Wiener Filter-Magnetoencephalography of Visual Cortical Activity

Dynamic cortical connectivity alterations associated with Alzheimer's disease: An EEG and fNIRS integration study

Emerging evidence indicates that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain network. Exploring alterations in the AD brain network is therefore of great importance for understanding and treating the disease. This study employs an integrative functional near-infrared spectroscopy (fNIRS) – electroencephalography (EEG) analysis approach to explore dynamic, regional alterations in the AD-linked brain network. FNIRS and EEG data were simultaneously recorded from 14 participants (8 healthy controls and 6 patients with mild AD) during a digit verbal span task (DVST). FNIRS-based spatial constraints were used as priors for EEG source localization. Graph-based indices were then calculated from the reconstructed EEG sources to assess regional differences between the groups. Results show that patients with mild AD revealed weaker and suppressed cortical connectivity in the high alpha band and in beta band to the orbitofrontal and parietal regions. AD-induced brain networks, compared to the networks of age-matched healthy controls, were mainly characterized by lower degree, clustering coefficient at the frontal pole and medial orbitofrontal across all frequency ranges. Additionally, the AD group also consistently showed higher index values for these graph-based indices at the superior temporal sulcus. These findings not only validate the feasibility of utilizing the proposed integrated EEG-fNIRS analysis to better understand the spatiotemporal dynamics of brain activity, but also contribute to the development of network-based approaches for understanding the mechanisms that underlie the progression of AD.

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Dynamic brain networks of healthy controls and patients with mild AD are documented via an integrative fNIRS-EEG approach.

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FNIRS-based constraints are employed as spatial priors for EEG source localization.

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Mild AD group reveals weaker connectivity to the orbitofrontal and parietal regions in high alpha band and beta band.

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AD-linked brain networks are characterized by lower degree and clustering coefficient at the frontal area.

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AD group also reveals higher index values for these graph-based indices at the superior temporal sulcus.

Information spreading by a combination of MEG source estimation and multivariate pattern classification

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Visual field asymmetries in visual evoked responses

Behavioral responses to visual stimuli exhibit visual field asymmetries, but cortical folding and the close proximity of visual cortical areas make electrophysiological comparisons between different stimulus locations problematic. Retinotopy-constrained source estimation (RCSE) uses distributed dipole models simultaneously constrained by multiple stimulus locations to provide separation between individual visual areas that is not possible with conventional source estimation methods. Magnetoencephalography and RCSE were used to estimate time courses of activity in V1, V2, V3, and V3A. Responses to left and right hemifield stimuli were not significantly different. Peak latencies for peripheral stimuli were significantly shorter than those for perifoveal stimuli in V1, V2, and V3A, likely related to the greater proportion of magnocellular input to V1 in the periphery. Consistent with previous results, sensor magnitudes for lower field stimuli were about twice as large as for upper field, which is only partially explained by the proximity to sensors for lower field cortical sources in V1, V2, and V3. V3A exhibited both latency and amplitude differences for upper and lower field responses. There were no differences for V3, consistent with previous suggestions that dorsal and ventral V3 are two halves of a single visual area, rather than distinct areas V3 and VP.

Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information

For practical brain-machine interfaces (BMIs), electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are the only current methods that are non-invasive and available in non-laboratory environments. However, the use of EEG and NIRS involves certain inherent problems. EEG signals are generally a mixture of neural activity from broad areas, some of which may not be related to the task targeted by BMI, hence impairing BMI performance. NIRS has an inherent time delay as it measures blood flow, which therefore detracts from practical real-time BMI utility. To try to improve real environment EEG-NIRS-based BMIs, we propose here a novel methodology in which the subjects' mental states are decoded from cortical currents estimated from EEG, with the help of information from NIRS. Using a Variational Bayesian Multimodal EncephaloGraphy (VBMEG) methodology, we incorporated a novel form of NIRS-based prior to capture event related desynchronization from isolated current sources on the cortical surface. Then, we applied a Bayesian logistic regression technique to decode subjects' mental states from further sparsified current sources. Applying our methodology to a spatial attention task, we found our EEG-NIRS-based decoder exhibited significant performance improvement over decoding methods based on EEG sensor signals alone. The advancement of our methodology, decoding from current sources sparsely isolated on the cortex, was also supported by neuroscientific considerations; intraparietal sulcus, a region known to be involved in spatial attention, was a key responsible region in our task. These results suggest that our methodology is not only a practical option for EEG-NIRS-based BMI applications, but also a potential tool to investigate brain activity in non-laboratory and naturalistic environments.

Optimization of retinotopy constrained source estimation constrained by prior

Studying how the timing and amplitude of visual evoked responses (VERs) vary between visual areas is important for understanding visual processing but is complicated by difficulties in reliably estimating VERs in individual visual areas using noninvasive brain measurements. Retinotopy constrained source estimation (RCSE) addresses this challenge by using multiple, retinotopically mapped stimulus locations to simultaneously constrain estimates of VERs in visual areas V1, V2, and V3, taking advantage of the spatial precision of fMRI retinotopy and the temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG). Nonlinear optimization of dipole locations, guided by a group-constrained RCSE solution as a prior, improved the robustness of RCSE. This approach facilitated the analysis of differences in timing and amplitude of VERs between V1, V2, and V3, elicited by stimuli with varying luminance contrast in a sample of eight adult humans. The V1 peak response was 37% larger than that of V2 and 74% larger than that of V3, and also ~10-20 ms earlier. Normalized contrast response functions were nearly identical for the three areas. Results without dipole optimization, or with other nonlinear methods not constrained by prior estimates were similar but suffered from greater between-subject variability. The increased reliability of estimates offered by this approach may be particularly valuable when using a smaller number of stimulus locations, enabling a greater variety of stimulus and task manipulations.

Improved method for retinotopy constrained source estimation of visual-evoked responses

Retinotopy constrained source estimation (RCSE) is a method for noninvasively measuring the time courses of activation in early visual areas using magnetoencephalography (MEG) or electroencephalography (EEG). Unlike conventional equivalent current dipole or distributed source models, the use of multiple, retinotopically mapped stimulus locations to simultaneously constrain the solutions allows for the estimation of independent waveforms for visual areas V1, V2, and V3, despite their close proximity to each other. We describe modifications that improve the reliability and efficiency of this method. First, we find that increasing the number and size of visual stimuli results in source estimates that are less susceptible to noise. Second, to create a more accurate forward solution, we have explicitly modeled the cortical point spread of individual visual stimuli. Dipoles are represented as extended patches on the cortical surface, which take into account the estimated receptive field size at each location in V1, V2, and V3 as well as the contributions from contralateral, ipsilateral, dorsal, and ventral portions of the visual areas. Third, we implemented a map fitting procedure to deform a template to match individual subject retinotopic maps derived from functional magnetic resonance imaging (fMRI). This improves the efficiency of the overall method by allowing automated dipole selection, and it makes the results less sensitive to physiological noise in fMRI retinotopy data. Finally, the iteratively reweighted least squares (IRLS) method was used to reduce the contribution from stimulus locations with high residual error for robust estimation of visual evoked responses.

Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior

Previous simulation and experimental studies have demonstrated that the application of Variational Bayesian Multimodal EncephaloGraphy (VBMEG) to magnetoencephalography (MEG) data can be used to estimate cortical currents with high spatio-temporal resolution, by incorporating functional magnetic resonance imaging (fMRI) activity as a hierarchical prior. However, the use of combined MEG and fMRI is restricted by the high costs involved, a lack of portability and high sensitivity to body-motion artifacts. One possible solution for overcoming these limitations is to use a combination of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This study therefore aimed to extend the possible applications of VBMEG to include EEG data with NIRS activity as a hierarchical prior. Using computer simulations and real experimental data, we evaluated the performance of VBMEG applied to EEG data under different conditions, including different numbers of EEG sensors and different prior information. The results suggest that VBMEG with NIRS prior performs well, even with as few as 19 EEG sensors. These findings indicate the potential value of clinically applying VBMEG using a combination of EEG and NIRS.

Evaluation of hierarchical Bayesian method through retinotopic brain activities reconstruction from fMRI and MEG signals

A hierarchical Bayesian method estimated current sources from MEG data, incorporating an fMRI constraint as a hierarchical prior whose strength is controlled by hyperparameters. A previous study [Sato, M., Yoshioka, T., Kajihara, S., Toyama, K., Goda, N., Doya, K., Kawato, M., 2004. Hierarchical Bayesian estimation for MEG inverse problem. Neuroimage 23, 806-826] demonstrated that fMRI information improves the localization accuracy for simulated data. The goal of the present study is to confirm the usefulness of the hierarchical Bayesian method by the real MEG and fMRI experiments using visual stimuli with a fan-shaped checkerboard pattern presented in four visual quadrants. The proper range of hyperparameters was systematically analyzed using goodness of estimate measures for the estimated currents. The robustness with respect to false-positive activities in the fMRI information was also evaluated by using noisy priors constructed by adding artificial noises to real fMRI signals. It was shown that with appropriate hyperparameter values, the retinotopic organization and temporal dynamics in the early visual area were reconstructed, which were in a close correspondence with the known brain imaging and electrophysiology of the humans and monkeys. The false-positive effects of the noisy priors were suppressed by using appropriate hyperparameter values. The hierarchical Bayesian method also was capable of reconstructing retinotopic sequential activation in V1 with fine spatiotemporal resolution, from MEG data elicited by sequential stimulation of the four visual quadrants with the fan-shaped checker board pattern at much shorter intervals (150 and 400 ms) than the temporal resolution of fMRI. These results indicate the potential capability for the hierarchical Bayesian method combining MEG with fMRI to improve the spatiotemporal resolution of noninvasive brain activity measurement.

Time course of superior temporal sulcus activity in response to eye gaze: a combined fMRI and MEG study

The human superior temporal sulcus (STS) has been suggested to be involved in gaze processing, but temporal data regarding this issue are lacking. We investigated this topic by combining fMRI and MEG in four normal subjects. Photographs of faces with either averted or straight eye gazes were presented and subjects passively viewed the stimuli. First, we analyzed the brain areas involved using fMRI. A group analysis revealed activation of the STS for averted compared to straight gazes, which was confirmed in all subjects. We then measured brain activity using MEG, and conducted a 3D spatial filter analysis. The STS showed higher activity in response to averted versus straight gazes during the 150-200 ms period, peaking at around 170 ms, after stimulus onset. In contrast, the fusiform gyrus, which was detected by the main effect of stimulus presentations in fMRI analysis, exhibited comparable activity across straight and averted gazes at about 170 ms. These results indicate involvement of the human STS in rapid processing of the eye gaze of another individual.

A comparison of functional MRI and magnetoencephalography for receptive language mapping

We compared functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) for the mapping of receptive language function. Participants performed the same language task in the two different imaging environments. MEG activation profiles showed prominent bilateral activity in superior temporal gyrus and left-lateralized activity in middle temporal gyrus. fMRI activation profiles revealed bilateral activity in prefrontal, superior temporal, middle temporal, and visual areas. Laterality quotients derived from the two modalities showed poor agreement between the two methods for commonly active regions of interest. Locations of peak activity also varied considerably within participants between the two methods.

Kalman filtering for reliable estimation of BBB permeability

INTRODUCTION

The blood-brain barrier (BBB) plays an important role in the pathophysiology of a number of central nervous system disorders. In the past, a number of laboratory techniques have been proposed to quantify permeability coefficient ki, an important index of barrier function. Recently, magnetic resonance imaging (MRI) has been used to estimate ki based on graphical plot technique. The MR technique was found to be in good agreement with the gold standard, quantitative autoradiography (QAR). However, a reduced image signal-to-noise ratio, among other factors such as partial volume effects, did not allow reliable estimation of permeability coefficients. This proof-of-principle study proposes the use of Kalman filter as a filtering technique for a reliable estimation of permeability coefficients. The results are compared to those obtained using the Wiener filter technique.

MATERIALS AND METHODS

MRI experiments were performed in Wistar rats (N=2) using a 4.7-T Bruker Biospec MR system (Bruker Biospin, Billerica, MA). After acquiring localizer images, T2-weighted diffusion-weighted imaging images were acquired. Finally, a rapid T1 mapping protocol was implemented to acquire one pre-gadolinium diethylenetriamine pentaacetic acid baseline data set followed by postinjection data sets at 3-min intervals for 45 min. Data were postprocessed with and without the application of Kalman and Wiener filters to obtain an estimate of ki.

RESULTS AND DISCUSSION

Comparing T1 maps, Patlak plots and permeability maps with and without the Kalman filtering presented several interesting observations. Kalman-filtered Patlak plots, compared to nonfiltered plots, showed that discrete data points on the plot were closer to the line fit. The number of time points used for the construction of the graphical plot had no effect on permeability coefficient estimates when the Kalman filter was used. A box-and-whiskers plot showed longer Y-error bars for nonfiltered and Wiener data compared to Kalman-filtered data. These observations suggest that it may be possible to obtain reliable permeability coefficient estimates in a short study time by applying the Kalman filter to the data. Future work involves investigating the application of this filter on a large-sample-size animal study and evaluating the role of partial volume effects on BBB permeability estimation.

Hierarchical Bayesian estimation for MEG inverse problem

Source current estimation from MEG measurement is an ill-posed problem that requires prior assumptions about brain activity and an efficient estimation algorithm. In this article, we propose a new hierarchical Bayesian method introducing a hierarchical prior that can effectively incorporate both structural and functional MRI data. In our method, the variance of the source current at each source location is considered an unknown parameter and estimated from the observed MEG data and prior information by using the Variational Bayesian method. The fMRI information can be imposed as prior information on the variance distribution rather than the variance itself so that it gives a soft constraint on the variance. A spatial smoothness constraint, that the neural activity within a few millimeter radius tends to be similar due to the neural connections, can also be implemented as a hierarchical prior. The proposed method provides a unified theory to deal with the following three situations: (1) MEG with no other data, (2) MEG with structural MRI data on cortical surfaces, and (3) MEG with both structural MRI and fMRI data. We investigated the performance of our method and conventional linear inverse methods under these three conditions. Simulation results indicate that our method has better accuracy and spatial resolution than the conventional linear inverse methods under all three conditions. It is also shown that accuracy of our method improves as MRI and fMRI information becomes available. Simulation results demonstrate that our method appropriately resolves the inverse problem even if fMRI data convey inaccurate information, while the Wiener filter method is seriously deteriorated by inaccurate fMRI information.