Unimodal, multimodal and machine-learning techniques to identifying structural, functional and connectivity dynamics underlying empathic accuracy

直觉他人感受状态的能力是人际交往的重要组成部分。这种能力被认为依赖于换位思考（PT）和共情关注（EC），它们可能分别代表了考虑他人观点的能力和动机（Keysers Zaki, 2009; Arbuckle Arbuckle & Shane, 2016）。然而，这项研究仍处于起步阶段。首先，迄今为止，大多数研究都是评估参与者试图理解他人精神状态时的神经活动，只有少数研究评估了成功理解这些精神状态时的神经回路（被称为“共情准确性”（eACC））。其次，迄今为止，大多数工作只报道了单一的成像模式(即。大脑结构、功能或连通性)，无法记录潜在重要的多模态模式。因此，目前对支持准确理解他人感觉状态的跨模态大脑动力学知之甚少。目前的提案描述了我们实验室在这个领域的下一个计划项目，旨在针对这些特定的限制。目的1涉及使用标准的单变量方法来评估结构（t1 - t2序列）、功能（fMRI）和静息状态功能连接数据，以识别PT、EC和eACC背后的复杂模式。目标2涉及使用联合独立成分分析（jICA）将所有三种模式“融合”到单个数据矩阵中，以便充分考虑eACC的跨模式预测因子。最后，Aim 3涉及使用机器学习技术来构建和训练eACC的多模态预测模型，并在两个独立的样本外数据集中测试该模型的可泛化性。这些研究将填补知识上的重要空白，朝着我们实验室描绘他人思想/感受表征的神经机制的长期目标迈进。这些研究的结果将在国际会议上发表，并在高级学术期刊上发表，这将引起研究认知和情感过程的神经基础的学者的极大兴趣。他们还将通过提供大规模多模态神经成像研究的收集、分析、解释和伦理方面的曝光和培训，为本科生和研究生的培训做出贡献。这些学生在心理学、神经科学、复杂的分析和机器学习方法方面接受了不同的培训，他们将成为加拿大下一代的研究人员，从而直接为NSERC的使命做出贡献，即多样化和激励未来NSE研究人员的新社区。

The ability to intuit another's feeling states is a critical component of human interaction. This ability is believed to rely on both perspective-taking (PT) and empathic concern (EC), which together may represent the ability, and motivation, to consider another's point of view, respectively (Keysers Zaki, 2009; Arbuckle Arbuckle & Shane, 2016). However, this research remains nascent. First, most work to date has evaluated neural activity while participants attempt to understand another's mental state, with only a handful of studies evaluating neural circuits during the successful understanding of those mental states (referred to as `empathic accuracy' (eACC)). Second, most work to date has reported only single imaging modalities (ie. brain structure, function or connectivity), that cannot document potentially important multimodal patterns. Thus, little is currently known about cross-modal brain dynamics that support the accurate understanding of another's feeling states. The present proposal describes our lab's next intended projects in this space, aimed at targeting these specific limitations. Aim 1 involves use of standard univariate methods to evaluate structural (T1-T2-sequences), functional (fMRI), and resting state functional connectivity data, to identify complex patterns underlying PT, EC and eACC. Aim 2 involves the use of joint Independent Component Analysis (jICA) to "fuse" all three modalities into a single data matrix, to allow for full consideration of cross-modal predictors of eACC. Finally, Aim 3 involves use of machine-learning techniques to construct and train a multimodal predictive model of eACC, and to test generalizability of that model in two independent, out-of-sample datasets. These studies will fill important gaps in knowledge, towards our lab's long-term goals of delineating the neural mechanisms underlying representation of other's thoughts/feelings. Results from these studies will be presented at international conferences and published in high-tier academic journals, and will be of considerable interest to academics studying the neural underpinnings of cognitive and emotional processes. They will also contribute to student training at both the undergraduate and graduate level, by providing exposure and training in collection, analysis, interpretation and ethics of a large-scale multimodal neuroimaging study. These students, diversely trained in psychology, neuroscience, and sophisticated analytical and machine learning methods, will serve as the next generation of Canadian researchers, thereby contributing directly to NSERC's stated mission of diversifying and energizing a new community of future NSE researchers.