Neuroscience

Articles and news from the latest research reports.

Posts tagged motor movements

170 notes

Using the brain to forecast decisions You’re waiting at a bus stop, expecting the bus to arrive any time. You watch the road. Nothing yet. A little later you start to pace. More time passes. “Maybe there is some problem”, you think. Finally, you give up and raise your arm and hail a taxi. Just as you pull away, you glimpse the bus gliding up. Did you have a choice to wait a bit longer? Or was giving up too soon the inevitable and predictable result of a chain of neural events? In research published on 09/28/2014 in the journal Nature Neuroscience, scientists show that neural recordings can be used to forecast when spontaneous decisions will take place. “Experiments like this have been used to argue that free will is an illusion,” says Zachary Mainen, a neuroscientist at the Champalimaud Centre for the Unknown, in Lisbon, Portugal, who led the study, “but we think that interpretation is mistaken.” The scientists used recordings of neurons in an area of the brain involved in planning movements to try to predict when a rat would give up waiting for a delayed tone. “We know they were not just responding to a stimulus, but spontaneously deciding when to give up, because the timing of their choice varied unpredictably from trial to trial” said Mainen. The researchers discovered that neurons in the premotor cortex could predict the animals’ actions more than one second in advance. According to Mainen, “This is remarkable because in similar experiments, humans report deciding when to move only around two tenths of a second before the movement.” However, the scientists claim that this kind of predictive activity does not mean that the brain has decided. “Our data can be explained very well by a theory of decision-making known as an ‘integration-to-bound’ model” says Mainen. According to this theory, individual brain cells cast votes for or against a particular action, such as raising an arm. Circuits within the brain keep a tally of the votes in favor of each action and when a threshold is reached it is triggered. Critically, like individual voters in an election, individual neurons influence a decision but do not determine the outcome. Mainen explained: “Elections can be forecast by polling, and the more data available, the better the prediction, but these forecasts are never 100% accurate and being able to partly predict an election does not mean that its results are predetermined. In the same way, being able to use neural activity to predict a decision does not mean that a decision has already taken place.” The scientists also described a second population of neurons whose activity is theorized to reflect the running tally of votes for a particular action. This activity, described as “ramping”, had previously been reported only in humans and other primates. According to Masayoshi Murakami, co-author of the paper, “we believe these data provide strong evidence that the brain is performing integration to a threshold, but there are still many unknowns.” Said Mainen, “what is the origin of the variability is a huge question. Until we understand that, we cannot say we understand how a decision works”.

Using the brain to forecast decisions

You’re waiting at a bus stop, expecting the bus to arrive any time. You watch the road. Nothing yet. A little later you start to pace. More time passes. “Maybe there is some problem”, you think. Finally, you give up and raise your arm and hail a taxi. Just as you pull away, you glimpse the bus gliding up. Did you have a choice to wait a bit longer? Or was giving up too soon the inevitable and predictable result of a chain of neural events?

In research published on 09/28/2014 in the journal Nature Neuroscience, scientists show that neural recordings can be used to forecast when spontaneous decisions will take place. “Experiments like this have been used to argue that free will is an illusion,” says Zachary Mainen, a neuroscientist at the Champalimaud Centre for the Unknown, in Lisbon, Portugal, who led the study, “but we think that interpretation is mistaken.”

The scientists used recordings of neurons in an area of the brain involved in planning movements to try to predict when a rat would give up waiting for a delayed tone. “We know they were not just responding to a stimulus, but spontaneously deciding when to give up, because the timing of their choice varied unpredictably from trial to trial” said Mainen. The researchers discovered that neurons in the premotor cortex could predict the animals’ actions more than one second in advance. According to Mainen, “This is remarkable because in similar experiments, humans report deciding when to move only around two tenths of a second before the movement.”

However, the scientists claim that this kind of predictive activity does not mean that the brain has decided. “Our data can be explained very well by a theory of decision-making known as an ‘integration-to-bound’ model” says Mainen. According to this theory, individual brain cells cast votes for or against a particular action, such as raising an arm. Circuits within the brain keep a tally of the votes in favor of each action and when a threshold is reached it is triggered. Critically, like individual voters in an election, individual neurons influence a decision but do not determine the outcome. Mainen explained: “Elections can be forecast by polling, and the more data available, the better the prediction, but these forecasts are never 100% accurate and being able to partly predict an election does not mean that its results are predetermined. In the same way, being able to use neural activity to predict a decision does not mean that a decision has already taken place.”

The scientists also described a second population of neurons whose activity is theorized to reflect the running tally of votes for a particular action. This activity, described as “ramping”, had previously been reported only in humans and other primates. According to Masayoshi Murakami, co-author of the paper, “we believe these data provide strong evidence that the brain is performing integration to a threshold, but there are still many unknowns.” Said Mainen, “what is the origin of the variability is a huge question. Until we understand that, we cannot say we understand how a decision works”.

Filed under motor cortex motor movements decision making neural activity neuroscience science

145 notes

Modelling how neurons work together A newly-developed, highly accurate representation of the way in which neurons behave when performing movements such as reaching could not only enhance understanding of the complex dynamics at work in the brain, but aid in the development of robotic limbs which are capable of more complex and natural movements. Researchers from the University of Cambridge, working in collaboration with the University of Oxford and the Ecole Polytechnique Fédérale de Lausanne (EPFL), have developed a new model of a neural network, offering a novel theory of how neurons work together when performing complex movements. The results are published in the 18 June edition of the journal Neuron. While an action such as reaching for a cup of coffee may seem straightforward, the millions of neurons in the brain’s motor cortex must work together to prepare and execute the movement before the coffee ever reaches our lips. When we reach for the much-needed cup of coffee, the neurons spring into action, sending a series of signals from the brain to the hand. These signals are transmitted across synapses – the junctions between neurons. Determining exactly how the neurons work together to execute these movements is difficult, however. The new theory was inspired by recent experiments carried out at Stanford University, which had uncovered some key aspects of the signals that neurons emit before, during and after the movement. “There is a remarkable synergy in the activity recorded simultaneously in hundreds of neurons,” said Dr Guillaume Hennequin of the University’s Department of Engineering, who led the research. “In contrast, previous models of cortical circuit dynamics predict a lot of redundancy, and therefore poorly explain what happens in the motor cortex during movements.” Better models of how neurons behave will not only aid in our understanding of the brain, but could also be used to design prosthetic limbs controlled via electrodes implanted in the brain. “Our theory could provide a more accurate guess of how neurons would want to signal both movement intention and execution to the robotic limb,” said Dr Hennequin. The behaviour of neurons in the motor cortex can be likened to a mousetrap or a spring-loaded box, in which the springs are waiting to be released and are let go once the lid is opened or the mouse takes the bait. As we plan a movement, the ‘neural springs’ are progressively flexed and compressed. When released, they orchestrate a series of neural activity bursts, all of which takes place in the blink of an eye. The signals transmitted by the synapses in the motor cortex during complex movements can be either excitatory or inhibitory, which are in essence mirror reflections of each other. The signals cancel each other out for the most part, leaving occasional bursts of activity. Using control theory, a branch of mathematics well-suited to the study of complex interacting systems such as the brain, the researchers devised a model of neural behaviour which achieves a balance between the excitatory and inhibitory synaptic signals. The model can accurately reproduce a range of multidimensional movement patterns. The researchers found that neurons in the motor cortex might not be wired together with nearly as much randomness as had been previously thought. “Our model shows that the inhibitory synapses might be tuned to stabilise the dynamics of these brain networks,” said Dr Hennequin. “We think that accurate models like these can really aid in the understanding of the incredibly complex dynamics at work in the human brain.” Future directions for the research include building a more realistic, ‘closed-loop’ model of movement generation in which feedback from the limbs is actively used by the brain to correct for small errors in movement execution. This will expose the new theory to the more thorough scrutiny of physiological and behavioural validation, potentially leading to a more complete mechanistic understanding of complex movements.

Modelling how neurons work together

A newly-developed, highly accurate representation of the way in which neurons behave when performing movements such as reaching could not only enhance understanding of the complex dynamics at work in the brain, but aid in the development of robotic limbs which are capable of more complex and natural movements.

Researchers from the University of Cambridge, working in collaboration with the University of Oxford and the Ecole Polytechnique Fédérale de Lausanne (EPFL), have developed a new model of a neural network, offering a novel theory of how neurons work together when performing complex movements. The results are published in the 18 June edition of the journal Neuron.

While an action such as reaching for a cup of coffee may seem straightforward, the millions of neurons in the brain’s motor cortex must work together to prepare and execute the movement before the coffee ever reaches our lips. When we reach for the much-needed cup of coffee, the neurons spring into action, sending a series of signals from the brain to the hand. These signals are transmitted across synapses – the junctions between neurons.

Determining exactly how the neurons work together to execute these movements is difficult, however. The new theory was inspired by recent experiments carried out at Stanford University, which had uncovered some key aspects of the signals that neurons emit before, during and after the movement. “There is a remarkable synergy in the activity recorded simultaneously in hundreds of neurons,” said Dr Guillaume Hennequin of the University’s Department of Engineering, who led the research. “In contrast, previous models of cortical circuit dynamics predict a lot of redundancy, and therefore poorly explain what happens in the motor cortex during movements.”

Better models of how neurons behave will not only aid in our understanding of the brain, but could also be used to design prosthetic limbs controlled via electrodes implanted in the brain. “Our theory could provide a more accurate guess of how neurons would want to signal both movement intention and execution to the robotic limb,” said Dr Hennequin.

The behaviour of neurons in the motor cortex can be likened to a mousetrap or a spring-loaded box, in which the springs are waiting to be released and are let go once the lid is opened or the mouse takes the bait. As we plan a movement, the ‘neural springs’ are progressively flexed and compressed. When released, they orchestrate a series of neural activity bursts, all of which takes place in the blink of an eye.

The signals transmitted by the synapses in the motor cortex during complex movements can be either excitatory or inhibitory, which are in essence mirror reflections of each other. The signals cancel each other out for the most part, leaving occasional bursts of activity.

Using control theory, a branch of mathematics well-suited to the study of complex interacting systems such as the brain, the researchers devised a model of neural behaviour which achieves a balance between the excitatory and inhibitory synaptic signals. The model can accurately reproduce a range of multidimensional movement patterns.

The researchers found that neurons in the motor cortex might not be wired together with nearly as much randomness as had been previously thought. “Our model shows that the inhibitory synapses might be tuned to stabilise the dynamics of these brain networks,” said Dr Hennequin. “We think that accurate models like these can really aid in the understanding of the incredibly complex dynamics at work in the human brain.”

Future directions for the research include building a more realistic, ‘closed-loop’ model of movement generation in which feedback from the limbs is actively used by the brain to correct for small errors in movement execution. This will expose the new theory to the more thorough scrutiny of physiological and behavioural validation, potentially leading to a more complete mechanistic understanding of complex movements.

Filed under neurons neural networks motor cortex motor movements prosthetic limbs robotics neuroscience science

151 notes

CC to the brain: How neurons control fine motor behavior of the arm Motor commands issued by the brain to activate arm muscles take two different routes. As the research group led by Professor Silvia Arber at the Basel University Biozentrum and the Friedrich Miescher Institute for Biomedical Research has now discovered, many neurons in the spinal cord send their instructions not only towards the musculature, but at the same time also back to the brain via an exquisitely organized network. This dual information stream provides the neural basis for accurate control of arm and hand movements. These findings have now been published in “Cell”. Movement is a fundamental capability of humans and animals, involving the highly complex interplay of brain, nerves and muscles. Movements of our arms and hands, in particular, call for extremely precise coordination. The brain sends a constant stream of commands via the spinal cord to our muscles to execute a wide variety of movements. This stream of information from the brain reaches interneurons in the spinal cord, which then transmit the commands via further circuits to motor neurons innervating muscles. The research group led by Silvia Arber at the Biozentrum of the University of Basel and the Friedrich Miescher Institute for Biomedical Research has now elucidated the organization of a second information pathway taken by these commands. cc to the brain: one command – two directions The scientists showed that many interneurons in the mouse spinal cord not only transmit their signals via motor neurons to the target muscle, but also simultaneously send a copy of this information back to the brain. Chiara Pivetta, first author of the publication, explains: “The motor command to the muscle is sent in two different directions – in one direction, to trigger the desired muscular contraction and in the other, to inform the brain that the command has actually been passed on to the musculature.” In analogy to e‑mail transmission, the information is thus not only sent to the recipient but also to the original requester. Information to brainstem nucleus segregated by function What happens to the information sent by spinal interneurons to the brain? As Arber’s group discovered, this input is segregated by function and spatially organized within a brainstem nucleus. Information from different types of interneurons thus flows to different areas of the nucleus. For example, spinal information that will influence left-right coordination of a movement is collected at a different site than information affecting the speed of a movement. Fine motor skills supported by dual information stream Arber comments: “From one millisecond to the next, this extremely precise feedback ensures that commands are correctly transmitted and that – via the signals sent back to the brain from the spinal cord – the resulting movement is immediately coordinated with the brain and adjusted.” Interestingly, the scientists only observed this kind of information flow to the brain for arm, but not for leg control. “What this shows,” says Arber, “is that this information pathway is most likely important for fine motor skills. Compared to the leg, movements of our arm and especially our hands have to be far more precise. Evidently, our body can only ensure this level of accuracy in motor control with constant feedback of information.” In further studies, Silvia Arber’s group now plans to investigate what happens if the flow of information back to the brain is disrupted in specific ways. Since some interneurons facilitate and others inhibit movement, such studies could provide additional insights into the functionality of circuits controlling movement.

CC to the brain: How neurons control fine motor behavior of the arm

Motor commands issued by the brain to activate arm muscles take two different routes. As the research group led by Professor Silvia Arber at the Basel University Biozentrum and the Friedrich Miescher Institute for Biomedical Research has now discovered, many neurons in the spinal cord send their instructions not only towards the musculature, but at the same time also back to the brain via an exquisitely organized network. This dual information stream provides the neural basis for accurate control of arm and hand movements. These findings have now been published in “Cell”.

Movement is a fundamental capability of humans and animals, involving the highly complex interplay of brain, nerves and muscles. Movements of our arms and hands, in particular, call for extremely precise coordination. The brain sends a constant stream of commands via the spinal cord to our muscles to execute a wide variety of movements. This stream of information from the brain reaches interneurons in the spinal cord, which then transmit the commands via further circuits to motor neurons innervating muscles. The research group led by Silvia Arber at the Biozentrum of the University of Basel and the Friedrich Miescher Institute for Biomedical Research has now elucidated the organization of a second information pathway taken by these commands.

cc to the brain: one command – two directions

The scientists showed that many interneurons in the mouse spinal cord not only transmit their signals via motor neurons to the target muscle, but also simultaneously send a copy of this information back to the brain. Chiara Pivetta, first author of the publication, explains: “The motor command to the muscle is sent in two different directions – in one direction, to trigger the desired muscular contraction and in the other, to inform the brain that the command has actually been passed on to the musculature.” In analogy to e‑mail transmission, the information is thus not only sent to the recipient but also to the original requester.

Information to brainstem nucleus segregated by function

What happens to the information sent by spinal interneurons to the brain? As Arber’s group discovered, this input is segregated by function and spatially organized within a brainstem nucleus. Information from different types of interneurons thus flows to different areas of the nucleus. For example, spinal information that will influence left-right coordination of a movement is collected at a different site than information affecting the speed of a movement.

Fine motor skills supported by dual information stream

Arber comments: “From one millisecond to the next, this extremely precise feedback ensures that commands are correctly transmitted and that – via the signals sent back to the brain from the spinal cord – the resulting movement is immediately coordinated with the brain and adjusted.” Interestingly, the scientists only observed this kind of information flow to the brain for arm, but not for leg control. “What this shows,” says Arber, “is that this information pathway is most likely important for fine motor skills. Compared to the leg, movements of our arm and especially our hands have to be far more precise. Evidently, our body can only ensure this level of accuracy in motor control with constant feedback of information.”

In further studies, Silvia Arber’s group now plans to investigate what happens if the flow of information back to the brain is disrupted in specific ways. Since some interneurons facilitate and others inhibit movement, such studies could provide additional insights into the functionality of circuits controlling movement.

Filed under arm movement motor movements spinal cord interneurons motor neurons neuroscience science

152 notes

Discovery Shows Cerebellum Plays Important Role In Sensing Limb Position And Movement Kennedy Krieger Institute researchers find that damage to the cerebellum impairs ability to predict motion outcomes and discrimination between limb positions. Researchers at the Kennedy Krieger Institute announced today study findings showing, for the first time, the link between the brain’s cerebellum and proprioception, or the body’s ability to sense movement and joint and limb position. Published in The Journal of Neuroscience, the study uncovers a previously unknown perceptual deficit among cerebellar patients, suggesting that damage to this portion of the brain can directly impact a person’s ability to sense the position of their limbs and predict movement. This discovery could prompt future researchers to reexamine physical therapy tactics for cerebellar patients, who often have impaired coordination or appear clumsy. The sense of proprioception arises from the brain’s readout of signals from receptors in muscles, joints and ligaments, but also from the brain’s predictions of how motor commands will move the limb. The latter can only contribute to proprioception when a person actively moves their own body. To date, researchers and neurologists believed that proprioception did not occur in the cerebellum, and thus, damage to the cerebellum did not affect proprioception. “Proprioception was previously not considered a factor in the therapy or recovery of cerebellar patients. In fact, previous research has shown that individuals with cerebellum damage and no other clinical neurological impairments have normal proprioception when their limbs are moved passively in a clinical setting,” says Amy J. Bastian, Ph.D., PT, director of the Motion Analysis Laboratory at Kennedy Krieger Institute. “However, when these patients move their limbs actively, they exhibit significant proprioceptive impairment.” Additionally, researchers found that proprioception in healthy subjects was impaired when unpredictable dynamics, or small disturbances to the cerebellum, were incorporated into active movement. This suggests that muscle activity alone is likely insufficient to improve perception of limb placement, and proprioception should be taken into consideration when determining therapeutic practices for cerebellar patients. Study Results and Methodology The study compared 11 healthy people (control group) to 11 patients with cerebellar damage (caused by spinocerebellar ataxia, sporadic cerebellar ataxia or autosomal-dominant cerebellar ataxia type III) but no evidence of white matter damage, spontaneous nystagmus or atrophy to the brainstem. None of the patients included in the study had sensory loss assessed by conventional clinical measures of proprioception and tactile sensation. Participants were compared in three psychophysical tasks designed to assess passive proprioception, active proprioception with simple dynamics, and active proprioception with complex, unpredictable dynamics designed to disrupt the cerebellum. All tasks relied on proprioceptive sense without vision. Results showed that: Cerebellar patients had no deficits in passive proprioception Unlike control subjects, cerebellar patients did not show an improvement between passive and active proprioception with simple dynamics Control patients performed similarly to patients in an active proprioception task with unpredictable, small disruptions to their movement. This study was supported by the Kennedy Krieger Institute, the Johns Hopkins University and the National Institutes of Health.

Discovery Shows Cerebellum Plays Important Role In Sensing Limb Position And Movement

Kennedy Krieger Institute researchers find that damage to the cerebellum impairs ability to predict motion outcomes and discrimination between limb positions.

Researchers at the Kennedy Krieger Institute announced today study findings showing, for the first time, the link between the brain’s cerebellum and proprioception, or the body’s ability to sense movement and joint and limb position. Published in The Journal of Neuroscience, the study uncovers a previously unknown perceptual deficit among cerebellar patients, suggesting that damage to this portion of the brain can directly impact a person’s ability to sense the position of their limbs and predict movement. This discovery could prompt future researchers to reexamine physical therapy tactics for cerebellar patients, who often have impaired coordination or appear clumsy.

The sense of proprioception arises from the brain’s readout of signals from receptors in muscles, joints and ligaments, but also from the brain’s predictions of how motor commands will move the limb. The latter can only contribute to proprioception when a person actively moves their own body. To date, researchers and neurologists believed that proprioception did not occur in the cerebellum, and thus, damage to the cerebellum did not affect proprioception.

“Proprioception was previously not considered a factor in the therapy or recovery of cerebellar patients. In fact, previous research has shown that individuals with cerebellum damage and no other clinical neurological impairments have normal proprioception when their limbs are moved passively in a clinical setting,” says Amy J. Bastian, Ph.D., PT, director of the Motion Analysis Laboratory at Kennedy Krieger Institute. “However, when these patients move their limbs actively, they exhibit significant proprioceptive impairment.”

Additionally, researchers found that proprioception in healthy subjects was impaired when unpredictable dynamics, or small disturbances to the cerebellum, were incorporated into active movement. This suggests that muscle activity alone is likely insufficient to improve perception of limb placement, and proprioception should be taken into consideration when determining therapeutic practices for cerebellar patients.

Study Results and Methodology

The study compared 11 healthy people (control group) to 11 patients with cerebellar damage (caused by spinocerebellar ataxia, sporadic cerebellar ataxia or autosomal-dominant cerebellar ataxia type III) but no evidence of white matter damage, spontaneous nystagmus or atrophy to the brainstem. None of the patients included in the study had sensory loss assessed by conventional clinical measures of proprioception and tactile sensation.

Participants were compared in three psychophysical tasks designed to assess passive proprioception, active proprioception with simple dynamics, and active proprioception with complex, unpredictable dynamics designed to disrupt the cerebellum. All tasks relied on proprioceptive sense without vision.

Results showed that:

  • Cerebellar patients had no deficits in passive proprioception
  • Unlike control subjects, cerebellar patients did not show an improvement between passive and active proprioception with simple dynamics
  • Control patients performed similarly to patients in an active proprioception task with unpredictable, small disruptions to their movement.

This study was supported by the Kennedy Krieger Institute, the Johns Hopkins University and the National Institutes of Health.

Filed under cerebellum proprioception motor movements neuroscience science

64 notes

Helpful for robotics: brain uses old information for new movements

Information from the senses has an important influence on how we move. For instance, you can see and feel when a mug is filled with hot coffee, and you lift it in a different way than if the mug were empty. Neuroscientist Julian Tramper discovered that the brain uses two forms of old information in order to execute new movements well. This discovery can be useful for the field of robotics. Tramper will receive his doctorate on Thursday 24 April from Radboud University Nijmegen

Every time you move, the brain deals with two problems. First, there is a slight delay in the sensory information needed to execute the movement. Second, the command from the brain directing the muscles to move is not entirely clear, because neuronal signals contain a certain amount of natural static interference. According to Tramper, the brain has a clever way of getting around both problems: It combines the old information from the senses with experience gained through similar movements made in the past. This means that our senses use two forms of old information in order to make new movements.

Computer versus test subject
Understanding the brain processes behind movement can be of great importance to fields like robotics. Therefore Tramper is trying to model his findings so that it will be possible to use them in robots in the future. He has already succeeded in this for certain hand-eye coordination experiments, to the extent that a computer can perform at about the same level as human test subjects. As a post-doctoral researcher within the Donders Institute, Tramper is researching how these types of models can be integrated into bio-inspired robots (robots based on biological principles).

SpaceCog
Tramper is currently working on a project called SpaceCog. The goal of this project is to develop a robot which can independently orient itself in space, something that humans do automatically. This is difficult to achieve, because each time a robot moves, it must reinterpret the information from its cameras and other sensors in order to determine whether the changes to its input are the result of its own movement or an external cause. The researchers involved in SpaceCog want to figure out how our brain has solved this problem. Tramper has three years to come up with a good computer model addressing this issue.

Looking towards the future
Tramper is studying hand-eye coordination by having test subjects play a special computer game. The subjects use a game controller to move a digital right hand and left hand on a screen. They have to move the two hands independently of one another and make them each follow a particular path in order to reach a final destination (see film 1). It turned out that the test subject’s eyes moved ahead of the digital hands. In other words, the eyes looked at a point that the hands would reach in the future (see film 2). This type of eye movement is called smooth pursuit, and before now it had only been detected in the case of external stimuli, when a subject was following an object’s movement. Tramper detected smooth pursuit eye movements at locations the hands had not yet reached, meaning these movements were triggered by internal stimuli.

Smooth pursuit
Tramper explains, ‘We’d previously demonstrated for other types of eye movement that the eye anticipates and moves in advance of external movement  To our surprise, this is also the case with smooth pursuit. It is probable that this is a compromise between where you are at a particular moment and where you want to get to. When moving, you need to keep track of your current location (which is constantly changing) and your target destination. Smooth pursuit eye movements can help you do this by letting your eye “hover” between both locations. If we can teach robots to do something like this, it will help make their movements much more natural. This will increase the number of ways in which robots can be put to work.’

(Source: ru.nl)

Filed under sensory information robots robotics motor movements hand-eye coordination SpaceCog neuroscience science

118 notes

Pitt/UPMC Team Describes Technology that Lets Spinal Cord-Injured Man Control Robot Arm with Thoughts Researchers at the University of Pittsburgh School of Medicine and UPMC describe in PLoS ONE how an electrode array sitting on top of the brain enabled a 30-year-old paralyzed man to control the movement of a character on a computer screen in three dimensions with just his thoughts. It also enabled him to move a robot arm to touch a friend’s hand for the first time in the seven years since he was injured in a motorcycle accident. With brain-computer interface (BCI) technology, the thoughts of Tim Hemmes, who sustained a spinal cord injury that left him unable to move his body below the shoulders, were interpreted by computer algorithms and translated into intended movement of a computer cursor and, later, a robot arm, explained lead investigator Wei Wang, Ph.D., assistant professor, Department of Physical Medicine and Rehabilitation, Pitt School of Medicine. “When Tim reached out to high-five me with the robotic arm, we knew this technology had the potential to help people who cannot move their own arms achieve greater independence,” said Dr. Wang, reflecting on a memorable scene from September 2011 that was re-told in stories around the world. “It’s very important that we continue this effort to fulfill the promise we saw that day.” Six weeks before the implantation surgery, the team conducted functional magnetic resonance imaging (fMRI) of Mr. Hemmes’ brain while he watched videos of arm movement. They used that information to place a postage stamp-size electrocortigraphy (ECoG) grid of 28 recording electrodes on the surface of the brain region that fMRI showed controlled right arm and hand movement. Wires from the device were tunneled under the skin of his neck to emerge from his chest where they could be connected to computer cables as necessary. For 12 days at his home and nine days in the research lab, Mr. Hemmes began the testing protocol by watching a virtual arm move, which triggered neural signals that were sensed by the electrodes. Distinct signal patterns for particular observed movements were used to guide the up and down motion of a ball on a computer screen. Soon after mastering movement of the ball in two dimensions, namely up/down and right/left, he was able to also move it in/out with accuracy on a 3-dimensional display. “During the learning process, the computer helped Tim hit his target smoothly by restricting how far off course the ball could wander,” Dr. Wang said. “We gradually took off the ‘training wheels,’ as we called it, and he was soon doing the tasks by himself with 100 percent brain control.” The robot arm was developed by Johns Hopkins University’s Applied Physics Laboratory. Currently, Jan Scheuermann, of Whitehall, Pa., is testing another BCI technology at Pitt/UPMC.

Pitt/UPMC Team Describes Technology that Lets Spinal Cord-Injured Man Control Robot Arm with Thoughts

Researchers at the University of Pittsburgh School of Medicine and UPMC describe in PLoS ONE how an electrode array sitting on top of the brain enabled a 30-year-old paralyzed man to control the movement of a character on a computer screen in three dimensions with just his thoughts. It also enabled him to move a robot arm to touch a friend’s hand for the first time in the seven years since he was injured in a motorcycle accident.

With brain-computer interface (BCI) technology, the thoughts of Tim Hemmes, who sustained a spinal cord injury that left him unable to move his body below the shoulders, were interpreted by computer algorithms and translated into intended movement of a computer cursor and, later, a robot arm, explained lead investigator Wei Wang, Ph.D., assistant professor, Department of Physical Medicine and Rehabilitation, Pitt School of Medicine.

“When Tim reached out to high-five me with the robotic arm, we knew this technology had the potential to help people who cannot move their own arms achieve greater independence,” said Dr. Wang, reflecting on a memorable scene from September 2011 that was re-told in stories around the world. “It’s very important that we continue this effort to fulfill the promise we saw that day.”

Six weeks before the implantation surgery, the team conducted functional magnetic resonance imaging (fMRI) of Mr. Hemmes’ brain while he watched videos of arm movement. They used that information to place a postage stamp-size electrocortigraphy (ECoG) grid of 28 recording electrodes on the surface of the brain region that fMRI showed controlled right arm and hand movement. Wires from the device were tunneled under the skin of his neck to emerge from his chest where they could be connected to computer cables as necessary.

For 12 days at his home and nine days in the research lab, Mr. Hemmes began the testing protocol by watching a virtual arm move, which triggered neural signals that were sensed by the electrodes. Distinct signal patterns for particular observed movements were used to guide the up and down motion of a ball on a computer screen. Soon after mastering movement of the ball in two dimensions, namely up/down and right/left, he was able to also move it in/out with accuracy on a 3-dimensional display.

“During the learning process, the computer helped Tim hit his target smoothly by restricting how far off course the ball could wander,” Dr. Wang said. “We gradually took off the ‘training wheels,’ as we called it, and he was soon doing the tasks by himself with 100 percent brain control.”

The robot arm was developed by Johns Hopkins University’s Applied Physics Laboratory. Currently, Jan Scheuermann, of Whitehall, Pa., is testing another BCI technology at Pitt/UPMC.

Filed under BCI spinal cord injury robotic arm motor movements neural activity robotics neuroscience science

free counters