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Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources

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Abstract

Electrical activity recorded on the scalp using electroencephalography (EEG) results from the mixing of signals originating from different regions of the brain as well as from artifactual sources. In order to investigate the role of distinct brain areas in a given experiment, the signal recorded on the sensors is typically projected back into the brain (source reconstruction) using algorithms that address the so-called EEG “inverse problem”. Once the activity of sources located inside of the brain has been reconstructed, it is often desirable to study the statistical dependencies among them, in particular to quantify directional dynamical interactions between brain areas. Unfortunately, even when performing source reconstruction, the superposition of signals that is due to the propagation of activity from sources to sensors cannot be completely undone, resulting in potentially biased estimates of directional functional connectivity. Here we perform a set of simulations involving interacting sources to quantify source connectivity estimation performance as a function of the location of the sources, their distance to each other, the noise level, the source reconstruction algorithm, and the connectivity estimator. The generated source activity was projected onto the scalp and projected back to the cortical level using two source reconstruction algorithms, linearly constrained minimum variance beamforming and ‘Exact’ low-resolution tomography (eLORETA). In source space, directed connectivity was estimated using multi-variate Granger causality and time-reversed Granger causality, and compared with the imposed ground truth. Our results demonstrate that all considered factors significantly affect the connectivity estimation performance.

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Data Availability

The code necessary to reproduce these simulations is available at: https://github.com/paolop21/simulation_source_connectivity. Simulations were run in parallel on the HPC platform of the University of Ghent, with a running time depending on the available nodes on the network. An iteration of the simulation on a Dell Inspiron 5567, intel i7, 16 GB RAM takes 2,4 s. The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Super-computer Center), funded by Ghent University, FWO and the Flemish Government—department EWI. The results of the simulations and the structures necessary to run the code are available at: https://zenodo.org/record/1157196#.WmZ1K6jiY2w.

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Author notes

  1. Alessandra Anzolin and Paolo Presti have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy

    Alessandra Anzolin, Paolo Presti & Laura Astolfi

  2. Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy

    Alessandra Anzolin & Laura Astolfi

  3. Department of Data Analysis, Faculty of Psychology and Educational Sciences, University of Ghent, 2 Henri Dunantlaan, 9000, Ghent, Belgium

    Alessandra Anzolin, Paolo Presti, Frederik Van De Steen & Daniele Marinazzo

  4. Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany

    Stefan Haufe

  5. MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China

    Daniele Marinazzo

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  1. Alessandra Anzolin
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Correspondence to Daniele Marinazzo.

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This is one of several papers published together in Brain Topography on the “Special Issue: Controversies in EEG Source Analysis”.

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Anzolin, A., Presti, P., Van De Steen, F. et al. Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources. Brain Topogr 32, 655–674 (2019). https://doi.org/10.1007/s10548-019-00705-z

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