The dynamics of neural information processing are complicated, and determining the sequence and mode of activation is equally important to determining which areas activate. Magnetoencephalography (MEG) and electroencephalography (EEG) noninvasively measure the electromagnetic fields directly generated by neuronal currents in the human brain. The potential to accurately localize these signals has emerged with the advent of dense, whole-head sensor arrays. Adaptive spatial filtering techniques such as beamforming are often used reconstruct the sources of MEG/EEG activity. I have developed extensions to apply beamforming to more experimental paradigms as well as a method for more accurate validation with intracranial EEG.

Beamformers poorly resolve brain sources that are strongly correlated temporally with one another, as might be expected for an auditory experiment. I presented a method to reject the contribution of potentially interfering sources in a user-defined suppression region while allowing for source reconstruction at other specified regions. Performance of the algorithm was validated with data from simulations and an auditory MEG experiment.

Few methods exist for localizing spectral power changes with MEG. I described a novel method that uses beamformers optimized for time-frequency source reconstruction from MEG data. The performance of the method was demonstrated with simulated sources and was also applied to real MEG data from a finger movement task. Modulations in both the beta band and, importantly, the high gamma band were revealed in sensorimotor cortex and found to be statistically significant across subjects. These results were additionally validated by intracranial EEG data from two epilepsy patients. Another compelling finding was high frequency activity (30-300 Hz) in the cerebellum.

Finally, while intracranial recordings are considered the gold standard for validating noninvasive measurements, often electrode locations are not precisely known. One common method, CT-MRI coregistration, may result in a localization error of more than 10 mm. To address this, I developed a procedure to link preoperative MRIs, surgical photographs, and postimplant X-rays with projective transforms.

The dynamics of neural information processing are complicated, and determining the sequence and mode of activation is equally important to determining which areas activate. Magnetoencephalography (MEG) and electroencephalography (EEG) noninvasively measure the electromagnetic fields directly generated by neuronal currents in the human brain. The potential to accurately localize these signals has emerged with the advent of dense, whole-head sensor arrays. Adaptive spatial filtering techniques such as beamforming are often used reconstruct the sources of MEG/EEG activity. I have developed extensions to apply beamforming to more experimental paradigms as well as a method for more accurate validation with intracranial EEG.

Beamformers poorly resolve brain sources that are strongly correlated temporally with one another, as might be expected for an auditory experiment. I presented a method to reject the contribution of potentially interfering sources in a user-defined suppression region while allowing for source reconstruction at other specified regions. Performance of the algorithm was validated with data from simulations and an auditory MEG experiment.

Few methods exist for localizing spectral power changes with MEG. I described a novel method that uses beamformers optimized for time-frequency source reconstruction from MEG data. The performance of the method was demonstrated with simulated sources and was also applied to real MEG data from a finger movement task. Modulations in both the beta band and, importantly, the high gamma band were revealed in sensorimotor cortex and found to be statistically significant across subjects. These results were additionally validated by intracranial EEG data from two epilepsy patients. Another compelling finding was high frequency activity (30-300 Hz) in the cerebellum.

Finally, while intracranial recordings are considered the gold standard for validating noninvasive measurements, often electrode locations are not precisely known. One common method, CT-MRI coregistration, may result in a localization error of more than 10 mm. To address this, I developed a procedure to link preoperative MRIs, surgical photographs, and postimplant X-rays with projective transforms.

Many tomographic source localization algorithms used in biomagnetic imaging assume, explicitly or sometimes implicitly, that the source activity at different brain locations are either independent or that the correlation structure between sources is known. Among these algorithms is a class of adaptive spatial filters known as beamformers, which have superior spatiotemporal resolution abilities. The performance of beamformers is robust to weakly coherent sources. However, these algorithms are extremely sensitive to the presence of strongly coherent sources. A frequent mode of failure in bearnformers occurs with reconstruction of auditory evoked fields (AEFs), in which bilateral auditory cortices are highly coherent in their activation. Here, we present a novel beamformer that suppresses activation from regions with interfering coherent sources. First, a volume containing the interfering sources is defined. The lead field matrix for this volume is computed and reduced into a few significant columns using singular value decomposition (SVD). A vector beamformer is then constructed by rejecting the contribution of sources in the suppression region while allowing for source reconstruction at other specified regions. Performance of this algorithm was first validated with simulated data. Subsequent tests of this modified beamformer were performed on bilateral AEF data. An unmodified vector beamformer using whole head coverage misplaces the source medially. After defining a suppression region containing the temporal cortex on one side, the described method consistently results in clear focal activations at expected regions of the contralateral superior temporal plane.

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. 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 real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12-30 Hz) and a 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. We also revealed observed high gamma activity in the cerebellum. The proposed algorithm is highly parallelizable and runs efficiently on modern high performance computing clusters. This method enables noninvasive five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics.