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High gamma oscillations (70-150 Hz; HG) are rapidly evolving, spatially localized neurophysiological signals that are believed to be the best representative signature of engaged neural populations. The HG band has been best characterized from invasive electrophysiological approaches such as electrocorticography (ECoG) because of the increased signal-to-noise ratio that results when by-passing the scalp and skull. Despite the recent observation that HG activity can be detected non-invasively by electroencephalography (EEG), it is unclear to what extent EEG can accurately resolve the spatial distribution of HG signals during active task engagement. We have overcome some of the limitations inherent to acquiring HG signals across the scalp by utilizing individual head anatomy in combination with an inverse modeling method. We applied a linearly constrained minimum variance beamformer (LCMV) method on EEG data during a motor imagery paradigm to extract a time-frequency spectrogram at every voxel location on the cortex. To confirm spatially distributed patterns of HG responses, we contrasted overlapping maps of the EEG HG signal with BOLD fMRI data acquired from the same set of neurologically normal subjects during a separate session. We show that scalp-based HG band activity detected by EEG during motor imagery spatially co-localizes with BOLD fMRI data. Taken together, these results suggest that EEG can accurately resolve spatially specific estimates of local cortical high frequency signals, potentially opening an avenue for non-invasive measurement of HG potentials from diverse sets of neurologically impaired populations for diagnostic and therapeutic purposes

Cortical networks and quantitative measures of connectivity are integral to the study of brain function. Despite lack of direct connections between left and right subthalamic nuclei (STN), there are apparent physiological connections. During clinical examination of patients with Parkinson’s Disease (PD), this connectivity is exploited to enhance signs of PD, yet our understanding of this connectivity is limited. We hypothesized that movement leads to synchronization of neural oscillations in bilateral STN, and we implemented phase coherence, a measure of phase-locking between cortical sites in a narrow frequency band, to demonstrate this synchronization. We analyzed task specific phase synchronization and causality between left and right STN local field potentials (LFP) recorded from both hemispheres simultaneously during a cued movement task in four subjects with PD who underwent Deep Brain Stimulation (DBS) surgery. We used a data driven approach to determine inter-hemispheric channel pairs and frequencies with a task specific increase in phase locking.We found significant phase locking between hemispheres in alpha frequency (8-12 Hz) in all subjects concurrent with movement of either hand. In all subjects, phase synchronization increased over baseline upon or prior to hand movement onset and lasted until the motion ceased. Left and right hand movement showed similar patterns. Granger causality at the phase-locking frequencies between synchronized electrodes revealed a unidirectional causality from right to left STN regardless of which side was moved.Phase synchronization across hemispheres between basal ganglia supports existence of a bilateral network having lateralized regions of specialization for motor processing. Our results suggest this bilateral network is activated by a unilateral motor program. Understanding phase synchronization in natural brain functions is critical to development of future DBS systems that augment goal directed behavioral function.