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

直觉判断他人的感觉状态的能力是人类互动的关键组成部分。这种能力被认为依赖于观点采择(PT)和移情关注(EC)，这两个因素一起可能分别代表了考虑他人观点的能力和动机(Keysers Zaki，2009；Aruckle Aruckle EMPERATIC Accuracy)。其次，到目前为止，大多数工作只报告了单一的成像方式(即。大脑结构、功能或连通性)，无法记录潜在重要的多模式模式。因此，目前对支持准确理解他人感觉状态的跨模式大脑动力学知之甚少。 本提案描述了我们实验室在这一领域的下一个预期项目，旨在针对这些特定的限制。目的1使用标准单变量方法评估结构(T1-T2序列)、功能(FMRI)和静息状态功能连接数据，以确定PT、EC和EACC的复杂模式。目标2涉及使用联合独立分量分析(JICA)将所有三种模式“融合”成单一数据矩阵，以便充分考虑EACC的跨模式预报器。最后，目标3涉及使用机器学习技术来构建和训练EACC的多模式预测模型，并在两个独立的样本外数据集中测试该模型的泛化能力。这些研究将填补知识的重要空白，朝着我们实验室的长期目标--描绘表达他人想法/感觉的潜在神经机制--迈进。 这些研究的结果将在国际会议上发表，并发表在高级学术期刊上，研究认知和情绪过程的神经基础的学者将会非常感兴趣。他们还将通过提供大规模多模式神经成像研究的收集、分析、解释和道德方面的接触和培训，为本科生和研究生一级的学生培训做出贡献。这些学生在心理学、神经科学以及复杂的分析和机器学习方法方面接受了不同的培训，他们将作为下一代加拿大研究人员，从而直接为NSERC宣布的多样化和激励未来NSE研究人员的新社区做出贡献。

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 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.