Dynamical complex network approaches for the analysis and modeling of large scale brain activities during cognitive processes

我们建议研究认知过程的神经网络基础。这将通过使用非线性动态复杂网络原理分析和建模大规模事件相关脑反应（ERP），并通过融合电生理和结构神经成像数据来完成。在动态复杂网络分析和建模[非线性动力学组Humboldt-Universität zu Beriin]，生物心理学[心理学系Humboldt-Universität zu Beriin]和神经科学信号和信息处理[计算机科学系Humboldt-Universität zu Berlin]的专业知识之间的密切合作中，我们将追求以下目标：(i)从心理学实验中获得高分辨率的ERP数据，这些实验具有与阅读和（作为基准）面部处理相关的定义明确的认知过程。（ii）提取传感器和源空间的功能和有效（因果）连接，以监测认知过程中大脑活动的连续认知子过程。这些大规模大脑网络的分析将通过基于分层复杂网络概念的先进工具来执行，以表征与不同实验条件相关的时间进化连接。（iii）发展生物物理复杂动态网络模型，可以解释类似erp的活动，代表参与所研究过程的大脑区域的激活。这种模型的耦合参数将使我们能够识别处理流中的因果连通性。为了改进分析和建模，我们将：(a)获得参与者的结构MRT，用于准确的脑活动源定位；(b)使用DTI和/或HARDI或DSI等扩散成像和足够的神经束成像方法，将结构连接信息整合到网络建模中，估计参与者的大脑结构网络。

We propose to study neural network foundations of cognifive processes. This wilt be done by analyzing and modeling large-scale event-related brain responses (ERP) using the principles of nonlinear dynamical complex networks, and by fusioning of electrophysiological and structural neuroimaging data. In a close collaboration between the expertise from dynamical complex network analysis and modeling [Group of Nonlinear Dynamics Humboldt-Universität zu Beriin], biological psychology [Department of Psychology, Humboldt-Universität zu Beriin], and signal- and informafion processing in the neurosciences [Department of Computer Science,Humboldt-Universität zu Berlin], we will pursue the following goals:(i) Obtain high-resolution ERP data from psychological experiments with well-defined cognitive processes that are relevant for reading and (as benchmark) face processing.(ii) Extract functional and effective (causal) connectivity in sensor and source spaces to monitor the successive cognifive subprocesses of the brain activity during cognifion.The analysis of these large-scale brain networks will be performed by advanced toolsbased on the concept of hierarchical complex networks to characterise the fime evolving connecfivity associated with different experimental condifions.(iii) Develop biophysical complex dynamical network models that can account for ERP-like activity, representing the acfivation of brain regions involved in the processes understudy. The coupling parameters of such a model will allow us to identify the causal connectivity in the processing streamIn order to improve the analysis and the modeling, we will: (a) obtain structural MRT of the participants to be used in accurate source localisation of the brain activity, and (b) esfimate structural networks of the brains of the participants, using diffusion imaging such us DTI and/or HARDI or DSI and sufficient tractography methods to integrate the structural connectivity information into the network modeling.