Weak evidence for neural correlates of task-switching in macaque V1.

A central goal of systems neuroscience is to understand how populations of sensory neurons encode and relay information to the rest of the brain. Three key quantities of interest are ) how mean neural activity depends on the stimulus (sensitivity), ) how neural activity (co)varies around the mean (noise correlations), and ) how predictive these variations are of the subject's behavior (choice probability). Previous empirical work suggests that both choice probability and noise correlations are affected by task training, with decision-related information fed back to sensory areas and aligned to neural sensitivity on a task-by-task basis.

Bottom-Up and Top-Down Input Augment the Variability of Cortical Neurons.

Neurons in the cerebral cortex respond inconsistently to a repeated sensory stimulus, yet they underlie our stable sensory experiences. Although the nature of this variability is unknown, its ubiquity has encouraged the general view that each cell produces random spike patterns that noisily represent its response rate. In contrast, here we show that reversibly inactivating distant sources of either bottom-up or top-down input to cortical visual areas in the alert primate reduces both the spike train irregularity and the trial-to-trial variability of single neurons.

Corticocortical feedback increases the spatial extent of normalization.

Normalization has been proposed as a canonical computation operating across different brain regions, sensory modalities, and species. It provides a good phenomenological description of non-linear response properties in primary visual cortex (V1), including the contrast response function and surround suppression. Despite its widespread application throughout the visual system, the underlying neural mechanisms remain largely unknown.

Mechanical ventilation guided by electrical impedance tomography in experimental acute lung injury.

Objective: To utilize real-time electrical impedance tomography to guide lung protective ventilation in an animal model of acute respiratory distress syndrome.

Design: Prospective animal study.

Setting: Animal research center.

Interaction of dependent and non-dependent regions of the acutely injured lung during a stepwise recruitment manoeuvre.

The benefit of treating acute lung injury with recruitment manoeuvres is controversial. An impediment to settling this debate is the difficulty in visualizing how distinct lung regions respond to the manoeuvre. Here, regional lung mechanics were studied by electrical impedance tomography (EIT) during a stepwise recruitment manoeuvre in a porcine model with acute lung injury.

Comparison of 2 lung recruitment strategies in children with acute lung injury.

Background: Lung recruitment maneuvers are frequently used in the treatment of children with lung injury. Here we describe a pilot study to compare the acute effects of 2 commonly used lung recruitment maneuvers on lung volume, gas exchange, and hemodynamic profiles in children with acute lung injury.

Methods: In a prospective, non-randomized, crossover pilot study, 10 intubated pediatric subjects with lung injury sequentially underwent: a period of observation; a sustained inflation (SI) maneuver of 40 cm H2O for 40 seconds and open-lung ventilation; a staircase recruitment strategy (SRS) (which utilized 5 cm H2O increments in airway pressure, from a starting plateau pressure of 30 cm H2O and PEEP of 15 cm H2O); a downwards PEEP titration; and a 1 hour period of observation with PEEP set 2 cm H2O above closing PEEP.

Reversal of dependent lung collapse predicts response to lung recruitment in children with early acute lung injury.

Objective: To describe the resolution of regional atelectasis and the development of regional lung overdistension during a lung-recruitment protocol in children with acute lung injury.

Design: Prospective interventional trial.

Setting: Pediatric intensive care unit.

A unified approach for EIT imaging of regional overdistension and atelectasis in acute lung injury.

Patients with acute lung injury or acute respiratory distress syndrome (ALI/ARDS) are vulnerable to ventilator-induced lung injury. Although this syndrome affects the lung heterogeneously, mechanical ventilation is not guided by regional indicators of potential lung injury. We used electrical impedance tomography (EIT) to estimate the extent of regional lung overdistension and atelectasis during mechanical ventilation.

Data-driven classification of ventilated lung tissues using electrical impedance tomography.

Current methods for identifying ventilated lung regions utilizing electrical impedance tomography images rely on dividing the image into arbitrary regions of interest (ROI), manually delineating ROI, or forming ROI with pixels whose signal properties surpass an arbitrary threshold. In this paper, we propose a novel application of a data-driven classification method to identify ventilated lung ROI based on forming k clusters from pixels with correlated signals. A standard first-order model for lung mechanics is then applied to determine which ROI correspond to ventilated lung tissue.

A Bayesian hierarchical correlation model for fMRI cluster analysis.

Data-driven cluster analysis is potentially suitable to search for, and discriminate between, distinct response signals in blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI), which appear during cerebrovascular disease. In contrast to model-driven methods, which test for a particular BOLD signal whose shape must be given beforehand, data-driven methods generate a set of BOLD signals directly from the fMRI data by clustering voxels into groups with correlated time signals. Here, we address the problem of selecting only the clusters that represent genuine responses to the experimental stimulus by modeling the correlation structure of the clustered data using a Bayesian hierarchical model.

Selection criteria for the analysis of data-driven clusters in cerebral FMRI.

Functional MRI (fMRI) may be possible without a priori models of the cerebral hemodynamic response. First, such data-driven fMRI requires that all cerebral territories with distinct patterns be identified. Second, a systematic selection method is necessary to prevent the subjective interpretation of the identified territories.

Direct EIT Jacobian calculations for conductivity change and electrode movement.

Electrical impedance tomography (EIT) is very sensitive to deformations of the medium boundary shape. For lung imaging, breathing and changes in posture move the electrodes and change the chest shape, resulting in image artefacts. Several approaches have been proposed to improve the reconstructed images; most methods reconstruct both the boundary deformation and conductivity change from the measured data.

Reconstruction of conductivity changes and electrode movements based on EIT temporal sequences.

Electrical impedance tomography (EIT) reconstructs a conductivity change image within a body from electrical measurements on the body surface; while it has relatively low spatial resolution, it has a high temporal resolution. One key difficulty with EIT measurements is due to the movement and position uncertainty of the electrodes, especially due to breathing and posture change. In this paper, we develop an approach to reconstruct both the conductivity change image and the electrode movements from the temporal sequence of EIT measurements.

Imaging of conductivity changes and electrode movement in EIT.

Electrical impedance tomography (EIT) attempts to reconstruct the internal impedance distribution in a medium from electrical measurements at electrodes on the medium surface. One key difficulty with EIT measurements is due to the position uncertainty of the electrodes, especially for medical applications, in which the body surface moves during breathing and posture change. In this paper, we develop a new approach which directly reconstructs both electrode movements and internal conductivity changes for difference EIT.