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

• Antragsteller: Professor Dr. Jürgen Kurths
• Fachliche Zuordnung: Allgemeine, Kognitive und Mathematische Psychologie
• Förderung: Förderung von 2007 bis 2015
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 34181293

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.

• DFG-Verfahren: Forschungsgruppen
• Teilprojekt zu: FOR 868: Computational Modeling of Behavioral, Cognitive, and Neural Dynamics
• Beteiligte Personen: Professorin Dr.-Ing. Galina Ivanova; Professor Dr. Werner Sommer