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Neurodynamic progression

Identifying the causal relationship between different brain areas responsible for certain tasks has been of great interest as it provides further advancement towards the decoding of the human brain. The understanding of how active brain areas are functionally connected requires tools of analysis that are capable of exploring the flow of underlying information. In this project, we aim to develop routines that are capable of assessing true source level coupling of active brain regions involved in processing information flows in response to repetitive events, such as movement, or responses to auditory or visual stimuli. For this purpose, cross-trial phase statistics (CTPS) are applied to decomposed MEG signals using independent component analysis (ICA). In this way, we are able to identify even weak brain responses from the set of independent components (ICs). After back-transformation of identified ICs, the signal-to-noise ratio of the MEG signals is greatly improved, thus allowing for further single trial analysis. The causal relationship between the extracted regions of interest, which are involved in the network responsible for the certain tasks under study, are then analysed by means of the generalised partial directed coherence (GPDC) a multivariate extension of Granger Causality in the frequency domain. Using a multivariate autoregressive model, the GPDC describes the share of information flow from brain area A to brain area B with respect to all interactions of A to other structures. Thereby, it is able to disentangle direct and indirect causations. GPDC is applied to the time series data of the identified brain regions in order to investigate the neurodynamics of the network during information processing.

Neurodynamic progression

The Figure shows a causal network indicating top-down processing from motor to visual areas during gaze attention cueing.

References

Dammers, J., Fasoula, A., George, N., Schwartz, D., 2014. Enhanced Causality Analysis in Source Space based on Cross trial Phase Statistics, in: 19th International Conference on Biomagnetism, Halifax, Canada. Halifax, p. 1.