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.

Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG.

Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields generated by neuronal activity in subcortical structures can be recorded noninvasively, using magnetoencephalography (MEG) and electroencephalography (EEG). However, these subcortical signals are much weaker than those generated by cortical activity. In addition, we show here that it is difficult to resolve subcortical sources because distributed cortical activity can explain the MEG and EEG patterns generated by deep sources. We then demonstrate that if the cortical activity is spatially sparse, both cortical and subcortical sources can be resolved with M/EEG. Building on this insight, we develop a hierarchical sparse inverse solution for M/EEG. We assess the performance of this algorithm on realistic simulations and auditory evoked response data, and show that thalamic and brainstem sources can be correctly estimated in the presence of cortical activity. Our work provides alternative perspectives and tools for characterizing electrophysiological activity in subcortical structures in the human brain.

Tracking the sleep onset process: an empirical model of behavioral and physiological dynamics.

The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP.

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.

MODELO DE UN SIMULADOR DE LA MANIOBRA DE LA ENDOSCOPIA GÁSTRICA BASADO EN VIDEO / SIMPLE SIMULATOR OF GASTROINTESTINAL ENDOSCOPY WITH INCORPORATION OF REAL VIDEO IMAGES

En el siguiente artículo se expone un modelo simple del procedimiento de endoscopia gástrica y un modelo plástico del estómago y de la distensión estomacal. El uso correcto de imágenes ayuda al desarrollo de sistemas de realidad virtual, y presenta más realismo a la simulación. El objetivo del trabajo consiste en experimentar la posibilidad de construir sistemas simuladores de pacientes en Colombia, utilizando la tecnología localmente disponible, a bajo costo y destinados para la formación de estudiantes de medicina.

The following paper deals with a simple model of a gastric endoscopy procedure and a plastic model of the stomach and its distension. The correct use of imaging helps in the development of virtual reality systems, and provides a greater realism to the simulation itself. The goal is to experience the possibility of building patient simulator systems in Colombia, using locally available technology, at low costs and intended for the training of medical students.