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

• Erstellungsjahr: 2016

Zusammenfassung der Projektergebnisse

The investigation of the human brain is one of the most demanding and rapidly developing fields. New methods and acquisition technologies open new possibilities for investigation. Various technologies for exploring the neural structure and its function are available, such as MRI, diffusion weighted MRI, functional MRI, EEG, and MEG to name a few. The data obtained can be analyzed separately or in combination using so-called fusion procedures. At the same time the complexity of these methods and technologies is constantly growing. In the present project we obtained progress in applying sophisticated novel methods to brain data. Firstly, we applied dynamic network modelling to high resolution EEG data obtained from language and face processing experiments. These efforts not only yielded new methods but also novel findings about dynamic brain network activity in specific conditions of the cognitive tasks employed. These new approaches were also applied to a set of data from an aging study from which we obtained evidence of specific alterations in cognitively declined elderly relative to young and healthy elderly participants. Toolboxes for these methods were developed and made available. Secondly, we addressed the special challenge of the fusion of white matter networks with functional networks. Within the work performed, we explored a methodology and developed a proof of concept for the fusion of white matter tracts and EEG signals in order to provide means for a more detailed investigation of human cognition. We are confident that the combination of the different modalities of acquisition could decisively contribute to deepen the understanding of the brain phenomena. First steps were taken within the A3 project but still more work is required to develop and validate our methods in order to provide readily accessible tools for basic and applied research on human cognition. Understanding the intricate interactions of structural networks on the one hand and functional networks on the other hand would be a groundbreaking success and is the overarching aim of (cognitive) neuroscience. We are confident that our work helped reaching this ambitious objective.

Projektbezogene Publikationen (Auswahl)

• (2011). Functional network analysis reveals differences in the semantic priming task. Journal of Neuroscience Methods, 197(2):333–9
  Schinkel, S., Zamora-López, G., Dimigen, O., Sommer, W., and Kurths, J.
  
• (2012). Order patterns networks (ORPAN) – a method to estimate time-evolving functional connectivity from multivariate time series. Frontiers in Computational Neuroscience, 6
  Schinkel, S., Zamora-López, G., Dimigen, O., Sommer, W., and Kurths, J.
  
• (2012). Order patterns networks (ORPAN)–concept and applications. Neural Coding 2012, page 117
  Schinkel, S., Zamora-López, G., Dimigen, O., Sommer, W., and Kurths, J.
  
• (2013). Age-related task sensitivity of frontal eeg entropy during encoding predicts retrieval. Brain Topography, 26(4), 547-557
  O’Hora, D., Schinkel, S., Hogan, M. J., Kilmartin, L., Keane, M., Lai, R., and Upton, N.
  
• (2013). High-level feature extraction from electrophysiological brain signals in the time-frequency domain. Biomed Tech, 58:1
  Heideklang, R. and Ivanova, G.
  
• (2013). Impact of filtering on region of interest estimation from diffusion weighted brain images. Biomedical Engineering/Biomedizinische Technik
  Perkunder, H. and Ivanova, G.
  
• (2013). Multiple circular-circular correlation coefficients for the quantification of phase synchronization processes in the brain. Biomedizinische Technik/Biomedical Engineering, 58(2):141–155
  Pauen, K. and Ivanova, G.
  
• (2013). Recurrence plots 25 years later – gaining confidence in dynamical transitions. EPL (Europhysics Letters), 101(2):20007
  Marwan, N., Schinkel, S., and Kurths, J.
  
• (2014). Modulation of the n170 adaptation profile by higher level factors. Biological psychology, 97:27–34
  Schinkel, S., Ivanova, G., Kurths, J., and Sommer, W.
  
• (2015). Visual analytics for correlationbased comparison of time series ensembles. In Computer Graphics Forum, volume 34, 411–420. Wiley Online Library
  Köthur, P., Witt, C., Sips, M., Marwan, N., Schinkel, S., and Dransch, D.