Modelo de arborización dendrítica basado en reconstrucciones de motoneuronas frénicas en ratas adultas

El área superficial de las dendritas en motoneuronas frénicas (PhrMNs) ha sido estimada anteriormente mediante técnicas estereológicas basadas en suposiciones geométricas, y medida en tres dimensiones (3D) utilizando microscopía confocal. Dado que el 97% del área receptora de una motoneurona corresponde a sus dendritas, la ramificación y extensión dendrítica son fisiológicamente importantes para determinar la salida de sus campos receptivos. Sin embargo, limitaciones inherentes a las estimaciones basadas en morfología neuronal y la tinción incompleta de los árboles dendríticos mediante técnicas retrógradas han dificultado los estudios sistemáticos de la morfología dendrítica en PhrMNs. En este estudio, se utilizó una nueva técnica que mejora la tinción dendrítica de las PhrMNs en preparaciones fijadas ligeramente. La reconstrucción dendrítica en 3D se logró con gran precisión utilizando microscopía confocal en PhrMNs de ratas adultas. Luego de una etapa de pre-procesamiento, la segmentación de los árboles dendríticos se realizó semi-automáticamente en 3D y usando mediciones directas del área superficial, se derivó un modelo cuadrático para estimar dicha área partiendo del diámetro de la dendrita primaria (r2 = 0.932; p<0.0001). Este método podría mejorar la evaluación de la plasticidad neuronal en respuesta a trauma u otras enfermedades permitiendo la estimación de la arborización dendrítica en PhrMNs, ya que el diámetro de la dendrita primaria puede obtenerse confiablemente de numerosas técnicas de tinción retrógrada.Stereological techniques that rely on morphological assumptions and direct three-dimensional (3D) confocal measurements have been previously used to estimate the dendritic surface areas of phrenic motoneurons (PhrMNs). Given that 97% of a motoneuron’s receptive area is provided by dendrites, dendritic branching and overall extension are physiologically important in determining the output of their synaptic receptive fields. However, limitations intrinsic to shape-based estimations and incomplete labeling of dendritic trees by retrograde techniques have hindered systematic approaches to examine dendritic morphology of PhrMNs. In this study, a novel method that improves dendritic filling of PhrMNs in lightly-fixed samples was used. Confocal microscopy allowed accurate 3D reconstruction of dendritic arbors from adult rat PhrMNs. Following pre-processing, segmentation was semi-automatically performed in 3D, and direct measurements of dendritic surface area were obtained. A quadratic model for estimating dendritic tree surface area based on measurements of primary dendrite diameter was derived (r2 = 0.932; p<0.0001). This method may enhance interpretation of motoneuron plasticity in response to injury or disease by permitting estimations of dendritic arborization of PhrMNs since measurements of primary dendrite diameter can be reliably obtained from a number of retrograde labeling techniques

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

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

Towards Finding A Link Between Neuronal Oscillations, Declarative Memory, and Viewing Behavior

Thesis (Master's)--University of Washington, 2020Lesion studies in humans have shown that the ability to acquire new declarative memories is a distinct cerebral function, separable from other perceptual and cognitive abilities, that relies on medial temporal lobe (MTL) structures such as the hippocampus and its surrounding cortex. For decades, looking behavior has been used as a covert measure of declarative memory. Importantly, not only do experience-dependent changes in viewing behavior often depend on the integrity of the MTL, but eye movements have also been shown to modulate neuronal activity within its structures. However, little is known about the neural mechanisms by which memory interacts with eye movements. While the MTL presumably plays a role in the consolidation of declarative memories via its extensive reciprocal connections with putative memory storage sites in neocortex, this ability is also thought to involve the synchronized activation of ensembles of neurons distributed throughout the brain. By combining intracranial electrophysiological recordings and infrared eye tracking in human epilepsy patients, this study aims to explore the hypothesis that oscillatory activity within the MTL, and between the MTL and neocortex, mediates the neural interactions underlying memory formation during free visual exploration

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.National Institutes of Health (U.S.) (New Innovator Award DP2-OD006454)National Institutes of Health (U.S.) (R01-EB006385-01)National Institutes of Health (U.S.) (P41-RR014075-11

Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics

<div><p>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.</p></div

Heterogeneity in healthy subjects: An SOP phenotype with alpha power dropout before the cessation of behavioral activity.

<p>The (A) spectrogram, (B) behavioral responses, (C) the wake probability curve, (D) and the clinical hypnogram are shown for a subject with this SOP. The wake probability curve captures persistence of behavioral responses after alpha power declines, a feature that is not evident in hypnogram-based binary models of sleep onset (E). The Bayesian likelihood analysis (F) shows that wake probability significantly outperforms (99.99% Bayesian credible interval of the difference distribution falls above zero) all of the instantaneous transition models in the ability to correctly predict the behavioral responses for this subject.</p

A close up of the sleep/wake transition period of the SOP from the same subject from Figure 3 illustrates the interplay of the EEG and behavior during the SOP.

<p>The corresponding (A) spectrogram, (B) behavioral responses, (C) the wake probability curve, (D) and the clinical hypnogram are shown. In (C), the distribution median (curve), and 95% confidence intervals (shaded region) are shown.</p

Tracking a fragmented SOP. A comparison of the (A) spectrogram, (B) behavioral responses, (C) the wake probability curve, (D) and the clinical hypnogram.

<p>In (C), the distribution median (curve), and 95% confidence intervals (shaded region) are shown. The probability of wakefulness tracks both the gradual time course of the initial descent into sleep, as well as the rapid changes during the arousal period.</p