EP 115. Effective connectivity of subcortical–cortical networks revealed by simultaneous scalp and depth EEG recordings in humans

Functional magnetic resonance imaging (fMRI) studies showed that the brain at rest exhibits spontaneous BOLD (Blood Oxygenation Level Dependent) fluctuations over time that correlate between functionally connected brain areas ( Biswal et al., 1995 ). However, the lower temporal resolution of fMRI precludes the study of neuronal coding and temporal information flow with an appropriate level of temporal acuity in these networks. EEG is better suited for the study of the temporal dynamics in such networks because of its millisecond resolution; however, due to its limited spatial resolution, contributions from subcortical structures are likely not well represented. Not surprisingly though, results from fMRI resting state studies clearly indicate that subcortical structures are important key players in these networks. In order to gain better insights into the interactions of cortical–subcortical circuits at rest, we performed recordings from multiple scalp electrodes simultaneously with chronically implanted deep brain stimulation electrodes in subcortical targets of human subjects. Computed partial directed coherence based on Granger causality ( Plomp et al., 2014 ) was performed to identify the major drivers of the network and the sites that were subject to them. Here we report on obsessive–compulsive disorder patients who were bilaterally implanted in the nucleus accumbens as well as Tourette syndrome patients with bilateral thalamic implants. In all patients, local circumscribed networks surrounding specific subcortical electrodes were identified as major drivers of the larger pathological network. Recordings which were performed with 256-channel scalp EEG electrodes were subject to source localization methods and connectivity analysis was performed in the source space ( Coito et al., 2015 ). Our cortical–subcortical simultaneous recordings provide the platform for a deeper understanding of the temporal dynamics of whole-brain networks in humans, which includes the intimate contribution of deep structures and opens new ways of identifying neurophysiological markers of these psychiatric diseases.