Abstract
The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. In this paper, we present a novel adaptive spatial filtering algorithm optimized for robust source time–frequency reconstruction from magnetoencephalography (MEG) and electroencephalography (EEG) data. The efficacy of the method is demonstrated with simulated sources and is also applied to real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12–30 Hz) and high gamma band (65–90 Hz) in sensorimotor cortex. The performance is validated by both across-subjects statistical comparisons and by intracranial electrocorticography (ECoG) data from two epilepsy patients. Interestingly, we also reliably observed high frequency activity (30–300 Hz) in the cerebellum, although with variable locations and frequencies across subjects. The proposed algorithm is highly parallelizable and runs efficiently on modern high-performance computing clusters. This method enables the ultimate promise of MEG and EEG for five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics during cognition.
Highlights
Magnetoencephalography (MEG) and electroencephalography (EEG) are functional neuroimaging techniques with millisecond time resolution (Hämäläinen et al, 1993)
The broadband beamformer correctly placed the peak of beta event-related desynchronization (ERD) at (25, 30, 100) mm. (See Figure 3.) the spatiotemporal extent of both high gamma event-related synchronization (ERS) sources were not as cleanly resolved
With our novel time-frequency optimized beamformer techniques, MEG can resolve sources of transient power changes across multiple frequency bands, including high gamma activity
Summary
Magnetoencephalography (MEG) and electroencephalography (EEG) are functional neuroimaging techniques with millisecond time resolution (Hämäläinen et al, 1993). MEG and EEG have been used to study evoked responses, i.e., activity that is both time-locked and phase-locked to a stimulus or task These analyses assume a model of neural activity in which responses are additive and/or phases are reset (Hanslmayr et al, 2007). It has been well-known that ongoing MEG/EEG oscillations can be suppressed in response to a stimulus or task since the earliest EEG research (Berger, 1930); this possibility is not accounted for by the evoked model. Averaging assumes trial-to-trial phase locking, which may not be valid for many complex cognitive paradigms
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