Functional Assessment Measures – Duke Aging Center

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Hand grasps neuro assessment group we demonstrate that primates can learn to reach and grasp virtual objects by controlling hand grasps neuro assessment group robot arm through a closed-loop brain—machine interface BMIc that uses multiple mathematical models to extract several motor parameters i.

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 hand grasps neuro assessment group 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.

PLoS Biol 1 2 : e Hand grasps neuro assessment group is an open-access article distributed under the terms of the Public Library of Science Open-Access License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Competing interests: The authors have declared that no conflicts of interest exist. Traumatic lesions of the central nervous system as well as neurodegenerative disorders continue to inflict devastating, and so far irreparable, motor deficits in large numbers of patients.

Every year, spinal cord injuries alone are responsible hand grasps neuro assessment group the occurrence of about 11, new cases of permanent paralysis in the United States Nobunaga et al. These cases add up to an already sizeable population of patients, estimated atin the United States Nobunaga et al.

Until very recently, the main thrust of basic research on restoration of motor functions after spinal cord injuries focused on reconstructing the connectivity and functionality of damaged nerve fibers Ramon-Cueto et al. While this repair strategy has produced encouraging results, such as limited restoration of limb mobility in animals, the goal of restoring complex motor behaviors, such as reaching and grasping, remains a major challenge.

Two decades ago, an alternative method for restoring motor behaviors in severely paralyzed patients was proposed Schmidt Initial experimental support for a cortically driven bypass came from the studies conducted by Fetz and collaborators Fetz ; Fetz and Finocchio; Fetz and Bakerwho demonstrated that macaque monkeys hand grasps neuro assessment group learn to selectively adjust the firing rate of individual cortical neurons to attain a particular level of cell activity if provided with sensory feedback that signaled the level of neuronal firing.

Recent studies in rodents Chapin et al. These experiments have demonstrated that animals can learn to utilize their brain activity to control the displacements of computer cursors Serruya et al.

Despite these initial results, several fundamental issues regarding the operation of BMIs, ranging from basic electrophysiological issues to multiple engineering bottlenecks, remain a matter of considerable debate Nicolelis; Donoghue Research groups that propose the use of single-unit activity also diverge on the assessments of whether small eight to thirty Serruya et al.

The issues of what signal to use and how much neuronal tissue to sample are linked to the question of what type of motor commands may be extracted from hand grasps neuro assessment group activity. Up to now, animal studies have demonstrated the capability of extracting a single motor parameter e.

While it is well known that cortical neuronal activity can encode a variety of motor parameters Fetz ; Messier and Kalaska ; Johnsonit is not clear which cortical areas would provide the best input for a BMI designed to restore multiple features of upper-limb function. A couple of laboratories have focused on the primary motor cortex M1 Serruya et al.

Our previous studies have suggested that, because of the distributed nature of motor planning in the brain, neuronal samples from multiple frontal and parietal cortical areas ought to be employed to operate such devices Wessberg et al.

Another important issue that has received little attention is how the interposition of an artificial actuator such as a robot arm in the control loop impacts the BMI and the subject's performance. Finally, more data are needed to evaluate the extent, relevance, and behavioral meaning of cortical reorganization that can be triggered by operation of a BMIc.

A possibility of such reorganization is supported by results in plasticity in M1 neurons in a force-field adaptation task Li et al. In this paper, we present the results from a series of long-term studies in macaque monkeys to address several of the fundamental issues that currently shape the debate on BMIs. In particular, we demonstrate for what we believe is the first time the ability of the same ensemble of cells in closed-loop mode to control two distinct movements of a robotic arm: reaching and grasping.

In addition, we demonstrate how the monkeys learn to control a real robotic actuator using a BMIc. We also report on how they overcome the robot dynamics and return to the same level of performance without modification of the task.

Finally, we quantitatively compare the contribution of neural populations in multiple cortical areas needed to create this control and analyze changes in these contributions during learning.

Using the experimental apparatus illustrated in Figure 1 A, monkeys were trained in three different tasks: a reaching task task hand grasps neuro assessment group Figure 1 Ba hand-gripping task task 2; Figure 1 Band a reach-and-grasp task task 3; Figure 1 B.

A Behavioral setup and control loops, consisting of the data acquisition system, the computer running multiple linear models in real time, the robot arm equipped with a gripper, and the visual display.

The pole was equipped with a gripping force transducer. Robot position was translated into cursor position on the screen, and feedback of the gripping force was provided by changing the cursor size. B Schematics of three behavioral tasks. In task 1, hand grasps neuro assessment group monkey's goal was to move the cursor to a visual target green that appeared at random locations on the screen.

In task 2, the pole was hand grasps neuro assessment group, and the monkey had to grasp a virtual object by developing a particular gripping force instructed by two red circles displayed on the screen. Task 3 was a hand grasps neuro assessment group of tasks 1 and 2. The monkey had to move the cursor to the target and then develop a gripping force necessary to grasp a virtual object. C—E Behavioral performance for two monkeys in tasks 1—3.

The percentage of correctly completed trials increased, while the time to conclude a trial decreased with training. This was true for both pole blue and brain red control. Horizontal green lines indicate chance performance obtained from the random walk model.

The introduction of the robot arm into the BMIc control loop resulted in a drop in behavioral performance. In approximately seven training sessions, the animal's behavioral performance gradually returned to the initial values.

This effect took place during both pole and brain control. F Stability of model predictions of hand velocity during long pole-control sessions more hand grasps neuro assessment group 50 min for two monkeys performing task 1. The first 10 min of performance were used to train the model, and then its coefficients were frozen.

Model predictions remained highly accurate for tens of minutes. G Surface EMGs of arm muscles recorded in task 1 for pole hand grasps neuro assessment group left and brain control without arm movements right.

Top plots show the X-coordinate of the cursor; plots below display EMGs of wrist flexors, wrist extensors, and biceps. EMG modulations were absent in brain control. We used multiple linear models, similar to those described in our previous studies Wessberg et al. Although all these parameters were extracted in real time on each session, only some of them were used to control the BMIc, depending on each of the three tasks the monkeys had to solve in a given day.

In each recording session, an initial min period was used for training of these models. During this period, monkeys used a hand-held pole either to move a cursor on the screen or to change the cursor size by application of gripping force to the pole.

During brain control mode, animals initially produced arm movements, but they soon realized that these were not necessary and ceased to produce them for periods of time. To systematically study this phenomenon, we removed the pole after the monkey ceased to produce arm movements in a session.

In all experiments, visual feedback i. When the robot was used, cursor position indicated to the animal the X and Y coordinates of the robot hand. The cursor size provided feedback of the force measured by the sensors on the robot's gripper.

Hand grasps neuro assessment group time delay between the output of the linear model and the response of the robot was in the range of 60—90 ms. For each task, training continued until the animal reached high levels of performance in brain control mode.

During this learning period, both the animal's and the BMIc's performance were assessed using hand grasps neuro assessment group measures. Chance performance was assessed using Monte Carlo simulations of random walks.

Contributions of individual neurons and the overall contribution of different cortical areas to the prediction of multiple motor signals were evaluated. In addition, changes in directional tuning of the neurons that resulted from using the BMIc were quantified. Figure hand grasps neuro assessment group C— 1 E illustrates procedural motor learning as animals interacted with the BMIc in each of the three tasks.

Improvement in behavioral performance with the BMIc was indicated by a significant increase in the percentage of hand grasps neuro assessment group completed successfully Figure 1 C— 1 E, top graphs and by a hand grasps neuro assessment group in movement time Figure 1 C— 1 E, bottom graphs.

For task 1, both monkeys had some training in pole control of task 1 data not shown several weeks before the series of successive daily sessions illustrated in Figure 1 C. For both tasks 1 and 2, after a relatively small number of daily training sessions, the monkeys' performance in brain control reached levels similar to those during pole control Figure 1 C and 1 D.

For tasks 2 and 3, all behavioral data are plotted, given that in both cases pole and brain controls were used since the first day of training. Behavioral improvement was also observed in task 3, hand grasps neuro assessment group combined elements of tasks 1 and hand grasps neuro assessment group Figure 1 E. In all three tasks, the levels of performance attained during brain control mode by far exceeded those predicted by a random walk model dashed and dotted lines in Figure 1 C— 1 E.

Moreover, both animals could operate the BMIc without any overt arm movement and muscle activity, as hand grasps neuro assessment group by the lack of EMG activity in several arm muscles Figure 1 G. The ratios of the standard deviation of the muscle activity during pole versus brain control for these muscles were A key novel feature of this study was the introduction of the robot equipped with a gripper into the control loop of the BMIc after the animals had learned the task.

Figure 1 C shows that because the intrinsic dynamics of the robot produced a lag between the pole movement and the cursor movement, the monkeys' performance initially declined. With time, however, the performance rapidly returned to the same levels as seen in previous training sessions Figure 1 C. It is critical to note that the high accuracy in the control of the robot was achieved by using velocity control in the BMIc, which produced smooth predicted trajectories, and by the fine tuning of robot controller parameters.

These parameters were fixed across sessions in both monkeys. The controller sent velocity commands to the robot every 60—90 ms. Each of these commands compensated hand grasps neuro assessment group potential position errors of the robot hand that resulted from previous commands. In all experiments, the animals continuously received visual feedback of their performance.

Unlike previous results in owl monkeys from experiments in which an open-loop BMI was implemented Wessberg and Nicolelisafter the model parameters were fixed, its hand grasps neuro assessment group did not drift substantially from initial best performance, even during 1-h recordings.

Hand grasps neuro assessment group possibility is that the presence of continuous visual feedback helped to stabilize model performance. Throughout learning of all three behavioral tasks, populations of neurons distributed in multiple frontal and parietal cortical areas exhibited task-related modulations of their firing rates. Using multiple linear models running in parallel, several motor signals were extracted from those modulations.

To evaluate the performance of the models in extracting different motor parameters, the models were first trained using 15 min of pole control data and then subsequent data were predicted.

Hand grasps neuro assessment group 2 B shows the model prediction of EMG activity. These results show that elaborate hand movements, such as the ones required to solve task 3, could be predicted from brain activity using a BMIc with the simultaneous application of multiple linear models. A Motor parameters blue and their prediction using linear models red.

C Contribution of neurons from the same ensemble to predictions of hand position top hand grasps neuro assessment group, velocity middleand gripping force bottom. Contributions were measured as correlation coefficients R between the recorded motor parameters and their values predicted by the linear model.

The color bar at the bottom hand grasps neuro assessment group cortical areas where the neurons were located. Each neuron contributed to prediction of multiple parameters of movements, and each area contained information about all parameters.

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