Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data

In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data.     

Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation

Source current estimation from electromagnetic (MEG and EEG) signals is an ill-posed problem that often produces blurry or inaccurately positioned estimates. The two modalities have distinct factors limiting the resolution, e.g., MEG cannot detect radially oriented sources, while EEG is sensitive to accuracy of the head model. This makes combined EEG + MEG estimation techniques desirable, but different acquisition noise statistics, complexity of the head models, and lack of pertinent metrics all complicate the assessment of the resulting improvements. We investigated analytically the effect of including EEG recordings in MEG studies versus the addition of new MEG channels when computing noise-normalized minimum ℓ₂-norm estimates. Three-compartment boundary-element forward models were constructed using structural MRI scans for four subjects. Singular value analysis of the resulting forward models predicted better performance of the EEG + MEG case in the form of higher matrix rank. MNE inverse operators for EEG, MEG and EEG + MEG were constructed using the sensor noise covariance estimated from data. Metrics derived from the resolution matrices predicted higher spatial resolution in EEG + MEG as compared to MEG due to decreased spread (lower spatial dispersion, higher resolution index) with no reduction in dipole localization error. The effect was apparent in all source locations, with increased magnitude for deep areas such as the cingulate cortex. We were also able to corroborate the results for the somatosensory cortex using median nerve responses.     

The influence of brain tissue anisotropy on human EEG and MEG.

The influence of gray and white matter tissue anisotropy on the human electroencephalogram (EEG) and magnetoencephalogram (MEG) was examined with a high resolution finite element model of the head of an adult male subject. The conductivity tensor data for gray and white matter were estimated from magnetic resonance diffusion tensor imaging. Simulations were carried out with single dipoles or small extended sources in the cortical gray matter. The inclusion of anisotropic volume conduction in the brain was found to have a minor influence on the topology of EEG and MEG (and hence source localization). We found a major influence on the amplitude of EEG and MEG (and hence source strength estimation) due to the change in conductivity and the inclusion of anisotropy. We expect that inclusion of tissue anisotropy information will improve source estimation procedures.     

A novel integrated MEG and EEG analysis method for dipolar sources

The ability of magnetoencephalography (MEG) to accurately localize neuronal currents and obtain tangential components of the source is largely due to MEG's insensitivity to the conductivity profile of the head tissues. However, MEG cannot reliably detect the radial component of the neuronal current. In contrast, the localization accuracy of electroencephalography (EEG) is not as good as MEG, but EEG can detect both the tangential and radial components of the source. In the present study, we investigated the conductivity dependence in a new approach that combines MEG and EEG to accurately obtain, not only the location and tangential components, but also the radial component of the source. In this approach, the source location and tangential components are obtained from MEG alone, and optimal conductivity values of the EEG model are estimated by best-fitting EEG signal, while precisely matching the tangential components of the source in EEG and MEG. Then, the radial components are obtained from EEG using the previously estimated optimal conductivity values. Computer simulations testing this integrated approach demonstrated two main findings. First, there are well-organized optimal combinations of the conductivity values that provide an accurate fit to the combined MEG and EEG data. Second, the radial component, in addition to the location and tangential components, can be obtained with high accuracy without needing to know the precise conductivity profile of the head. We then demonstrated that this new approach performed reliably in an analysis of the 20-ms component from human somatosensory responses elicited by electric median-nerve stimulation.     

Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain

This paper presents a computationally efficient source estimation algorithm that localizes cortical oscillations and their phase relationships. The proposed method employs wavelet-transformed magnetoencephalography (MEG) data and uses anatomical MRI to constrain the current locations to the cortical mantle. In addition, the locations of the sources can be further confined with the help of functional MRI (fMRI) data. As a result, we obtain spatiotemporal maps of spectral power and phase relationships. As an example, we show how the phase locking value (PLV), that is, the trial-by-trial phase relationship between the stimulus and response, can be imaged on the cortex. We apply the method to spontaneous, evoked, and driven cortical oscillations measured with MEG. We test the method of combining MEG, structural MRI, and fMRI using simulated cortical oscillations along Heschl's gyrus (HG). We also analyze sustained auditory gamma-band neuromagnetic fields from MEG and fMRI measurements. Our results show that combining the MEG recording with fMRI improves source localization for the non-noise-normalized wavelet power. In contrast, noise-normalized spectral power or PLV localization may not benefit from the fMRI constraint. We show that if the thresholds are not properly chosen, noise-normalized spectral power or PLV estimates may contain false (phantom) sources, independent of the inclusion of the fMRI prior information. The proposed algorithm can be used for evoked MEG/EEG and block-designed or event-related fMRI paradigms, or for spontaneous MEG data sets. Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain can provide further understanding of large-scale neural activity and communication between different brain regions.     

Source analysis of interictal spikes in polymicrogyria: loss of relevant cortical fissures requires simultaneous EEG to avoid MEG misinterpretation

PURPOSE: Multiple source analysis of interictal EEG and MEG spikes was used to identify irritative zones in polymicrogyria (PMG). Spike onset times and source localization were compared between both modalities. PMG is characterized by a marked loss of deep cortical fissures. Hence, differences between EEG and MEG were expected since MEG signals are predominantly generated from tangentially orientated neurons in fissures. PATIENTS: We studied 7 children and young adults (age 7.5 to 19 years) with localization-related epilepsy and unilateral polymicrogyria (PMG) as defined from anatomical MRI. METHODS: 122-channel whole-head MEG and 32-channel EEG were recorded simultaneously for 25 to 40 min. Using the BESA program, interictal spikes were identified visually and used as templates to search for similar spatio-temporal spike patterns throughout the recording. Detected similar spikes (r > 0.85) were averaged, high-pass filtered (5 Hz) to enhance spike onset, and subjected to multiple spatio-temporal source analysis. Source localization was visualized by superposition on T1-weighted MRI and compared to the lesion. RESULTS: Nine spike types were identified in seven patients (2 types in 2 patients). Eight out of nine EEG sources and seven MEG sources modeling spike onset were localized within the visible lesion. EEG spike onset preceded MEG significantly in two spike types by 19 and 25 ms. This was related to radial onset activity in EEG while MEG localized propagated activity. In one case, the earliest MEG spike activity was localized to the normal hemisphere while the preceding radial EEG onset activity was localized within the lesion. Distances between EEG and MEG onset sources varied markedly between 9 and 51 mm in the eight spike types with concordant lateralization. CONCLUSION: Interictal irritative zones were localized within the lesion in PMG comparable to other malformations, e.g., FCD. Discrepancies in MEG and EEG were related to the lack of deep fissures in PMG. In two cases, MEG was blind to the onset of radial interictal spike activity and localized propagated spike activity. In two other cases, MEG localized to the more peripheral parts of the irritative zone. Simultaneous EEG recordings with MEG and multiple source analysis are required to avoid problems of MEG interpretation.     

MEG source localization under multiple constraints: an extended Bayesian framework

To use Electroencephalography (EEG) and Magnetoencephalography (MEG) as functional brain 3D imaging techniques, identifiable distributed source models are required. The reconstruction of EEG/MEG sources rests on inverting these models and is ill-posed because the solution does not depend continuously on the data and there is no unique solution in the absence of prior information or constraints. We have described a general framework that can account for several priors in a common inverse solution. An empirical Bayesian framework based on hierarchical linear models was proposed for the analysis of functional neuroimaging data [Friston, K., Penny, W., Phillips, C., Kiebel, S., Hinton, G., Ashburner, J., 2002. Classical and Bayesian inference in neuroimaging: theory. NeuroImage 16, 465-483] and was evaluated recently in the context of EEG [Phillips, C., Mattout, J., Rugg, M.D., Maquet, P., Friston, K., 2005. An empirical Bayesian solution to the source reconstruction problem in EEG. NeuroImage 24, 997-1011]. The approach consists of estimating the expected source distribution and its conditional variance that is constrained by an empirically determined mixture of prior variance components. Estimation uses Expectation-Maximization (EM) to give the Restricted Maximum Likelihood (ReML) estimate of the variance components (in terms of hyperparameters) and the Maximum A Posteriori (MAP) estimate of the source parameters. In this paper, we extend the framework to compare different combinations of priors, using a second level of inference based on Bayesian model selection. Using Monte-Carlo simulations, ReML is first compared to a classic Weighted Minimum Norm (WMN) solution under a single constraint. Then, the ReML estimates are evaluated using various combinations of priors. Both standard criterion and ROC-based measures were used to assess localization and detection performance. The empirical Bayes approach proved useful as: (1) ReML was significantly better than WMN for single priors; (2) valid location priors improved ReML source localization; (3) invalid location priors did not significantly impair performance. Finally, we show how model selection, using the log-evidence, can be used to select the best combination of priors. This enables a global strategy for multiple prior-based regularization of the MEG/EEG source reconstruction.     

MEG/EEG sources of the 170-ms response to faces are co-localized in the fusiform gyrus

The 170-ms electrophysiological processing stage (N170 in EEG, M170 in MEG) is considered an important computational step in face processing. Hence its neuronal sources have been modelled in several studies. The current study aimed to specify the relation of the dipolar sources underlying N170 and M170. Whole head EEG and MEG were measured simultaneously during the presentation of unfamiliar faces. An Independent Component Analysis (ICA) was applied to the data prior to localization. N170 and M170 were then modelled with a pair of dipoles in a four-shell ellipse (EEG)/homogeneous sphere (MEG) arranged symmetrically across midline. The dipole locations were projected onto the individual structural MR brain images. Dipoles were localized in fusiform gyri in ten out of eleven individuals for EEG and in seven out of eleven for MEG. N170 and M170 were co-localized in the fusiform gyrus in six individuals. The ICA shifted some of the single-subject dipoles up from cerebellum to fusiform gyrus mainly due to the removal of cardiac activity. The group mean dipole locations were also found in posterior fusiform gyri, and did not differ significantly between EEG and MEG. The result was replicated in a repeated measurement 3 months later.     

Investigations of dipole localization accuracy in MEG using the bootstrap

We describe the use of the nonparametric bootstrap to investigate the accuracy of current dipole localization from magnetoencephalography (MEG) studies of event-related neural activity. The bootstrap is well suited to the analysis of event-related MEG data since the experiments are repeated tens or even hundreds of times and averaged to achieve acceptable signal-to-noise ratios (SNRs). The set of repetitions or epochs can be viewed as a set of independent realizations of the brain's response to the experiment. Bootstrap resamples can be generated by sampling with replacement from these epochs and averaging. In this study, we applied the bootstrap resampling technique to MEG data from somatotopic experimental and simulated data. Four fingers of the right and left hand of a healthy subject were electrically stimulated, and about 400 trials per stimulation were recorded and averaged in order to measure the somatotopic mapping of the fingers in the S1 area of the brain. Based on single-trial recordings for each finger we performed 5000 bootstrap resamples. We reconstructed dipoles from these resampled averages using the Recursively Applied and Projected (RAP)-MUSIC source localization algorithm. We also performed a simulation for two dipolar sources with overlapping time courses embedded in realistic background brain activity generated using the prestimulus segments of the somatotopic data. To find correspondences between multiple sources in each bootstrap, sample dipoles with similar time series and forward fields were assumed to represent the same source. These dipoles were then clustered by a Gaussian Mixture Model (GMM) clustering algorithm using their combined normalized time series and topographies as feature vectors. The mean and standard deviation of the dipole position and the dipole time series in each cluster were computed to provide estimates of the accuracy of the reconstructed source locations and time series.     

Geometrical interpretation of fMRI-guided MEG/EEG inverse estimates

Magneto- and electroencephalography (MEG/EEG) and functional magnetic resonance imaging (fMRI) provide complementary information about the functional organization of the human brain. An important advantage of MEG/EEG is the millisecond time resolution in detecting electrical activity in the cerebral cortex. The interpretation of MEG/EEG signals, however, is limited by the difficulty of determining the spatial distribution of the neural activity. Functional MRI can help in the MEG/EEG source analysis by suggesting likely locations of activity. We present a geometric interpretation of fMRI-guided inverse solutions in which the MEG/EEG source estimate minimizes a distance to a subspace defined by the fMRI data. In this subspace regularization (SSR) approach, the fMRI bias does not assume preferred amplitudes for MEG/EEG sources, only locations. Characteristic dependence of the source estimates on the regularization parameters is illustrated with simulations. When the fMRI locations match the true MEG/EEG source locations, they serve to bias the underdetermined MEG/EEG inverse solution toward the fMRI loci. Importantly, when the fMRI loci do not match the true MEG/EEG loci, the solution is insensitive to those fMRI loci.     

Magnetoencephalographic yield of interictal spikes in temporal lobe epilepsy. Comparison with scalp EEG recordings

To compare magnetoencephalography (MEG) with scalp electroencephalography (EEG) in the detection of interictal spikes in temporal lobe epilepsy (TLE), we simultaneously recorded MEG and scalp EEG with a whole-scalp neuromagnetometer in 46 TLE patients. We visually searched interictal spikes on MEG and EEG channels and classified them into three types according to their presentation on MEG alone (M-spikes), EEG alone (E-spikes), or concomitantly on both modalities (M/E-spikes). The M-spikes and M/E-spikes were localized with MEG equivalent current dipole modeling. We analyzed the relative contribution of MEG and EEG in the overall yield of spike detection and also compared M-spikes with M/E-spikes in terms of dipole locations and strengths. During the 30- to 40-min MEG recordings, interictal spikes were obtained in 36 (78.3%) of the 46 patients. Among the 36 patients, most spikes were M/E-spikes (68.3%), some were M-spikes (22.1%), and some were E-spikes (9.7%). In comparison with EEG, MEG gave better spike yield in patients with lateral TLE. Sources of M/E- and M-spikes were situated in the same anatomical regions, whereas the average dipole strength was larger for M/E- than M-spikes. In conclusion, some interictal spikes appeared selectively on either MEG or EEG channels in TLE patients although more spikes were simultaneously identified on both modalities. Thus, simultaneous MEG and EEG recordings help to enhance spike detection. Identification of M-spikes would offer important localization of irritative foci, especially in patients with lateral TLE.     

Source cancellation profiles of electroencephalography and magnetoencephalography

Recorded electric potentials and magnetic fields due to cortical electrical activity have spatial spread even if their underlying brain sources are focal. Consequently, as a result of source cancellation, loss in signal amplitude and reduction in the effective signal-to-noise ratio can be expected when distributed sources are active simultaneously. Here we investigate the cancellation effects of EEG and MEG through the use of an anatomically correct forward model based on structural MRI acquired from 7 healthy adults. A boundary element model (BEM) with four compartments (brain, cerebrospinal fluid, skull and scalp) and highly accurate cortical meshes (~ 300,000 vertices) were generated. Distributed source activations were simulated using contiguous patches of active dipoles. To investigate cancellation effects in both EEG and MEG, quantitative indices were defined (source enhancement, cortical orientation disparity) and computed for varying values of the patch radius as well as for automatically parcellated gyri and sulci. Results were calculated for each cortical location, averaged over all subjects using a probabilistic atlas, and quantitatively compared between MEG and EEG. As expected, MEG sensors were found to be maximally sensitive to signals due to sources tangential to the scalp, and minimally sensitive to radial sources. Compared to EEG, however, MEG was found to be much more sensitive to signals generated antero-medially, notably in the anterior cingulate gyrus. Given that sources of activation cancel each other according to the orientation disparity of the cortex, this study provides useful methods and results for quantifying the effect of source orientation disparity upon source cancellation.     

Dynamics of parietofrontal networks underlying visuospatial short-term memory encoding

Brain imaging studies in TEP, functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have shown that visuospatial short-term memory tasks depend on dorsal parietofrontal networks. Knowing the spatiotemporal dynamics of this network would provide further understanding of the neural bases of the encoding process. We combined magnetoencephalography (MEG) with EEG and fMRI techniques to study this network in a task, in which participants had to judge the symmetry in position of two dots, presented either simultaneously ("immediate comparison") or successively ("memorization" of a first dot and "delayed comparison", after 3 s, with a second dot). With EEG, larger amplitude was observed in the parietocentral P3b component (350-500 ms) in the immediate and "delayed comparisons" than in "memorization" condition, where topography at this time was more anterior and right lateralized. MEG provided a more accurate localization and temporal variations of sources, revealing a strong M4 component at 450 ms in the "memorization" condition, with two sources localized in parietal and right premotor regions. These localizations are consistent with both fMRI foci and EEG cortical current source densities (CSD), but only MEG revealed the strong increase in premotor region at 450 ms related to "memorization". These combined results suggest that EEG P3B and MEG M4 components reflect two different dynamics in parietofrontal networks: the parietocentral P3b indexes a decision mechanism during the immediate and "delayed comparisons", whereas the MEG M4 component, with a larger right premotor source, reflects the encoding process in visuospatial short-term memory.     

Correlation of sensorimotor activation with functional magnetic resonance imaging and magnetoencephalography in presurgical functional imaging: a spatial analysis.

In this study we investigated the spatial heterotopy of MEG and fMRI localizations after sensory and motor stimulation tasks. Both methods are frequently used to study the topology of the primary and secondary motor cortex, as well as a tool for presurgical brain mapping. fMRI was performed with a 1.5T MR system, using echo-planar imaging with a motor and a sensory task. Somatosensory and motor evoked fields were recorded with a biomagnetometer. fMRI activation was determined with a cross-correlation analysis. MEG source localization was performed with a single equivalent current dipole model and a current density localization approach. Distances between MEG and fMRI activation sites were measured within the same anatomical 3-D-MR image set. The central region could be identified by MEG and fMRI in 33 of 34 cases. However, MEG and fMRI localization results showed significantly different activation sites for the motor and sensory task with a distance of 10 and 15 mm, respectively. This reflects the different neurophysiological mechanisms: direct neuronal current flow (MEG) and secondary changes in cerebral blood flow and oxygenation level of activated versus non activated brain structures (fMRI). The result of our study has clinical implications when MEG and fMRI localizations are used for pre- and intraoperative brain mapping. Although both modalities are useful for the estimation of the motor cortex, a single modality may err in the exact topographical labeling of the motor cortex. In some unclear cases a combination of both methods should be used in order to avoid neurological deficits.     

A spatiotemporal dynamic distributed solution to the MEG inverse problem

MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic Maximum a Posteriori Expectation-Maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods.     

A new method to identify multiple sources of oscillatory activity from magnetoencephalographic data

Identifying the sources of oscillatory activity in the human brain is a challenging problem in current magnetoencephalography (MEG) and electroencephalography (EEG) research. The fluctuations in phase and amplitude of cortical oscillations preclude signal averaging over successive sections of the data without a priori assumptions. In addition, several sources at different locations often produce oscillatory activity at similar frequencies. For example, spontaneous oscillatory activity in the 8- to 13-Hz band is produced simultaneously at least in the posterior parts of the brain and bilaterally in the sensorimotor cortices. The previous approaches of identifying sources of oscillatory activity by dipole modeling of bandpass filtered data are quite laborious and require that multiple criteria are defined by an experienced user. In this work we introduce a convenient method for source localization using minimum current estimates in the frequency domain. Individual current estimates are calculated for the Fourier transforms of successive sections of continuous data. These current estimates are then averaged. The algorithm was tested on simulated and measured MEG data and compared with conventional dipole modeling. The main advantage of the proposed method is that it provides an efficient approach for simultaneous estimation of multiple sources of oscillatory activity in the same frequency band.     

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.     

Joint EEG/fMRI state space model for the detection of directed interactions in human brains--a simulation study

An often addressed challenge in neuroscience research is the assignment of different tasks to specific brain regions. In many cases several brain regions are activated during a single task. Therefore, one is also interested in the temporal evolution of brain activity to infer causal relations between activated brain regions. These causal relations may be described by a directed, task specific network which consists of activated brain regions as vertices and directed edges. The edges describe the causal relations. Inference of the task specific brain network from measurements like electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) is challenging, due to the low spatial resolution of the former and the low temporal resolution of the latter. Here, we present a simulation study investigating a possible combined analysis of simultaneously measured EEG and fMRI data to address the challenge specified above. A nonlinear state space model is used to distinguish between the underlying brain states and the (simulated) EEG/fMRI measurements. We make use of a modified unscented Kalman filter and a corresponding unscented smoother for the estimation of the underlying neural activity. Model parameters are estimated using an expectation-maximization algorithm, which exploits the partial linearity of our model. Inference of the brain network structure is then achieved using directed partial correlation, a measure for Granger causality. The results indicate that the convolution effect of the fMRI forward model imposes a big challenge for the parameter estimation and reduces the influence of the fMRI in combined EEG-fMRI models. It remains to be investigated whether other models or similar combinations of other modalities such as, e.g., EEG and magnetoencephalography can increase the profit of the promising idea of combining various modalities.     

Event-related single-shot volumetric functional magnetic resonance inverse imaging of visual processing

Developments in multi-channel radio-frequency (RF) coil array technology have enabled functional magnetic resonance imaging (fMRI) with higher degrees of spatial and temporal resolution. While modest improvement in temporal acceleration has been achieved by increasing the number of RF coils, the maximum attainable acceleration in parallel MRI acquisition is intrinsically limited only by the amount of independent spatial information in the combined array channels. Since the geometric configuration of a large-n MRI head coil array is similar to that used in EEG electrode or MEG SQUID sensor arrays, the source localization algorithms used in MEG or EEG source imaging can be extended to also process MRI coil array data, resulting in greatly improved temporal resolution by minimizing k-space traversal during signal acquisition. Using a novel approach, we acquire multi-channel MRI head coil array data and then apply inverse reconstruction methods to obtain volumetric fMRI estimates of blood oxygenation level dependent (BOLD) contrast at unprecedented whole-brain acquisition rates of 100 ms. We call this combination of techniques magnetic resonance Inverse Imaging (InI), a method that provides estimates of dynamic spatially-resolved signal change that can be used to construct statistical maps of task-related brain activity. We demonstrate the sensitivity and inter-subject reliability of volumetric InI using an event-related design to probe the hemodynamic signal modulations in primary visual cortex. Robust results from both single subject and group analyses demonstrate the sensitivity and feasibility of using volumetric InI in high temporal resolution investigations of human brain function.     

Head movements of children in MEG: quantification, effects on source estimation, and compensation

Head movements during magnetoencephalography (MEG) recordings may lead to inaccurate localization of brain activity. This can be particularly problematic for studies with children. We quantified head movements in 8- to 12-year-old children performing a cognitive task and examined how the movements affected source estimation. Each child was presented auditory word stimuli in five 4-min runs. The mean change in the MEG sensor locations during the experiment ranged from 3 to 26mm across subjects. The variation in the head position was largest in the up-down direction. The mean localization error in equivalent current dipole (ECD) simulations was 12mm for runs with the most head movement, with the frontal cortex appearing to be most prone to errors due to head movements. In addition, we examined the effect of head movements on two types of source estimates, ECDs and minimum-norm estimates (MNE), for an auditory evoked response. Application of a recently introduced signal space separation (SSS) method to compensate for the head movements was found to increase the goodness-of-fit of the ECDs, reduce the spatial confidence intervals of the ECDs, and enhance the peak amplitude in the MNE. These results are indicative of the SSS method being able to compensate for the spatial smoothing of the signals caused by head movements. Overall, the results suggest that MEG source estimates are relatively robust against head movements in children, and that confounds due to head movements can be successfully dealt with in MEG studies of developmental cognition.