THEMATIC AREA Z

3rd FUNDING PERIOD
THEMATIC AREA Z

Thematic area Z focuses both on the management and administration (Z1), the graduate school of the SFB 936 (Z2)as well as the collaboration among projects from the thematic areas A, B, and C (Z3).
Project Z1: Central tasks of the Collaborative Research Centre
Prof. Dr. Andreas Engel
Dept. of Neurophysiology and Pathophysioloy, UKE
Prof. Dr. Christian Gerloff
Dept. of Neurology, UKE

Project Z2: Integrated research training group
Prof. Dr. Andreas Engel
Dept. of Neurophysiology and Pathophysioloy, UKE
Prof. Dr. Christian Gerloff
Dept. of Neurology, UKE

This project is responsible for the coordination of the Integrated Research Training Group, which has successfully been implemented in the first funding period. The Training Group currently involves 18 PhD students, 1 MD/PhD students and 20 medical doctoral students. The qualification program features lectures, seminars and courses on scientific topics of the SFB, workshops and seminars for training of additional skills and an annual retreat. Visiting scientists contribute to the lectures, seminars and journal clubs offered for the doctoral students. The complete course program has been formally approved by the deanery of the UKE for the awarding of the credits. The Training Group has been integrated with other structured graduate training groups, both within the Medical Faculty and across faculties.
Project Z3: Analysis and modeling of multi-site interactions in the brain
Prof. Dr. Tobias Donner
Dept. of Neurophysiology and Pathophysiology, UKE
Prof. Dr. Claus Hilgetag
Institute of Computational Neuroscience, UKE
Dr. Guido Nolte
Dept. of Neurophysiology and Pathophysiology, UKE

This project provides centralized theoretical expertise in data evaluation, complex network analysis as well as modeling of brain dynamics that supports and enhances the specific work in individual research projects. Thus, this central project will support the processing of behavioral data, brain activity data, and connectivity metrics from diverse empirical approaches using state-of-the-art forward and inverse methods for EEG and MEG data as well as advanced measures of structural, functional and effective connectivity. Moreover, the project will characterize the network organization of neuronal connectivity data, and will use these data as the basis for the computational modeling of connectivity-based brain dynamics and behavior in the healthy and diseased brain.
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