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  How to avoid measurement of spurious inter-regional functional connectivity from EEG: A simulation study

Pellegrini, F., Nikulin, V. V., & Haufe, S. (2022). How to avoid measurement of spurious inter-regional functional connectivity from EEG: A simulation study. Clinical Neurophysiology, 137, e60-e61.

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Pellegrini, F.1, Author
Nikulin, Vadim V.2, Author              
Haufe, S.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

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 Abstract: Introduction: Aggregating statistical dependencies between multivariate time series is important to characterise functional connectivity (FC) between brain regions. However, it is still unclear how to reliably detect true FC from source-reconstructed M/EEG data. This study generates ground truth data and compares the performances of various pipelines that estimate directed FC (Time-Reversed Granger Causality TRGC1) and undirected linear FC (imaginary part of coherency, absolute part of coherency and multivariate interaction measure MIM2) between regions. Material and Methods: To simulate EEG-like sensor signals, we proceeded as follows: 1. Ground truth source activity was generated as white noise time series filtered in the alpha band. Between one and five pairs of sources interacted with certain time delays. 2. After adding individual 1/f noise, the source signals were projected to sensor space. 3. White sensor noise was added. Afterwards, sensor data were projected to source level by applying one of four tested inverse solutions: Dynamic imaging of coherent sources (DICS), Linearly Constrained Minimum Variance source projection (LCMV), eLORETA, and Champagne. All tested connectivity analysis pipelines calculate one connectivity score for every region combination. The pipelines‘ ability to detect the true interactions was evaluated by percentile rank. Results: The best-performing FC pipeline consists of the following steps: 1. Source projection with a beamformer inverse solution. 2. Principal component analysis within every region. 3. Selection of a fixed number of strongest principal components as basis for further analysis. 4. Calculation of the MIM for every region pair in case of undirected FC and calculation of the TRGC in case of directed FC. Worst performance was obtained with pipelines estimating undirected FC with the absolute value of coherency. DICS source projection resulted in good detection of undirected FC, but failure to detect the direction of FC with TRGC. Discussion: In this study, we tested several connectivity analysis pipelines that are used in the literature. Our simulation clearly shows that many of them do not detect true interactions reliably. To use the winning pipeline of this study could greatly increase the validity of future experimental connectivity studies.

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Language(s): eng - English
 Dates: 2022-05
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1016/j.clinph.2022.01.111
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Title: Clinical Neurophysiology
  Other : Clin. Neurophysiol.
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 137 Sequence Number: - Start / End Page: e60 - e61 Identifier: ISSN: 1388-2457
CoNE: https://pure.mpg.de/cone/journals/resource/954926941726