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基于同步EEG-fMRI采集的情绪认知重评数据特征融合分析研究

邹凌 严永 杨彪 李文杰 潘昌杰 周仁来

邹凌, 严永, 杨彪, 李文杰, 潘昌杰, 周仁来. 基于同步EEG-fMRI采集的情绪认知重评数据特征融合分析研究. 自动化学报, 2016, 42(5): 771-781. doi: 10.16383/j.aas.2016.c150545
引用本文: 邹凌, 严永, 杨彪, 李文杰, 潘昌杰, 周仁来. 基于同步EEG-fMRI采集的情绪认知重评数据特征融合分析研究. 自动化学报, 2016, 42(5): 771-781. doi: 10.16383/j.aas.2016.c150545
ZOU Ling, YAN Yong, YANG Biao, LI Wen-Jie, PAN Chang-Jie, ZHOU Ren-Lai. Feature Fusion Analysis of Simultaneously Recorded EEG-fMRI in Emotion Cognitive Reappraisal. ACTA AUTOMATICA SINICA, 2016, 42(5): 771-781. doi: 10.16383/j.aas.2016.c150545
Citation: ZOU Ling, YAN Yong, YANG Biao, LI Wen-Jie, PAN Chang-Jie, ZHOU Ren-Lai. Feature Fusion Analysis of Simultaneously Recorded EEG-fMRI in Emotion Cognitive Reappraisal. ACTA AUTOMATICA SINICA, 2016, 42(5): 771-781. doi: 10.16383/j.aas.2016.c150545

基于同步EEG-fMRI采集的情绪认知重评数据特征融合分析研究

doi: 10.16383/j.aas.2016.c150545
基金项目: 

常州市科技项目 CE20145055

国家自然科学基金项目 61201096, 51307010

详细信息
    作者简介:

    严永 常州大学信息科学与工程学院硕士研究生.主要研究方向为同步EEG-fMRI数据分析.E-mail:15861850725@163.com;

    杨彪 常州大学信息科学与工程学院讲师.2014年在东南大学仪器科学与技术学院获得博士学位.主要研究方向为计算机视觉和脑电成像技术.E-mail:yb6864171@cczu.edu.cn

    李文杰 常州大学信息科学与工程学院讲师.主要研究方向为信号与信息处理.E-mail:lwj212@126.com;

    潘昌杰 常州大学护理学院副教授.主要研究方向为心血管疾病影像诊断,功能MRI信号处理.E-mail:pcj424815@sina.com

    周仁来 南京大学社会学院心理学系教授,博士.主要研究方向为1)情绪:情绪能力评估与调节,情绪障碍诊断与矫正,情绪加工的认知与神经机制;2)记忆:记忆能力评估与训练,情绪与记忆相互作用,记忆加工的认知与神经机制.E-mail:rlzhou@nju.edu.cn

    通讯作者:

    邹凌 常州大学信息科学与工程学院教授.2004年在浙江大学电气学院获得博士学位.主要研究方向为同步EEG-fMRI多模态分析,生物医学信号处理与模式识别.本文通信作者.E-mail:zouling@cczu.edu.cn

Feature Fusion Analysis of Simultaneously Recorded EEG-fMRI in Emotion Cognitive Reappraisal

Funds: 

Science and Technology Program of Changzhou CE20145055

National Natural Science Foundation of China 61201096, 51307010

More Information
    Author Bio:

    Master student at the School of Information Science & Engineering, Changzhou University. His main research interest is simultaneously acquired EEG-fMRI data analysis

    Lecturer at the School of Information Science & Engineering, Changzhou University. He received his Ph.D. degree at the School of Instrument Science & Technology, Southeast University in 2014. His main research interest is computer vision and neuroimaging technology

    Lecturer at the School of Information Science & Engineering, Changzhou University. His main interest is signal and information processing

    Associate professor at the Nursing Institute, Changzhou University. His main research directions is the imaging diagnosis of cardiovascular disease and fMRI signal processing

    Ph.D., professor at the School of Social and Behavioral Science, Nanjing University. His research interest covers 1) emotion: measurement and regulation of emotion competency, diagnosis and intervention of emotion disorder; and cognitive and neural mechanism of emotion perception; 2) memory: measure and training of memory ability, interaction of emotion and memory, cognitive and neural mechanism of memory process

    Corresponding author: ZOU Ling Professor at Faculty of Information Science & Engineering, Changzhou University. She received her Ph.D. degree in control science and control engineering from Zhejiang University in 2004. Her research interest covers simultaneous fusion analysis of EEG/fMRI, biomedical signal processing and pattern recognition. Corresponding author of this paper
  • 摘要: 脑电(Electroencephalography, EEG)与功能磁共振成像(Functional magnetic resonance imaging, fMRI)为脑科学研究提供了互补的时空信息. 为研究大脑在对情绪图片采取认知重评策略时的神经活动, 基于同步采集的EEG-fMRI数据, 应用典型相关分析、经验模态分解及k-均值聚类等算法对融合情绪数据进行交叉关联和盲源分离, 得到空间上的fMRI图像和与之对应的EEG时间演变信号. 结果表明: 时域上, CCA分离出的脑电成分在认知重评状态下有明显的晚期正电位(Late positive potential, LPP) (潜伏期200ms~900ms)出现, 而且认知重评策略诱发下的LPP 波幅明显小于观看负性诱发的LPP波幅(F(1, 224)= 28.72, P<0.01), 而大于观看中性诱发的LPP波幅(F(1, 224)= 63.32, P<0.01); 与之对应的空域上, 可以明显地看出和情绪调节相关的扣带回, 额叶、颞叶等区域有明显激活区, 采用情绪认知重评策略时的脑区激活强度明显小于观看负性状态, 而大于观看中性, 且观看中性状态下被激活的与情绪相关的区域相对较少. 研究表明, 这种融合数据分析技术通过计算两种模态数据之间潜在的线性相关性, 可以有效地分离出大脑在时空上神经活动情况, 达到了同时描绘出大脑神经活动的时间信息与空间信息的效果.
  • 图  1  EEG-fMRI数据融合框架

    Fig.  1  Framework of EEG-fMRI data fusion

    图  2  同步EEG-fMRI数据采集系统结构图

    Fig.  2  Constructional detail of simultaneously EEG-fMRI data acquisition system

    图  3  情绪认知重评实验范式

    Fig.  3  Experimental paradigm

    图  4  EEG-fMRI融合分析具体步骤

    Fig.  4  Specific steps for of EEG-fMRI fusion analysis

    图  5  某个被试降低负性状态下EEG-fMRI特征提取结果

    Fig.  5  Outcome of EEG-fMRI feature extraction in decrease-negative condition by one subject

    图  6  观看中性状态下EEG-fMRI相关成分平均结果

    Fig.  6  Average outcome of EEG-fMRI correlation components by look-neutral condition

    图  7  观看负性状态下EEG-fMRI相关成分平均结果

    Fig.  7  Average outcome of EEG-fMRI correlation components by look-neutral condition

    图  8  降低负性状态下EEG-fMRI相关成分平均结果

    Fig.  8  Average outcome of EEG-fMRI correlation components by decrease-negative condition

    图  9  基于CCA算法的所有被试不同情绪状态的EEG相关成分叠加平均

    Fig.  9  Outcome of averaging all of subjects' EEG correlation components based on CCA algorithm by three emotion conditions

    表  1  三种情绪状态下具有较大相关性EEG-fMRI相关成分

    Table  1  Correlation component of EEG-fMRI which has higher correlation by three emotion conditions

    相关成分 相关系数
    观看中性观看负性降低负性
    成分10.9440.9660.913
    成分20.8880.9470.801
    成分30.8410.8770.731
    成分40.7410.8160.716
    成分50.6810.7650.606
    成分6N/A0.6600.537
    成分7 N/A 0.584 N/A
    下载: 导出CSV

    表  3  观看负性状态下fMRI相关成分叠加平均激活区域

    Table  3  Average ROIs of fMRI correlation components by decrease-negative

    观看中性状态fMRI激活区域
    ROIs(编号) AAL标签 Z score
    颞下回(90) Temporal Inf R 3.980
    额中回(8) Frontal Mid R 3.767
    眶部额下回(15) Frontal Mid Orb R 2.264
    前扣带和旁扣带回(31) Frontal Sup R 1.970
    颞极: 颞中回(88) Temporal Pole Mid R 1.674
    杏仁核(41) Frontal Mid L 1.492
    背外侧额上回(3) Frontal Sup L 1.100
    颞下回(89) Temporal Inf L 0.675
    颞中回(86) Temporal Mid R 0.297
    三角部额下回(13) Frontal Inf Tri L 0.037
    眶内额上回(26) Frontal Mid Orb R 0.004
    颞极: 颞中回(87) Temporal Pole Mid L -0.12
    背外侧额上回(4) Cingulum Ant L -0.284
    内侧额上回(24) Frontal Sup Medial R -0.287
    下载: 导出CSV

    表  4  降低负性状态下fMRI相关成分叠加平均激活区域

    Table  4  Average ROIs of fMRI correlation components by decrease-negative condition

    观看中性状态fMRI激活区域
    ROIs(编号) AAL标签 Z score
    丘脑(77) Thalamus L 1.260
    前扣带和旁扣带脑回(31) Cingulum Ant L 0.824
    杏仁核(41) Amygdala L 0.512
    眶部额下回(15) Frontal Inf Orb L 0.207
    内侧和旁扣带脑回(34) Cingulum Mid R -0.066
    岛盖部额下回(11) Frontal Inf Oper L -0.116
    内侧和旁扣带脑回(33) Cingulum Mid L -0.327
    海马旁回(39) ParaHippocampal L -0.417
    颞中回(85) Temporal Mid L -0.476
    中央前回(1) Precentral L -0.483
    海马旁回(40) ParaHippocampal R -0.487
    内侧额上回(23) Frontal Sup Medial L -0.498
    后扣带回(35) Cingulum Post L -0.529
    前扣带和旁扣带脑回(32) Cingulum Ant R -0.533
    眶部额上回(5) Frontal Sup Orb L -0.587
    距状裂周围皮层(43) Calcarine L -0.59
    内侧额上回(24) Frontal Sup Medial R -0.594
    楔前叶(68) Precuneus R -0.664
    颞下回(89) Temporal Inf L -0.683
    颞极: 颞上回(83) Temporal Pole Sup L -0.694
    梭状回(55) Fusiform L -0.709
    颞极: 颞中回(88) Temporal Pole Mid R -0.869
    下载: 导出CSV

    表  2  观看中性状态下fMRI相关成分叠加平均激活区域

    Table  2  Average ROIs of fMRI correlation components by look-neutral condition

    观看中性状态fMRI激活区域
    ROIs(编号) AAL标签 Z score
    尾状核(71) Caudate L 0.078
    嗅皮质(22) Olfactory R -0.027
    脑岛(29) Insula L -0.044
    豆状壳核(74) Putamen R -0.076
    海马旁回(39) ParaHippocampal L -0.111
    顶上回(59) Parietal Sup L -0.13
    尾状核(72) Caudate R -0.171
    枕中回(52) Occipital Mid R -0.333
    眶部额上回(6) Frontal Sup Orb R -0.347
    丘脑(77) Thalamus L -0.356
    杏仁核(41) Amygdala L -0.414
    海马(38) Hippocampus R -0.46
    颞中回(86) Temporal Mid R -0.471
    海马旁回(40) ParaHippocampal R -0.474
    海马(37) Hippocampus L -0.536
    三角部额下回(13) Frontal Inf Tri L -0.561
    眶部额下回(15) Frontal Inf Orb L -0.578
    岛盖部额下回(11) Frontal Inf Oper L -0.592
    颞极: 颞上回(83) Temporal Pole Sup L -0.609
    额中回(7) Frontal Mid L -0.619
    顶上回(60) Parietal Inf R -0.677
    豆状壳核(73) Putamen L -0.785
    前扣带和旁扣带脑回(31) Rectus R -0.789
    内侧额上回(23) Frontal Sup Medial L -0.968
    下载: 导出CSV
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  • 收稿日期:  2015-09-02
  • 录用日期:  2016-01-25
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