Localization of individual area neuronal activity

The Characteristics of Power Spectral Density in Bipolar Disorder at the Resting State

Bipolar disorder (BD) is a common psychiatric disorder, but its pathophysiology is not fully elucidated. The current study focused on its electrophysiological characteristics, especially power spectral density (PSD). Resting state with eyes opened magnetoencephalography data were collected from 21 patients with BD and 22 healthy controls. The whole brain's PSD was calculated from source reconstructed waveforms at each frequency band (delta: 1-3 Hz, theta: 4-7 Hz, alpha: 8-12 Hz, low beta: 13-19 Hz, high beta: 20-29 Hz, and gamma: 30-80 Hz). We compared PSD values on the marked vertices at each frequency band between healthy and patient groups using a Mann-Whitney rank test to examine the relationship between significantly different PSD and clinical measures. The PSD in patients with BD was significantly decreased in lower frequency bands, mainly in the default mode network (DMN) areas (bilateral medial prefrontal cortex, bilateral precuneus, left inferior parietal lobe, and right temporal cortex in the alpha band) and salience network areas (SAL; left anterior insula [AI] at the delta band, anterior cingulate cortex at the theta band, and right AI at the alpha band). No significant differences in PSD were observed at low beta and high beta. PSD was not correlated with age or other clinical scales. Altered PSDs of the DMN and SAL were observed in the delta, theta, and alpha bands. These alterations contribute to the vulnerability of BD through the disturbance of self-referential mental activity and switching between the default mode and frontoparietal networks.

Late responses in the anterior insula reflect the cognitive component of pain: evidence of nonpain processing

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Distinguishing sensory and cognitive aspects of pain-related insular activity and the temporal profile of anterior insula activity suggested a key role of cognitive modulation.

Introduction:

Pain is a complex experience influenced by sensory and psychological factors. The insula is considered to be a core part of the pain network in the brain. Previous studies have suggested a relationship between the posterior insula (PI) and sensory processing, and between the anterior insula (AI) and cognitive–affective factors.

Objectives:

Our aim was to distinguish sensory and cognitive responses in pain-related insular activities.

Methods:

We recorded spatiotemporal insular activation patterns of healthy participants (n = 20) during pain or tactile processing with painful or nonpainful movie stimuli, using a magnetoencephalography. We compared the peak latency between PI and AI activities in each stimulus condition, and between pain and tactile processing in each response. The peak latency and amplitude between different movies were then examined to explore the effects of cognitive influence. A visual analogue scale was used to assess subjective perception.

Results:

The results revealed one clear PI activity and 2 AI activities (early and late) in insular responses induced by pain/tactile stimulation. The early response transmitted from the PI to AI was observed during sensory-associated brain activity, whereas the late AI response was observed during cognitive-associated activity. In addition, we found that painful movie stimuli had a significant influence on both late AI activity and subjective perception, caused by nonpainful actual stimulation.

Conclusions:

The current findings suggested that late AI activation reflects the processing of cognitive pain information, whereas the PI and early AI responses reflect sensory processing.

Weighted Blind Source Separation Can Decompose the Frequency Mismatch Response by Deviant Concatenation: An MEG Study

The mismatch response (MMR) is thought to be a neurophysiological measure of novel auditory detection that could serve as a translational biomarker of various neurological diseases. When recorded with electroencephalography (EEG) or magnetoencephalography (MEG), the MMR is traditionally extracted by subtracting the event-related potential/field (ERP/ERF) elicited in response to “deviant” sounds that occur randomly within a train of repetitive “standard” sounds. However, there are several problems with such a subtraction, which include increased noise and the neural adaptation problem. On the basis of the original theory underlying MMR (i.e., the memory-comparison process), the MMR should be present only in deviant epochs. Therefore, we proposed a novel method called weighted-BSST/k, which uses only the deviant response to derive the MMR. Deviant concatenation and weight assignment are the primary procedures of weighted-BSST/k, which maximize the benefits of time-delayed correlation. We hypothesized that this novel weighted-BSST/k method highlights responses related to the detection of the deviant stimulus and is more sensitive than independent component analysis (ICA). To test this hypothesis and the validity and efficacy of the weighted-BSST/k in comparison with ICA (infomax), we evaluated the methods in 12 healthy adults. Auditory stimuli were presented at a constant rate of 2 Hz. Frequency MMRs at a sensor level were obtained from the bilateral temporal lobes with the subtraction approach at 96–276 ms (the MMR time range), defined based on spatio-temporal cluster permutation analysis. In the application of the weighted-BSST/k, the deviant responses were given a constant weight using a rectangular window on the MMR time range. The ERF elicited by the weighted deviant responses demonstrated one or a few dominant components representing the MMR that fitted well with that of the sensor space analysis using the conventional subtraction approach. In contrast, infomax or weighted-infomax revealed many minor or pseudo components as constituents of the MMR. Our single-trial, contrast-free approach may assist in using the MMR in basic and clinical research, and it opens a new and potentially useful way to analyze event-related MEG/EEG data.

Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study

BACKGROUND

Bipolar disorder (BD) is a serious psychiatric disorder that is associated with a high suicide rate, and for which no clinical biomarker has yet been identified. To address this issue, we investigated the use of magnetoencephalography (MEG) as a new prospective tool. MEG has been used to evaluate frequency-specific connectivity between brain regions; however, no previous study has investigated the frequency-specific resting-state connectome in patients with BD. This resting-state MEG study explored the oscillatory representations of clinical symptoms of BD via graph analysis.

METHODS

In this prospective case-control study, 17 patients with BD and 22 healthy controls (HCs) underwent resting-state MEG and evaluations for depressive and manic symptoms. After estimating the source current distribution, orthogonalized envelope correlations between multiple brain regions were evaluated for each frequency band. We separated regions-of-interest into seven left and right network modules, including the frontoparietal network (FPN), limbic network (LM), salience network (SAL), and default mode network (DMN), to compare the intra- and inter-community edges between the two groups.

RESULTS

In the BD group, we found significantly increased inter-community edges of the right LM-right DMN at the gamma band, and decreased inter-community edges of the right SAL-right FPN at the delta band and the left SAL-right SAL at the theta band. Intra-community edges in the left LM at the high beta band were significantly higher in the BD group than in the HC group. The number of connections in the left LM at the high beta band showed positive correlations with the subjective and objective depressive symptoms in the BD group.

CONCLUSION

We introduced graph theory into resting-state MEG studies to investigate the functional connectivity in patients with BD. To the best of our knowledge, this is a novel approach that may be beneficial in the diagnosis of BD. This study describes the spontaneous oscillatory brain networks that compensate for the time-domain issues associated with functional magnetic resonance imaging. These findings suggest that the connectivity of the LM at the beta band may be a good objective biological biomarker of the depressive symptoms associated with BD.

Prospects for Future Methodological Development and Application of Magnetoencephalography Devices in Psychiatry

Magnetoencephalography (MEG) is a functional neuroimaging tool that can record activity from the entire cortex on the order of milliseconds. MEG has been used to investigate numerous psychiatric disorders, such as schizophrenia, bipolar disorder, major depression, dementia, and autism spectrum disorder. Although several review papers on the subject have been published, perspectives and opinions regarding the use of MEG in psychiatric research have primarily been discussed from a psychiatric research point of view. Owing to a newly developed MEG sensor, the use of MEG devices will soon enter a critical period, and now is a good time to discuss the future of MEG use in psychiatric research. In this paper, we will discuss MEG devices from a methodological point of view. We will first introduce the utilization of MEG in psychiatric research and the development of its technology. Then, we will describe the principle theory of MEG and common algorithms, which are useful for applying MEG tools to psychiatric research. Next, we will consider three topics-child psychiatry, resting-state networks, and cortico-subcortical networks-and address the future use of MEG in psychiatry from a broader perspective. Finally, we will introduce the newly developed device, the optically-pumped magnetometer, and discuss its future use in MEG systems in psychiatric research from a methodological point of view. We believe that state-of-the-art electrophysiological tools, such as this new MEG system, will further contribute to our understanding of the core pathology in various psychiatric disorders and translational research.

A novel method for extracting interictal epileptiform discharges in multi-channel MEG: Use of fractional type of blind source separation

OBJECTIVE

Visual inspection of interictal epileptiform discharges (IEDs) in multi-channel MEG requires a time-consuming evaluation process and often leads to inconsistent results due to variability of IED waveforms. Here, we propose a novel extraction method for IEDs using a T/k type of blind source separation (BSS_T/k).

METHODS

We applied BSS_T/k with seven patients with focal epilepsy to test the accuracy of identification of IEDs. We conducted comparisons of the results of BSS components with those obtained by visual inspection in sensor-space analysis.

RESULTS

BSS_T/k provided better signal estimation of IEDs compared with sensor-space analysis. Importantly, BSS_T/k was able to uncover IEDs that could not be detected by visual inspection. Furthermore, IED components were clearly extracted while preserving spike and wave morphology. Variable IED waveforms were decomposed into one dominant component.

CONCLUSIONS

BSS_T/k was able to visualize the spreading signals over multiple channels into a single component from a single epileptogenic zone. BSS_T/k can be applied to focal epilepsy with a simple parameter setting.

SIGNIFICANCE

Our novel method was able to highlight IEDs with increased accuracy for identification of IEDs from multi-channel MEG data.

Spatiotemporal brain dynamics of auditory temporal assimilation

Time is a fundamental dimension, but millisecond-level judgments sometimes lead to perceptual illusions. We previously introduced a “time-shrinking illusion” using a psychological paradigm that induces auditory temporal assimilation (ATA). In ATA, the duration of two successive intervals (T₁ and T₂), marked by three auditory stimuli, can be perceived as equal when they are not. Here, we investigate the spatiotemporal profile of human temporal judgments using magnetoencephalography (MEG). Behavioural results showed typical ATA: participants judged T₁ and T₂ as equal when T₂ − T₁ ≤ +80 ms. MEG source-localisation analysis demonstrated that regional activity differences between judgment and no-judgment conditions emerged in the temporoparietal junction (TPJ) during T₂. This observation in the TPJ may indicate its involvement in the encoding process when T₁ ≠ T₂. Activation in the inferior frontal gyrus (IFG) was enhanced irrespective of the stimulus patterns when participants engaged in temporal judgment. Furthermore, just after the final marker, activity in the IFG was enhanced specifically for the time-shrinking pattern. This indicates that activity in the IFG is also related to the illusory perception of time-interval equality. Based on these observations, we propose neural signatures for judgments of temporal equality in the human brain.

Evaluation of performance to detect default mode network among some algorithms applied to resting-state fMRI data

Significant correlation exists in the blood-oxygen-level-dependent (BOLD) signals of resting-state fMRI across different regions in the brain. These regions form the default mode network (DMN), salience network (SN), sensory networks, and others. Among these, the DMN is widely investigated in relation to various mental diseases. Several analytic methods are available for obtaining the DMN activity from individuals' fMRI time-series signals, but a fully effective method has not yet been established. In the present study, we examined a functional connectivity analysis and three algorithms of blind source separation including independent component analysis, second-order blind identification, and non-negative matrix factorization using a set of resting-state fMRI data measured for twelve young participants. Results showed that the second-order blind identification yielded superior performance for the DMN detection, indicating significant activation in all DMN regions based on statistical parametric maps.

Unifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis

Unsupervised analysis of the dynamics (nonstationarity) of functional brain connectivity during rest has recently received a lot of attention in the neuroimaging and neuroengineering communities. Most studies have used functional magnetic resonance imaging, but electroencephalography (EEG) and magnetoencephalography (MEG) also hold great promise for analyzing nonstationary functional connectivity with high temporal resolution. Previous EEG/MEG analyses divided the problem into two consecutive stages: the separation of neural sources and then the connectivity analysis of the separated sources. Such nonoptimal division into two stages may bias the result because of the different prior assumptions made about the data in the two stages. We propose a unified method for separating EEG/MEG sources and learning their functional connectivity (coactivation) patterns. We combine blind source separation (BSS) with unsupervised clustering of the activity levels of the sources in a single probabilistic model. A BSS is performed on the Hilbert transforms of band-limited EEG/MEG signals, and coactivation patterns are learned by a mixture model of source envelopes. Simulation studies show that the unified approach often outperforms conventional two-stage methods, indicating further the benefit of using Hilbert transforms to deal with oscillatory sources. Experiments on resting-state EEG data, acquired in conjunction with a cued motor imagery or nonimagery task, also show that the states (clusters) obtained by the proposed method often correlate better with physiologically meaningful quantities than those obtained by a two-stage method.

Integrated approach for studying adaptation mechanisms in the human somatosensory cortical network

Magnetoencephalography and independent component analysis (ICA) was utilized to study and characterize neural adaptation in the somatosensory cortical network. Repetitive punctate tactile stimuli were applied unilaterally to the dominant hand and face using a custom-built pneumatic stimulator called the TAC-Cell. ICA-based source estimation from the evoked neuromagnetic responses indicated cortical activity in the contralateral primary somatosensory cortex (SI) for face stimulation, while hand stimulation resulted in robust contralateral SI and posterior parietal cortex (PPC) activation. Activity was also observed in the secondary somatosensory cortical area (SII) with reduced amplitude and higher variability across subjects. There was a significant difference in adaptation rate between SI and higher-order somatosensory cortices for hand stimulation. Adaptation was significantly dependent on stimulus frequency and pulse index within the stimulus train for both hand and face stimulation. The peak latency of the activity was significantly dependent on stimulation site (hand vs. face) and cortical area (SI vs. PPC). The difference in the peak latency of activity in SI and PPC is presumed to reflect a hierarchical serial-processing mechanism in the somatosensory cortex.

Ocular and cardiac artifact rejection for real-time analysis in MEG

BACKGROUND

Recently, magnetoencephalography (MEG) based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods for neuroscience research. It is well known that artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming process.

NEW METHOD

The method (referred to as ocular and cardiac artifact rejection for real-time analysis, OCARTA) is based on constrained independent component analysis (cICA), where a priori information of the underlying source signals is used to optimize and accelerate signal decomposition. Thereby, prior information is incorporated by using the subject's individual cardiac and ocular activity. The algorithm automatically uses different separation strategies depending on the underlying source activity.

RESULTS

OCARTA was tested and applied to data from three different but most commonly used MEG systems (4D-Neuroimaging, VSM MedTech Inc. and Elekta Neuromag). Ocular and cardiac artifacts were effectively reduced within one iteration at a time delay of 1ms performed on a standard PC (Intel Core i5-2410M).

COMPARISON WITH EXISTING METHODS

The artifact rejection results achieved with OCARTA are in line with the results reported for offline ICA-based artifact rejection methods.

CONCLUSION

Due to the fast and subject-specific signal decomposition the new approach introduced here is capable of real-time ocular and cardiac artifact rejection.

Neurodynamics of somatosensory cortices studied by magnetoencephelography

From the viewpoint of statistical inverse problems, identification of transfer functions in feedback models is applied for neurodynamics of somatosensory cortices, and brain communication among active regions can be expressed in terms of transfer functions. However, brain activities have been investigated mainly by averaged waveforms in the conventional magnetoencephalography analysis, and thus brain communication among active regions has not yet been identified. It is shown that brain communication among two more than three brain regions is determined, when fluctuations related to concatenate averaged waveforms can be obtained by using a suitable blind source separation method. In blind identification of feedback model, some transfer functions or their impulse responses between output variables of current dipoles corresponding to active regions are identified from reconstructed time series data of fluctuations by the method of inverse problem. Neurodynamics of somatosensory cortices in 5 Hz median nerve stimuli can be shown by cerebral communication among active regions of somatosensory cortices in terms of impulse responses of feedback model.

A signal-processing pipeline for magnetoencephalography resting-state networks

To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level reconstruction of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been documented by means of a dedicated processing pipeline: MEG recordings are decomposed by independent component analysis (ICA) into artifact and brain components (ICs); next, the channel maps associated with the latter ones are projected into the source space and the resulting voxel-wise weights are used to linearly combine the IC time courses. An extensive description of the proposed pipeline is provided here, along with an assessment of its performances with respect to alternative approaches. The following investigations were carried out: (1) ICA decomposition algorithm. Synthetic data are used to assess the sensitivity of the ICA results to the decomposition algorithm, by testing FastICA, INFOMAX, and SOBI. FastICA with deflation approach, a standard solution, provides the best decomposition. (2) Recombination of brain ICs versus subtraction of artifactual ICs (at the channel level). Both the recombination of the brain ICs in the sensor space and the classical procedure of subtracting the artifactual ICs from the recordings provide a suitable reconstruction, with a lower distortion using the latter approach. (3) Recombination of brain ICs after localization versus localization of artifact-corrected recordings. The brain IC recombination after source localization, as implemented in the proposed pipeline, provides a lower source-level signal distortion. (4) Detection of RSNs. The accuracy in source-level reconstruction by the proposed pipeline is confirmed by an improved specificity in the retrieval of RSNs from experimental data.

Blind source separation of neural activities from magnetoencephalogram in periodical median nerve stimuli

Neural activities of cortices in periodical median nerve stimuli are studied from magnetoencephalogram. The fractional type of the decorrelation method is used for the blind source separation with temporal structure. The blind source separation method is proposed for selecting neural activities related to somatosensory stimulus from magnetoencephalogram by comparing cross-correlation functions between components of blind source separation.

Adaptive changes in the neuromagnetic response of the primary and association somatosensory areas following repetitive tactile hand stimulation in humans

Cortical adaptation in the primary somatosensory cortex (SI) has been probed using different stimulation modalities and recording techniques, in both human and animal studies. In contrast, considerably less knowledge has been gained about the adaptation profiles in other areas of the cortical somatosensory network. Using magnetoencephalography (MEG), we examined the patterns of short-term adaptation for evoked responses in SI and somatosensory association areas during tactile stimulation applied to the glabrous skin of the hand. Cutaneous stimuli were delivered as trains of serial pulses with a constant frequency of 2 Hz and 4 Hz in separate runs, and a constant inter-train interval of 5 s. The unilateral stimuli elicited transient responses to the serial pulses in the train, with several response components that were separated by independent component analysis. Subsequent source reconstruction techniques identified regional generators in the contralateral SI and somatosensory association areas in the posterior parietal cortex (PPC). Activity in the bilateral secondary somatosensory cortex (i.e., SII/PV) was also identified, although less consistently across subjects. The dynamics of the evoked activity in each area and the frequency-dependent adaptation effects were assessed from the changes in the relative amplitude of serial responses in each train. We show that the adaptation profiles in SI and PPC areas can be quantitatively characterized from neuromagnetic recordings using tactile stimulation, with the sensitivity to repetitive stimulation increasing from SI to PPC. A similar approach for SII/PV has proven less straightforward, potentially due to the tendency of these areas to respond selectively to certain stimuli.

Temporal dynamics of spontaneous MEG activity in brain networks

Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of band-limited power primarily within the theta, alpha, and beta bands-that is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses.

Dynamical activities of primary somatosensory cortices studied by magnetoencephalography

A blind identification method of transfer functions in feedback systems is introduced for examination of dynamical activities of cortices by magnetoencephalography study. Somatosensory activities are examined in 5 Hz periodical median nerve stimulus. In the present paper, we will try two careful preprocessing procedures for the identification method to obtain impulse responses between primary somatosensory cortices. Time series data of the somatosensory evoked field are obtained by using a blind source separation of the T/k type (fractional) decorrelation method. Time series data of current dipoles of primary somatosensory cortices are transformed from the time series data of the somatosensory evoked field by the inverse problem. Fluctuations of current dipoles of them are obtained after elimination of deterministic periodical evoked waveforms. An identification method based on feedback system theory is used for estimation of transfer functions in a feedback model from obtained fluctuations of currents dipoles of primary somatosensory cortices. Dynamical activities between them are presented by Bode diagrams of transfer functions and their impulse responses: the time delay of about 30 ms via corpus callosum is found in the impulse response of identified transfer function.

Wired for her face? Male attentional bias for female faces

Under conditions of inattention or deficits in orienting attention, special classes of stimuli (e.g. faces, bodies) are more likely to be perceived than other stimuli. This suggests that biologically salient visual stimuli automatically recruit attention, even when they are task-irrelevant or ignored. Here we report results from a behavioral experiment with female and male subjects and two magnetoencephalography (MEG) experiments with male subjects only, in which we investigated attentional capture with face and hand stimuli. In both the behavioral and MEG experiments, subjects were required to count the number of gender-specific targets from either face or hand categories within a block of stimuli. In the behavioral experiment, we found that male subjects were significantly more accurate in response to female than male face target blocks. There was no corresponding effect found in response to hand target blocks. Female subjects did not show a gender-based difference in response to face or hand target blocks. MEG results indicated that the male subjects' responses to face stimuli in primary visual cortex (V1) and the face-selective part of the fusiform gyrus (FG) were reduced when male face stimuli were not relevant to the task, whereas female faces maintained a strong response in these areas in both task-relevant and task-irrelevant conditions. These results suggest that within the male brain, female face stimuli are more resilient to suppression than male faces, once attention is drawn to the part of the visual field where the face appears.

Evoked magnetic fields of magnetoencephalography and their statistical property

In an evoked magnetic field of magnetoencephalography a wave form is calculated by averaging. We propose that the wave form is deterministic in the case of 5 Hz periodical stimuli. We have found with statistical accuracy that the wave form of a somatosensory evoked magnetic field is deterministic in 5 Hz periodical median nerve stimuli, since any stationary process is decomposed into a deterministic part and a nondeterministic part from the Wold decomposition theorem. For the decorrelation method of blind source separation we have obtained several components which have nonzero wave forms. Via the selected components time series data of a somatosensory evoked magnetic field generated from somatosensory cortexes have been separated from background brain noise by using a T/k (fractional) type decorrelation method.

Exploring the variability of single trials in somatosensory evoked responses using constrained source extraction and RMT

This paper describes the theoretical background of a new data-driven approach to encephalographic single-trial (ST) data analysis. Temporal constrained source extraction using sparse decomposition identifies signal topographies that closely match the shape characteristics of a reference signal, one response for each ST. The correlations between these ST topographies are computed for formal Correlation Matrix Analysis (CMA) based on Random Matrix Theory (RMT). The RMT-CMA provides clusters of similar ST topologies in a completely unsupervised manner. These patterns are then classified into deterministic set and noise using well established RMT results. The efficacy of the method is applied to EEG and MEG data of somatosensory evoked responses (SERs). The results demonstrate that the method can recover brain signals with time course resembling the reference signal and follow changes in strength and/or topography in time by simply stepping the reference signal through time.