A Subspace Pursuit-based Iterative Greedy Hierarchical solution to the neuromagnetic inverse problem.

Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent developments in source localization methodology have emphasized temporal as well as spatial constraints to improve source localization accuracy, but these methods can be computationally intense. Solutions emphasizing spatial sparsity hold tremendous promise, since the underlying neurophysiological processes generating MEG signals are often sparse in nature, whether in the form of focal sources, or distributed sources representing large-scale functional networks. Recent developments in the theory of compressed sensing (CS) provide a rigorous framework to estimate signals with sparse structure. In particular, a class of CS algorithms referred to as greedy pursuit algorithms can provide both high recovery accuracy and low computational complexity. Greedy pursuit algorithms are difficult to apply directly to the MEG inverse problem because of the high-dimensional structure of the MEG source space and the high spatial correlation in MEG measurements. In this paper, we develop a novel greedy pursuit algorithm for sparse MEG source localization that overcomes these fundamental problems. This algorithm, which we refer to as the Subspace Pursuit-based Iterative Greedy Hierarchical (SPIGH) inverse solution, exhibits very low computational complexity while achieving very high localization accuracy. We evaluate the performance of the proposed algorithm using comprehensive simulations, as well as the analysis of human MEG data during spontaneous brain activity and somatosensory stimuli. These studies reveal substantial performance gains provided by the SPIGH algorithm in terms of computational complexity, localization accuracy, and robustness.

A spatiotemporal dynamic distributed solution to the MEG inverse problem.

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

Computationally Efficient Algorithms for Sparse, Dynamic Solutions to the EEG Source Localization Problem.

OBJECTIVE: Electroencephalography (EEG) and magnetoencephalography noninvasively record scalp electromagnetic fields generated by cerebral currents, revealing millisecond-level brain dynamics useful for neuroscience and clinical applications. Estimating the currents that generate these fields, i.e., source localization, is an ill-conditioned inverse problem. Solutions to this problem have focused on spatial continuity constraints, dynamic modeling, or sparsity constraints. The combination of these key ideas could offer significant performance improvements, but substantial computational costs pose a challenge for practical application of such approaches. Here, we propose a new method for EEG source localization that combines: 1) covariance estimation for both source and measurement noises; 2) linear state-space dynamics; and 3) sparsity constraints, using 4) novel computationally efficient estimation algorithms. METHODS: For source covariance estimation, we use a locally smooth basis alongside sparsity enforcing priors. For EEG measurement noise covariance estimation, we use an inverse Wishart prior density. We estimate these model parameters using an expectation-maximization algorithm that employs steady-state filtering and smoothing to expedite computations. RESULTS: We characterized the performance of our method by analyzing simulated data and experimental recordings of eyes-closed alpha oscillations. Our sparsity enforcing priors significantly improved estimation of both the spatial distribution and time course of simulated data, while improving computational time by more than 12-fold over previous dynamic methods. CONCLUSION: We developed and demonstrated a novel method for improved EEG source localization employing spatial covariance estimation, dynamics, and sparsity. SIGNIFICANCE: Our approach provides substantial performance improvements over existing methods using computationally efficient algorithms that will facilitate practical applications in both neuroscience and medicine.

Simultaneous electroencephalography and functional magnetic resonance imaging of general anesthesia.

It has been long appreciated that anesthetic drugs induce stereotyped changes in electroencephalogram (EEG), but the relationships between the EEG and underlying brain function remain poorly understood. Functional imaging methods including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), have become important tools for studying how anesthetic drugs act in the human brain to induce the state of general anesthesia. To date, no investigation has combined functional MRI with EEG to study general anesthesia. We report here a paradigm for conducting combined fMRI and EEG studies of human subjects under general anesthesia. We discuss the several technical and safety problems that must be solved to undertake this type of multimodal functional imaging and show combined recordings from a human subject. Combined fMRI and EEG exploits simultaneously the high spatial resolution of fMRI and the high temporal resolution of EEG. In addition, combined fMRI and EEG offers a direct way to relate established EEG patterns induced by general anesthesia to changes in neural activity in specific brain regions as measured by changes in fMRI blood oxygen level dependent (BOLD) signals.

Reconstrucción tridimensional de estructuras óseas a partir de estudios tomográficos para su manipulación en modelos de elementos finitos / Three dimensional reconstruction of osseous structures based on tomographic studies for the manipulation of finite element models

La reconstrucción y visualización tridimensional de estructuras óseas es una herramienta útil para el diagnóstico clínico a partir de imágenes médicas. Además, la aplicación de diferentes técnicas de ingeniería, como el diseño asistido por computador y modelaciones numéricas de dichas estructuras, brindan un apoyo significativo a los profesionales e investigadores del área de la salud para la realización de procedimientos clínicos más acertados. Este artículo presenta un método de reconstrucción tridimensional de estructuras óseas a partir de imágenes topográficas planas en formato DICOM, que consta de un módulo de procesamientos de las imágenes MATLAB 6.5 y un módulo de reconstrucción, manipulación y visualización de estructuras sólidas en el software CAD/CAM ProENGINEER WILDFIRE para su posterior utilización en software de electos finitos...