Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.
Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.
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DOI：
10.1371/journal.pcbi.1002441
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发表时间：
2012
影响因子：
4.3
通讯作者：
Hirsch J
中科院分区：
文献类型：
--
作者：
Pantazatos SP;Talati A;Pavlidis P;Hirsch J
对无人值守的威胁相关刺激的处理，如恐惧面孔，以前已经使用组功能磁共振(FMRI)方法进行了研究。然而，识别大脑活动的特征，包含足够的信息来解码，或“脑读”，无人看管(隐含)的恐惧感知仍然是一个活跃的研究目标。在这里，我们使用来自不同受试者的训练数据来检验这一假设，即大规模功能连接(FC)模式解码单个个体内隐含感知的面孔的情绪表达。FMRI和模块化设计被用来在内隐(任务无关)呈现恐惧和中性面孔的过程中获取粗体信号。采用线性滤波特征选择的模式分类器(线性核支持向量机，简称为支持向量机)使用成对的FC作为特征来预测隐含呈现的人脸的情感表情。我们绘制了分类精度与前N个选定特征的数量的关系图，并观察到15-40个特征的分类精度显著高于随机精度(90-100%)。在恐惧面孔呈现过程中，角回和海马区之间信息最丰富和正向调制最多的FC区域，而丘脑是最大的贡献区，与双侧颞中回和脑岛的正向调制联系。其他预测恐惧的FCS包括枕上区和顶区、小脑和前额叶皮质。相比之下，空间活动的模式(与互动相反)在破译内隐恐惧方面相对缺乏信息量。这些发现表明，全脑互动模式是无人值守的恐惧情绪处理的敏感和信息标志。同时，我们展示并提出了一种敏感的、探索性的方法来识别大规模的、条件相关的FC。与基于模型的分组方法相比，目前的方法没有考虑多个功能连接的多变量联合响应，并且不受信号损失和多个比较校正的需要的阻碍。从血氧水平依赖(BOLD)功能磁共振成像(FMRI)获得的整个大脑的成对关联(大规模功能连接)模式日益成为脑活动的特征。通常，这是在静息状态(即没有呈现刺激)时进行的，以基于个体差异或诊断来区分受试者。在目前的工作中，我们确定了这样的模式，这些模式是对威胁相关刺激的无人值守处理的敏感信号，允许一个人在注意与表情无关的刺激特征时，“大脑阅读”一个人是看到一张中性的脸还是恐惧的脸。这些结果进一步了解了健康受试者威胁检测和面部情感加工的神经机制，也可能有助于我们进一步理解各种障碍，如焦虑和自闭症，这些障碍在这些过程中表现出异常。同时，我们提出了一种探索性的和敏感的方法来识别条件依赖的大规模功能连通性。这种方法不是基于对受试者之间的平均功能连接和两个条件之间的对比的统计推断，而是基于当尝试在两个条件之间进行预测时每个功能连接的信息贡献，使用基于机器学习的多变量模式分析来自不同的受试者的训练数据。
Processing of unattended threat-related stimuli, such as fearful faces, has been previously examined using group functional magnetic resonance (fMRI) approaches. However, the identification of features of brain activity containing sufficient information to decode, or “brain-read”, unattended (implicit) fear perception remains an active research goal. Here we test the hypothesis that patterns of large-scale functional connectivity (FC) decode the emotional expression of implicitly perceived faces within single individuals using training data from separate subjects. fMRI and a blocked design were used to acquire BOLD signals during implicit (task-unrelated) presentation of fearful and neutral faces. A pattern classifier (linear kernel Support Vector Machine, or SVM) with linear filter feature selection used pair-wise FC as features to predict the emotional expression of implicitly presented faces. We plotted classification accuracy vs. number of top N selected features and observed that significantly higher than chance accuracies (between 90–100%) were achieved with 15–40 features. During fearful face presentation, the most informative and positively modulated FC was between angular gyrus and hippocampus, while the greatest overall contributing region was the thalamus, with positively modulated connections to bilateral middle temporal gyrus and insula. Other FCs that predicted fear included superior-occipital and parietal regions, cerebellum and prefrontal cortex. By comparison, patterns of spatial activity (as opposed to interactivity) were relatively uninformative in decoding implicit fear. These findings indicate that whole-brain patterns of interactivity are a sensitive and informative signature of unattended fearful emotion processing. At the same time, we demonstrate and propose a sensitive and exploratory approach for the identification of large-scale, condition-dependent FC. In contrast to model-based, group approaches, the current approach does not discount the multivariate, joint responses of multiple functional connections and is not hampered by signal loss and the need for multiple comparisons correction. Brain activity is increasingly characterized by patterns of pair-wise correlations (large-scale functional connectivity) across the whole brain obtained from Blood Oxygen Level Dependent (BOLD) functional magnetic resonance imaging (fMRI). Typically this is done during resting states (i.e. no presented stimulus) to differentiate subjects based on individual variation or diagnosis. In the current work, we identify such patterns that are a sensitive signature of unattended processing of threat-related stimuli, allowing one to “brain-read” whether an individual was presented with a neutral or fearful face while they attended to non-expression-related stimulus features. These results further the understanding of the neural mechanisms sub-serving threat-detection and facial affect processing in healthy subjects, and may also help further our understanding of various disorders, such as anxiety and autism, which exhibit anomalies in these processes. At the same time, we propose an exploratory and sensitive approach for the identification of condition-dependent, large-scale functional connectivity. This approach is not based on statistical inference on functional connections averaged across subjects and contrasted between two conditions, but rather based on the informative contribution of each functional connection when attempting to predict between two conditions, using machine-learning based multivariate pattern analysis on training data from separate subjects.
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