Doug Ranken

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 subjects 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.

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.

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.

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.

Multidipole analysis of simulated epileptic spikes with real background activity

&NA; This simulated magnetoencephalographic study was designed to determine the variability in source parameters with real subject background activity when applying multidipole spatial‐temporal dipole analyses, for which the correct model was compared with undermodeled and overmodeled cases. The simulated sources were created from patches of the cortical surface of each subjects MRI. One‐ and two‐source frontal lobe spikes were generated in two cortical regions seen commonly in frontal lobe epilepsy patients tested at our site (orbital frontal and premotor cortex). In general, the modeling results were adequate for the correct model order and the correct model order plus one. In addition, if the localization error was less than 10 mm from the simulated source, the peak latency of the spike and orientation were very reliable, but the peak amplitude was not. The additional source in the overmodeled condition, on the other hand, was not localized reliably across the different epochs within subjects. The results suggest that consistency of the spike localization and inconsistency of other sources will allow one to determine reliably the appropriate model order in real data, and therefore determine single and multifocal spike generators.

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.

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.

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.

Nonlinear analysis of biological systems using short M-sequences and sparse-stimulation techniques

The m-sequence pseudorandom signal has been shown to be a more effective probing signal than traditional Gaussian white noise for studying nonlinear biological systems using cross-correlation techniques. The effectiveness is evidenced by the high signal-to-noise (S/N) ratio and speed of data acquisition. However, the “anomalies” that occur in the estimations of the cross-correlations represent an obstacle that prevents m-sequences from being more widely used for studying nonlinear systems. The sparse-stimulation method for measuring system kernels can help alleviate estimation errors caused by anomalies. In this paper, a “padded sparse-stimulation” method is evaluated, a modification of the “inserted sparse-stimulation” technique introduced by Sutter, along with a short m-sequence as a probing signal. Computer simulations show that both the “padded” and “inserted” methods can effectively eliminate the anomalies in the calculation of the second-order kernel, even when short m-sequences were used (length of 1023 for a binary m-sequence, and 728 for a ternary m-sequence). Preliminary experimental data from neuromagnetic studies of the human visual system are also presented, demonstrating that the system kernels can be measured with high signal-to-noise (S/N) ratios using short m-sequences.

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.