CRCNS: Collaborative Research: Mapping and Control of Large-Scale Neural Dynamics

脑刺激目前被用于治疗各种认知、神经和精神疾病，包括抑郁症、帕金森氏症和精神分裂症。尽管有这种广泛的应用，但大脑刺激的工作原理在很大程度上仍然是一个谜。该项目旨在通过研究和建模神经过程如何随着大脑刺激而变化来解决这一理解上的差距。研究人员的目标是制定评估和优化基于刺激的神经和精神疾病治疗的标准。随着科学的进步，研究人员将参与教育工作，将研究带到课堂上，并为学生增加跨机构的机会。研究人员将特别强调指导和鼓励妇女和少数民族走上科学和工程的学术道路。此外，研究人员正在将他们的努力结合到斯科卡尼奇网络可视化实习项目中，该项目每年夏天都会接待艺术本科生。研究人员提出了一套新的数学技术来描述和预测神经过程，以及它们如何随着大脑刺激而变化。加州大学河滨分校（美国种族最多样化的研究密集型机构之一）和宾夕法尼亚大学的研究人员将在理论、计算和实验研究方面进行合作，主要有三个研究项目：(1)复杂神经动力学的静态和动态建模，识别人脑中感兴趣的多分辨率区域，它们的相互联系的性质，以及它们在观察到的认知动力学中的功能；(2)分析神经系统的全网络动态特性，基于可控性指标表征大脑区域，设计用于复杂神经过程调制的非破坏性控制算法；(3)在各种各样的经验设置中验证模型和控制策略，以设计和预测神经和精神疾病的临床干预结果。该项目的成功将加深对复杂神经生物学系统的理解，构建人类大脑及其动态过程的新图谱，并开发治疗性脑刺激方案的非破坏性控制技术。

Brain stimulation is currently being used to treat a variety of cognitive, neurological, and psychiatric disorders, including depression, Parkinson's disease, and schizophrenia. Despite this broad use, how brain stimulation works remains largely a mystery. This project aims to address this gap in understanding by studying and modeling how neural processes change with brain stimulation. The investigators aim to develop criteria by which to evaluate and optimize stimulation-based treatments of neurological and psychiatric disorders. To accompany the scientific advances, the investigators will engage in educational efforts to bring the research to the classroom and to enhance cross-institutional opportunities for students. The investigators will place special emphasis on mentoring and encouraging women and minorities on the academic path in science and engineering. In addition, the investigators are combining their efforts in the Skirkanich Internship in Network Visualization, which hosts undergraduate art students each summer. The investigators propose a new set of mathematical techniques to describe and predict neural processes and how they change with brain stimulation. Investigators at the University of California at Riverside (one of the most ethnically diverse research-intensive institutions in the U.S.) and the University of Pennsylvania will collaborate on theoretical, computational, and experimental research with three main research initiatives: (1) static and dynamic modeling of complex neural dynamics, identification of multi-resolution regions of interest in the human brain, the nature of their interconnections, and their function in observed cognitive dynamics; (2) analysis of network-wide dynamic properties of neural systems, characterization of brain regions based on controllability metrics, and design of non-disruptive control algorithms for the modulation of complex neural processes; (3) validation of models and control strategies in a wide variety of empirical settings to engineer and predict the outcomes of clinical interventions in neurological and psychiatric disorders. The success of this project will enable a deepened understanding of complex neurobiological systems, construct novel maps of the human brain and its dynamic processes, and develop non-disruptive control techniques for therapeutic brain stimulation protocols.