Abstract
Objective. Stereo-electroencephalography (SEEG) has recently gained importance in analyzing brain functions. Its high temporal resolution and spatial specificity make it a powerful tool to investigate the strength, direction, and spectral content of brain networks interactions, especially when these connections are stimulus-evoked. However, choosing the best approach to evaluate the flow of information is not trivial, due to the lack of validated methods explicitly designed for SEEG. Approach. We propose a novel non-parametric statistical test for event-related causality (ERC) assessment on SEEG recordings. Here, we refer to the ERC as the causality evoked by a particular part of the stimulus (a response window (RW)). We also present a data surrogation method to evaluate the performance of a causality estimation algorithm. We finally validated our pipeline using surrogate SEEG data derived from an experimentally collected dataset, and compared the most used and successful measures to estimate effective connectivity, belonging to the Geweke–Granger causality framework. Main results. Here we show that our workflow correctly identified all the directed connections in the RW of the surrogate data and prove the robustness of the procedure against synthetic noise with amplitude exceeding physiological-plausible values. Among the causality measures tested, partial directed coherence performed best. Significance. This is the first non-parametric statistical test for ERC estimation explicitly designed for SEEG datasets. The pipeline, in principle, can also be applied to the analysis of any type of time-varying estimator, if there exists a clearly defined RW.
Highlights
Stereoelectroencephalography (SEEG) is traditionally used by clinicians to locate epileptic foci in patients with drug-resistant epilepsy [1, 2] and has recently gained importance to investigate the neuronal structures involved in important brain functions [3,4,5], or as driving signal for brain-computer interfaces [6]
SEEG recordings allow us to gather data by combining electroencephalography (EEG) or magnetoencephalography (MEG) temporal resolutions of milliseconds with unmatched spatial specificity [7] and without the common limitations associated with non-invasive recordings [4]
The number of spurious connections detected by the RW Mean was almost null, with just 8 for the wDTF in one subject and 2 for the short-time direct DTF (SdDTF) in another
Summary
Stereoelectroencephalography (SEEG) is traditionally used by clinicians to locate epileptic foci in patients with drug-resistant epilepsy [1, 2] and has recently gained importance to investigate the neuronal structures involved in important brain functions [3,4,5], or as driving signal for brain-computer interfaces [6]. SEEG recordings allow us to gather data by combining electroencephalography (EEG) or magnetoencephalography (MEG) temporal resolutions of milliseconds with unmatched spatial specificity [7] and without the common limitations associated with non-invasive recordings (e.g. artifacts, inverse problem, source leakage, etc) [4]. Recent works using SEEG recordings have shown that cognitive tasks cause event-related spectral perturbations of brain activity at frequencies up to 300 Hz [3, 8,9,10,11,12]. High gamma activity arising from a portion of the network engaged by a task may causally induce high gamma activity in another brain region [13]. The combination of high spatial and temporal resolution, together with the possibility to record Fast oscillatory neuronal activity can play an important role in organizing neurons in large-scale networks [14,15,16].
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