Multiscale Oscillatory Dynamics in Cortical Function

项目摘要拟议的项目旨在开发大脑皮层振荡的模型，因为它们被认为是以精确的方式编码所有不同的大脑状态动态。如今，我们能够在多个尺度上获得神经活动的测量，从具有详细化学相互作用的分子水平开始，到具有突触电流和放电动态的局部记录的细胞水平，到具有神经元组装记录的网络，到具有来自多个皮层区域的大脑激活记录的系统。1）它与特定脑状态下的生理假设和实验记录相一致; 2）它具有层次性;以多个分辨率描述大脑活动，以及将大脑状态转换描述为信息-状态细化，以及3）这个模型将进一步加深我们对大脑皮层动力学中几种明显现象的理解：1）它将解释大脑在同步方面经历的招募过程，2）它将解释同步过程的鲁棒性，3）它将解释大脑状态转换动力学的平滑性，4）它将解释同步细胞数量与同步频率之间的关系，5）它将解释基于内部刺激和自下而上的感觉信息处理的自上而下的动态进化的动力学和整合，（5）解释执行的时序特性和效率，并将推导出的详细皮层模型与我们现有的运动控制系统的系统级模型结合起来。特别是，我们的目标是研究振荡在组成皮层子系统之间的信息传递中的作用，并随后在头皮EEG记录中识别运动规划的独特特征。一方面，如果将它们用作预测因子，它们可以指向旨在使模型无效的重要实验活动，这将导致我们对大脑过程的理解取得实质性进展。另一方面，这些模型作为诊断工具至关重要，例如，此类模型可用于检测癫痫发作和脑肿瘤、校准麻醉期间的药物作用、研究认知任务特征以及研究精神发育迟滞。最后，他们可以提供适当的动态描述，一个适当的设计neural prosthesit.Intellectual优点：拟议的研究提供了一个桥梁之间的神经生理学，neu-roanatomy，和工程通过发展一个一致的计算模型的皮层活动。该研究由一个多学科小组进行，该小组在一般系统的建模，分析和设计方面具有专业知识，特别是神经系统的建模和分析，并通过NSF的支持进行了合作。建议的研究将通过我们与马萨诸塞州综合医院的工作人员正在进行的运动系统控制合作来增加实验部分。广泛的影响：除了上面提到的效用之外，这项研究正在系统地缩小生物学家，统计学家，计算机科学家和系统理论家对神经系统的理解的碎片化研究之间的差距。有了这种统一，计算生物学的发展将有助于理解并有可能治愈许多已知的神经疾病。这项研究的进展消除了诸如科学和工程等严重分离的领域之间的界限。这种多学科研究需要不同专业领域的不同群体之间的全面互动。这项研究将导致博士论文涵盖这两个领域所证明的我们的KDI项目。此外，跨学科的课程可以结构化，以提供更好的本科层次的多学科培训。

Project SummaryThe proposed project aims at developing a model of cortical oscillation in the brain as they arebelieved to encode in a precise way all the different brain-state dynamics. Nowadays, we are ableto obtain measurements of neural activities at multiple scales starting at the molecular level withdetailed chemical interactions, to cellular levels with localized recordings of synaptic currents andfiring dynamic, to networks with recordings from neuronal assemblies, to systems with recordingsof brain activation from multiple cortical areas.The model will have the following attributes: 1) it is consistent with physiological hypothesesand experimental recordings under specific brain states, 2) it is hierarchical, describing the brainactivity at multiple resolutions as well as describing brain state transitions as information-staterefinements and 3) it is amenable to efficient simulations.This model will further our understanding of several phenomena evident in the cortical dy-namics of the brain: 1) it will explain the recruitment process the brain undergoes in terms ofsynchronization, 2) it will explain the robustness of the synchronization process, 3) it will explainthe smoothness in the dynamics of brain-state transitions, 4) it will explain the relationship be-tween the number of synchronized cells and the frequency of synchronization, 5) it will explainthe dynamics and integration of the top-down dynamic evolution based on internal stimuli andthe bottom-up processing of sensory information, and 5) it will explain the timing properties andefficiency in execution.The derived detailed cortical model will then be integrated with our existing system-levelmodel of the motor control system. In particular, we aim to study the role of oscillations ininformation transfer across constituent cortical subsystems, and to subsequently identify uniquesignatures of motion planning in scalp EEG recordings.Models of cortical oscillations will have utility in various ways. On one hand, if used aspredictors, they can point to important experimental activities designed to invalidate the models,which will result in a substantial progress of our understanding of the brain processes. On theother hand, these models are critical as a diagnostic tool, for example, such models can be usedto detect seizures and brain tumors, to calibrate drug action during anesthesia, to study cognitivetask signatures, and to study mental retardation. Finally, they can provide the appropriatedynamic description for a proper design of neural prosthesis.Intellectual Merit: The proposed research provides a bridge between neurophysiology, neu-roanatomy, and engineering through the development of a consistent computational model ofcortical activities. The research is conducted by a multi-disciplinary group with expertise inboth modeling, analysis and design of systems in general, as well as modeling and analysis ofthe nervous system in particular, with a track record of collaboration through NSF support. Theproposed research will be augmented with an experimental component through our ongoing motorsystem control collaboration with workers in Massachusetts General Hospital.Broad Impact: Beyond the utility mentioned above, this research is systematically closing thegap between the fragmented research on understanding the nervous system conducted by biolo-gists, statisticians, computer scientists and system theorists. With this unification, computationalbiology developments will aid in understanding and potentially curing much of the neural diseasesknown.Progress in this research removes the boundaries between the strongly segregated fields such assciences and engineering. Such multi-disciplinary research requires complete interactions betweendifferent groups with different domains of expertise. This research will result in PhD theses cov-ering both such fields as demonstrated by our KDI project. In addition, courses across disciplinescan be structured to provide better multi-disciplinary training at the undergraduate level.