A Computational Framework for EEG Causal Oscillatory Connectivity

• Published in: Proceedings of machine learning research Vol. 223; p. 40
• Main Authors: Rawls, Eric, Gilmore, Casey, Kummerfeld, Erich, Lim, Kelvin, Nienow, Tasha
• Format: Journal Article
• Language: English
• Published: United States 01.08.2023
• Subjects: Oscillations; Transcranial Direct Current Stimulation; Traumatic Brain Injury; Causal Discovery; EEG
• ISSN: 2640-3498; 2640-3498

Here we advance a new approach for measuring EEG causal oscillatory connectivity, capitalizing on recent advances in causal discovery analysis for skewed time series data and in spectral parameterization of time-frequency (TF) data. We first parameterize EEG TF data into separate oscillatory and aperiodic components. We then measure causal interactions between separated oscillatory data with the recently proposed causal connectivity method Greedy Adjacencies and Non-Gaussian Orientations (GANGO). We apply GANGO to contemporaneous time series, then we extend the GANGO method to lagged data that control for temporal autocorrelation. We apply this approach to EEG data acquired in the context of a clinical trial investigating noninvasive transcranial direct current stimulation to treat executive dysfunction following mild Traumatic Brain Injury (mTBI). First, we analyze whole-scalp oscillatory connectivity patterns using community detection. Then we demonstrate that tDCS increases the effect size of causal theta-band oscillatory connections between prefrontal sensors and the rest of the scalp, while simultaneously decreasing causal alpha-band oscillatory connections between prefrontal sensors and the rest of the scalp. Improved executive functioning following tDCS could result from increased prefrontal causal theta oscillatory influence, and decreased prefrontal alpha-band causal oscillatory influence.