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

点击翻译按钮获取中文摘要

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.