

More Information
    Author Bio:

    WANG Xin Ph. D. candidate at Yanshan University. She received her master degree in 2020 from Yanshan University. Her research interest covers medical information processing and afiective computing

    AN Zhan-Zhou He received his master degree in 2019 from Yanshan University. His research interest covers medical information processing and afiective computing

    CAI Er-Juan She received her master degree in 2018 from Yanshan University. Her research interest covers the EEG information processing and computing for autism children

    KANG Jian-Nan Lecturer at Hebei University. She received her master degree in 2006 from Yanshan University. Her main research interest is the EEG information processing for autism children

    Corresponding author: LI Xin Professor at Yanshan University. She received her Ph. D. degree in 2008 from Yanshan University. Her research interest covers medical information processing and afiective computing. Corresponding author of this paper
  • 摘要: 脑功能网络是分析复杂网络之间连接关系的一种有效方法, 对脑功能障碍分析具有重要意义. 本文基于频域Granger因果分析的定向传递函数(Direction-transfer function, DTF), 构建了各频段的脑功能网络. 采用图论方法分析最佳阈值下经颅直流电刺激(Transcranial direct current stimulation, tDCS)干预前后孤独症(Autism spectrum disorder, ASD)儿童脑网络的平均度、全局效率和平均局部效率等特征, 并对比了经颅直流电刺激对孤独症儿童脑功能状态辅助干预效果. 结果发现刺激前组在各频段的图论特征均低于刺激后组($P<0.05$), 其中Theta频段和低-beta频段的局部效率统计性差异显著, 表明在一定程度上tDCS干预是ASD儿童治疗的有效手段.
    Recommended by Associate Editor ZHANG Dao-Qiang
    1)  本文责任编委 张道强
  • 图  1  国际标准10/20系统的16通道电极分布

    Fig.  1  16-channel electrode distribution in international standard 10/20 system

    图  2  刺激前组受试儿童各个频段的DTF连接矩阵图

    Fig.  2  The DTF connection matrix of each frequency band of children in the group post-tDCS

    图  3  刺激后组受试儿童各个频段的DTF连接矩阵图

    Fig.  3  The DTF connection matrix of each frequency band of children in the group pre-tDCS

    图  4  间接因果关系示意图

    Fig.  4  Schematic diagram of indirect causality

    表  1  各频段下刺激前后两组ASD儿童全脑DTF总和

    Table  1  Total global brain DTF of ASD children in both groups before and after stimulation

    DTFsum Theta Alpha Low-beta High-beta Gamma
    Before 14.88 $ \pm $ 0.015** 14.86 $ \pm $ 0.011*** 14.82 $ \pm $ 0.020*** 14.84 $ \pm $ 0.012** 12.41 $ \pm $ 0.054***
    After 14.96 $ \pm $ 0.006 14.95 $ \pm $ 0.005 14.93 $ \pm $ 0.006 14.91 $ \pm $ 0.005 12.98 $ \pm $ 0.084
    下载: 导出CSV

    表  2  Theta频段平均度、全局效率、平均局部效率阈值统计表

    Table  2  Threshold statistics of theta band average, global efficiency, average local efficiency

    Theta $ D $ $ E_{Glob} $ $ E_{Loc} $
    $ p<0.05 $* 0.001 $ \sim $ 0.081 0.001 $ \sim $ 0.078 0.095 $ \sim $ 0.1 0.001 $ \sim $ 0.058
    $ p<0.01 $** 0.001 $ \sim $ 0.080 0.001 $ \sim $ 0.008 0.01 $ \sim $ 0.013 0.001 0.002 0.01 $ \sim $ 0.053
    0.072 $ \sim $ 0.074 0.099 0.1
    $ p<0.005 $*** 0.051 $ \sim $ 0.077 0.014 $ \sim $ 0.047
    下载: 导出CSV

    表  3  Alpha频段平均度、全局效率、平均局部效率阈值统计表

    Table  3  Threshold statistics of alpha band average, global efficiency, average local efficiency

    Alpha $ D $ $ E_{Glob} $ $ E_{Loc} $
    $ p<0.05 $* 0.001 $ \sim $ 0.045 0.052 $ \sim $ 0.059 0.001 $ \sim $ 0.038 0.001 $ \sim $ 0.083
    $ p<0.01 $** 0.003 $ \sim $ 0.012 0.018 $ \sim $ 0.021 0.006 $ \sim $ 0.013 0.015 0.002 $ \sim $ 0.083 0.047
    0.049 $ \sim $ 0.067
    $ p<0.005 $***
    下载: 导出CSV

    表  4  Low-beta频段平均度、全局效率、平均局部效率阈值统计表

    Table  4  Threshold statistics of low-beta band average, global efficiency, average local efficiency

    Low-beta $ D $ $ E_{Glob} $ $ E_{Loc} $
    $ p<0.05 $* 0.001 $ \sim $ 0.063 0.093 $ \sim $ 0.1 0.001 $ \sim $ 0.042 0.088 $ \sim $ 0.1 0.001 $ \sim $ 0.072
    $ p<0.01 $** 0.002 $ \sim $ 0.012 0.021 $ \sim $ 0.025 0.003 $ \sim $ 0.013 0.093 $ \sim $ 0.1 0.002 $ \sim $ 0.004 0.012 $ \sim $ 0.042
    0.027 0.038 0.046 $ \sim $ 0.048
    $ p<0.005 $*** 0.018 0.025 $ \sim $ 0.03
    0.032 $ \sim $ 0.038
    下载: 导出CSV

    表  5  High-beta频段平均度、全局效率、平均局部效率阈值统计表

    Table  5  Threshold statistics of high-beta band average, global efficiency, average local efficiency

    High-beta $ D $ $ E_{Glob} $ $ E_{Loc} $
    $ p<0.05 $* 0.001 $ \sim $ 0.056 0.092 $ \sim $ 0.1 0.001 $ \sim $ 0.053 0.098 $ \sim $ 0.1 0.001 $ \sim $ 0.038
    $ p<0.01 $** 0.002 $ \sim $ 0.03 0.032 $ \sim $ 0.034 0.002 $ \sim $ 0.018 0.022 $ \sim $ 0.024 0.002 0.003 0.014 $ \sim $ 0.018
    0.036 0.01 0.011
    $ p<0.005 $***
    下载: 导出CSV

    表  6  Gamma频段平均度、全局效率、平均局部效率阈值统计表

    Table  6  Threshold statistics of gamma band average, global efficiency, average local efficiency

    Gamma $ D $ $ E_{Glob} $ $ E_{Loc} $
    $ p<0.05 $* 0.001 $ \sim $ 0.083 all 0.001 $ \sim $ 0.017 0.026 $ \sim $ 0.036
    0.038
    $ p<0.01 $** 0.002 $ \sim $ 0.078 all 0.002 0.003 0.028 0.029
    $ p<0.005 $*** 0.046 $ \sim $ 0.071 0.053 $ \sim $ 0.095
      注: all表示Eglob在阈值全范围内差异显著度均满足$ p<0.05 $*和$ p<0.01 $**.
    下载: 导出CSV

    表  7  各频段下刺激前后两组受试儿童脑网络各图论参数的阈值

    Table  7  Threshold of graph parameters of brain network in two groups of children before and after stimulation

    Theta Alpha Low-beta High-beta Gamma
    T 0.045 0.038 0.025 0.036 0.029
    D ** * ** ** **
    $ \rm{E_{Glob}} $ * * * * **
    $ \rm{E_{Loc}} $ *** ** *** * **
      注: 显著度*$ p<0.05 $, **$ p<0.01 $, ***$ p<0.001 $
    下载: 导出CSV

    表  8  最佳阈值下刺激前后脑网络平均度

    Table  8  Average degree of brain network before and after stimulation under optimal threshold

    频段 Before After
    Theta 13.44 $ \pm $ 0.389 14.65 $ \pm $ 0.300**
    Alpha 15.59 $ \pm $ 0.526 16.69 $ \pm $ 0.343*
    Low-beta 16.92 $ \pm $ 0.609 18.52 $ \pm $ 0.393**
    High-beta 16.81 $ \pm $ 0.580 18.42 $ \pm $ 0.402**
    Gamma 16.78 $ \pm $ 0.519 18.56 $ \pm $ 0.379**
      注: 显著度*$ p<0.05 $, **$ p<0.01 $, ***$ p<0.001 $
    下载: 导出CSV

    表  9  最佳阈值下刺激前后脑网络全局效率

    Table  9  Global efficiency of pre-and hindbrain network stimulation under optimal threshold

    频段 Before After
    Theta 0.54 $ \pm $ 0.017 0.58 $ \pm $ 0.013*
    Alpha 0.62 $ \pm $ 0.021 0.66 $ \pm $ 0.013*
    Low-beta 0.65 $ \pm $ 0.024 0.70 $ \pm $ 0.015*
    High-beta 0.65 $ \pm $ 0.022 0.70 $ \pm $ 0.016*
    Gamma 0.68 $ \pm $ 0.023 0.76 $ \pm $ 0.014**
      注: 显著度*$ p<0.05 $, **$ p<0.01 $, ***$ p<0.001 $
    下载: 导出CSV

    表  10  最佳阈值下刺激前后脑网络局部效率

    Table  10  Local efficiency of pre-and hindbrain network stimulation under optimal threshold

    频段 Before After
    Theta 0.90 $ \pm $ 0.004 0.91 $ \pm $ 0.003***
    Alpha 0.91 $ \pm $ 0.002 0.92 $ \pm $ 0.002**
    Low-beta 0.93 $ \pm $ 0.002 0.94 $ \pm $ 0.002***
    High-beta 0.93 $ \pm $ 0.001 0.94 $ \pm $ 0.002*
    Gamma 0.89 $ \pm $ 0.002 0.76 $ \pm $ 0.002**
      注: 显著度*$ p<0.05 $, **$ p<0.01 $, ***$ p<0.001 $
    下载: 导出CSV
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  • 收稿日期:  2018-11-05
  • 录用日期:  2019-05-06
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