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 TRGC 1 ) and undirected linear FC (imaginary part of coherency, absolute part of coherency and multivariate interaction measure MIM 2 ) 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.