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 1) how mean neural activity depends on the stimulus (sensitivity), 2) how neural activity (co)varies around the mean (noise correlations), and 3) 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. We used Utah arrays to record activity from populations of primary visual cortex (V1) neurons from two macaque monkeys that were trained to switch between two coarse orientation-discrimination tasks. Surprisingly, we find no evidence for significant trial-by-trial changes in noise covariance between tasks, nor do we find a consistent relationship between neural sensitivity and choice probability, despite recording from well-tuned task-sensitive neurons, many of which were histologically confirmed to be in supragranular V1, and despite behavioral evidence that the monkeys switched their strategy between tasks. Thus our data at best provide weak support for the hypothesis that trial-by-trial task-switching induces changes to noise correlations and choice probabilities in V1. However, our data agree with a recent finding of a single "choice axis" across tasks. They also raise the intriguing possibility that choice-related signals in early sensory areas are less indicative of task learning per se and instead reflect perceptual learning that occurs in highly overtrained subjects.NEW & NOTEWORTHY Converging evidence suggests that decision processes affect sensory neural activity, and this has informed numerous theories of neural processing. We set out to replicate and extend previous results on decision-related information and noise correlations in V1 of macaque monkeys. However, in our data, we find little evidence for a number of expected effects. Our null results therefore call attention to differences in task training, stimulus design, recording, and analysis techniques between our and prior studies.

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. A simple model in which a fraction of the pre-synaptic input is silenced can reproduce this reduction in variability, provided that there exist temporal correlations primarily within, but not between, excitatory and inhibitory input pools. A large component of the variability of cortical neurons may therefore arise from synchronous input produced by signals arriving from multiple sources.

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. We recently observed that corticocortical feedback contributes to surround suppression in V1, raising the possibility that feedback acts through normalization. To test this idea, we characterized area summation and contrast response properties in V1 with and without feedback from V2 and V3 in alert macaques and applied a standard normalization model to the data. Area summation properties were well explained by a form of divisive normalization, which computes the ratio between a neuron's driving input and the spatially integrated activity of a "normalization pool." Feedback inactivation reduced surround suppression by shrinking the spatial extent of the normalization pool. This effect was independent of the gain modulation thought to mediate the influence of contrast on area summation, which remained intact during feedback inactivation. Contrast sensitivity within the receptive field center was also unaffected by feedback inactivation, providing further evidence that feedback participates in normalization independent of the circuit mechanisms involved in modulating contrast gain and saturation. These results suggest that corticocortical feedback contributes to surround suppression by increasing the visuotopic extent of normalization and, via this mechanism, feedback can play a critical role in contextual information processing.

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. SUBJECTS: Twelve Yorkshire swine (15 kg). INTERVENTIONS: Lung injury was induced with saline lavage and augmented using large tidal volumes. The control group (n = 6) was ventilated using ARDSnet guidelines, and the electrical impedance tomography-guided group (n = 6) was ventilated using guidance with real-time electrical impedance tomography lung imaging. Regional electrical impedance tomography-derived compliance was used to maximize the recruitment of dependent lung and minimize overdistension of nondependent lung areas. Tidal volume was 6 mL/kg in both groups. Computed tomography was performed in a subset of animals to define the anatomic correlates of electrical impedance tomography imaging (n = 5). Interleukin-8 was quantified in serum and bronchoalveolar lavage samples. Sections of dependent and nondependent regions of the lung were fixed in formalin for histopathologic analysis. MEASUREMENTS AND MAIN RESULTS: Positive end-expiratory pressure levels were higher in the electrical impedance tomography-guided group (14.3 cm H2O vs. 8.6 cm H2O; p < 0.0001), whereas plateau pressures did not differ. Global respiratory system compliance was improved in the electrical impedance tomography-guided group (6.9 mL/cm H2O vs. 4.7 mL/cm H2O; p = 0.013). Regional electrical impedance tomography-derived compliance of the most dependent lung region was increased in the electrical impedance tomography group (1.78 mL/cm H2O vs. 0.99 mL/cm H2O; p = 0.001). Pao2/FIO2 ratio was higher and oxygenation index was lower in the electrical impedance tomography-guided group (Pao2/FIO2: 388 mm Hg vs. 113 mm Hg, p < 0.0001; oxygentation index, 6.4 vs. 15.7; p = 0.02) (all averages over the 6-hr time course). The presence of hyaline membranes (HM) and airway fibrin (AF) was significantly reduced in the electrical impedance tomography-guided group (HMEIT 42% samples vs. HMCONTROL 67% samples, p < 0.01; AFEIT 75% samples vs. AFCONTROL 100% samples, p < 0.01). Interleukin-8 level (bronchoalveolar lavage) did not differ between the groups. The upper and lower 95% limits of agreement between electrical impedance tomography and computed tomography were ± 16%. CONCLUSIONS: Electrical impedance tomography-guided ventilation resulted in improved respiratory mechanics, improved gas exchange, and reduced histologic evidence of ventilator-induced lung injury in an animal model. This is the first prospective use of electrical impedance tomography-derived variables to improve outcomes in the setting of acute lung injury.

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. The following interaction between dependent and non-dependent regions consistently occurred: atelectasis in the most dependent region was reversed only after the non-dependent region became overdistended. EIT estimates of overdistension and atelectasis were validated by histological examination of lung tissue, confirming that the dependent region was primarily atelectatic and the non-dependent region was primarily overdistended. The pulmonary pressure-volume equation, originally designed for modelling measurements at the airway opening, was adapted for EIT-based regional estimates of overdistension and atelectasis. The adaptation accurately modelled the regional EIT data from dependent and non-dependent regions (R(2) > 0.93, P < 0.0001) and predicted their interaction during recruitment. In conclusion, EIT imaging of regional lung mechanics reveals that overdistension in the non-dependent region precedes atelectasis reversal in the dependent region during a stepwise recruitment manoeuvre.

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. RESULTS: Arterial blood gases, lung mechanics, hemodynamics, and functional residual capacity were recorded following each step of the study and following each increment of the SRS. Both SI and SRS were effective in raising PaO2 and functional residual capacity. During the SRS maneuver we noted significant increases in dead-space ventilation, a decrease in carbon dioxide elimination, an increase in PaCO2, and a decrease in compliance of the respiratory system. Lung recruitment was not sustained following the decremental PEEP titration. CONCLUSIONS: SRS is effective in opening the lung in children with early acute lung injury, and is hemodynamically well tolerated. However, attention must be paid to PaCO2 during the SRS. Even minutes following lung recruitment, lungs may derecruit when PEEP is lowered beyond the closing pressure.

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. PATIENTS: Ten children with early (<72 hrs) acute lung injury. INTERVENTIONS: Sustained inflation maneuver (positive airway pressure of 40 cm H2O for 40 secs), followed by a stepwise recruitment maneuver (escalating plateau pressures by 5 cm H2O every 15 mins) until physiologic lung recruitment, defined by PaO2 + PaCO2 ≥400 mm Hg, was achieved. Regional lung volumes and mechanics were measured using electrical impedance tomography. MEASUREMENTS AND MAIN RESULTS: Patients that responded to the stepwise lung-recruitment maneuver had atelectasis in 54% of the dependent lung regions, while nonresponders had atelectasis in 10% of the dependent lung regions (p = .032). In the pressure step preceding physiologic lung recruitment, a significant reversal of atelectasis occurred in 17% of the dependent lung regions (p = .016). Stepwise recruitment overdistended 8% of the dependent lung regions in responders, but 58% of the same regions in nonresponders (p < .001). Lung compliance in dependent lung regions increased in responders, while compliance in nonresponders did not improve. In contrast to the stepwise recruitment maneuver, the sustained inflation did not produce significant changes in atelectasis or oxygenation: atelectasis was only reversed in 12% of the lung (p = .122), and there was only a modest improvement in oxygenation (27 ± 14 mm Hg, p = .088). CONCLUSIONS: Reversal of atelectasis in the most dependent lung region preceded improvements in gas exchange during a stepwise lung-recruitment strategy. Lung recruitment of dependent lung areas was accompanied by considerable overdistension of nondependent lung regions. Larger amounts of atelectasis in dependent lung areas were associated with a positive response to a stepwise lung-recruitment maneuver.

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. Techniques for tidal breath detection, lung identification, and regional compliance estimation were combined with the Graz consensus on EIT lung imaging (GREIT) algorithm. Nine ALI/ARDS patients were monitored during stepwise increases and decreases in airway pressure. Our method detected individual breaths with 96.0% sensitivity and 97.6% specificity. The duration and volume of tidal breaths erred on average by 0.2 s and 5%, respectively. Respiratory system compliance from EIT and ventilator measurements had a correlation coefficient of 0.80. Stepwise increases in pressure could reverse atelectasis in 17% of the lung. At the highest pressures, 73% of the lung became overdistended. During stepwise decreases in pressure, previously-atelectatic regions remained open at sub-baseline pressures. We recommend that the proposed approach be used in collaborative research of EIT-guided ventilation strategies for ALI/ARDS.

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. We applied the method in an experimental study of 16 mechanically ventilated swine in the supine position, which underwent changes in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (F(I)O(2)). In each stage of the experimental protocol, the method performed best with k = 4 and consistently identified 3 lung tissue ROI and 1 boundary tissue ROI in 15 of the 16 subjects. When testing for changes from baseline in lung position, tidal volume, and respiratory system compliance, we found that PEEP displaced the ventilated lung region dorsally by 2 cm, decreased tidal volume by 1.3%, and increased the respiratory system compliance time constant by 0.3 s. F(I)O(2) decreased tidal volume by 0.7%. All effects were tested at p < 0.05 with n = 16. These findings suggest that the proposed ROI detection method is robust and sensitive to ventilation dynamics in the experimental setting.

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. The model is empirically justified by demonstrating the hierarchical organization of the voxel correlations after cluster analysis. BOLD signal discrimination is demonstrated using: 1) simulations that contain multiple pathological BOLD response signals; and 2) fMRI data acquired during an event-related motor task. These demonstrations are compared with results from a model-driven method based on the general linear model. Our simulations show that the data-driven method can discriminate between the BOLD response signals, while the model-driven method only finds one signal. For fMRI, the data-driven method distinguishes between the BOLD signals appearing in the sensorimotor cortex and those in basal ganglia and putamen, while the model-driven method combines these signals into one activation map. We conclude that the proposed data-driven method provides an objective framework to identify and discriminate between distinct BOLD response signals.

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. This paper addresses the second point by proposing a novel method for the automated interpretation of identified territories in data-driven fMRI. Selection criteria are formulated using: 1) the temporal cross-correlation between each identified territory and the paradigm and 2) the spatial contiguity of the corresponding voxel map. Ten event-design fMRI data sets are analyzed with one prominent algorithm, fuzzy c-means clustering, before applying the selection criteria. For comparison, these data are also analyzed with an established, model-based method: statistical parametric mapping. Both methods produced similar results and identified potential activation in the expected territory of the sensorimotor cortex in all ten data sets. Moreover, the proposed method classified distinct territories in separate clusters. Selected clusters have a mean temporal correlation coefficient of 0.39+/-0.07 (n=19) with a mean 2.7+/-1.4 second response delay. At most, four separate contiguous territories were observed in 87% of these clusters. These results suggest that the proposed method may be effective for exploratory fMRI studies where the hemodynamic response is perturbed during cerebrovascular disease.

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. Since both the conductivity change and electrode movement are slow with respect to the data frame rate, there are significant temporal correlations which we formulate as priors for the regularized image reconstruction model. Image reconstruction is posed in terms of a regularization matrix and a Jacobian matrix which are augmented for the conductivity change and electrode movement, and then further augmented to concatenate the d previous and future frames. Results are shown for simulation, phantom and human data, and show that the proposed algorithm yields improved resolution and noise performance in comparison to a conventional one-step reconstruction method.

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. These techniques require the calculation of the 'movement Jacobian', reflecting measurement changes due to the boundary deformation. Previous papers have calculated this Jacobian using perturbation techniques, which are slow (requiring multiple solutions of the forward problem) and become inaccurate with increasing finite element model size. This effect has limited reconstruction algorithms for deformable media to mostly 2D. To address this problem, we propose a direct method to calculate the Jacobian, based on a formulation of the derivatives of the finite element system matrix with respect to geometry changes. An illustrative example of these calculations is given, as well as a comparison between the proposed method and a perturbation method. Results show this method is approximately 300 times faster; and for larger model sizes, the perturbation method begins to diverge from those from the direct method proposed.

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. The reconstruction problem is formulated in terms of a regularized inverse, using an augmented Jacobian, sensitive to impedance change and electrode movement. A reconstruction prior term is computed to impose a smoothness constraint on both the spatial distribution of impedance change and electrode movement. A one-step regularized imaging algorithm is then implemented based on the augmented Jacobian and smoothness constraint. Images were reconstructed using the algorithm of this paper with data from simulated 2D and 3D conductivity changes and electrode movements, and from saline phantom measurements. Results showed good reconstruction of the actual electrode movements, as well as a dramatic reduction in image artefacts compared to images from the standard algorithm, which did not account for electrode movement.