Autochthonous Thelazia callipaeda Infection in Dog, New York, USA, 2020.

We report a case of autochthonous infection of the eye worm Thelazia callipaeda in a dog in the northeastern United States. Integrated morphologic identification and molecular diagnosis confirmed the species. Phylogenetic analysis suggested introduction from Europe. The zoonotic potential of this parasite warrants broader surveillance and increased awareness among physicians and veterinarians.

Temporally Segmented Directionality in the Motor Cortex.

Developing models of the dynamic and complex patterns of information processing that take place during behavior is a major thrust of systems neuroscience. An underlying assumption of many models is that the same set of rules applies across different conditions. This has been the case for directional tuning during volitional movement; a single cosine function has been remarkably robust for describing the encoding of movement direction in different types of neurons, in many locations of the nervous system, and even across species. However, detailed examination of the tuning time course in motor cortex suggests that direction coding may be labile. Here, we show that there are discrete time epochs within single reaches, between which individual neurons change their tuning. Our findings suggest that motor cortical activity patterns may reflect consistent changes in the state of the control system during center-out reaching. These transitions are likely linked to different behavioral components, suggesting that the task defines changes in the operational structure of the control system.

Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations.

OBJECTIVE: In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using a brain-machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject's left motor cortex. APPROACH: Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously. MAIN RESULTS: Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping. SIGNIFICANCE: Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.

Outcomes in HIV-infected adults with tuberculosis at clinics with and without co-located HIV clinics in Botswana.

SETTING: Gaborone, Botswana. OBJECTIVE: To determine if starting anti-tuberculosis treatment at clinics in Gaborone without co-located human immunodeficiency virus (HIV) clinics would delay time to highly active antiretroviral therapy (HAART) initiation and be associated with lower survival compared to starting anti-tuberculosis treatment at clinics with on-site HIV clinics. DESIGN: Retrospective cohort study. Subjects were HAART-naïve, aged ≥ 21 years with pulmonary tuberculosis (TB), HIV and CD4 counts ≤ 250 cells/mm(3) initiating anti-tuberculosis treatment between 2005 and 2010. Survival at completion of anti-tuberculosis treatment or at 6 months post-treatment initiation and time to HAART after anti-tuberculosis treatment initiation were compared by clinic type. RESULTS: Respectively 259 and 80 patients from clinics without and with on-site HIV facilities qualified for the study. Age, sex, CD4, baseline sputum smears and loss to follow-up rate were similar by clinic type. Mortality did not differ between clinics without or with on-site HIV clinics (20/250, 8.0% vs. 8/79, 10.1%, relative risk 0.79, 95%CI 0.36-1.72), nor did median time to HAART initiation (respectively 63 and 66 days, P = 0.53). CONCLUSION: In urban areas where TB and HIV programs are separate, geographic co-location alone without further integration may not reduce mortality or time to HAART initiation among co-infected patients.

State-space control of prosthetic hand shape.

In the field of neuroprosthetic control, there is an emerging need for simplified control of high-dimensional devices. Advances in robotic technology have led to the development of prosthetic arms that now approach the look and number of degrees of freedom (DoF) of a natural arm. These arms, and especially hands, now have more controllable DoFs than the number of control DoFs available in many applications. In natural movements, high correlations exist between multiple joints, such as finger flexions. Therefore, discrepancy between the number of control and effector DoFs can be overcome by a control scheme that maps low-DoF control space to high-DoF joint space. Imperfect effectors, sensor noise and interactions with external objects require the use of feedback controllers. The incorporation of feedback in a system where the command is in a different space, however, is challenging, requiring a potentially difficult inverse high-DoF to low-DoF transformation. Here we present a solution to this problem based on the Extended Kalman Filter.

A fuzzy logic model for hand posture control using human cortical activity recorded by micro-ECog electrodes.

This paper presents a fuzzy logic model to decode the hand posture from electro-cortico graphic (ECoG) activity of the motor cortical areas. One subject was implanted with a micro-ECoG electrode array on the surface of the motor cortex. Neural signals were recorded from 14 electrodes on this array while Subject participated in three reach and grasp sessions. In each session, Subject reached and grasped a wooden toy hammer for five times. Optimal channels/electrodes which were active during the task were selected. Power spectral densities of optimal channels averaged over a time period of 1/2 second before the onset of the movement and 1 second after the onset of the movement were fed into a fuzzy logic model. This model decoded whether the posture of the hand is open or closed with 80% accuracy. Hand postures along the task time were decoded by using the output from the fuzzy logic model by two methods (i) velocity based decoding (ii) acceleration based decoding. The latter performed better when hand postures predicted by the model were compared to postures recorded by a data glove during the experiment. This fuzzy logic model was imported to MATLABSIMULINK to control a virtual hand.

Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements.

In this study human motor cortical activity was recorded with a customized micro-ECoG grid during individual finger movements. The quality of the recorded neural signals was characterized in the frequency domain from three different perspectives: (1) coherence between neural signals recorded from different electrodes, (2) modulation of neural signals by finger movement, and (3) accuracy of finger movement decoding. It was found that, for the high frequency band (60-120 Hz), coherence between neighboring micro-ECoG electrodes was 0.3. In addition, the high frequency band showed significant modulation by finger movement both temporally and spatially, and a classification accuracy of 73% (chance level: 20%) was achieved for individual finger movement using neural signals recorded from the micro-ECoG grid. These results suggest that the micro-ECoG grid presented here offers sufficient spatial and temporal resolution for the development of minimally-invasive brain-computer interface applications.

Statistical Signal Processing and the Motor Cortex.

Over the past few decades, developments in technology have significantly improved the ability to measure activity in the brain. This has spurred a great deal of research into brain function and its relation to external stimuli, and has important implications in medicine and other fields. As a result of improved understanding of brain function, it is now possible to build devices that provide direct interfaces between the brain and the external world. We describe some of the current understanding of function of the motor cortex region. We then discuss a typical likelihood-based state-space model and filtering based approach to address the problems associated with building a motor cortical-controlled cursor or robotic prosthetic device. As a variation on previous work using this approach, we introduce the idea of using Markov chain Monte Carlo methods for parameter estimation in this context. By doing this instead of performing maximum likelihood estimation, it is possible to expand the range of possible models that can be explored, at a cost in terms of computational load. We demonstrate results obtained applying this methodology to experimental data gathered from a monkey.

Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles.

We have recently developed a closed-loop environment in which we can test the ability of primates to control the motion of a virtual device using ensembles of simultaneously recorded neurons /29/. Here we use a maximum likelihood method to assess the information about task performance contained in the neuronal ensemble. We trained two animals to control the motion of a computer cursor in three dimensions. Initially the animals controlled cursor motion using arm movements, but eventually they learned to drive the cursor directly from cortical activity. Using a population vector (PV) based upon the relation between cortical activity and arm motion, the animals were able to control the cursor directly from the brain in a closed-loop environment, but with difficulty. We added a supervised learning method that modified the parameters of the PV according to task performance (adaptive PV), and found that animals were able to exert much finer control over the cursor motion from brain signals. Here we describe a maximum likelihood method (ML) to assess the information about target contained in neuronal ensemble activity. Using this method, we compared the information about target contained in the ensemble during arm control, during brain control early in the adaptive PV, and during brain control after the adaptive PV had settled and the animal could drive the cursor reliably and with fine gradations. During the arm-control task, the ML was able to determine the target of the movement in as few as 10% of the trials, and as many as 75% of the trials, with an average of 65%. This average dropped when the animals used a population vector to control motion of the cursor. On average we could determine the target in around 35% of the trials. This low percentage was also reflected in poor control of the cursor, so that the animal was unable to reach the target in a large percentage of trials. Supervised adjustment of the population vector parameters produced new weighting coefficients and directional tuning parameters for many neurons. This produced a much better performance of the brain-controlled cursor motion. It was also reflected in the maximum likelihood measure of cell activity, producing the correct target based only on neuronal activity in over 80% of the trials on average. The changes in maximum likelihood estimates of target location based on ensemble firing show that an animal's ability to regulate the motion of a cortically controlled device is not crucially dependent on the experimenter's ability to estimate intention from neuronal activity.

Extraction algorithms for cortical control of arm prosthetics.

Now that recordings of multiple, individual action potentials are being made with chronic electrodes, it seems that previous work showing simple encoding of movement parameters in these spike trains can be used as a real-time control signal for prosthetic arms. Efficient extraction algorithms can compensate for the limited ensemble sample acquired with this emerging technology.

On the relationship between joint angular velocity and motor cortical discharge during reaching.

Single-unit activity in area M1 was recorded in awake, behaving monkeys during a three-dimensional (3D) reaching task performed in a virtual reality environment. This study compares motor cortical discharge rate to both the hand's velocity and the arm's joint angular velocities. Hand velocity is considered a parameter of extrinsic space because it is measured in the Cartesian coordinate system of the monkey's workspace. Joint angular velocity is considered a parameter of intrinsic space because it is measured relative to adjacent arm/body segments. In the initial analysis, velocity was measured as the difference in hand position or joint posture between the beginning and ending of the reach. Cortical discharge rate was taken as the mean activity between these two times. This discharge rate was compared through a regression analysis to either an extrinsic-coordinate model based on the three components of hand velocity or to an intrinsic-coordinate model based on seven joint angular velocities. The model showed that velocities about four degrees-of-freedom (elbow flexion/extension, shoulder flexion/extension, shoulder internal/external rotation, and shoulder adduction/abduction) were those best represented in the sampled population of recorded activity. Patterns of activity recorded across the cortical population at each point in time throughout the task were used in a second analysis to predict the temporal profiles of joint angular velocity and hand velocity. The population of cortical units from area M1 matched the hand velocity and three of the four major joint angular velocities. However, shoulder adduction/abduction could not be predicted even though individual cells showed good correlation to movement on this axis. This was also the only major degree-of-freedom not well correlated to hand velocity, suggesting that the other apparent relations between joint angular velocity and neuronal activity may be due to intrinsic-extrinsic correlations inherent in reaching movements.

Work toward real-time control of a cortical neural prothesis.

Implantable devices that interact directly with the human nervous system have been gaining acceptance in the field of medicine since the 1960's. More recently, as is noted by the FDA approval of a deep brain stimulator for movement disorders, interest has shifted toward direct communication with the central nervous system (CNS). Deep brain stimulation (DBS) can have a remarkable effect on the lives of those with certain types of disabilities such as Parkinson's disease, Essential Tremor, and dystonia. To correct for many of the motor impairments not treatable by DBS (e.g. quadriplegia), it would be desirable to extract from the CNS a control signal for movement. A direct interface with motor cortical neurons could provide an optimal signal for restoring movement. In order to accomplish this, a real-time conversion of simultaneously recorded neural activity to an online command for movement is required. A system has been established to isolate the cellular activity of a group of motor neurons and interpret their movement-related information with a minimal delay. The real-time interpretation of cortical activity on a millisecond time scale provides an integral first step in the development of a direct brain-computer interface (BCI).

Arm trajectory and representation of movement processing in motor cortical activity.

We review experiments in which single-cell primary motor cortical activity was recorded from Rhesus monkeys (Macaca mulatta) while they performed reaching and drawing tasks. The directional tuning curves generated during reaching were modulated by the speed of movement and this was reflected in the magnitude of population vectors calculated from firing rates of a large population of cells. Directional and speed representation in the firing rates of these cells is robust across both reaching and drawing. Several behavioural invariants related to the speed of drawing were represented in the time-series of population vectors. This high fidelity neural representation of velocity found in motor cortex can be used to visualize the dynamics of motor cortical activity during drawing and suggests that the cost function governing the rate of drawing is bound by neural processing.

Motor cortical representation of speed and direction during reaching.

The motor cortical substrate associated with reaching was studied as monkeys moved their hands from a central position to one of eight targets spaced around a circle. Single-cell activity patterns were recorded in the proximal arm area of motor cortex during the task. In addition to the well-studied average directional selectivity ("preferred direction") of single-cell activity, we also found the time-varying speed of movement to be represented in the cortical activity. A single equation relating motor cortical discharge rate to these two parameters was developed. This equation, which has both independent (speed only) and interactive (speed and direction) components, described a large portion of the time-varying motor cortical activity during the task. Electromyographic activity from a number of upper arm muscles was recorded during this task. Muscle activity was also found to be directionally tuned; however, the distributions of preferred directions were found to be significantly different from cortical activity. In addition, the effect of speed on cortical and muscle activity was also found to be significantly different.

Motor cortical activity during drawing movements: population representation during spiral tracing.

Monkeys traced spirals on a planar surface as unitary activity was recorded from either premotor or primary motor cortex. Using the population vector algorithm, the hand's trajectory could be accurately visualized with the cortical activity throughout the task. The time interval between this prediction and the corresponding movement varied linearly with the instantaneous radius of curvature; the prediction interval was longer when the path of the finger was more curved (smaller radius). The intervals in the premotor cortex fell into two groups, whereas those in the primary motor cortex formed a single group. This suggests that the change in prediction interval is a property of a single population in primary motor cortex, with the possibility that this outcome is due to the different properties generated by the simultaneous action of separate subpopulations in premotor cortex. Electromyographic (EMG) activity and joint kinematics were also measured in this task. These parameters varied harmonically throughout the task with many of the same characteristics as those of single cortical cells. Neither the lags between joint-angular velocities and hand velocity nor the lags between EMG and hand velocity could explain the changes in prediction interval between cortical activity and hand velocity. The simple spatial and temporal relationship between cortical activity and finger trajectory suggests that the figural aspects of this task are major components of cortical activity.

Motor cortical activity during drawing movements: population representation during lemniscate tracing.

Activity was recorded extracellularly from single cells in motor and premotor cortex as monkeys traced figure-eights on a touch-sensitive computer monitor using the index finger. Each unit was recorded individually, and the responses collected from four hemispheres (3 primary motor and 1 dorsal premotor) were analyzed as a population. Population vectors constructed from this activity accurately and isomorphically represented the shape of the drawn figures showing that they represent the spatial aspect of the task well. These observations were extended by examining the temporal relation between this neural representation and finger displacement. Movements generated during this task were made in four kinematic segments. This segmentation was clearly evident in a time series of population vectors. In addition, the (2)/(3) power law described for human drawing was also evident in the neural correlate of the monkey hand trajectory. Movement direction and speed changed continuously during the task. Within each segment, speed and direction changed reciprocally. The prediction interval between the population vector and movement direction increased in the middle of the segments where curvature was high, but decreased in straight portions at the beginning and end of each segment. In contrast to direction, prediction intervals between the movement speed and population vector length were near-constant with only a modest modulation in each segment. Population vectors predicted direction (vector angle) and speed (vector length) throughout the drawing task. Joint angular velocity and arm muscle EMG were well correlated to hand direction, suggesting that kinematic and kinetic parameters are correlated in these tasks.

Self-organization of firing activities in monkey's motor cortex: trajectory computation from spike signals.

The population vector method has been developed to combine the simultaneous direction-related activities of a population of motor cortical neurons to predict the trajectory of the arm movement. In this article, we consider a self-organizing model of a neural representation of the arm trajectory based on neuronal discharge rates. As self-organizing feature map (SOFM) is used to select the optimal set of weights in the model to determine the contribution of an individual neuron to an overall movement representation. The correspondence between movement directions and discharge patterns of the motor cortical neurons is established in the output map. The topology-preserving property of the SOFM is used to analyze the recorded data of a behaving monkey. The data used in this analysis were taken while the monkey was tracing spirals and doing center-->out movements. The arm trajectory could be well predicted using such a statistical model based on the motor cortex neuronal firing information. The SOFM method is compared with the population vector method, which extracts information related to trajectory by assuming that each cell has a fixed preferred direction during the task. This implies that these cells are acting along lines labeled only for direction. However, extradirectional information is carried in these cell responses. The SOFM has the capability of extracting not only direction-related information but also other parameters that are consistently represented in the activity of the recorded population of cells.

Neural signals for command control and feedback in functional neuromuscular stimulation: a review.

In current functional neuromuscular stimulation systems (FNS), control and feedback signals are usually provided by external sensors and switches, which pose problems such as donning and calibration time, cosmesis, and mechanical vulnerability. Artificial sensors are difficult to build and are insufficiently biocompatible and reliable for implantation. With the advent of methods for electrical interfacing with nerves and muscles, natural sensors are being considered as an alternative source of feedback and command signals for FNS. Decision making methods for higher level control can perform equally well with natural or artificial sensors. Recording nerve cuff electrodes have been developed and tested in animals and demonstrated to be feasible in humans for control of dorsiflexion in foot-drop and grasp in quadriplegia. Electromyographic signals, being one thousand times larger than electroneurograms, are easier to measure but have not been able to provide reliable indicators (e.g., of muscle fatigue) that would be useful in FNS systems. Animal studies have shown that information about the shape and movement of arm trajectories can be extracted from brain cortical activity, suggesting that FNS may ultimately be directly controllable from the central nervous system.

A method for detecting the time course of correlation between single-unit activity and EMG during a behavioral task.

The chance that a change in excitability of one neuron leads to a change in excitability of another is likely to vary within a single volitional act. This temporal variability in functional connectivity is impossible to assess with standard analytical procedures to accurately that measure the correlation between such elements. This reports describes a technique designed to overcome this limitation by expressing a correlation measure calculated repeatedly in short epochs throughout a behavioral trial. The activity of two elements, a motor cortical neuron and a shoulder muscle, that might take place during a drawing task was first simulated so that the correlation could be manipulated. Various correlation algorithms (standard cross-correlation, spike-triggered average, impulse-response function, impulse-response surface) were tested with these data. Spike trains from a monkey's motor cortex and rectified EMG from its posterior deltoid muscle were compared using the same techniques and shown to have a correlation that changed in a characteristic manner throughout a task that required the monkey to draw a sinusoid.