Spatial Temporal Analysis of Multi-Subject Neuroimaging Data for Human Emotion Studies

这个跨学科的研究项目将开发新的统计方法来分析从人类情感心理学研究中收集的多学科刺激诱发功能磁共振成像（fMRI）数据。该项目将加深人们对人类大脑中与情绪相关的回路如何在社会支持与外部产生的情绪压力相结合的情况下发挥作用的理解。最终，该项目将有助于了解大脑如何通过情绪的社会调节来使用社会支持。这些知识将有助于未来在这一领域的研究。这项研究的结果将有助于临床研究人员对影响儿童和成人的许多神经发育和情感障碍的神经病理学感兴趣。该项目将为本科生和研究生（特别是那些来自代表性不足群体的学生）提供参与涉及人脑数据的高级统计和多学科研究的机会。项目成果，包括科学发现和开发的软件，将通过公共存储库向公众提供。开发的统计模型和计算方法将解决分析fMRI数据的典型挑战，包括大量数据大小，复杂的空间和时间属性以及弱信噪比。新的低秩多元一般线性模型用于多受试者、刺激诱发的fMRI数据，其特征是大脑活动在不同区域、受试者和刺激类型之间共有的共同特性，与非参数方法相比，它们需要更少的参数来表征大脑活动的变化。因此，fMRI数据分析的新方法具有同时减少模型参数，提高估计效率和足够的模型灵活性的特点。该项目将开发新的非凸优化算法，以解决分析fMRI数据的计算挑战。本研究将运用上述方法对人类情绪进行功能磁共振成像研究，探讨不同社会接触条件下大脑对消极情绪刺激的反应差异，并确定不同社会接触条件下情绪相关脑功能与伴随情感感受的关系。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This interdisciplinary research project will develop new statistical methods to analyze multi-subject, stimulus-evoked functional magnetic resonance imaging (fMRI) data collected from a psychology study of human emotion. The project will increase understanding of how human brain circuits associated with emotions function in a context that combines social support with externally generated emotional stress. Ultimately, the project will contribute knowledge of how the brain uses social support via the social regulation of emotion. This knowledge will facilitate future research in this area. The results of this research will assist clinical researchers interested in the neuropathology of many neurodevelopmental and affective disorders affecting children and adults. The project will provide the opportunity for undergraduate and graduate students (especially those from underrepresented groups) to participate in advanced statistical and multidisciplinary research involving human brain data. Project results, including scientific findings and developed software, will be made publicly available using public repositories.The statistical models and computational methods to be developed will address typical challenges in analyzing fMRI data, including massive data size, complex spatial and temporal properties, and a weak signal-to-noise ratio. The new low-rank multivariate general linear models for multi-subject, stimulus-evoked fMRI data feature the brain activity's common properties shared across different regions, subjects, and stimulus types, and they require fewer parameters than nonparametric methods to characterize variation in brain activity. As such, the new approaches to fMRI data analysis are characterized by simultaneously reduced model parameters, increased estimation efficiency, and sufficient model flexibility. This project will develop new nonconvex optimization algorithms to address the computational challenges in analyzing fMRI data. Applying the developed methods to a fMRI study of human emotion, the investigators will examine the difference in brain responses to negative emotional stimuli under different social contact conditions and identify the association between emotion-related brain functions and concomitant affective feelings under different social contact conditions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.