EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons—Applications to Connectivity Studies

Abstract

Electrophysiological Source Imaging (ESI) is hampered by lack of “gold standards” for model validation. Concurrent electroencephalography (EEG) and electrocorticography (ECoG) experiments (EECoG) are useful for this purpose, especially primate models due to their flexibility and translational value for human research. Unfortunately, there is only one EECoG experiments in the public domain that we know of: the Multidimensional Recording (MDR) is based on a single monkey ( www.neurotycho.org ). The mining of this type of data is hindered by lack of specialized procedures to deal with: (1) Severe EECoG artifacts due to the experimental produces; (2) Sophisticated forward models that account for surgery induced skull defects and implanted ECoG electrode strips; (3) Reliable statistical procedures to estimate and compare source connectivity (partial correlation). We provide solutions to the processing issues just mentioned with EECoG-Comp: an open source platform ( https://github.com/Vincent-wq/EECoG-Comp ). EECoG lead fields calculated with FEM (Simbio) for MDR data are also provided and were used in other papers of this special issue. As a use case with the MDR, we show: (1) For real MDR data, 4 popular ESI methods (MNE, LCMV, eLORETA and SSBL) showed significant but moderate concordance with a usual standard, the ECoG Laplacian (standard partial ); (2) In both monkey and human simulations, all ESI methods as well as Laplacian had a significant but poor correspondence with the true source connectivity. These preliminary results may stimulate the development of improved ESI connectivity estimators but require the availability of more EECoG data sets to obtain neurobiologically valid inferences.

Time-evolving controllability of effective connectivity networks during seizure progression

“…Emergence of Synchronous Seizure Phases. In a challenge to a static brain controllability perspective, a number of studies find that multiple patterns of corticocortical coupling may appear on the same structural foundation, and that timeevolving EC networks may provide a more accurate view of the connectivity landscape (16,17,23,35,36). Correlative functional connectivity models may inadvertently incorporate common inputs to cortical brain regions, such as neural signals from projections of cholinergic and GABA-ergic ascending fibers, that do not reflect direct corticocortical influences (37).…”

“…For each epileptic event, effective connectivity networks were constructed from one-second time windows of both ictal and preictal data. All timeseries data from ECoG channel recordings were re-referenced to the common average reference to avoid broad field effects (23,28,34). Each channel was digitally filtered with 60Hz notch, 120Hz low-pass, and 1Hz high-pass filters using a 4th-order Butterworth design to remove line-noise, drift, and high frequency noise, respectively (34).…”

“…Unfortunately, the number of ECoG recording channels is smaller than the number of samples in a time window, which means that the inverse covariance matrix cannot be directly computed, and must instead be estimated. The Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) (18,19) method for regularized inverse covariance estimation is widely implemented due to its efficiency and simplicity (20)(21)(22)(23), and is what we implement in our work. The method uses a Gaussian graphical modeling approach to compute a regularized inverse covariance estimate Ω ≈ Σ −1 from a set of variables assumed to have a Gaussian distribution with mean µ and covariance Σ. GLASSO maximizes the penalized log-likelihood log detΩ − tr(ΩΣ) − ρ||Ω||1 [5] over all nonnegative definite matrices Ω from the sample covariance matrix Σ.…”

Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective

“…Among the notable works along this line are to use entropy (Nicolaou and Georgiou, 2012;Arunkumar et al, 2016Arunkumar et al, , 2017 and complexity measures (Gao et al, 2011a(Gao et al, , 2012bMartis et al, 2015;Medvedeva et al, 2016;Pratiher et al, 2016;Sikdar et al, 2018). However, the majority of the works published are based on electrocorticogram (ECoG), which is invasively obtained by means of electrodes applied directly over or inserted into the cerebral cortex (Wang et al, 2019). Clinically, the more widely available form of EEG is the non-invasive surface EEG.…”

Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent

“…Among the notable works along this line are to use entropy (Nicolaou and Georgiou, 2012;Arunkumar et al, 2016Arunkumar et al, , 2017 and complexity measures (Gao et al, 2011(Gao et al, , 2012bMartis et al, 2015;Medvedeva et al, 2016;Pratiher et al, 2016;Sikdar et al, 2018). The majority of the works published are however based on electrocorticogram (ECoG), which is invasively obtained by directly attaching electrodes to the cerebral cortex (Wang et al, 2019). Clinically, the more widely available form of EEG is the non-invasive scalp EEG.…”