Face activated neurodynamic cortical networks.

Previous neuroimaging studies have shown that complex visual stimuli, such as faces, activate multiple brain regions, yet little is known on the dynamics and complexity of the activated cortical networks during the entire measurable evoked response. In this study, we used simulated and face-evoked empirical MEG data from an oddball study to investigate the feasibility of accurate, efficient, and reliable spatio-temporal tracking of cortical pathways over prolonged time intervals. We applied a data-driven, semiautomated approach to spatio-temporal source localization with no prior assumptions on active cortical regions to explore non-invasively face-processing dynamics and their modulation by task. Simulations demonstrated that the use of multi-start downhill simplex and data-driven selections of time intervals submitted to the Calibrated Start Spatio-Temporal (CSST) algorithm resulted in improved accuracy of the source localization and the estimation of the onset of their activity. Locations and dynamics of the identified sources indicated a distributed cortical network involved in face processing whose complexity was task dependent. This MEG study provided the first non-invasive demonstration, agreeing with intracranial recordings, of an early onset of the activity in the fusiform face gyrus (FFG), and that frontal activation preceded parietal for responses elicited by target faces.

Early cortical responses are sensitive to changes in face stimuli.

Face-related processing has been demonstrated already in the early evoked response around 100 ms after stimulus. The aims of this study were to explore these early responses both at sensor and cortical source level and to explore to what extent they might be modulated by a change in face stimulus. Magnetoencephalographic (MEG) recordings, a visual oddball paradigm, and a semiautomated spatiotemporal source localization method were used to investigate cortical responses to changes in face stimuli. Upright and inverted faces were presented in an oddball paradigm with four conditions; standards and deviants differing in emotion or identity. The task in all conditions was silent counting of the target face with glasses. Deviant face stimuli elicited larger MEG responses at about 100 ms than standard ones did but only for upright faces. Spatiotemporal source localization up to 140 ms after stimulus revealed activation of parietal and temporal sources in addition to occipital ones, all of which demonstrated differences in locations and dynamics for standards, deviants, and targets. Peak latencies of the identified cortical sources were shorter for deviants than standards, again only for upright faces. Our results showed differences in cortical responses to standards and deviants that were more pronounced for upright than for inverted faces, suggesting early detection of face-related changes in visual stimulation. The observed effect provides new evidence for the face sensitivity of the early neuromagnetic response around 100 ms.

Somatosensory responses in normal aging, mild cognitive impairment, and Alzheimer's disease.

As a part of a larger study of normal aging and Alzheimer's disease (AD), which included patients with mild cognitive impairment (MCI), we investigated the response to median nerve stimulation in primary and secondary somatosensory areas. We hypothesized that the somatosensory response would be relatively spared given the reported late involvement of sensory areas in the progression of AD. We applied brief pulses of electric current to left and right median nerves to test the somatosensory response in normal elderly (NE), MCI, and AD. MEG responses were measured and were analyzed with a semi-automated source localization algorithm to characterize source locations and timecourses. We found an overall difference in the amplitude of the response of the primary somatosensory source (SI) based on diagnosis. Across the first three peaks of the SI response, the MCI patients exhibited a larger amplitude response than the NE and AD groups (P < 0.03). Additional relationships between neuropsychological measures and SI amplitude were also determined. There was no significant difference in amplitude for the contralateral secondary somatosensory source across diagnostic category. These results suggest that somatosensory cortex is affected early in the progression of AD and may have some consequence on behavioral and functional measures.

Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC.

A number of brain imaging techniques have been developed in order to investigate brain function and to develop diagnostic tools for various brain disorders. Each modality has strengths as well as weaknesses compared to the others. Recent work has explored how multiple modalities can be integrated effectively so that they complement one another while maintaining their individual strengths. Bayesian inference employing Markov Chain Monte Carlo (MCMC) techniques provides a straightforward way to combine disparate forms of information while dealing with the uncertainty in each. In this paper we introduce methods of Bayesian inference as a way to integrate different forms of brain imaging data in a probabilistic framework. We formulate Bayesian integration of magnetoencephalography (MEG) data and functional magnetic resonance imaging (fMRI) data by incorporating fMRI data into a spatial prior. The usefulness and feasibility of the method are verified through testing with both simulated and empirical data.

Probabilistic forward model for electroencephalography source analysis.

Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer-Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates.

Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data.

The performance of parametric magnetoencephalography (MEG) and electroencephalography (EEG) source localization approaches can be degraded by the use of poor background noise covariance estimates. In general, estimation of the noise covariance for spatiotemporal analysis is difficult mainly due to the limited noise information available. Furthermore, its estimation requires a large amount of storage and a one-time but very large (and sometimes intractable) calculation or its inverse. To overcome these difficulties, noise covariance models consisting of one pair or a sum of multi-pairs of Kronecker products of spatial covariance and temporal covariance have been proposed. However, these approaches cannot be applied when the noise information is very limited, i.e., the amount of noise information is less than the degrees of freedom of the noise covariance models. A common example of this is when only averaged noise data are available for a limited prestimulus region (typically at most a few hundred milliseconds duration). For such cases, a diagonal spatiotemporal noise covariance model consisting of sensor variances with no spatial or temporal correlation has been the common choice for spatiotemporal analysis. In this work, we propose a different noise covariance model which consists of diagonal spatial noise covariance and Toeplitz temporal noise covariance. It can easily be estimated from limited noise information, and no time-consuming optimization and data-processing are required. Thus, it can be used as an alternative choice when one-pair or multi-pair noise covariance models cannot be estimated due to lack of noise information. To verify its capability we used Bayesian inference dipole analysis and a number of simulated and empirical datasets. We compared this covariance model with other existing covariance models such as conventional diagonal covariance, one-pair and multi-pair noise covariance models, when noise information is sufficient to estimate them. We found that our proposed noise covariance model yields better localization performance than a diagonal noise covariance, while it performs slightly worse than one-pair or multi-pair noise covariance models - although these require much more noise information. Finally, we present some localization results on median nerve stimulus empirical MEG data for our proposed noise covariance model.

Frequency-following and connectivity of different visual areas in response to contrast-reversal stimulation.

The sensitivity of visual areas to different temporal frequencies, as well as the functional connections between these areas, was examined using magnetoencephalography (MEG). Alternating circular sinusoids (0, 3.1, 8.7 and 14 Hz) were presented to foveal and peripheral locations in the visual field to target ventral and dorsal stream structures, respectively. It was hypothesized that higher temporal frequencies would preferentially activate dorsal stream structures. To determine the effect of frequency on the cortical response we analyzed the late time interval (220-770 ms) using a multi-dipole spatio-temporal analysis approach to provide source locations and timecourses for each condition. As an exploratory aspect, we performed cross-correlation analysis on the source timecourses to determine which sources responded similarly within conditions. Contrary to predictions, dorsal stream areas were not activated more frequently during high temporal frequency stimulation. However, across cortical sources the frequency-following response showed a difference, with significantly higher power at the second harmonic for the 3.1 and 8.7 Hz stimulation and at the first and second harmonics for the 14 Hz stimulation with this pattern seen robustly in area V1. Cross-correlations of the source timecourses showed that both low- and high-order visual areas, including dorsal and ventral stream areas, were significantly correlated in the late time interval. The results imply that frequency information is transferred to higher-order visual areas without translation. Despite the less complex waveforms seen in the late interval of time, the cross-correlation results show that visual, temporal and parietal cortical areas are intricately involved in late-interval visual processing.

Improving source detection and separation in a spatiotemporal Bayesian inference dipole analysis.

Most existing spatiotemporal multi-dipole approaches for MEG/EEG source localization assume that the dipoles are active for the full time range being analysed. If the actual time range of activity of sources is significantly shorter than the time range being analysed, the detectability, localization and time-course determination of such sources may be adversely affected, especially for weak sources. In order to improve detectability and reconstruction of such sources, it is natural to add active time range information (starting time point and ending time point of source activation) for each candidate source as unknown parameters in the analysis. However, this adds additional nonlinear free parameters that could burden the analysis and could be unfeasible for some methods. Recently, we described a spatiotemporal Bayesian inference multi-dipole analysis for the MEG/EEG inverse problem. This approach treated the number of dipoles as a free parameter, produced realistic uncertainty estimates using a Markov chain Monte Carlo numerical sampling of the posterior distribution and included a method to reduce the unwanted effects of local minima. In this paper, our spatiotemporal Bayesian inference multi-dipole analysis is extended to incorporate active time range parameters of starting and stopping time points. The properties of this analysis in comparison to the previous one without active time range parameters are demonstrated through extensive studies using both simulated and empirical MEG data.

Aging changes and gender differences in response to median nerve stimulation measured with MEG.

OBJECTIVE: The current study uses magnetoencephalography (MEG) to characterize age-related changes and gender differences in the amplitudes and timing of cortical sources evoked by median nerve stimulation. METHODS: Thirty-four healthy subjects from two age groups: 20-29 and >64 years of age were examined. After measuring the MEG responses, we modeled the data using a spatio-temporal multi-dipole modeling approach to determine the source locations and their associated timecourses. RESULTS: We found early, large amplitude responses in the elderly in primary somatosensory (approximately 20 ms) and pre-central sulcus timecourses (approximately 22 ms) and lower amplitude responses in the elderly later in primary somatosensory (approximately 32 ms) and contralateral secondary somatosensory timecourses (approximately 90 ms). In addition, females had larger peak amplitude responses than males in the contralateral secondary somatosensory timecourse (approximately 28 and 51 ms). CONCLUSIONS: These results show that the median nerve stimulation paradigm provides considerable sensitivity to age- and gender-related differences. The results are consistent with the theory that increased amplitudes identified in the elderly may be associated with decreased inhibition. SIGNIFICANCE: The results emphasize that an examination of two discrete age groups, collapsed across gender, cannot provide a complete understanding of the fundamental changes that occur in the brain across the lifetime.

Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.

Recently, we described a Bayesian inference approach to the MEG/EEG inverse problem that used numerical techniques to estimate the full posterior probability distributions of likely solutions upon which all inferences were based [Schmidt, D.M., George, J.S., Wood, C.C., 1999. Bayesian inference applied to the electromagnetic inverse problem. Human Brain Mapping 7, 195; Schmidt, D.M., George, J.S., Ranken, D.M., Wood, C.C., 2001. Spatial-temporal bayesian inference for MEG/EEG. In: Nenonen, J., Ilmoniemi, R. J., Katila, T. (Eds.), Biomag 2000: 12th International Conference on Biomagnetism. Espoo, Norway, p. 671]. Schmidt et al. (1999) focused on the analysis of data at a single point in time employing an extended region source model. They subsequently extended their work to a spatiotemporal Bayesian inference analysis of the full spatiotemporal MEG/EEG data set. Here, we formulate spatiotemporal Bayesian inference analysis using a multi-dipole model of neural activity. This approach is faster than the extended region model, does not require use of the subject's anatomical information, does not require prior determination of the number of dipoles, and yields quantitative probabilistic inferences. In addition, we have incorporated the ability to handle much more complex and realistic estimates of the background noise, which may be represented as a sum of Kronecker products of temporal and spatial noise covariance components. This reduces the effects of undermodeling noise. In order to reduce the rigidity of the multi-dipole formulation which commonly causes problems due to multiple local minima, we treat the given covariance of the background as uncertain and marginalize over it in the analysis. Markov Chain Monte Carlo (MCMC) was used to sample the many possible likely solutions. The spatiotemporal Bayesian dipole analysis is demonstrated using simulated and empirical whole-head MEG data.

Differentiability of simulated MEG hippocampal, medial temporal and neocortical temporal epileptic spike activity.

Previous studies have shown that magnetoencephalography (MEG) can measure hippocampal activity, despite the cylindrical shape and deep location in the brain. The current study extended this work by examining the ability to differentiate the hippocampal subfields, parahippocampal cortex, and neocortical temporal sources using simulated interictal epileptic activity. A model of the hippocampus was generated on the MRIs of five subjects. CA1, CA3, and dentate gyrus of the hippocampus were activated as well as entorhinal cortex, presubiculum, and neocortical temporal cortex. In addition, pairs of sources were activated sequentially to emulate various hypotheses of mesial temporal lobe seizure generation. The simulated MEG activity was added to real background brain activity from the five subjects and modeled using a multidipole spatiotemporal modeling technique. The waveforms and source locations/orientations for hippocampal and parahippocampal sources were differentiable from neocortical temporal sources. In addition, hippocampal and parahippocampal sources were differentiated to varying degrees depending on source. The sequential activation of hippocampal and parahippocampal sources was adequately modeled by a single source; however, these sources were not resolvable when they overlapped in time. These results suggest that MEG has the sensitivity to distinguish parahippocampal and hippocampal spike generators in mesial temporal lobe epilepsy.

Investigation of the normal proximal somatomotor system using magnetoencephalography.

OBJECTIVE: The role of the ipsilateral cortex in proximal muscle control in normal human subjects is still under debate. One clinical finding, rapid recovery of proximal muscle relative to distal muscle use following stroke, has led to the suggestion that the ipsilateral as well as the contralateral motor cortex may be involved in normal proximal muscle control. The primary goal of this project was to identify contralateral and ipsilateral motor cortex activation associated with proximal muscle movement in normal subjects using magnetoencephalography (MEG). METHODS: We developed protocols for a self-paced bicep motor task and a deltoid, electrical-stimulation somatosensory task. The MEG data were analyzed using automated multi-dipole spatiotemporal modeling techniques to localize the sources and characterize the associated timing of these sources. RESULTS: Reliable contralateral primary motor and somatosensory sources localized to areas consistent with the homunculus. Ipsilateral M1 activation was only found in 2/12 hemispheres. CONCLUSIONS: Robust contralateral motor cortex activation and sparse ipsilateral motor cortex activation suggest that the ipsilateral motor cortex is not involved in normal proximal muscle control. SIGNIFICANCE: The results suggest that proximal and distal muscle control is similar in normal subjects in the sense that proximal muscle control is primarily governed by the contralateral motor cortex.

Central versus peripheral visual field stimulation results in timing differences in dorsal stream sources as measured with MEG.

Small, achromatic circular sinusoids were presented in the central and peripheral visual fields to investigate dorsal visual stream activation. It was hypothesized that peripheral stimulation would lead to faster onset latencies, as well as preferentially activate dorsal stream visual areas relative to central field stimulation. Although both central and peripheral stimulation activated similar areas, the onset latencies of neuromagnetic sources in two dorsal stream areas were found to be significantly shorter for peripheral versus central field stimulation. The results suggest that information from central versus peripheral fields arrives in the higher-order visual areas via different routes.