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面向认知表现预测的时−空共变混合深度学习模型

李晴 徐雪远 邬霞

李晴, 徐雪远, 邬霞. 面向认知表现预测的时−空共变混合深度学习模型. 自动化学报, 2022, 48(12): 1−10 doi: 10.16383/j.aas.c220025
引用本文: 李晴, 徐雪远, 邬霞. 面向认知表现预测的时−空共变混合深度学习模型. 自动化学报, 2022, 48(12): 1−10 doi: 10.16383/j.aas.c220025
Li Qing, Xu Xue-Yuan, Wu Xia. Spatio-temporal co-variant hybrid deep learning framework for cognitive performance prediction. Acta Automatica Sinica, 2022, 48(12): 1−10 doi: 10.16383/j.aas.c220025
Citation: Li Qing, Xu Xue-Yuan, Wu Xia. Spatio-temporal co-variant hybrid deep learning framework for cognitive performance prediction. Acta Automatica Sinica, 2022, 48(12): 1−10 doi: 10.16383/j.aas.c220025

面向认知表现预测的时−空共变混合深度学习模型

doi: 10.16383/j.aas.c220025
基金项目: 北京市自然科学基金(4212037)资助
详细信息
    作者简介:

    李晴:北京师范大学认知神经科学与学习国家重点实验室博士后. 2022年获北京师范大学博士学位. 主要研究方向为脑影像智能分析. E-mail: liqing@bnu.edu.cn

    徐雪远:2022年获北京师范大学人工智能学院博士学位. 主要研究方向为脑信号智能分析. E-mail: xuxueyuan@mail.bnu.edu.cn

    邬霞:北京师范大学人工智能学院教授. 2008年获北京师范大学认知神经科学与学习研究所博士学位. 主要研究方向为医学图像处理. 本文通信作者. E-mail: wuxia@bnu.edu.cn

Spatio-temporal Co-variant Hybrid Deep Learning Framework for Cognitive Performance Prediction

Funds: Supported by Natural Science Foundation of Beijing (4212037)
More Information
    Author Bio:

    LI Qing Postdoctoral researcher at the State Key Laboratory of Co-gnitive Neuroscience and Learning, Beijing Normal University. She received her Ph.D. degree from Beijing Normal University in 2022. Her main research interest is brain imaging intelligent analysis

    XU Xue-Yuan He received his Ph.D. degree from Beijing Normal University in 2022. His main resea-rch interest is brain signal intelligent analysis

    WU Xia Professor at the School of Artificial Intelligence, Beijing Normal University. She received her Ph.D. degree from Beijing Normal University in 2008. Her main research interest is medical image processing. Corresponding author of this paper

  • 摘要: 认知表现预测已经成为当前大脑研究的重要课题. 功能磁共振成像技术由于同时具有较好的时间和空间分辨率, 有潜力为认知表现预测提供数据支持. 为了解决基于功能磁共振成像数据对认知表现进行预测时大脑所具有的时−空共变难刻画问题, 提出了一种新型基于大脑学习机制的时−空共变混合深度学习模型, 即深度稀疏自编码器与循环全连接网络混合模型, 以混合神经网络模型的损失函数误差作为认知表现预测能力的评价标准. 在人类连接组项目数据集上的实验结果表明, 提出的时−空共变混合模型能够有效和稳健地预测认知表现, 并提取到与人脑学习、记忆相关的有意义的脑影像特征, 从而为认知表现预测提供技术支持.
  • 图  1  基于大脑学习机制的时−空共变混合DSRAE-RFNet模型框架

    Fig.  1  The overview of learning mechanism based spatio-temporal co-variant hybrid deep learning framework (DSRAE-RFNet)

    图  2  DSRAE-RFNet模型在3组被试上对RT、ACC分别进行预测时的MSE损失下降图

    Fig.  2  The MSE loss when predicting RT and ACC with DSRAE-RFNet model on three groups participants

    图  3  DSRAE-RFNet模型在3组被试上对RT、ACC的预测结果

    Fig.  3  The predictive results of RT and ACC with DSRAE-RFNet model on three groups participants

    图  4  反应时间表现预测结果

    Fig.  4  Reaction time performance predicting results

    图  5  反应时间与准确率表现预测过程中习得的大脑时−空共变特征

    Fig.  5  The brain spatio-temporal co-variant features learned from the RT and ACC performance prediction processes

    图  6  准确率表现预测结果

    Fig.  6  Accuracy performance predicting results

    图  7  DSRAE-RFNet及比较算法在单个GPU上单个运行次数的运行时间

    Fig.  7  Running time of DSRAE-RFNet and comparable methods on a single GPU during one epoch

    表  1  工作记忆任务: fMRI数据信息

    Table  1  Working memory task: fMRI data information

    参数任务信息
    重复时间数量405 个
    扫描持续时间5 分 01 秒
    任务组块数量8 个
    刺激名称0-back: 被试判断当前呈现内容是否
    与预先规定内容一致
    2-back: 被试判断当前呈现内容是否
    与 2 个位置之前的呈现内容一致
    cue: 任务初始阶段或 block 间的间隔
    下载: 导出CSV

    表  2  工作记忆任务: 认知表现数据信息

    Table  2  Cognitive performance data information

    刺激反应时间 (RT)准确率 (ACC)
    0-back总体反应时间 总体准确率
    人体刺激反应时间 人体刺激准确率
    面孔刺激反应时间 面孔刺激准确率
    地点刺激反应时间 地点刺激准确率
    工具刺激反应时间 工具刺激准确率
    2-back总体反应时间 总体准确率
    人体刺激反应时间 人体刺激准确率
    面孔刺激反应时间 面孔刺激准确率
    地点刺激反应时间 地点刺激准确率
    工具刺激反应时间 工具刺激准确率
    下载: 导出CSV

    表  3  与未采用学习机制模型比较的认知表现预测结果

    Table  3  Cognitive performance prediction results compared with the model without learning mechanism

    认知表现组别没有采用学习机制采用学习机制
    RT第 1 组0.4550.700
    第 2 组0.3300.740
    第 3 组0.6800.776
    ACC第 1 组0.4490.429
    第 2 组0.3880.477
    第 3 组0.5230.536
    下载: 导出CSV

    表  4  与其他预测模型比较的认知表现预测结果

    Table  4  Cognitive performance prediction results compared with the other predictive model

    组别RTACC
    第 1 组第 2 组第 3 组第 1 组第 2 组第 3 组
    ICA0.6770.6610.7650.0900.5050.449
    GLM0.4510.3690.2920.2320.2240.343
    RNN0.6960.7470.7190.3900.4530.511
    LSTM0.6850.6740.7310.3690.3680.345
    AE0.0780.5190.6630.1780.2890.202
    DSRNN[33]0.6920.5760.7330.4130.3540.356
    DVAE[34]0.1570.6440.5120.4110.2420.216
    STAAE[35]0.5880.4310.5320.0660.1220.466
    DCAE[10]0.3840.4380.4650.2250.2000.089
    本文算法0.7000.7400.7760.4290.4770.536
    下载: 导出CSV

    表  5  对不同认知任务的认知表现预测结果

    Table  5  Cognitive performance prediction results for different cognitive tasks

    认知表现组别情感任务语言任务关系任务工作记忆任务
    RT第1组0.1600.7200.6670.700
    第2组0.2800.8000.9900.740
    第3组0.2400.7200.7600.776
    ACC第1组0.4000.5200.6930.429
    第2组0.3200.7200.8200.477
    第3组0.1400.7600.8400.536
    下载: 导出CSV
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  • 收稿日期:  2022-01-10
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