Sensor array design of optically pumped magnetometers for accurately estimating source currents.

An optically pumped magnetometer (OPM) is a new generation of magnetoencephalography (MEG) devices that is small, light, and works at room temperature. Due to these characteristics, OPMs enable flexible and wearable MEG systems. On the other hand, if we have a limited number of OPM sensors, we need to carefully design their sensor arrays depending on our purposes and regions of interests (ROIs). In this study, we propose a method that designs OPM sensor arrays for accurately estimating the cortical currents at the ROIs. Based on the resolution matrix of minimum norm estimate (MNE), our method sequentially determines the position of each sensor to optimize its inverse filter pointing to the ROIs and suppressing the signal leakage from the other areas. We call this method the Sensor array Optimization based on Resolution Matrix (SORM). We conducted simple and realistic simulation tests to evaluate its characteristics and efficacy for real OPM-MEG data. SORM designed the sensor arrays so that their leadfield matrices had high effective ranks as well as high sensitivities to ROIs. Although SORM is based on MNE, the sensor arrays designed by SORM were effective not only when we estimated the cortical currents by MNE but also when we did so by other methods. With real OPM-MEG data we confirmed its validity for real data. These analyses suggest that SORM is especially useful when we want to accurately estimate ROIs' activities with a limited number of OPM sensors, such as brain-machine interfaces and diagnosing brain diseases.

Whole-brain propagating patterns in human resting-state brain activities.

Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. "Whole-brain" propagating activities may also reflect a process that integrates information distributed over the entire brain, such as visual and motor information. Here we reveal whole-brain propagating activities from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. We simultaneously recorded the MEGs and EEGs and estimated the source currents from both measurements. Then using our recently proposed algorithm, we extracted repetitive spatiotemporal patterns from the source currents. The estimated patterns consisted of multiple frequency components, each of which transiently exhibited the frequency-specific resting-state networks (RSNs) of functional MRIs (fMRIs), such as the default mode and sensorimotor networks. A simulation test suggested that the spatiotemporal patterns reflected the phase alignment of the multiple frequency oscillators induced by the propagating activities along the anatomical connectivity. These results argue that whole-brain propagating activities transiently exhibited multiple RSNs in their multiple frequency components, suggesting that they reflected a process to integrate the information distributed over the frequencies and networks.

Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval.

Estimation of hyper-parameters for a hierarchical model of combined cortical and extra-brain current sources in the MEG inverse problem.

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.

Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography.

High-density diffuse optical tomography (HD-DOT) is an emerging technique for visualizing the internal state of biological tissues. The large number of overlapping measurement channels due to the use of high-density probe arrays permits the reconstruction of the internal optical properties, even with a reflectance-only measurement. However, accurate three-dimensional reconstruction is still a challenging problem. First, the exponentially decaying sensitivity causes a systematic depth-localization error. Second, the nature of diffusive light makes the image blurred. In this paper, we propose a three-dimensional reconstruction method that overcomes these two problems by introducing sensitivity-normalized regularization and sparsity into the hierarchical Bayesian method. Phantom experiments were performed to validate the proposed method under three conditions of probe interval: 26 mm, 18.4 mm, and 13 mm. We found that two absorbers with distances shorter than the probe interval could be discriminated under the high-density conditions of 18.4-mm and 13-mm intervals. This discrimination ability was possible even if the depths of the two absorbers were different from each other. These results show the high spatial resolution of the proposed method in both depth and horizontal directions.

Common cortical areas have different neural mechanisms for covert and overt visual pursuits.

Although humans can direct their attention to visual targets with or without eye movements, it remains unclear how different brain mechanisms control visual attention and eye movements together and/or separately. Here, we measured MEG and fMRI data during covert/overt visual pursuit tasks and estimated cortical currents using our previously developed extra-dipole, hierarchical Bayesian method. Then, we predicted the time series of target positions and velocities from the estimated cortical currents of each task using a sparse machine-learning algorithm. The predicted target positions/velocities had high temporal correlations with actual visual target kinetics. Additionally, we investigated the generalization ability of predictive models among three conditions: control, covert, and overt pursuit tasks. When training and testing data were the same tasks, the largest reconstructed accuracies were overt, followed by covert and control, in that order. When training and testing data were selected from different tasks, accuracies were in reverse order. These results are well explained by the assumption that predictive models consist of combinations of three computational brain functions: visual information-processing, maintenance of attention, and eye-movement control. Our results indicate that separate subsets of neurons in the same cortical regions control visual attention and eye movements differently.

Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates.

Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchronization transitions with EEG/MEG. However, there is still little understanding of the relationship between the slow fluctuations measured with fMRI and the fast synchronization transitions with EEG/MEG. In this study, as a first step toward evaluating experimental evidence of resting state activity at two different time scales but in a unified way, we investigate connectome dynamics models that simultaneously explain resting-state functional connectivity (rsFC) and EEG microstates. Here, we introduce empirical rsFC and microstates as evaluation criteria of simulated neuronal dynamics obtained by the Larter-Breakspear model in one cortical region connected with those in other cortical regions based on structural connectivity. We optimized the global coupling strength and the local gain parameter (variance of the excitatory and inhibitory threshold) of the simulated neuronal dynamics by fitting both rsFC and microstate spatial patterns to those of experimental ones. As a result, we found that simulated neuronal dynamics in a narrow optimal parameter range simultaneously reproduced empirical rsFC and microstates. Two parameter groups had different inter-regional interdependence. One type of dynamics was synchronized across the whole brain region, and the other type was synchronized between brain regions with strong structural connectivity. In other words, both fast synchronization transitions and slow BOLD fluctuation changed based on structural connectivity in the two parameter groups. Empirical microstates were similar to simulated microstates in the two parameter groups. Thus, fast synchronization transitions correlated with slow BOLD fluctuation based on structural connectivity yielded characteristics of microstates. Our results demonstrate that a bottom-up approach, which extends the single neuronal dynamics model based on empirical observations into a neural mass dynamics model and integrates structural connectivity, effectively reveals both macroscopic fast, and slow resting-state network dynamics.

Impact of language on functional connectivity for audiovisual speech integration.

Visual information about lip and facial movements plays a role in audiovisual (AV) speech perception. Although this has been widely confirmed, previous behavioural studies have shown interlanguage differences, that is, native Japanese speakers do not integrate auditory and visual speech as closely as native English speakers. To elucidate the neural basis of such interlanguage differences, 22 native English speakers and 24 native Japanese speakers were examined in behavioural or functional Magnetic Resonance Imaging (fMRI) experiments while mono-syllabic speech was presented under AV, auditory-only, or visual-only conditions for speech identification. Behavioural results indicated that the English speakers identified visual speech more quickly than the Japanese speakers, and that the temporal facilitation effect of congruent visual speech was significant in the English speakers but not in the Japanese speakers. Using fMRI data, we examined the functional connectivity among brain regions important for auditory-visual interplay. The results indicated that the English speakers had significantly stronger connectivity between the visual motion area MT and the Heschl's gyrus compared with the Japanese speakers, which may subserve lower-level visual influences on speech perception in English speakers in a multisensory environment. These results suggested that linguistic experience strongly affects neural connectivity involved in AV speech integration.

Intracerebral pain processing in a Yoga Master who claims not to feel pain during meditation.

We recorded magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) following noxious laser stimulation in a Yoga Master who claims not to feel pain when meditating. As for background MEG activity, the power of alpha frequency bands peaking at around 10 Hz was much increased during meditation over occipital, parietal and temporal regions, when compared with the non-meditative state, which might mean the subject was very relaxed, though he did not fall asleep, during meditation. Primary pain-related cortical activities recorded from primary (SI) and secondary somatosensory cortices (SII) by MEG were very weak or absent during meditation. As for fMRI recording, there were remarkable changes in levels of activity in the thalamus, SII-insula (mainly the insula) and cingulate cortex between meditation and non-meditation. Activities in all three regions were increased during non-meditation, similar to results in normal subjects. In contrast, activities in all three regions were weaker during meditation, and the level was lower than the baseline in the thalamus. Recent neuroimaging and electrophysiological studies have clarified that the emotional aspect of pain perception mainly involves the insula and cingulate cortex. Though we cannot clearly explain this unusual condition in the Yoga Master, a change of multiple regions relating to pain perception could be responsible, since pain is a complex sensory and emotional experience.

Extended hierarchical Bayesian diffuse optical tomography for removing scalp artifact.

Functional near-infrared spectroscopy (fNIRS) can non-invasively measure hemodynamic responses in the cerebral cortex with a portable apparatus. However, the observation signal in fNIRS measurements is contaminated by the artifact signal from the hemodynamic response in the scalp. In this paper, we propose a method to separate the signals from the cortex and the scalp by estimating both hemodynamic changes by diffuse optical tomography (DOT). In the inverse problem of DOT, we introduce smooth regularization to the hemodynamic change in the scalp and sparse regularization to that in the cortex based on the nature of the hemodynamic responses. These appropriate regularization models, with the spatial information of optical paths of many measurement channels, allow three-dimensional reconstruction of both hemodynamic changes. We validate our proposed method through two-layer phantom experiments and MRI-based head-model simulations. In both experiments, the proposed method simultaneously estimates the superficial smooth activity in the scalp area and the deep localized activity in the cortical area.

Functional relationship between human rolandic oscillations and motor cortical excitability: an MEG study.

Synchronization and desynchronization of the neural rhythm in the brain play an important role in the orchestration of perception, motor action and conscious experience. Based on the results of electrocorticographic and magnetoencephalographic (MEG) recordings, it has been considered that human rolandic oscillations originate in the anterior bank of the central sulcus (20-Hz rhythm) and the postcentral cortex (10-Hz rhythm): the 20-Hz oscillation is closely related to motor function, while the 10-Hz rhythm is attributed mainly to sensory function. To test whether the rolandic oscillations are functionally relevant to the motor cortical excitability, we examined effects of 1-Hz repetitive transcranial magnetic stimulation (rTMS) of the left primary motor cortex (M1) on movement-related changes of the rolandic oscillations in 12 normal subjects. MEG data recorded during brisk extension of the right index finger in two different sessions (with and without rTMS conditioning) were compared. Motor-evoked potential (MEP) of the right hand muscle was also measured before and after rTMS to assess the motor cortical excitability. We found that 1-Hz rTMS over M1 significantly reduced the movement-related rebound of the 20-Hz oscillation in association with decreased motor cortical excitability. In particular, movement-related rebound of the 20-Hz rhythm was closely tied with motor cortical excitability. These findings further strengthen the notion of functional relevance of 20-Hz cortical oscillation to motor cortical excitability. In the framework of previous studies, the decrease in movement-related rebound may be regarded as a compensatory reaction to the inhibited cortical activity.