Craig S. Henriquez

Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

Magnetic resonance myocardial fiber-orientation mapping with direct histological correlation

Functional properties of the myocardium are mediated by the tissue structure. Consequently, proper physiological studies and modeling necessitate a precise knowledge of the fiber orientation. Magnetic resonance (MR) diffusion tensor imaging techniques have been used as a nondestructive means to characterize tissue fiber structure; however, the descriptions so far have been mostly qualitative. This study presents a direct, quantitative comparison of high-resolution MR fiber mapping and histology measurements in a block of excised canine myocardium. Results show an excellent correspondence of the measured fiber angles not only on a point-by-point basis (average difference of -2.30 ± 0.98°, n = 239) but also in the transmural rotation of the helix angles (average correlation coefficient of 0.942 ± 0.008 with average false-positive probability of 0.004 ± 0.001, n = 24). These data strongly support the hypothesis that the eigenvector of the largest MR diffusion tensor eigenvalue coincides with the orientation of the local myocardial fibers and underscore the potential of MR imaging as a noninvasive, three-dimensional modality to characterize tissue fiber architecture.Functional properties of the myocardium are mediated by the tissue structure. Consequently, proper physiological studies and modeling necessitate a precise knowledge of the fiber orientation. Magnetic resonance (MR) diffusion tensor imaging techniques have been used as a nondestructive means to characterize tissue fiber structure; however, the descriptions so far have been mostly qualitative. This study presents a direct, quantitative comparison of high-resolution MR fiber mapping and histology measurements in a block of excised canine myocardium. Results show an excellent correspondence of the measured fiber angles not only on a point-by-point basis (average difference of -2.30 +/- 0.98 degrees, n = 239) but also in the transmural rotation of the helix angles (average correlation coefficient of 0.942 +/- 0.008 with average false-positive probability of 0.004 +/- 0.001, n = 24). These data strongly support the hypothesis that the eigenvector of the largest MR diffusion tensor eigenvalue coincides with the orientation of the local myocardial fibers and underscore the potential of MR imaging as a noninvasive, three-dimensional modality to characterize tissue fiber architecture.

Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface

Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood. Here, we show that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI. Initially, monkeys controlled the actuator by moving a hand-held pole. During this period, the BMI was trained to predict the actuator velocity. As the monkeys started using their cortical activity to control the actuator, the activity of individual neurons and neuronal populations became less representative of the animals hand movements while representing the movements of the actuator. As a result of this adaptation, the animals could eventually stop moving their hands yet continue to control the actuator. These results show that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animals own limb.

Anisotropy, fiber curvature, and bath loading effects on activation in thin and thick cardiac tissue preparations: simulations in a three-dimensional bidomain model.

Anisotropy Effects in a 3D Bidomain. Introduction: A modeling study is presented to explore the effects of tissue conductivity, fiber orientation, and presence of an adjoining extracellular volume conductor on electrical conduction in cardiac muscle. Simulated results are compared with those of classical in vitro experiments on superfused thin layer preparations and on whole hearts.

Continuous Oximetry/Capnometry Monitoring Reveals Frequent Desaturation and Bradypnea During Patient-Controlled Analgesia

BACKGROUND:The most serious complication of patient-controlled analgesia (PCA) is respiratory depression (RD). The incidence of RD in the literature is derived from intermittent sampling of pulse oximetry (Spo2) and respiratory rate and defined as a deviation below an arbitrary threshold. METHODS:We monitored postsurgical patients in a hospital ward receiving morphine or meperidine PCA with continuous oximetry and capnography. Nurses responding to audible monitor bedside alarms documented respiratory status and interventions. RESULTS:A total of 178 patients were included in the analysis, 12% and 41% of whom had episodes of desaturation (Spo2 <90%) and bradypnea (respiratory rate <10) lasting 3 min or more. One patient required “rescue” with positive pressure ventilation, and none required naloxone. Patients over 65 years of age and the morbidly obese were at greater risk for desaturation. Patients over 65 years of age were also more likely to have bradypnea, whereas the morbidly obese and patients receiving continuous infusions were less likely to have bradypnea. CONCLUSIONS:Our incidence of RD by bradypnea is significantly higher than the 1%–2% incidence in the literature, using the same threshold criteria but more stringent duration criteria, while our incidence of RD based on desaturation is consistent with previous estimates. We conclude that continuous respiratory monitoring is optimal for the safe administration of PCA, because any RD event can progress to respiratory arrest if undetected. Better alarm algorithms must be implemented to reduce the frequent alarms triggered by threshold criteria for RD.

Stable Ensemble Performance with Single-Neuron Variability during Reaching Movements in Primates

Significant variability in firing properties of individual neurons was observed while two monkeys, chronically implanted with multielectrode arrays in frontal and parietal cortical areas, performed a continuous arm movement task. Although the degree of correlation between the firing of single neurons and movement parameters was nonstationary, stable predictions of arm movements could be obtained from the activity of neuronal ensembles. This result adds support to the idea that movement parameters are redundantly encoded in the motor cortex, such that brain networks can achieve the same behavioral goals through different patterns and relative contribution of individual neuron activity. This has important implications for neural prosthetics, suggesting that accurate operation of a brain-machine interface requires recording from large neuronal ensembles to minimize the effect of variability and ensuring stable performance over long periods of time.

Unscented Kalman Filter for Brain-Machine Interfaces

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation.

Study of unipolar electrogram morphology in a computer model of atrial fibrillation.

Introduction: Electrograms exhibit a wide variety of morphologies during atrial fibrillation (AF). The basis of these time courses, however, is not completely understood. In this study, data from computer models were studied to relate features of the signals to the underlying dynamics and tissue substrate.

Genesis of complex fractionated atrial electrograms in zones of slow conduction: a computer model of microfibrosis.

BACKGROUND Complex fractionated atrial electrograms are used as potential targets for catheter ablation therapy of atrial fibrillation. Although fibrosis has been associated with the presence of fractionated electrograms, characterizing the substrate through the inspection of electrograms is challenging. OBJECTIVE This study sought to determine how progression of microfibrosis and slow conduction affect electrogram morphology. METHODS A microstructure computer model representing a monolayer of cardiac cells was developed. Slow conduction was induced by: (1) sodium channel blockade, (2) uniform reduction in cell-to-cell coupling, and (3) microfibrosis incorporated as a set of collagenous septa disconnecting transverse coupling. The density (0 to 30%) and length (30 to 945 microm) of these collagenous septa were varied. Unipolar and bipolar electrograms were computed during paced rhythm for a set of electrodes with different tip sizes. RESULTS The analysis of unipolar electrograms with realistic temporal and spatial filtering showed that increasing the density and length of collagenous septa decreased conduction velocity by up to 75% and increased the amount of fractionation (up to 14 deflections) and asymmetry of the electrograms. In contrast, slow conduction induced by sodium channel blockade or uniformly reduced coupling did not result in electrogram fractionation. When a larger electrode was used, electrogram amplitude was smaller and fractionation increased in a substrate-dependent way. CONCLUSION Microscale obstacles cause significant changes to electrogram waveforms. Conduction velocity and electrogram amplitude and degree of fractionation can be used to discriminate the nature of the substrate and characteristics of fibrosis, giving rise to slow conduction.

Study of atrial arrhythmias in a computer model based on magnetic resonance images of human atria.

The maintenance of multiple wavelets appears to be a consistent feature of atrial fibrillation (AF). In this paper, we investigate possible mechanisms of initiation and perpetuation of multiple wavelets in a computer model of AF. We developed a simplified model of human atria that uses an ionic-based membrane model and whose geometry is derived from a segmented magnetic resonance imaging data set. The three-dimensional surface has a realistic size and includes obstacles corresponding to the location of major vessels and valves, but it does not take into account anisotropy. The main advantage of this approach is its ability to simulate long duration arrhythmias (up to 40 s). Clinically relevant initiation protocols, such as single-site burst pacing, were used. The dynamics of simulated AF were investigated in models with different action potential durations and restitution properties, controlled by the conductance of the slow inward current in a modified Luo-Rudy model. The simulation studies show that (1) single-site burst pacing protocol can be used to induce wave breaks even in tissue with uniform membrane properties, (2) the restitution-based wave breaks in an atrial model with realistic size and conduction velocities are transient, and (3) a significant reduction in action potential duration (even with apparently flat restitution) increases the duration of AF. (c) 2002 American Institute of Physics.