Douglas M. Ranken

Multi-start downhill simplex method for spatio-temporal source localization in magnetoencephalography

A multi-start downhill simplex method is examined as a global minimization technique for fitting multidipole, spatio-temporal magnetoencephalography (MEG) data. This procedure has been performed on both simulated and empirical human visual data, known to exhibit complex field patterns due to multiple sources. Unlike some other non-linear fitting techniques the multi-start downhill simplex method does not require users to provide initial guesses for the dipole parameters, hence the fitting procedure is less time-consuming, more objective, and user-friendly. In addition, this method offers more than one adequate solution thus providing a range of uncertainty for the estimated parameters. The Multi-start downhill simplex method is used to fit the non-linear dipole spatial parameters, while the linear temporal parameters are fit using a separate linear fitting procedure. Singular value decomposition (SVD) is also used in order to improve the procedure for determining the adequate number of modeled dipoles.

Evaluation of boundary element methods for the EEG forward problem: effect of linear interpolation

The authors implement the approach for solving the boundary integral equation for the electroencephalography (EEG) forward problem proposed by de Munck (1992), in which the electric potential varies linearly across each plane triangle of the mesh. Previous solutions have assumed the potential is constant across an element. The authors calculate the electric potential and systematically investigate the effect of different mesh choices and dipole locations by using a three concentric sphere head model for which there is an analytic solution. Implementing the linear interpolation approximation results in errors that are approximately half those of the same mesh when the potential is assumed to be constant, and provides a reliable method for solving the problem.<<ETX>>

Modelling the magnetic signature of neuronal tissue

Neuronal communication in the brain involves electrochemical currents, which produce magnetic fields. Stimulus-evoked brain responses lead to changes in these fields and can be studied using magneto- and electro-encephalography (MEG/EEG). In this paper we model the spatiotemporal distribution of the magnetic field of a physiologically idealized but anatomically realistic neuron to assess the possibility of using magnetic resonance imaging (MRI) for directly mapping the neuronal currents in the human brain. Our results show that the magnetic field several centimeters from the centre of the neuron is well approximated by a dipole source, but the field close to the neuron is not, a finding particularly important for understanding the possible contrast mechanism underlying the use of MRI to detect and locate these currents. We discuss the importance of the spatiotemporal characteristics of the magnetic field in cortical tissue for evaluating and optimizing an experiment based on this mechanism and establish an upper bound for the expected MRI signal change due to stimulus-induced cortical response. Our simulations show that the expected change of the signal magnitude is 1.6% and its phase shift is 1 degrees . An unexpected finding of this work is that the cortical orientation with respect to the external magnetic field has little effect on the predicted MRI contrast. This encouraging result shows that magnetic resonance contrast directly based on the neuronal currents present in the cortex is theoretically a feasible imaging technique. MRI contrast generation based on neuronal currents depends on the dendritic architecture and we obtained high-resolution optical images of cortical tissue to discuss the spatial structure of the magnetic field in grey matter.

Single vs. paired visual stimulation: superposition of early neuromagnetic responses and retinotopy in extrastriate cortex in humans

Neuromagnetic techniques were used in conjunction with magnetic resonance imaging (MRI) techniques to: (1) localize and characterize cortical sources evoked by visual stimuli presented at different locations in the lower right visual field; (2) examine the superposition of cortical responses by comparing the summation of responses to the presentation of single stimuli with responses to paired stimuli; and (3) examine the spatial resolution of magnetoencephalographic (MEG) techniques by comparing the identified source locations evoked by the presentation of single vs. paired stimuli. Using multi-dipole, non-linear minimization analyses, three sources were localized for each stimulus condition during the initial 80-170 ms poststimulus interval for all subjects. In addition to an occipital source, two extrastriate sources were identified: occipital-parietal and occipital-temporal. Each source evidenced a systematic shift in location associated with changes in stimulus placement parallel to the vertical meridian. To our knowledge, this is the first demonstration of retinotopic organization of extrastriate areas, using non-invasive neuromagnetic techniques. The paired presentation of stimuli reflected superposition of the responses evoked by single stimuli but only for early activity up to 150 ms poststimulus. Undersummation was evident after 150 ms. All sources identified for single stimuli were also identified in the paired-stimulus responses; but at the expense of larger errors for some of the estimated parameters.

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.

Anatomical constraints for neuromagnetic source models

The localization of neural electromagnetic sources from measurements at the head surface requires the solution of an inverse problem; that is, the determination of the number, location, spatial configuration, strength, and time-course of the neuronal currents that give rise to the magnetic field or potential distribution. In most general form, the neuromagnetic and electrical inverse problems are ill-posed and have no unique solution; however, approximate solutions are possible if assumptions are made regarding the shape and conductivity of the head and the number and configuration of neuronal currents responsible for the surface distributions. To help resolve ambiguities and to reduce the number and range of free parameters required to model complex neuromagnetic sources, the authors are investigating strategies to constrain the locations of allowable sources, based on a knowledge of individual anatomy. The key assumption, justified by both physiological evidence and theoretical considerations, is that the dominant neuromagnetic sources which contribute to surface field distributions reside within the cortex. It is demonstrated that anatomically constrained source modeling strategies can produce significant improvements in source localization; however, the conclusion is that additional improvements in model fitting or source reconstruction procedures are required.

MEG-SIM: A Web Portal for Testing MEG Analysis Methods using Realistic Simulated and Empirical Data

MEG and EEG measure electrophysiological activity in the brain with exquisite temporal resolution. Because of this unique strength relative to noninvasive hemodynamic-based measures (fMRI, PET), the complementary nature of hemodynamic and electrophysiological techniques is becoming more widely recognized (e.g., Human Connectome Project). However, the available analysis methods for solving the inverse problem for MEG and EEG have not been compared and standardized to the extent that they have for fMRI/PET. A number of factors, including the non-uniqueness of the solution to the inverse problem for MEG/EEG, have led to multiple analysis techniques which have not been tested on consistent datasets, making direct comparisons of techniques challenging (or impossible). Since each of the methods is known to have their own set of strengths and weaknesses, it would be beneficial to quantify them. Toward this end, we are announcing the establishment of a website containing an extensive series of realistic simulated data for testing purposes (http://cobre.mrn.org/megsim/). Here, we present: 1) a brief overview of the basic types of inverse procedures; 2) the rationale and description of the testbed created; and 3) cases emphasizing functional connectivity (e.g., oscillatory activity) suitable for a wide assortment of analyses including independent component analysis (ICA), Granger Causality/Directed transfer function, and single-trial analysis.

MRIVIEW: An interactive computational tool for investigation of brain structure and function

MRIVIEW is a software system that uses image processing and visualization to provide neuroscience researchers with an integrated environment for combining functional and anatomical information. Key features of the software include semi-automated segmentation of volumetric head data and an interactive coordinate reconciliation method which utilizes surface visualization. The current system is a precursor to a computational brain atlas. We describe features this atlas will incorporate, including methods under development for visualizing brain functional data obtained from several different research modalities.<<ETX>>

Size matters: MEG empirical and simulation study on source localization of the earliest visual activity in the occipital cortex

While the relationship between sensory stimulation and tasks and the size of the cortical activations is generally unknown, the visual modality offers a unique possibility of an experimental manipulation of stimulus size-related increases of the spatial extent of cortical activation even during the earliest activity in the retinotopically organized primary visual cortex. We used magnetoecephalography (MEG), visual stimuli of increasing size, and numerical simulations on realistic cortical surfaces to explore the effects of increasing spatial extent of the activated cortical sources on the neuromagnetic fields, location estimation biases, and source resolution. Source localization was performed assuming multiple dipoles in a sphere model using an efficient, automatically restarted multi-start simplex minimizer within the Calibrated Start Spatio-Temporal (CSST) algorithm. We found size-related effects on amplitude and latencies and differences in relative locations of the earliest occipital sources evoked by stimuli of increasing size presented at the same eccentricity. This finding was confirmed by single patch simulations. Additionally, simulations of multiple extended sources demonstrated size-related increase in limits in source resolution for bilaterally simulated sources, biases in location estimates for a given separation of sources, and limits in source resolution due to source multiplicity within a hemisphere.

Three-Dimensional Volumetric Reconstruction for Neuromagnetic Source Localization

Neuromagnetic measurements in conjunction with appropriate mathematical models permit the localization of centers of activity in the human brain. For large evoked response components, Monte Carlo error analyses suggest that location uncertainty due to measurement noise may be as low as 1 mm. However, in order to achieve and exploit this degree of accuracy in source localization it is necessary to improve procedures for documenting the location of sensors with respect to the head, and for locating anatomical sources which account for observed fields.