Difference between revisions of "Week9"

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[[Image:Fig2.jpg| thumb |right | Figure 2A-B. Model Predictions (Carmena et al., 2003) ]]
 
[[Image:Fig2.jpg| thumb |right | Figure 2A-B. Model Predictions (Carmena et al., 2003) ]]
  
Linear model very accurately predicts hand position, velocity, and gripping force. (Figure 2) Actual muscle contractions were not predicted so well. Each area recorded from contributed some information about all 3 parameters to differing degrees. Primary motor cortex seemed to contain the most information about all parameters. Figures 2g-2i show that single-unit recordings contain more information than multi-unit recordings, but that deficit can be overcome by recording from more channels.
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The linear model predicts hand position, velocity, and gripping force very accurately. (Figure 2A-B) Actual muscle contractions were not predicted so well. Each area recorded from contributed some information about all 3 parameters to differing degrees. Primary motor cortex seemed to contain the most information about all parameters. Single-unit recordings contain more information than multi-unit recordings, but that deficit can be overcome by recording from more channels.
  
 
With training, information extracted from brain regions increases. Before training, M1 had the best predictive power, with a correlation value of ~0.4, where other areas were around 0.25-0.3. After training, all areas had a correlation value of close to 0.6. The velocity line in 3f is flat because this particular model is one built to predict hand position.
 
With training, information extracted from brain regions increases. Before training, M1 had the best predictive power, with a correlation value of ~0.4, where other areas were around 0.25-0.3. After training, all areas had a correlation value of close to 0.6. The velocity line in 3f is flat because this particular model is one built to predict hand position.
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[[Image:Fig4.jpg| thumb |right | Figure 4A-D. Directional Tuning in Different Conditions (Carmena et al., 2003) ]]
 
[[Image:Fig4.jpg| thumb |right | Figure 4A-D. Directional Tuning in Different Conditions (Carmena et al., 2003) ]]
  
Figure 4a-d: In pole control, the entire ensemble is responsive to all angles; in brain control, maximum response is more limited. In direct pole control, the neuronal ensemble as a whole was evenly spread out over all directions. In brain control, the ensemble is more biased to respond to one particular direction. r values indicate how correlated one plot is to another. This shows that the neurons are changing their responsive properties in different situations.
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Figure 4a-d: In pole control, the entire neural ensemble is responsive to all angles; in brain control, the response is greatest to a narrower range of angles. In direct pole control, the neuronal ensemble as a whole was evenly spread out over all directions. In brain control, the ensemble is more biased to respond to one particular direction. r values indicate how correlated one plot is to another. This shows that the neurons are changing their responsive properties in different situations.
  
 
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Individual neurons have a higher tuning depth in direct pole control. Tuning depth is the maximum response minus the minimum response; it is a measure of selectivity. In general, there was more tuning depth during pole control than any other condition. However, some neurons exhibited more selectivity in pole control, while others were more selective during brain control.
Figure 4e-j: Individual neurons have a higher tuning depth in direct pole control. Tuning depth is the maximum response minus the minimum response. A higher value shows greater selectivity. In general, there was more tuning depth during pole control than any other condition. 4h-j show tuning response curves for 3 separate neurons in 3 conditions. Some neurons are more selective in pole control, while others are more selective in brain control.
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Revision as of 00:18, 19 March 2010

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MANUSCRIPT ID

  • Neuronal ensemble control of prosthetic devices by a human with tetraplegia
  • Leigh R. Hochberg, Mijail D. Serruya, Gerhard M. Friehs, Jon A. Mukand, Maryam Saleh, Abraham H. Caplan, Almut Branner, David Chen, Richard D. Penn and John P. Donoghue, Nature 442, 164-171 (13 July 2006)
  • PURPOSE: Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a 'neural cursor' with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.
  • Neuromotor Prostheses • Tetraplegia • Neuronal ensembles • Control Signal • Linear Filter

MANUSCRIPT Details

  • Introduction/Aims
  • Previous work: Current assistive technologies rely on devices for which an extant function provides a signal that substitutes for missing actions. For example, cameras can monitor eye movements that can be used to point a computer cursor. Although these surrogate devices have been available for some time, they are typically limited in utility, cumbersome to maintain, and disruptive of natural actions. For instance, gaze towards objects of interest disrupts eye-based control. By contrast, an NMP is a type of brain–computer interface (BCI) that can guide movement by harnessing the existing neural substrate for that action—that is, neuronal activity patterns in motor areas.

For this technology to be successful, there are two basic requirements:

-There must be persistent activity in the relevant brain areas (in this case M1), despite injury to spinal cord. -This activity must be under the subject's voluntary control (this allows the construction of a control signal).




  • Methods

The others obtained resectioned brain tissue from the temporal lobe of 5 patients diagnosed with MTLE, along with one non-epileptic patient (diagnosed with glioblastoma) and one patient diagnosed with lateral temporal lobe epilepsy (LTLE)

Tissue was used in one of two ways: 1) Tissue was homogenized in order create membrane vesicles. These vesicles were then injected into xenopus oocytes and allowed to incorporate. 2) Neocortical was prepared into slices.

Whole cell patch clamping was used to record currents of oocytes or pyramidal neurons in response to exogenously puffed GABA. This was done in either the presence or absense of LEV.

  • Results

1) In oocytes it was found that, compared to non-epileptic tissue, the GABA current of epileptic tissue showed significant run down. This rundown was prevented by LEV such that there was no difference in response between epileptic and non-epileptic tissue. There was no difference between epipleptic patients who were taking LEV prior to resection and those that were not.

2) A dose response curve for LEV was constructed. At low doses there is decreasing rundown with increasing concentrations of LEV. In fact, at a dose of 1 uM LEV causes a use dependent increase of the GABA response. At all doses tested above 1 uM LEV there is a run down of ~20% which is comparable to that seen in non-epileptic tissue.

3) The initial results were were confirmed using neocortical slices. They found that, like in oocytes, neocortical slices from epileptics showed a rundown of the GABA current. Again, like ocytes, this run down was prevented by the presence of LEV.

4) They used tissue from the subiculum rather than the neocortex. They found that like cortical tissue there was a rundown of the GABA response in epileptic tissue, however unlike cortical tissue this rundown was not effected by LEV.

5) They tested the effect of LEV on recover from rundown. They applied LEV after GABA rundown was elicited and found that LEV enhanced the recovery.

6) They tested the effect of LEV on tissue from patient diagnosed with LTLE (which is not associated with rundown) and found that there was no effect on overall current or desensitization.

7) Last, because BDNF also prevents rundown they tested whether the effect of LEV is exerted through BDNF. They found that the effect of LEV was unaffected by blockade of the BDNF receptor TrkB. Like BDNF, however, the effect of LEV was blocked by inhibiting PKC.

  • Discussion

This paper confirms previous studies which associated MTLE with a rundown of GABAA currents. It adds to this by showing that the antiepileptic drug LEV prevents this rundown and enhances recovery, suggesting that the rundown is at the core of the disease state. The lack of effect in the subiculum also fits with model because in the subiculum GABA can be excitatory. Thus, maintenance of rundown would act to prevent seizures.

Lastly, this paper validates the use of oocytes as model system for studying epilepsy and suggests that it may serve as a useful way of screening antiepileptic drugs.

MANUSCRIPT ID

  • Title

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


  • Reference

Carmena JM, Lebedev Ma, Crist RE, et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS biology. 2003;1(2):E42. Available at: http://www.ncbi.nlm.nih.gov/pubmed/14624244.


  • Abstract

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.


  • Keywords

BMI, Brain-Machine Interface, Closed Loop, Directional Tuning, Primary Motor Cortex, Macaque, Linear Model, Robotics, Artificial Intelligence


  • Input Author

JJM


MANUSCRIPT DETAILS

  • Introduction/Aims


Spinal cord injuries and neurodegeneration lead to thousands of motor deficits every year. Typically, research is aimed at reconstructing connectivity and function of damaged areas. This paper aims to bypass the damaged area entirely by recording from motor control areas and using those signals to control an external robotic prosthesis.


Primary motor cortex and surrounding areas probably control intentional motor movements, but there are many open questions regarding brain-machine interfaces recording from these areas. These questions include:

*What type of brain signal is the best input? Single unit? Multiunit? Local Field Potentials?

*Which cortical brain region(s) should we record from and contain the most information about motor movements?

*What types of motor commands can be extracted from cortical activity?

*How does an artifiicial actuator like a robot arm affect performance?

*How does use of a BMI reorganize cortical functionality?

This paper aims to explore these questions.


  • Methods



The subjects in this study were two adult female macaque monkeys. Each monkey had training in interacting with a computer while in a chair. Recording arrays were implanted in primary motor cortex, dorsal premotor cortex, and supplementary motor cortex. Monkey 1 also had a recording array in primary sensory cortex. Monkey 2 also had a recording array in the medial intraparietal area of posterior parietal cortex.


The experimental set-up was as follows. The monkeys were seated in a constrained chair facing a computer screen. In some trials the monkeys controlled a cursor on the screen by moving and squeezing a pole with their left arm. In other trials the pole was absent. Multiunit recording data from the abovementioned cortical areas was passed into a data acquisition box. A linear model was trained on the neural recording data to predict the position and squeeze strength of the cursor. In pole-control mode, the monkey directly controlled a robot arm; in brain-control mode, the linear model controlled the robot arm. The cursor's position on the screen was a reflection of the robot arm's position.


Figure 1A. Experimental set-upCarmena et al., 2003)
Figure 1B. Experimental tasks (Carmena et al., 2003)
Figure 1C-E. Improvement Over Trials (Carmena et al., 2003)


In task 1, the monkey simply had to move the cursor to a target on the screen, and was given a juice reward upon successful completion in less than 5 seconds.

In task 2, the monkey had to apply the correct amount of force to expand the cursor to the proper size.

Task 3 was a combination of task 1 and 2. The monkey had to move the cursor to the target and then apply the correct pressure.

The linear model was a simple linear regression model, which finds the matrix of weights A that minimizes the mean squared error of the actual training outputs Y vs. X*A. Y is the output matrix over time, and X is the input matrix over time. A was found by the equation A = inv(X'X)X'Y, where inv(X) represents the inverse operation on a matrix, and X' represents the transpose operation on a matrix. The first 10 minutes of pole control activity was used as training, then the values of A were frozen in place for prediction.


  • Results


Monkeys can learn the tasks in both pole control and brain control. Trials correct and average time to complete trials both improved in both pole-control and brain-control set-ups. The model's prediction of the three parameters was very stable over time.

Figure 2A-B. Model Predictions (Carmena et al., 2003)

The linear model predicts hand position, velocity, and gripping force very accurately. (Figure 2A-B) Actual muscle contractions were not predicted so well. Each area recorded from contributed some information about all 3 parameters to differing degrees. Primary motor cortex seemed to contain the most information about all parameters. Single-unit recordings contain more information than multi-unit recordings, but that deficit can be overcome by recording from more channels.

With training, information extracted from brain regions increases. Before training, M1 had the best predictive power, with a correlation value of ~0.4, where other areas were around 0.25-0.3. After training, all areas had a correlation value of close to 0.6. The velocity line in 3f is flat because this particular model is one built to predict hand position.

Figure 4A-D. Directional Tuning in Different Conditions (Carmena et al., 2003)

Figure 4a-d: In pole control, the entire neural ensemble is responsive to all angles; in brain control, the response is greatest to a narrower range of angles. In direct pole control, the neuronal ensemble as a whole was evenly spread out over all directions. In brain control, the ensemble is more biased to respond to one particular direction. r values indicate how correlated one plot is to another. This shows that the neurons are changing their responsive properties in different situations.

Individual neurons have a higher tuning depth in direct pole control. Tuning depth is the maximum response minus the minimum response; it is a measure of selectivity. In general, there was more tuning depth during pole control than any other condition. However, some neurons exhibited more selectivity in pole control, while others were more selective during brain control.


Figure 5C-D. Plasticity of Directional Tuning (Carmena et al., 2003)

Figure 5a-b: Directional tuning of an ensemble in pole control narrows and rotates slightly over training, while maintaining a high tuning depth for individual neurons. In early sessions, the ensemble's responses were scattered over all angles. In later sessions the neurons responded more synchronously to a particular direction.


Figure 5c-d: Similar directional plasticity is observed in brain control without the pole.


Figure 6A. Ensemble activity during gripping (Carmena et al., 2003)

Figure 6a-c: Neural firing for gripping force is very similar in both pole control and brain control. Here, M1 consistently has the most predictive correlation with gripping force, followed by S1. Both of these improved over training, while neurons from other areas did not exhibit more predictive power. 6c shows that M1 and S1 develop the deepest directional tuning, as well.

Position and force trajectories are very similar in pole control and brain control, and in both monkeys gripping force was the best predicted followed by position, and then velocity.

  • Summary

-Monkeys can learn to operate a robotic arm without overt self-arm movements.

-Large ensembles of neurons are preferred.

-Accurate predictions can be made with multiunit recordings, as opposed to single-unit recordings.

-Motor programming and execution are highly distributed in frontal and parietal areas.

-Neurons change their receptive fields with training, indicating that preexisting networks can assimilate a representation of the robotic arm and possibly lead to a sense of "ownership."


  • Discussion

There is considerable debate about what exactly the motor cortex encodes in primates, whether neurons there are directly controlling muscles, encoding movement intentions, or just responding to movement of the body.

This paper shows that it is very difficult to separate the effects of the motor cortex from the surrounding ares. All recorded areas contained information about arm movements. The authors claim that with enough data, and area could control a brain-machine interface; primary motor cortex could be avoided entirely. While this is good news for patients with damage to motor cortex, it complicates the discussion about the exact role of motor cortex. It is clear, however, that the motor cortex operates in tandem with the surrounding areas in order to produce movements.

The other major claim of this paper is that neurons in these motor control areas can vary significantly in their receptive fields, and these fields can change with training. Some neurons lose their tuning depth when arm movements are absent, but others become more tuned when there is no arm movement. Some lost their tuning when the monkey was still moving its arm, but the brain was controlling the cursor. The tuning directions and depths of the neurons changed over time, showing that motor cortex can be modified with training. This implies that the homunculus in the motor cortex is probably not static, but use- and experience-dependent. This is a good sign for patients hoping to control a robot arm in the future, in that with time the foreign arm may eventually be incorporated into the person's image of them self; the arm would become part of the body.

  • References