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Quantifying the effect of demixing approaches on directed connectivity estimated between reconstructed EEG sources

(2019) BRAIN TOPOGRAPHY. 32(4). p.655-674
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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.
Keywords
Brain Connectivity, Source Reconstruction, Granger Causality, Modelling, FUNCTIONAL CONNECTIVITY, CORTICAL CONNECTIVITY, SOURCE LOCALIZATION, VOLUME-CONDUCTION, BRAIN, RESOLUTION, MEG

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Citation

Please use this url to cite or link to this publication:

MLA
Anzolin, Alessandra et al. “Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources.” BRAIN TOPOGRAPHY 32.4 (2019): 655–674. Print.
APA
Anzolin, A., Presti, P., Van de Steen, F., Astolfi, L., Haufe, S., & Marinazzo, D. (2019). Quantifying the effect of demixing approaches on directed connectivity estimated between reconstructed EEG sources. BRAIN TOPOGRAPHY, 32(4), 655–674.
Chicago author-date
Anzolin, Alessandra, Paolo Presti, Frederik Van de Steen, Laura Astolfi, Stefan Haufe, and Daniele Marinazzo. 2019. “Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources.” Brain Topography 32 (4): 655–674.
Chicago author-date (all authors)
Anzolin, Alessandra, Paolo Presti, Frederik Van de Steen, Laura Astolfi, Stefan Haufe, and Daniele Marinazzo. 2019. “Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources.” Brain Topography 32 (4): 655–674.
Vancouver
1.
Anzolin A, Presti P, Van de Steen F, Astolfi L, Haufe S, Marinazzo D. Quantifying the effect of demixing approaches on directed connectivity estimated between reconstructed EEG sources. BRAIN TOPOGRAPHY. Springer; 2019;32(4):655–74.
IEEE
[1]
A. Anzolin, P. Presti, F. Van de Steen, L. Astolfi, S. Haufe, and D. Marinazzo, “Quantifying the effect of demixing approaches on directed connectivity estimated between reconstructed EEG sources,” BRAIN TOPOGRAPHY, vol. 32, no. 4, pp. 655–674, 2019.
@article{8612360,
  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.},
  author       = {Anzolin, Alessandra and Presti, Paolo and Van de Steen, Frederik and Astolfi, Laura and Haufe, Stefan and Marinazzo, Daniele},
  issn         = {0896-0267},
  journal      = {BRAIN TOPOGRAPHY},
  keywords     = {Brain Connectivity,Source Reconstruction,Granger Causality,Modelling,FUNCTIONAL CONNECTIVITY,CORTICAL CONNECTIVITY,SOURCE LOCALIZATION,VOLUME-CONDUCTION,BRAIN,RESOLUTION,MEG},
  language     = {eng},
  number       = {4},
  pages        = {655--674},
  publisher    = {Springer},
  title        = {Quantifying the effect of demixing approaches on directed connectivity estimated between reconstructed EEG sources},
  url          = {http://dx.doi.org/10.1007/s10548-019-00705-z},
  volume       = {32},
  year         = {2019},
}

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