Reciprocal Organizational Architecture of Human Brain Function

了解不同的大脑区域是如何共同工作的，对于了解健康和疾病中的大脑功能是基本的。分离和整合是大脑功能结构的一般原则。人类大脑功能的多样性是由多个并行的神经过程相互作用产生并实现的，每个神经过程在空间上分布在特定的大脑区域结构底物上。认知神经科学的一个基本问题是如何从功能磁共振成像(FMRI)数据中稳健地、忠实地重建并发功能网络，并定量地测量它们之间的网络级别的相互作用。佐治亚大学的刘天明博士将开发新的计算方法，研究不同大脑区域如何形成不同的网络。使用公开可用的人类连接计划(HCP)功能磁共振数据集，刘博士不仅将开发和评估一种新的组织架构理论来解释人类大脑功能，还将开发一套免费提供的软件工具来分析大脑网络活动。通过这个项目，刘博士将把研究融入教育和推广活动中。该项目产生的新颖教育材料将有助于培养下一代科学家。刘博士计划采用创新的词典学习方法稀疏地表示全脑fMRI信号，其中每个过完备基础词典的时间序列代表大脑网络的功能活动，其对应的参考权重向量代表这个大脑网络的空间地图。该项目的目标是(1)识别和表征大量可重复性和健壮的功能网络，包括任务诱发和休息状态网络，跨越HCP数据；(2)探索这些任务诱发和休息状态脑网络在空间上相互重叠的程度；(3)检验认知大脑功能是通过相互定位的高度异性恋区域和高度专业化区域的混合组合实现的假设。总体而言，该项目将为理解网络内部和网络间的相互作用贡献新的工具和见解，预计将有助于各种认知神经科学和神经工程研究。

Understanding how different brain regions work together is fundamental to the understanding of brain functions in both health and diseases. Segregation and integration is a general principle of the brain's functional architecture. The diverse range of human brain functions emerges from and is realized by the interaction of multiple concurrent neural processes, each of which is spatially distributed across specific structural substrate of brain areas. A fundamental question in cognitive neuroscience is how to robustly and faithfully reconstruct concurrent functional networks from functional magnetic resonance imaging (fMRI) data and quantitatively measure their network-level interactions. Dr. Tianming Liu of University of Georgia will develop novel computational methods to investigate how different brain regions form distinctive networks. Using the publicly available Human Connectome Project (HCP) fMRI datasets, Dr. Liu will not only develop and evaluate a novel theory of organizational architecture to explain human brain function but also produce a set of freely available software tools for analyzing brain network activity. Through this project, Dr. Liu will integrate research into educational and outreach activities. The novel educational materials generated from this project will contribute to cultivate the next generation of scientists. Dr. Liu plans to employ innovative dictionary learning methods to sparsely represent whole-brain fMRI signals, where the time series of each over-complete basis dictionary represents the functional activities of a brain network and its corresponding reference weight vector stands for the spatial map of this brain network. This project aims to (1) identify and characterize a large number of reproducible and robust functional networks, including both task-evoked and resting state networks, across the HCP data; (2) explore to what extent these task-evoked and resting state brain networks overlap spatially with each other; (3) test the hypothesis that cognitive brain functions are realized by hybrid combinations of reciprocally localized highly-heterogamous regions and highly-specialized regions. Collectively, this project will contribute novel tools and insights for understanding intra- and inter-network interactions, which are expected to benefit a variety of cognitive neuroscience and neural engineering studies.