Several behavioral and brain imaging studies have demonstrated a significant interaction between speech perception and speech production. In this study, auditory cortical responses to speech were examined during self-production and feedback alteration. Magnetic field recordings were obtained from both hemispheres in subjects who spoke while hearing controlled acoustic versions of their speech feedback via earphones. These responses were compared to recordings made while subjects listened to a tape playback of their production. The amplitude of tape playback was adjusted to match the amplitude of self-produced speech. Recordings of evoked responses to both self-produced and tape-recorded speech were obtained free of movement-related artifacts. Responses to self-produced speech were weaker than were responses to tape-recorded speech. Responses to tones were also weaker during speech production, when compared with responses to tones recorded in the presence of speech from tape playback. However, responses evoked by gated noise stimuli did not differ for recordings made during self-produced speech versus recordings made during tape-recorded speech playback. These data suggest that during speech production, the auditory cortex (1) attenuates its sensitivity and (2) modulates its activity as a function of the expected acoustic feedback.

This paper discusses the location bias and the spatial resolution in the reconstruction of a single dipole source by various spatial filtering techniques used for neuromagnetic imaging. We first analyze the location bias for several representative adaptive and non-adaptive spatial filters using their resolution kernels. This analysis theoretically validates previously reported empirical findings that standardized low-resolution electromagnetic tomography (sLORETA) has no location bias. We also find that the minimum-variance spatial filter does exhibit bias in the reconstructed location of a single source, but that this bias is eliminated by using the normalized lead field. We then focus on the comparison of sLORETA and the lead-field normalized minimum-variance spatial filter, and analyze the effect of noise on source location bias. We find that the signal-to-noise ratio (SNR) in the measurements determines whether the sLORETA reconstruction has source location bias, while the lead-field normalized minimum-variance spatial filter has no location bias even in the presence of noise. Finally, we compare the spatial resolution for sLORETA and the minimum-variance filter, and show that the minimum-variance filter attains much higher resolution than sLORETA does. The results of these analyses are validated by numerical experiments as well as by reconstructions based on two sets of evoked magnetic responses.

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, though 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.

We have developed a method suitable for reconstructing spatio-temporal activities of neural sources by using magnetoencephalogram (MEG) data. The method extends the adaptive beamformer technique originally proposed by Borgiotti and Kaplan to incorporate the vector beamformer formulation in which a set of three weight vectors are used to detect the source activity in three orthogonal directions. The weight vectors of the vector-extended version of the Borgiotti-Kaplan beamformer are then projected onto the signal subspace of the measurement covariance matrix to obtain the final form of the proposed beamformer's weight vectors. Our numerical experiments show that both spatial resolution and output signal-to-noise ratio of the proposed beamformer are significantly higher than those of the minimum-variance-based vector beamformer used in previous investigations. We also applied the proposed beamformer to two sets of auditory-evoked MEG data, and the results clearly demonstrated the method's capability of reconstructing spatio-temporal activities of neural sources.

The Wisconsin Card Sorting Test, which probes the ability to shift attention from one category of stimulus attributes to another (shifting cognitive sets), is the most common paradigm used to detect human frontal lobe pathology. However, the exact relationship of this card test to prefrontal function and the precise anatomical localization of the cognitive shifts involved are controversial. By isolating shift-related signals using the temporal resolution of functional magnetic resonance imaging, we reproducibly found transient activation of the posterior part of the bilateral inferior frontal sulci. This activation was larger as the number of dimensions (relevant stimulus attributes that had to be recognized) were increased. These results suggest that the inferior frontal areas play an essential role in the flexible shifting of cognitive sets.

We investigated the response inhibition function of the prefrontal cortex associated with the go/no-go task using functional magnetic resonance imaging in five human subjects. The go/no-go task consisted of go and no-go trials given randomly with roughly equal probability. In go trials a green square was presented and the subjects had to respond by promptly pushing a button using their right or left thumbs, but in no-go trials a red square was presented and subjects were instructed not to respond. When brain activity in no-go trials is dominant over that in go trials in areas in the prefrontal cortex, this no-go dominant brain activity would reflect the neural processes for inhibiting inherent response tendency. We used a new strategy of image data analysis by which transient brain activity in go or no-go trials can be analysed separately, and looked for the prefrontal areas in which the brain activity in no-go trials is dominant over that in go trials. We found the no-go dominant foci in the posterior part of the right inferior frontal sulcus reproducibly among the subjects. This was true whether the right or left hand was used. These results suggest that this region in the prefrontal cortex is related to the neural mechanisms underlying the response inhibition function.

To reconstruct neuromagnetic sources, the minimum-variance beamformer has been extended to incorporate the three-dimensional vector nature of the sources, and two types of extensions-the scalar-and vector-type extensions-have been proposed. This paper discusses the asymptotic signal-to-noise ratio (SNR) of the outputs of these two types of beamformers. We first show that these two types of beamformers give exactly the same output power and output SNR if the beamformer pointing direction is optimized. We then compare the output SNR of the beamformer with optimum direction to that of the conventional vector beamformer formulation where the beamformer pointing direction is not optimized. The comparison shows that the beamformer with optimum direction gives an output SNR superior to that of the conventional vector beamformer. Numerical examples validating the results of the analysis are presented.

The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and time course of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and unknown orientations and by the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to produce theoretically zero reconstruction error estimating multiple dipoles even in the presence of strong correlations and unknown orientations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach.