[Show abstract][Hide abstract] ABSTRACT: One of the major obstacles in estimating cortical currents from MEG signals is the disturbance caused by magnetic artifacts derived from extra-cortical current sources such as heartbeats and eye movements. To remove the effect of such extra-brain sources, we improved the hybrid hierarchical variational Bayesian method (hyVBED) proposed by Fujiwara et al. (NeuroImage, 2009). hyVBED simultaneously estimates cortical and extra-brain source currents by placing dipoles on cortical surfaces as well as extra-brain sources. This method requires EOG data for an EOG forward model that describes the relationship between eye dipoles and electric potentials. In contrast, our improved approach requires no EOG and less a priori knowledge about the current variance of extra-brain sources. We propose a new method, "extra-dipole," that optimally selects hyper-parameter values regarding current variances of the cortical surface and extra-brain source dipoles. With the selected parameter values, the cortical and extra-brain dipole currents were accurately estimated from the simulated MEG data. The performance of this method was demonstrated to be better than conventional approaches, such as principal component analysis and independent component analysis, which use only statistical properties of MEG signals. Furthermore, we applied our proposed method to measured MEG data during covert pursuit of a smoothly moving target and confirmed its effectiveness.
[Show abstract][Hide abstract] ABSTRACT: The magnetic fields generated by eye movements are major artifacts in MEG measurements. We propose a hybrid hierarchical variational Bayesian method to remove eye movement artifacts from MEG data. Our method is an extension of the hierarchical variational Bayesian method for MEG source localization proposed by Sato et al. [Sato, M., Yoshioka, T., Kajihara, S., Toyama, K., Goda, N., Doya, K., and Kawato, M., (2004). Hierarchical Bayesian estimation for MEG inverse problem. NeuroImage 23(3), 806-826]. First, we assumed a single dipole at each left and right eyeball as a source of eye artifacts. Second, we constructed an EOG forward model describing the relationship between eye dipoles and electric potentials, i.e., EOG. Based on the Bayesian framework, the proposed method concurrently estimates eye and brain current sources from both MEG and EOG data. Thereby the brain current sources can be isolated from eye artifacts. The new method was tested in two ways. In the simulation experiments, the performance of eye artifact removal was evaluated from various aspects; locations of brain current sources, temporal correlation between eye and brain current sources, the level of MEG observation noise and so on. In real MEG experiments, we measured MEG and EOG data during smooth pursuit eye movements for a horizontally or circularly moving target. Our method successfully removed eye artifacts from the simulated and real MEG data with the estimation of brain current sources that were located in eye movement related areas. Our method should be widely applicable to MEG data obtained in tasks with non-negligible eye movements.
[Show abstract][Hide abstract] ABSTRACT: Experiments of a magnetoencephalography (MEG) and an functional magnetic resonance imaging (fMRI) were conducted to reveal the cortical mechanisms related to covert pursuit to a moving visual target. Subject was asked to gaze a fixation point at the center of screen and to track covertly a horizontally moving target. The MEG was measured when the subjects were tracking the target covertly. Current sources of about 7,000 dipoles on the cortical surface were estimated from the MEG data by a hierarchical Bayesian method incorporating the fMRI data. We investigated whether the target velocity can be reconstructed from estimated current sources. One of the datasets was used for training of the weight parameter, and validation tests were conducted using other two datasets. The result showed that target velocities could be reconstructed from the current sources in the cortical areas, related to processing target motion in eye movements, such as primary visual cortex, lateral occipito-temporal cortex, parietal cortex, and pre-frontal cortex. This result suggested that these areas were responsible for tracking a moving target, in consistent with previous studies using noninvasive recording of brain function.
[Show abstract][Hide abstract] ABSTRACT: In smooth-pursuit eye movements (SPEM) with gain close to one, SPEM should be controlled mainly by prediction of target motion because retinal slip is nearly zero. We investigated the neural mechanisms of visual-target prediction by the three fMRI experiments. (1) Overt pursuit task: subjects pursued a sinusoidally moving target which blinked (blink condition) or did not blink (continuous condition). (2) Covert pursuit task: subjects covertly pursued the same target with eyes gazed at fixation point. (3) Attend-to-stationary target task: subjects brought attention on a stationary target with eyes gazed at fixation point. In the overt pursuit task, the SPEM gain and the delay in the blink condition were not very different from the continuous condition, indicating good prediction of the blinking target motion. Activities in the dorsolateral prefrontal, precentral, medial superior frontal, intraparietal, and lateral occipito-temporal cortexes increased in the blink-continuous subtraction. The V1 activity decreased for this contrast. In the covert pursuit task, only the anterior/superior LOTC activity remained in the blink-continuous subtraction. In the attend-to-stationary target task, the blink-continuous subtraction elicited no activation. Consequently, the a/sLOTC activity is responsible for target prediction rather than motor commands for eye movements or just target blinking such as visual saliency.
Full-text · Article · Mar 2006 · Neuroscience Research
[Show abstract][Hide abstract] ABSTRACT: The measurement of magnetoencephalographic (MEG) signals is contaminated by large magnetic artifacts, such as heart beats,
eye movements, and muscle activities, and so on. These artifacts can be orders of magnitude larger than the signal from the
brain, thus making cortical current estimation extremely difficult. This paper proposes a novel method to remove the effects
of artifacts by simultaneously estimating the cortical and artifactual dipole currents. By using proper prior information,
we show that this method can estimate the currents of artifacts and cortical activities simultaneously, and the estimated
cortical currents are more reasonable in comparison to those of previous methods.