A comparison of two-dimensional techniques for converting magnetocardiogram maps into effective current source distributions.

The aim of this study was to develop a method for converting the pseudo two-dimensional current given by a current-arrow map (CAM) into the physical current. The physical current distribution is obtained by the optimal solution in a least mean square sense with Tikhonov regularization (LMSTR). In the current dipole simulation, the current pattern differences (ΔJ) between the results of the CAM and the LMSTR with several regularization parameters (α = 10(-1)-10(-15)) are calculated. In magnetocardiographic (MCG) analysis, the depth (z(d)) of a reconstruction plane is chosen by using the coordinates of the sinus node, which is estimated from MCG signals at the early p-wave. The ΔJs at p-wave peaks, QRS-complex peaks, and T-wave peaks of MCG signals for healthy subjects are calculated. Furthermore, correlation coefficients and regression lines are also calculated from the current values of the CAM and the LMSTR during p-waves, QRS-complex, and T-waves of MCG signals. In the simulation, the ΔJs (α ≈ 10(-10)) had a minimal value. The ΔJs (α = 10(-10)) at p-wave peaks, QRS-complex peaks, and T-wave peaks of MCG signals for healthy subjects also had minimal value. The correlation coefficients of the current values given by the CAM and the LMSTR (α = 10(-10)) were greater than 0.9. Furthermore, slopes (y) of the regression lines are correlated with the depth (z(d)) (r = -0.93). Consequently, the CAM value can be transformed into the LMSTR current value by multiplying it by the slope (y) obtained from the depth (z(d)). In conclusion, the result given by the CAM can be converted into an effective physical current distribution by using the depth (z(d)).

Performance of prewhitening beamforming in MEG dual experimental conditions.

This paper presents an analysis on the performance of the prewhitening beamformer when applied to magnetoencephalography (MEG) experiments involving dual (task and control) conditions. We first analyze the method's robustness to two types of violations of the prerequisites for the prewhitening method that may arise in real-life two-condition experiments. In one type of violation, some sources exist only in the control condition but not in the task condition. In the other type of violation, some signal sources exist both in the control and the task conditions, and that they change intensity between the two conditions. Our analysis shows that the prewhitening method is very robust to these nonideal conditions. In this paper, we also present a theoretical analysis showing that the prewhitening method is considerably insensitive to overestimation of the signal-subspace dimensionality. Therefore, the prewhitening beamformer does not require accurate estimation of the signal subspace dimension. Results of our theoretical analyses are validated in numerical experiments and in experiments using a real MEG data set obtained during self-paced hand movements.

NUTMEG: a neuromagnetic source reconstruction toolbox.

We have developed an analysis toolbox called NUTMEG (Neurodynamic Utility Toolbox for Magnetoencephalography) for reconstructing the spatiotemporal dynamics of neural activations and overlaying them onto structural MR images. The toolbox runs under MATLAB in conjunction with SPM2 and can be used with the Linux/UNIX, Mac OS X, and even Windows platforms. Currently, evoked magnetic field data from 4-D Neuroimaging, CTF, and KIT systems can be imported to the toolbox for analysis. NUTMEG uses an eigenspace vector beamforming algorithm to generate a tomographic reconstruction of spatiotemporal magnetic source activity over selected time intervals and spatial regions. The MEG coordinate frame is coregistered with an anatomical MR image using fiducial locations and, optionally, head shape information. This allows the reconstruction to be superimposed onto an MRI to provide a convenient visual correspondence to neuroanatomy. Navigating through the MR volume automatically updates the displayed time series of activation for the selected voxel. Animations can also be generated to view the evolution of neural activity over time. Since NUTMEG displays activations using SPM2's engine, certain SPM functions such as brain rendering and spatial normalization may be applied as well. Finally, as a MATLAB package, the end user can easily add customized functions. Source code is available at http://bil.ucsf.edu/ and distributed under a BSD-style license.

Temporal characterization of memory retrieval processes: an fMRI study of the "tip of the tongue" phenomenon.

"Tip of the tongue" (TOT) is a natural phenomenon in which people cannot retrieve a target word immediately, even though they feel confident that they know the target. This provides us an opportunity to understand the human memory system, because cognitive components of memory retrieval such as retrieval effort and successful retrieval are temporally dissociated from each other during the TOT states. The purpose of the present study was to reveal the neural correlates of the cognitive components of the retrieval process by separating cognitive phases of the TOT phenomenon using event-related functional magnetic resonance imaging with multiple regression analysis. We demonstrated that the left dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex were activated at the time of successful retrieval, and the left DLPFC also showed activation when the subjects successfully retrieved the target names as compared to when they gave up. This result suggests that the left DLPFC is specific to the successful retrieval process. During the TOT state, a number of regions were activated, and this suggests that widely distributed brain regions are engaged when people make a hard effort to retrieve a proper name in the TOT state. Our new approach employing temporal resolution of the TOT phenomenon may contribute to the understanding of the mechanisms of the human memory system.

Serial activation of distinct cytoarchitectonic areas of the human S1 cortex after posterior tibial nerve stimulation.

MEG recordings visualized non-invasively a dynamic anterior-posterior activation in the pyramidal cell population of the human primary somatosensory cortex (S1) after posterior tibial nerve stimulation. Somatosensory evoked fields (SEFs) were recorded over the foot area in response to right posterior tibial nerve stimulation at the ankle in six normal subjects. A newly developed MEG vector beamformer technique applied to the SEFs revealed two distinct sources in the mesial wall of the left hemisphere around the primary P37m response typically separated by 1.3 cm. The first source was located in area 3b and oriented toward the contralateral hemisphere. The second source was assumed to be in an area near the marginal sulcus and the source orientation was directed posteriorly. The first source began to be active during the initial slope of the P37m. The second source was active after the P37m peak and the signal intensities of the first and second sources were equal at a mean latency of 2.6 ms after the peak of P37m. Then the first source became inactive and the second source was dominant after about 5 ms post-P37m peak. These findings suggest that a single peaked posterior tibial nerve P37m consists of partially overlapping two subcomponents generated in area 3b and an area near the marginal sulcus.

Dynamic activation of distinct cytoarchitectonic areas of the human SI cortex after median nerve stimulation.

MEG recordings visualized non-invasively a serial mediolateral activation of the human somatosensory 3b area followed by a stationary activation of area 1 after median nerve stimulation. Somatosensory evoked fields (SEFs) were recorded over the hand area contralateral to the right median nerve stimulation at the wrist in six normal subjects. A newly developed MEG vector beamformer technique applied to the SEFs revealed two distinct sources (areas 3b and 1) in the primary somatosensory cortex (SI) during the primary N20m-P22m response in all subjects. The first source was located in area 3b, which started to move sequentially toward mediolateral direction 0.7 ms prior to the peak of N20m and ended its movement 1.4 ms after the peak with a total distance of 11.2 mm. We speculate that the movement reflects a sequential mediolateral activation of the pyramidal cells in area 3b, which is mediated by horizontal connections running parallel to the cortical surface. The second source in area 1, located 5.6 mm medial and 4.2 mm posterior to the first source, was active 1.0 ms after the N20m peak. Then, the first source became inactive and the second source was dominant. In sharp contrast with the first source, the second source was stationary. The different behavior of these two components (moving vs stationary) indicates independent parallel inputs to area 3b and area 1 from the thalamus.

Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique.

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.

Estimating neural sources from each time-frequency component of magnetoencephalographic data.

We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. This method, referred to as the time-frequency multiple-signal-classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest. In this paper, we formulate the method based on the most general form of the quadratic time-frequency representations. We then apply it to two kinds of nonstationary MEG data: gamma-band (frequency range between 30-100 Hz) auditory activity data and spontaneous MEG data. Our method successfully detected the gamma-band source slightly medial to the N1m source location. The method was able to selectively localize sources for alpha-rhythm bursts at different locations. It also detected the mu-rhythm source from the alpha-rhythm-dominant MEG data that was measured with the subject's eyes closed. The results of these applications validate the effectiveness of the time-frequency MUSIC algorithm for selectively localizing sources having different time-frequency signatures.

Neural source estimation from a time-frequency component of somatic evoked high-frequency magnetic oscillations to posterior tibial nerve stimulation.

OBJECTIVE: High frequency oscillations (HFOs) evoked by posterior tibial nerve stimulation were recorded using magnetoencephalography (MEG). Time-frequency domain multiple signal classification (TF-MUSIC) algorithm was applied, and the usefulness of this method was demonstrated. METHODS: Ten normal subjects were studied. To localize sources for the HFOs of those somatosensory evoked fields, we applied two kinds of methods: the single moving dipole (SMD) method and the TF-MUSIC method. The SMD method was applied after digitally band-pass filtering the somatosensory response with a bandwidth of 500-800 Hz. To estimate the locations of sources with the TF-MUSIC algorithm, we first set the target region on the spectrogram of the somatosensory responses. Then, the procedure described in Section 2.2 was applied with this target region. RESULTS: A clear, isolated region was detected in 6 out of 10 subjects using a time-frequency spectrogram. The averaged distance of the dipole sources between the HFOs and the underlying P37m using the TF-MUSIC algorithm was smaller than using the SMD method. CONCLUSIONS: The TF-MUSIC algorithm is suitable for extracting a target response whose spectrum changes significantly during the observation.

MEG spatio-temporal analysis using a covariance matrix calculated from nonaveraged multiple-epoch data.

We propose a magnetoencephalographic (MEG) spatio-temporal analysis in which the measurement-covariance matrix is calculated using nonaveraged multiple epoch data. The proposed analysis has two advantages. First, a very narrow time window can be used for the source estimation. Second, accurate localization is possible even when the source activation has a time jitter. Experiments using auditory evoked MEG data clearly demonstrate these advantages.

Time-frequency MEG-MUSIC algorithm.

We propose a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. The method is based on the multiple-signal-classification (MUSIC) algorithm and it calculates a time--frequency matrix in which diagonal and off-diagonal terms are the auto and crosstime--frequency distributions of multichannel MEG recordings, respectively. The method averages this time-frequency matrix over the time--frequency region of interest. The locations of neural sources are then estimated by checking the orthogonality between the noise subspace of this averaged matrix and the sensor lead field. Accordingly, the method allows us to estimate the locations of neural sources from each time--frequency component. A computer simulation was performed to test the validity of the proposed method, and the results demonstrate its effectiveness.

Activation of lateral extrastriate areas during orthographic processing of Japanese characters studied with fMRI.

We investigated the early orthographic processing in the occipital cortex using Japanese morphogram by functional magnetic resonance imaging. Kanji (Japanese morphogram) is one system of character used in the Japanese language, each character of which has a specific meaning and pronunciations. To ensure that the effects of the general visual properties of Kanji were excluded from Kanji-related activation, we created strict control stimuli, the "scrambled Kanji" that had the same luminance, contrast, and retinotopical size as those of the original Kanji. In the Kanji vs scrambled Kanji task, we found significant activation in the left inferior occipital gyrus. However, we found no activation in earlier visual cortices, including the primary visual cortex, indicating that the scrambled Kanji served as an effective control stimulus for this task. In the Kanji vs blank screen task, much more areas, including earlier visual cortices, were activated. The activation that we found in the Kanji vs scrambled Kanji task was compatible with the results of previous studies of English letter strings by other groups, suggesting that the left inferior occipital gyrus plays an essential role in orthographic processing common to these two different writing systems.

No-go dominant brain activity in human inferior prefrontal cortex revealed by functional magnetic resonance imaging.

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.

MEG covariance difference analysis: a method to extract target source activities by using task and control measurements.

A method is proposed for extracting target dipolesource activities from two sets of evoked magnetoencephalographic (MEG) data, one measured using task stimuli and the other using control stimuli. The difference matrix between the two covariance matrices obtained from these two measurements is calculated, and a procedure similar to the MEG-multiple signal classification (MUSIC) algorithm is applied to this difference matrix to extract the target dipole-source configuration. This configuration corresponds to the source-configuration difference between the two measurements. Computer simulation verified the validity of the proposed method. The method was applied to actual evoked-field data obtained from simulated task-and-control experiments. In these measurements, a combination of auditory and somatosensory stimuli was used as the task stimulus and the somatosensory stimulus alone was used as the control stimulus. The proposed covariance difference analysis successfully extracted the target auditory source and eliminated the disturbance from the somatosensory sources.

Comparison of covariance-based and waveform-based subtraction methods in removing the interference from button-pressing finger movements.

A covariance matrix-based subtraction method has recently been proposed to remove interference using two MEG measurements: The first has both target and interfering activities and the second only has the interference. This paper compared covariance matrix-based subtraction with conventional waveform-based subtraction, which requires that the waveforms of interference be equal at every time point between the two measurements. Our analysis showed that covariance-subtraction only requires that the time-average of the squared intensity of interference be equal between the two measurements. As a result, the method is still effective when the onset of interference differs or even their measured waveforms differ between the two measurements. The covariance- and waveform-subtraction methods were both applied to remove the interference caused by response-button-pressing finger movements in auditory-evoked MEG measurements. The results of this application demonstrated the superiority of the covariance-subtraction method over the conventional waveform-subtraction method.

Transient activation of inferior prefrontal cortex during cognitive set shifting.

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.

Noise covariance incorporated MEG-MUSIC algorithm: a method for multiple-dipole estimation tolerant of the influence of background brain activity.

This paper proposes a method of localizing multiple current dipoles from spatio-temporal biomagnetic data. The method is based on the multiple signal classification (MUSIC) algorithm and is tolerant of the influence of background brain activity. In this method, the noise covariance matrix is estimated using a portion of the data that contains noise, but does not contain any signal information. Then, a modified noise subspace projector is formed using the generalized eigenvectors of the noise and measured-data covariance matrices. The MUSIC localizer is calculated using this noise subspace projector and the noise covariance matrix. The results from a computer simulation have verified the effectiveness of the method. The method was then applied to source estimation for auditory-evoked fields elicited by syllable speech sounds. The results strongly suggest the method's effectiveness in removing the influence of background activity.

Reconstruction of two-dimensional current distribution from tangential MCG measurement.

We describe a two-dimensional reconstruction method for tangential magnetocardiograms (MCGs). This method is based on two-dimensional Fourier analysis, and we used a new type of window function for tangential MCG to solve the problem of the small number of measurement points. By using this method, cardiac activity can be estimated as a two-dimensional current distribution. To determine the effectiveness of this method, we measured tangential MCGs of normal subjects, and compared the estimated current distribution with the actual cardiac muscle activity. Using this method, we were able to clearly show cardiac activity.

Detecting cortical activities from fMRI time-course data using the MUSIC algorithm with forward and backward covariance averaging.

A method is proposed for processing time-course fMRI data taken with successive single-shot echo-planar imaging. The proposed method uses a two-dimensional version of the multiple signal classification (MUSIC) algorithm and the technique called covariance averaging, both of which were developed in the field of sensor-array processing. The proposed method consists of four steps: calculate the averaged data covariance matrix, determine the number of activities using this covariance matrix, estimate the locations of the activities, and estimate their time evolution curves. Computer simulation results showed that a nearly fourfold improvement in the spatial resolution can be attained due to the method's super-resolution capability.

Generalized Wiener estimation of three-dimensional current distribution from biomagnetic measurements.

This paper proposes a method for estimating three-dimensional (3-D) biocurrent distribution from spatio-temporal biomagnetic data. This method is based on the principle of generalized Wiener estimation, and it is formulated based on the assumption that current sources are uncorrelated. Computer simulation demonstrates that the proposed method can reconstruct a 3-D current distribution where the conventional least-squares minimum-norm method fails. The influence of noise is also simulated, and the results indicate that a signal-to-noise ratio of more than 20 for the uncorrelated sensor noise is needed to implement the proposed method. The calculated point spread function shows that the proposed method has very high spatial resolution compared to the conventional minimum norm method. The results of computer simulation of the distributed current sources are also presented, including cases where current sources are correlated. These results suggest that no serious errors arise if the source correlation is weak.