Mapping human brain function with MEG and EEG: methods and validation

Three-dimensional source imaging from simultaneously recorded ERP and BOLD-fMRI

We present the 3-D EEG source images reconstructed by using the minimum norm least square (MNLS) method in combination with the functional magnetic resonance imaging (fMRI) statistical parametric mapping. For a group of five normal subjects, electroencephalogram (EEG) and fMRI signals responding to the full-view checkerboard pattern-reversal visual stimulation were recorded simultaneously and separately. The electrical activities in V1/V2 and V5 were successfully imaged in the N75-P100-N145 and P100-N145 components, respectively. The present results demonstrate the merits of high-resolution spatiotemporal functional neuroimaging by integrating the simultaneously recorded fMRI and EEG data.

Bi-phase locking - a tool for probing non-linear interaction in the human brain

We present a novel method for detecting frequency-frequency coupling between the electrical output of cortical areas as measured by electrocorticography (ECoG), electroencephalography (EEG) and magnetoencephalography (MEG), the biphase-locking value (bPLV). Our method is an extension of the well known phase-locking value (PLV) and is specifically sensitive to non-linear interactions, i.e. quadratic phase coupling across frequencies. Due to its sensitivity to non-linear interactions, it is robust to spurious synchronization arising from linear crosstalk, which is an especially useful property when analyzing data recorded by EEG/MEG. We discuss the statistical properties of the bPLV, specifically the distribution of the bPLV under assumption of random phases between the signals of interest. We also compare the bPLV to the PLV for cortical interactions that are computed for simulated EEG/MEG data. These data were mapped to the cortex using an inverse solution. We demonstrate our method for event related ECoG data recorded from the motor cortex of an epileptic patient, who performed a cued finger movement task. We find highly significant, movement related increase of the bPLV between the alpha (12 Hz) and high gamma (77 Hz) band in a pre-motor area, coupling to high gamma at 89 Hz in the motor cortex.

A novel sparse source imaging in reconstructing extended cortical current sources

We have developed a new sparse source imaging (SSI) method with the use of the L1-norm in EEG inverse problems to reconstruct extended cortical current sources. The new SSI method explores the sparseness in cortical current density variation maps (the transform domain) other than in the cortical current density maps (the original domain) from previously reported SSI methods. The new SSI is assessed by a series of computer simulations. The performance of SSI is compared with the well-known L2-norm MNE using the AUC metric. Our present simulation data indicate that the new SSI has significantly improved performance in reconstructing extended cortical current sources and estimating their cortical extents. The L2-norm MNE shows relatively poor performance in the same source imaging tasks. The new SSI method is also applicable to MEG source imaging.

Forward and backward connections in the brain: a DCM study of functional asymmetries

In this paper, we provide evidence for functional asymmetries in forward and backward connections that define hierarchical architectures in the brain. We exploit the fact that modulatory or nonlinear influences of one neuronal system on another (i.e., effective connectivity) entail coupling between different frequencies. Functional asymmetry in forward and backward connections was addressed by comparing dynamic causal models of MEG responses induced by visual processing of normal and scrambled faces. We compared models with and without nonlinear (between-frequency) coupling in both forward and backward connections. Bayesian model comparison indicated that the best model had nonlinear forward and backward connections. Using the best model we then quantified frequency-specific causal influences mediating observed spectral responses. We found a striking asymmetry between forward and backward connections; in which high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This suppression was significantly greater than the homologous coupling in the forward connections. Furthermore, exactly the asymmetry was observed when we examined face-selective coupling (i.e., coupling under faces minus scrambled faces). These results highlight the importance of nonlinear coupling among brain regions and point to a functional asymmetry between forward and backward connections in the human brain that is consistent with anatomical and physiological evidence from animal studies. This asymmetry is also consistent with functional architectures implied by theories of perceptual inference in the brain, based on hierarchical generative models.

Magnetoencephalography (MEG)

Magnetoencephalography (MEG) encompasses a family of non-contact, non-invasive techniques for detecting the magnetic field generated by the electrical activity of the brain, for analyzing this MEG signal and for using the results to study brain function. The overall purpose of MEG is to extract estimates of the spatiotemporal patterns of electrical activity in the brain from the measured magnetic field outside the head. The electrical activity in the brain is a manifestation of collective neuronal activity and, to a large extent, the currency of brain function. The estimates of brain activity derived from MEG can therefore be used to study mechanisms and processes that support normal brain function in humans and help us understand why, when and how they fail.

A novel ANCOVA design for analysis of MEG data with application to a visual attention study

Statistical inference from MEG-based distributed activation maps is well suited to the general linear modeling framework, a standard approach to the analysis of fMRI and PET neuroimaging studies. However, there are important differences from the other neuroimaging modalities related to how observations are created and fitted in GLM models, as well as how subsequent statistical inference is performed. In this paper, we demonstrate how MEG oscillatory components can be analyzed in this framework based on a custom ANCOVA model that takes into account baseline and inter-hemispheric effects, rather than a simpler ANOVA design. We present the methodology using as an example an MEG study of visual spatial attention, since the model design depends on the specific experiment and neuroscience hypotheses being tested. However, the techniques presented here can be readily adapted to accommodate other experimental paradigms. We create statistics that estimate the temporal evolution of attention effects on alpha power in several cortical regions. We present evidence for direction-specific attention effects on alpha activity in occipital and parietal regions and demonstrate the sub-second timing of these effects in each region. The results support a mechanism for anticipatory attentional deployment that dynamically modulates the local alpha synchrony in a network of parietal control and occipital sensory regions.

Mapping the connectivity with structural equation modeling in an fMRI study of shape-from-motion task

In this fMRI study, we explore the connectivity among brain regions in a shape-from-motion task using the causal mapping analysis of structural equation modeling (SEM). An important distinction of our approach is that we have adapted SEM from its traditional role in confirmatory analysis to provide utility as an exploratory mapping technique. Our current approaches include (I) detecting brain regions that fit well in a hypothesized neural network model, and (II) identifying the best connectivity model at each brain region. We demonstrate that SEM effectively detects the dorsal and ventral visual pathways from the covariance structure in fMRI data, confirming previous neuroscience results. Further, our SEM mapping methodology found that the two pathways interact through specific cortical areas such as the superior lateral occipital cortex in the perception of shape from motion.

Solution space reduction in the peripheral nerve source localization problem using forward field similarities

Improving our ability to localize bioelectric sources within a peripheral nerve would help us to monitor the control signals flowing to and from any limb or organ. This technology would provide a useful neuroscience tool, and could perhaps be incorporated into a neuroprosthesis interface. We propose to use measurements from a multi-contact nerve cuff to solve an inverse problem of bioelectric source localization within the peripheral nerve. Before the inverse problem can be addressed, the forward problem is solved using finite element modeling. A fine mesh improves the accuracy of the forward problem solution, but increases the number of variables to be solved for in the inverse problem. To alleviate this problem, variables corresponding to mesh elements that are not distinguishable by the measurement setup are grouped together, thus reducing the dimension of the inverse problem without impacting on the forward problem accuracy. A quantitative criterion for element distinguishability is derived using the columns of the leadfield matrix and information about the uncertainty in the measurements. Our results indicate that the number of variables in the inverse problem can be reduced by more than half using the proposed method, without having a detrimental impact on the quality of the localization.

Five-dimensional neuroimaging: localization of the time-frequency dynamics of cortical activity

The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. In this paper, we present a novel adaptive spatial filtering algorithm optimized for robust source time-frequency reconstruction from magnetoencephalography (MEG) and electroencephalography (EEG) data. The efficacy of the method is demonstrated with simulated sources and is also applied to real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12-30 Hz) and high gamma band (65-90 Hz) in sensorimotor cortex. The performance is validated by both across-subjects statistical comparisons and by intracranial electrocorticography (ECoG) data from two epilepsy patients. Interestingly, we also reliably observed high frequency activity (30-300 Hz) in the cerebellum, although with variable locations and frequencies across subjects. The proposed algorithm is highly parallelizable and runs efficiently on modern high-performance computing clusters. This method enables the ultimate promise of MEG and EEG for five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics during cognition.

Rapid interactions between the ventral visual stream and emotion-related structures rely on a two-pathway architecture

Visual attention can be driven by the affective significance of visual stimuli before full-fledged processing of the stimuli. Two kinds of models have been proposed to explain this phenomenon: models involving sequential processing along the ventral visual stream, with secondary feedback from emotion-related structures ("two-stage models"); and models including additional short-cut pathways directly reaching the emotion-related structures ("two-pathway models"). We tested which type of model would best predict real magnetoencephalographic responses in subjects presented with arousing visual stimuli, using realistic models of large-scale cerebral architecture and neural biophysics. The results strongly support a "two-pathway" hypothesis. Both standard models including the retinotectal pathway and nonstandard models including cortical-cortical long-range fasciculi appear plausible.

Evidence of a posterior cingulate involvement (Brodmann area 31) in dyslexia: a study based on source localization algorithm of event-related potentials

The study investigates the differences regarding the position of intracranial generators of P50 component of ERPs in 38 dyslexic children aged 11.47+/-2.12 years compared with their 19 healthy siblings aged 12.21+/-2.25. The dipoles were extracted by solving the inverse electromagnetic problem according to the recursively applied and projected multiple signal classification (RAP-MUSIC) algorithm approach. For improved localization of the main dipole the solutions were optimized using genetic algorithms. The statistical analysis revealed differences regarding the position of intracranial generators of low frequency of P50. Particularly, dyslexics showed main activity being located at posterior cingulate cortex (Brodmann's area 31) while controls exhibited main activity being located at retrosplenial cortex (Brodmann's area 30). These results may indicate a role for the posterior cingulate cortex in the pre-attentive processing operation of dyslexia beyond of its traditional function in terms of spatial attention and motor intention.

Dynamic causal modelling of induced responses

This paper describes a dynamic causal model (DCM) for induced or spectral responses as measured with the electroencephalogram (EEG) or the magnetoencephalogram (MEG). We model the time-varying power, over a range of frequencies, as the response of a distributed system of coupled electromagnetic sources to a spectral perturbation. The model parameters encode the frequency response to exogenous input and coupling among sources and different frequencies. The Bayesian inversion of this model, given data enables inferences about the parameters of a particular model and allows us to compare different models, or hypotheses. One key aspect of the model is that it differentiates between linear and non-linear coupling; which correspond to within and between-frequency coupling respectively. To establish the face validity of our approach, we generate synthetic data and test the identifiability of various parameters to ensure they can be estimated accurately, under different levels of noise. We then apply our model to EEG data from a face-perception experiment, to ask whether there is evidence for non-linear coupling between early visual cortex and fusiform areas.

Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation

OBJECTIVES

In this pilot study, we intended to assess whether a procedure based on blind source separation (BSS) and subsequent partial reconstruction of magnetoencephalogram (MEG) recordings might enhance the differences between MEGs from Alzheimer's disease (AD) patients and elderly control subjects.

MATERIALS AND METHODS

We analysed MEG background activity recordings acquired with a 148-channel whole-head magnetometer from 21 AD patients and 21 control subjects. Artefact-free epochs of 20 s were blindly decomposed using the algorithm for multiple unknown signals extraction (AMUSE), which arranges the extracted components by decreasing linear predictability. Thus, the components of diverse epochs and subjects could be easily compared. Every component was characterised with its median frequency and spectral entropy (denoted by fmedian and SpecEn, respectively). The differences between subject groups in these variables were statistically evaluated to find out which components could improve the subject classification. Then, these significant components were used to partially reconstruct the MEG recordings.

RESULTS

The statistical analysis showed that the AMUSE components which provided the largest differences between demented patients and control subjects were ordered together. Considering this analysis, we defined two subsets, denoted by BSS-{15,35} and BSS-{20,30}, which included 21 components (15-35) and 11 components (20-30), respectively. We partially reconstructed the MEGs with these subsets. Then, the classification performance was computed with a leave-one-out cross-validation procedure for the case where no BSS was applied and for the partial reconstructions BSS-{15,35} and BSS-{20,30}. The BSS and component selection procedure improved the classification accuracy from 69.05% to 83.33% using f(median) with BSS-{15,35} and from 61.91% to 73.81% using SpecEn with BSS-{20,30}.

CONCLUSION

These preliminary results lead us to think that the proposed procedure based on BSS and selection of significant components may improve the classification of AD patients using straightforward features from MEG recordings.

Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data

We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. Both methods localize source activity using a linear mixture of temporal basis functions (TBFs) learned from the data. In contrast to existing methods that use predetermined TBFs, we compute TBFs from data using a graphical factor analysis based model [Nagarajan, S.S., Attias, H.T., Hild, K.E., Sekihara, K., 2007a. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26, 3886-3910], which separates evoked or event-related source activity from ongoing spontaneous background brain activity. Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each voxel. We explicitly model, with two different robust parameterizations, the contribution from signals outside a voxel of interest. The two models differ in a trade-off of computational speed versus accuracy of learning the unknown interference contributions. Performance in simulations and real data, both with large noise and interference and/or correlated sources, demonstrates significant improvement over existing source localization methods.

fMRI-EEG integrated cortical source imaging by use of time-variant spatial constraints

In response to the need of establishing a high-resolution spatiotemporal neuroimaging technique, tremendous efforts have been focused on developing multimodal strategies that combine the complementary advantages of high-spatial-resolution functional magnetic resonance imaging (fMRI) and high-temporal-resolution electroencephalography (EEG) or magnetoencephalography (MEG). A critical challenge to the fMRI-EEG/MEG integration lies in the spatial mismatches between fMRI activations and instantaneous electrical source activities. Such mismatches are fundamentally due to the fact that fMRI and EEG/MEG signals are generated and collected in highly different time scales. In this paper, we propose a new theoretical framework to solve the problem of fMRI-EEG integrated cortical source imaging. The new framework has two principal technical advancements. First, by assuming a linear neurovascular coupling, a method is derived to quantify the fMRI signal in each voxel as proportional to the time integral of the power of local electrical current during the period of event-related potentials (ERP). Second, the EEG inverse problem is solved for every time instant using an adaptive Wiener filter, in which the prior time-variant source covariance matrix is estimated by combining the quantified fMRI responses and the segmented EEG signals before response averaging. A series of computer simulations were conducted to evaluate the proposed methods in terms of imaging the instantaneous cortical current density (CCD) distribution and estimating the source time courses with a millisecond temporal resolution. As shown in the simulation results, the instantaneous CCD reconstruction by using the proposed fMRI-EEG integration method was robust against both fMRI false positives and false negatives while retaining a spatial resolution nearly as high as that of fMRI. The proposed method could also reliably estimate the source waveforms when multiple sources were temporally correlated or uncorrelated, or were sustained or transient, or had some features in frequency or phase, or had even more complicated temporal dynamics. Moreover, applying the proposed method to real fMRI and EEG data acquired in a visual experiment yielded a time series of reconstructed CCD images, in agreement with the traditional view of hierarchical visual processing. In conclusion, the proposed method provides a reliable technique for the fMRI-EEG integration and represents a significant advancement over the conventional fMRI-weighted EEG (or MEG) source imaging techniques and is also applicable to the fMRI-MEG integrated source imaging.

Light exposure causes functional changes in the retina: increased photoreceptor cation channel permeability, photoreceptor apoptosis, and altered retinal metabolic function

Light exposure induces retinal photoreceptor degeneration and retinal remodeling in both the normal rat retina and in animal models of retinal degeneration. Although cation entry is one of the triggers leading to apoptosis, it is unclear if this event occurs in isolation, or whether a number of pathways lead to photoreceptor apoptosis following light exposure. Following light exposure, we investigated the characteristics of cation entry, apoptotic markers [using terminal deoxynucleotidyl transferase (EC 2.7.7.31) dUTP nick-end labeling (TUNEL) labeling] and metabolic properties of retina from Sprague-Dawley (SD) rats and a rat model of retinitis pigmentosa [proline-23-histidine (P23H) rat]. Assessment of cation channel permeability using agmatine (AGB) labeling showed that excessive cation gating accompanied the series of anomalies that occur prior to photoreceptor loss. Increased AGB labeling in photoreceptors was seen in parallel with the appearance of apoptotic photoreceptors detected by TUNEL labeling with only a smaller proportion of cells colocalizing both markers. However, SD and P23H retinal photoreceptors differed in the amounts and colocalization of AGB gating and TUNEL labeling as a function of light exposure. Finally, reduced retinal lactate dehydrogenase levels were found in SD and P23H rat retinas after a 24-h light exposure period. Short-term (2 h) exposure of the P23H rat retina caused an increase in lactate dehydrogenase activity suggesting increased metabolic demand. These results suggest that energy availability may be exacerbated during the early stages of light exposure in susceptible retinas. Also, the concomitant observation of increased ion gating and TUNEL labeling suggest the existence of at least two possible mechanisms in light-damaged retinas in both SD and the P23H rat retina.

EEG source imaging: correlating source locations and extents with electrocorticography and surgical resections in epilepsy patients

SUMMARY

It is desirable to estimate epileptogenic zones with both location and extent information from noninvasive EEG. In the present study, the authors use a subspace source localization method (FINE), combined with a local thresholding technique, to achieve such tasks. The performance of this method was evaluated in interictal spikes from three pediatric patients with medically intractable partial epilepsy. The thresholded subspace correlation, which is obtained from FINE scanning, is a favorable marker, which implies the extents of current sources associated with epileptic activities. The findings were validated by comparing the results with invasive electrocorticographic (ECoG) recordings of interictal spike activity. The surgical resections in these three patients correlated well with the epileptogenic zones identified from both EEG sources and ECoG potential distributions. The value of the proposed noninvasive technique for estimating epileptiform activity was supported by satisfactory surgery outcomes.

A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data

We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian methods and by exploiting knowledge about their timing and spatial covariance properties. Full posterior distributions are computed rather than just the MAP estimates. In simulation, the algorithm can accurately localize and estimate the time courses of several simultaneously active dipoles, with rotating or fixed orientation, at noise levels typical for averaged MEG data. The algorithm even performs reasonably at noise levels typical of an average of just a few trials. The algorithm is superior to beamforming techniques, which we show to be an approximation to our graphical model, in estimation of temporally correlated sources. Success of this algorithm using MEG data for localizing bilateral auditory cortex, low-SNR somatosensory activations, and for localizing an epileptic spike source are also demonstrated.

Functional tissue pulsatility imaging of the brain during visual stimulation

Functional tissue pulsatility imaging is a new ultrasonic technique being developed to map brain function by measuring changes in tissue pulsatility as a result of changes in blood flow with neuronal activation. The technique is based in principle on plethysmography, an older, nonultrasound technology for measuring expansion of a whole limb or body part as a result of perfusion. Perfused tissue expands by a fraction of a percent early in each cardiac cycle when arterial inflow exceeds venous outflow, and it relaxes later in the cardiac cycle when venous drainage dominates. Tissue pulsatility imaging (TPI) uses tissue Doppler signal processing methods to measure this pulsatile "plethysmographic" signal from hundreds or thousands of sample volumes in an ultrasound image plane. A feasibility study was conducted to determine if TPI could be used to detect regional brain activation during a visual contrast-reversing checkerboard block paradigm study. During a study, ultrasound data were collected transcranially from the occipital lobe as a subject viewed alternating blocks of a reversing checkerboard (stimulus condition) and a static, gray screen (control condition). Multivariate analysis of variance was used to identify sample volumes with significantly different pulsatility waveforms during the control and stimulus blocks. In 7 of 14 studies, consistent regions of activation were detected from tissue around the major vessels perfusing the visual cortex.

Ictal source analysis: localization and imaging of causal interactions in humans

We propose a new integrative approach to characterize the structure of seizures in the space, time, and frequency domains. Such characterization leads to a new technical development of ictal source analysis for the presurgical evaluation of epilepsy patients. The present new ictal source analysis method consists of three parts. First, a three-dimensional source scanning procedure is performed by a spatio-temporal FINE source localization method to locate the multiple sources responsible for the time evolving ictal rhythms at their onsets. Next, the dynamic behavior of the sources is modeled by a multivariate autoregressive process (MVAR). Lastly, the causal interaction patterns among the sources as a function of frequency are estimated from the MVAR modeling of the source temporal dynamics. The causal interaction patterns indicate the dynamic communications between sources, which are useful in distinguishing the primary sources responsible for the ictal onset from the secondary sources caused by the ictal propagation. The present ictal analysis strategy has been applied to a number of seizures from five epilepsy patients, and their results are consistent with observations from either MRI lesions or SPECT scans, which indicate its effectiveness. Each step of the ictal source analysis is statistically evaluated in order to guarantee the confidence in the results.

Parametric surface-source modeling and estimation with electroencephalography

Electroencephalography (EEG) is an important tool for studying the brain functions and is becoming popular in clinical practice. In this paper, we develop four parametric EEG models to estimate current sources that are spatially distributed on a surface. Our models approximate the source shape and extent explicitly and can be applied to localize extended sources which are often encountered, e.g., in epilepsy diagnosis. We assume a realistic head model and solve the EEG forward problem using the boundary element method. We present the source models with increasing degrees of freedom, provide the forward solutions, and derive the maximum-likelihood estimates as well as Cramér-Rao bounds of the unknown source parameters. In order to evaluate the applicability of the proposed models, we first compare their estimation performances with the dipole model's using several known source distributions. We then discuss the conditions under which we can distinguish between the proposed extended sources and the focal dipole using the generalized likelihood ratio test. We also apply our models to the electric measurements obtained from a phantom body in which an extended electric source is imbedded. We observe that the proposed model can capture the source extent information satisfactorily and the localization accuracy is better than the dipole model.

Spatial resolution of EEG cortical source imaging revealed by localization of retinotopic organization in human primary visual cortex

The aim of the present study is to investigate the spatial resolution of electroencephalography (EEG) cortical source imaging by localizing the retinotopic organization in the human primary visual cortex (V1). Retinotopic characteristics in V1 obtained from functional magnetic resonance imaging (fMRI) study were used as reference to assess the spatial resolution of EEG since fMRI can discriminate small changes in activation in visual field. It is well known that the activation of the early C1 component in the visual evoked potential (VEP) elicited by pattern onset stimuli coincides well with the activation in the striate cortex localized by fMRI. In the present experiments, we moved small circular checkerboard stimuli along horizontal meridian and compared the activations localized by EEG cortical source imaging with those from fMRI. Both fMRI and EEG cortical source imaging identified spatially correlated activity within V1 in each subject studied. The mean location error, between the fMRI-determined activation centers in V1 and the EEG source imaging activation peak estimated at equivalent C1 components (peak latency: 74.8+/-10.6 ms), was 7 mm (25% and 75% percentiles are 6.45 mm and 8.4 mm, respectively), which is less than the change in fMRI activation map by a 3 degrees visual field change (7.8 mm). Moreover, the source estimates at the earliest major VEP component showed statistically good correlation with those obtained by fMRI. The present results suggest that the spatial resolution of the EEG cortical source imaging can correctly discriminate cortical activation changes in V1 corresponding to less than 3 degrees visual field changes.

Task-based comparison of inverse methods in magnetoencephalography

Magnetoencephalography (MEG) provides unique insights into the spatio-temporal dynamics of neural activation in the human brain. Unfortunately, the accuracy with which neural sources can be localized is limited by the highly illposed nature of the inverse problem. A large number of inverse methods have been proposed that deal with this illposedness using a range of different modeling and regularization procedures. Here we describe an objective task-based framework for comparing different inverse methods. Using the free-response receiver operating characteristic (FROC) we compare the performance of matched filters, cortically constrained dipole scanning, and minimum norm imaging methods for the task of detecting focal cortical activation. Our results indicate that the scanning methods outperform matched filters and minimum norm imaging for the case of one and two 2 cm2 patches of cortical activity when the dynamics of the two patches are both strongly and weakly correlated and irrespective of the spacing of the two activated regions.

Modified beamformers for coherent source region suppression

Many tomographic source localization algorithms used in biomagnetic imaging assume, explicitly or sometimes implicitly, that the source activity at different brain locations are either independent or that the correlation structure between sources is known. Among these algorithms is a class of adaptive spatial filters known as beamformers, which have superior spatiotemporal resolution abilities. The performance of beamformers is robust to weakly coherent sources. However, these algorithms are extremely sensitive to the presence of strongly coherent sources. A frequent mode of failure in beamformers occurs with reconstruction of auditory evoked fields (AEFs), in which bilateral auditory cortices are highly coherent in their activation. Here, we present a novel beamformer that suppresses activation from regions with interfering coherent sources. First, a volume containing the interfering sources is defined. The lead field matrix for this volume is computed and reduced into a few significant columns using singular value decomposition (SVD). A vector beamformer is then constructed by rejecting the contribution of sources in the suppression region while allowing for source reconstruction at other specified regions. Performance of this algorithm was first validated with simulated data. Subsequent tests of this modified beamformer were performed on bilateral AEF data. An unmodified vector beamformer using whole head coverage misplaces the source medially. After defining a suppression region containing the temporal cortex on one side, the described method consistently results in clear focal activations at expected regions of the contralateral superior temporal plane.

The neural basis of the hemodynamic response nonlinearity in human primary visual cortex: Implications for neurovascular coupling mechanism

It has been well recognized that the nonlinear hemodynamic responses of the blood oxygenation level-dependent (BOLD) functional MRI (fMRI) are important and ubiquitous in a series of experimental paradigms, especially for the event-related fMRI. Although this phenomenon has been intensively studied and it has been found that the post-capillary venous expansion is an intrinsically nonlinear mechanical process, the existence of an additional neural basis for the nonlinearity has not been clearly shown. In this paper, we assessed the correlation between the electric and vascular indices by performing simultaneous electroencephalography (EEG) and fMRI recordings in humans during a series of visual stimulation (i.e., radial checkerboard). With changes of the visual stimulation frequencies (from 0.5 to 16 Hz) and contrasts (from 1% to 100%), both the event related potentials (ERPs) and hemodynamic responses show nonlinear behaviors. In particular, the mean power of the brain electric sources and the neuronal efficacies (as originally defined in the hemodynamics model [Friston et al. Neuroimage, 12, 466-477, 2000], here represent the vascular inputs) in primary visual cortex consistently show a linear correlation for all subjects. This indicates that the hemodynamic response nonlinearity found in this paper primarily reflects the nonlinearity of underlying neural activity. Most importantly, this finding underpins a nonlinear neurovascular coupling. Specifically, it is shown that the transferring function of the neurovascular coupling is likely a power transducer, which integrates the fast dynamics of neural activity into the vascular input of slow hemodynamics.

Neural localization of semantic context effects in electromagnetic and hemodynamic studies

Measures of electrical brain activity (event-related potentials, ERPs) have been useful in understanding language processing for several decades. Extant data suggest that the amplitude of the N400 component of the ERP is a general index of the ease or difficulty of retrieving stored conceptual knowledge associated with a word, which is dependent on both the stored representation itself, and the retrieval cues provided by the preceding context. Recordings from patients with brain damage, intracranial recordings, and magnetoencephalographic data implicate a (probably large portion of) the left temporal lobe as the largest source of the N400 semantic context effect, with a substantial but lesser contribution from the right temporal lobe. Event-related functional magnetic resonance (fMRI) studies using semantic context manipulations are dominated by observations of greater hemodynamic activity for incongruent sentence completions or semantically unrelated words than congruent or related words, consistent with the direction of the ERP effect. The locations of the hemodynamic effects show some variability across studies, but one commonly identified region is the left superior temporal gyrus, which is compatible with the electrophysiological results. A second commonly identified region in the fMRI studies is the left inferior frontal gyrus, which does not appear to make a substantial contribution to the N400 effect.

Functional cortical source imaging from simultaneously recorded ERP and fMRI

Feasibility of continuously and simultaneously recording visual evoked potentials (VEPs) with fMRI was assessed by quantitatively comparing cortical source images by means of receiver operating characteristic (ROC) curve analysis. The averaged EEG source images coincided well with simultaneously acquired fMRI activations. Strong correlation was found between the cortical source images of VEPs recorded inside and outside the scanner. Application of fMRI prior information strengthened correlation between estimated source images as well as resulted in source estimates with higher spatial resolution. The present results demonstrate that reliable cortical source images can be acquired during simultaneous fMRI scanning and they may be used for multimodal functional source imaging studies.

Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization

Dynamical causal modeling (DCM) of evoked responses is a new approach to making inferences about connectivity changes in hierarchical networks measured with electro- and magnetoencephalography (EEG and MEG). In a previous paper, we illustrated this concept using a lead field that was specified with infinite prior precision. With this prior, the spatial expression of each source area, in the sensors, is fixed. In this paper, we show that using lead field parameters with finite precision enables the data to inform the network's spatial configuration and its expression at the sensors. This means that lead field and coupling parameters can be estimated simultaneously. Alternatively, one can also view DCM for evoked responses as a source reconstruction approach with temporal, physiologically informed constraints. We will illustrate this idea using, for each area, a 4-shell equivalent current dipole (ECD) model with three location and three orientation parameters. Using synthetic and real data, we show that this approach furnishes accurate and robust conditional estimates of coupling among sources and their orientations.

Layer positioning of late-born cortical interneurons is dependent on Reelin but not p35 signaling

We tested the response of interneurons to the absence of Reelin signaling or p35 in the mouse neocortex. We provide three independent strands of evidence to demonstrate that layering of late-born (but not early-born) interneurons is regulated by Reelin signaling. First, early-born and late-born interneurons behaved differently in mice lacking Reelin or disabled 1 (Dab1). Early-born interneurons showed layer inversion, whereas late-born interneurons did not demonstrate layer inversion but were randomly distributed across the cortex. Second, in p35 mutant brains (in which Reelin signaling is intact), late-born interneurons are appropriately positioned in the upper layers despite the malpositioning of all other cortical neurons in these mice. Third, transplanted late-born interneuron precursors (wild type) into Dab1(-/-) cortices showed appropriate upper layer segregation. Together, these results indicate that, in the absence of Reelin signaling, late-born interneurons fail to laminate properly, and this is restored in an environment in which Reelin signaling is intact. These studies suggest different mechanisms for the stratification of cortical interneurons. Whereas the early-born interneurons appear to be associated with projection neuron layering, late-born interneurons rely on Reelin signaling for their correct lamination.

Evaluation of EEG localization methods using realistic simulations of interictal spikes

Performing an accurate localization of sources of interictal spikes from EEG scalp measurements is of particular interest during the presurgical investigation of epilepsy. The purpose of this paper is to study the ability of six distributed source localization methods to recover extended sources of activated cortex. Due to the frequent lack of a gold standard to evaluate source localization methods, our evaluation was performed in a controlled environment using realistic simulations of EEG interictal spikes, involving several anatomical locations with several spatial extents. Simulated data were corrupted by physiological EEG noise. Simulations involving pairs of sources with the same amplitude were also studied. In addition to standard validation criteria (e.g., geodesic distance or mean square error), we proposed an original criterion dedicated to assess detection accuracy, based on receiver operating characteristic (ROC) analysis. Six source localization methods were evaluated: the minimum norm, the minimum norm weighted by multivariate source prelocalization (MSP), cortical LORETA with or without additional minimum norm regularization, and two derivations of the maximum entropy on the mean (MEM) approach. Results showed that LORETA-based and MEM-based methods were able to accurately recover sources of different spatial extents, with the exception of sources in temporo-mesial and fronto-mesial regions. Several spurious sources were generated by those methods, however, whereas methods using the MSP always located very accurately the maximum of activity but not its spatial extent. These findings suggest that one should always take into account the results from different localization methods when analyzing real interictal spikes.

Recipes for the linear analysis of EEG

In this paper, we describe a simple set of "recipes" for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and non-neural current sources.

Multi-Resolution FOCUSS: A Source Imaging Technique Applied to MEG Data

A variety of techniques are available for imaging magnetoencephalographic (MEG) data to the corresponding cortical structures. Each performs a functional optimization that includes mathematical and physical restrictions on source activity. Unlike other imaging techniques, MR-FOCUSS (Multi-Resolution FOCal Underdetermined System Solution) utilizes a wavelet statistical operator that allows spatial resolution to be chosen appropriately for focal or extended sources. Control of focal imaging properties is achieved by specifying P in an l(P) norm distribution template used to construct the wavelets. In addition, incorporation of a multi-resolution wavelet operator desensitizes the mathematical algorithm to noise, (regularization). Like the FOCUSS imaging technique, an initial estimate of cortical activity is recursively enhanced to obtain the final high resolution imaging results. Studies of model MEG data representing all regions of a realistic cortical model are performed to quantify MR-FOCUSS imaging properties. These modeled data studies included single and multiple dipole sources as well as an extended source model. Thus, MR-FOCUSS is found to be very effective for imaging language processing for pre-surgical planning and provides a high-resolution method to image sequential activation of multiple correlated sources involved in language processing.

A finite difference method with reciprocity used to incorporate anisotropy in electroencephalogram dipole source localization

Many implementations of electroencephalogram (EEG) dipole source localization neglect the anisotropical conductivities inherent to brain tissues, such as the skull and white matter anisotropy. An examination of dipole localization errors is made in EEG source analysis, due to not incorporating the anisotropic properties of the conductivity of the skull and white matter. First, simulations were performed in a 5 shell spherical head model using the analytical formula. Test dipoles were placed in three orthogonal planes in the spherical head model. Neglecting the skull anisotropy results in a dipole localization error of, on average, 13.73 mm with a maximum of 24.51 mm. For white matter anisotropy these values are 11.21 mm and 26.3 mm, respectively. Next, a finite difference method (FDM), presented by Saleheen and Kwong (1997 IEEE Trans. Biomed. Eng. 44 800-9), is used to incorporate the anisotropy of the skull and white matter. The FDM method has been validated for EEG dipole source localization in head models with all compartments isotropic as well as in a head model with white matter anisotropy. In a head model with skull anisotropy the numerical method could only be validated if the 3D lattice was chosen very fine (grid size < or = 2 mm).