Neuroscience

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Posts tagged brainwaves

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Brainwaves reflect ability to beat built-in bias Many animals, including humans, harbor ingrained biases to act when they can obtain rewards and to remain inactive to avoid punishment. Sometimes, however those biases can steer us wrong. A new study finds that theta brainwave activity in the prefrontal cortex predicts how well people can overcome these biases when a better choice are available. Vertebrates are predisposed to act to gain rewards and to lie low to avoid punishment. Try to teach chickens to back away from food in order to obtain it, and you’ll fail, as researchers did in 1986. But humans are better thinkers than chickens. In the May 8 edition of the Journal of Neuroscience, researchers show that the level of theta brainwave activity in the prefrontal cortex predicts whether people will be able to overcome these ingrained biases when doing so is required to achieve a goal. The study helps explain a distinctly human mechanism of cognition, said the lead researchers at Brown University, and could be applied to studying and treating reward-seeking or punishment-avoidance conditions such as addiction or obsessive-compulsive disorder. Despite how we have evolved, life doesn’t always encourage acting to gain reward or freezing to avoid punishment. Sometimes we must restrain ourselves to gain a reward (baseball batters can get on base by not swinging at bad pitches) or take action to avoid a penalty (tax cheaters can come forward during amnesties). Acting counter to our ingrained Pavlovian biases is a matter of the brain recognizing the conflict between the rational course of action and the instinct. “We have suggested that more advanced brain mechanisms in the prefrontal are needed to exert cognitive control over behavior in these circumstances,” said Michael Frank, associate professor of cognitive, linguistic and psychological sciences and the paper’s senior author. “This study provides evidence that temporally specific brain activity within the prefrontal cortex is related to this ability, both between and within individuals.” Human vs. bias That brain activity could be measured and quantified as theta brainwaves. Brown postdoctoral researcher James Cavanagh led the research in which he recruited 34 people to play a custom-designed computer game while wearing EEG scalp monitors. The game involved four scenarios, all reinforced by putting a little real money on the line: the instinctual scenarios of clicking for a reward and not clicking to avoid a penalty, and the trickier scenarios of clicking to avoid penalty and not clicking to gain a reward. Over many rounds, players tried to learn what to do when presented with one of four distinct symbols, each of which corresponded to a different scenario. Cavanagh programmed the scenarios usually, but not always, to reward the proper behavior. For this reason, people had to pay attention to what was likely, rather than merely memorize a simple reliable pattern. Cavanagh and his co-authors measured how well people learned the proper action for each scenario. With the advantage of instinct, almost everyone learned to click for a reward. Most people also managed to learn not to click to avoid penalty and even managed in similar numbers to click to avoid penalty. Like the chickens, however, significantly fewer people could restrain themselves in order to gain a reward. Those who were bad at overcoming one Pavlovian bias were much more likely to fail at the other. While the subjects were playing the game, the experimenters also measured theta brainwave activity in each subject’s prefrontal cortex — for instance at the exact moment they saw the distinct symbols of the tasks. The main idea of the study was to correlate the subjects’ theta brain activity during the tasks with their ability to overcome ingrained bias when appropriate. Sure enough, the subject’s ability to repress Pavlovian bias was predicted by the enhancement of theta during the trials when the bias was unwanted, compared to when it provided proper guidance. “Some people are really good at it and some are not, and we were able to predict that from their brain activity,” Cavanagh said. This was not only true when comparing individual subjects, but also when comparing the subjects to themselves at different times (e.g., some subjects’ abilities wavered from task to task and the theta varied right along). Many psychological factors could have confounded the results — differential sensitivity to gains and losses, for example – but Cavanagh and Frank controlled for those with the help of a sophisticated computer model that accounts for and statistically disentangles the relationship of bias and theta from those other influences. Our better nature All of the study subjects were screened to ensure they were psychiatrically healthy. In these subjects, the study results not only confirmed that people harbor the ingrained biases, but that they differ in their ability to overcome them. Frank said the variations likely come from innate and situational factors. Evidence suggests that the degree of ingrained bias may have genetic and neurological roots, he said, but can also vary within the same individual based on factors such as fatigue or stress. For people with psychiatric disorders, Cavanagh said, the predictive value of measurable theta activity for behavioral patterns could become an important tool for diagnosis and predicting treatment outcomes. Frank, who is affiliated with the Brown Institute for Brain Science, added that the lab has begun studying whether people can improve behavior by purposely modulating theta activity. If so, that could lead to a therapy for addiction. “We are beginning studies that allow us to safely manipulate activity in specific frequencies like theta in the frontal cortex which will allow us to assess the causal role these signals may be playing,” he said. It’s not easy to work against primal intuition, but people have that ability and now researchers know how that ability is reflected in brains. “This tells us a lot about the neurobiology of why we’re special,” Cavanagh said.

Brainwaves reflect ability to beat built-in bias

Many animals, including humans, harbor ingrained biases to act when they can obtain rewards and to remain inactive to avoid punishment. Sometimes, however those biases can steer us wrong. A new study finds that theta brainwave activity in the prefrontal cortex predicts how well people can overcome these biases when a better choice are available.

Vertebrates are predisposed to act to gain rewards and to lie low to avoid punishment. Try to teach chickens to back away from food in order to obtain it, and you’ll fail, as researchers did in 1986. But humans are better thinkers than chickens. In the May 8 edition of the Journal of Neuroscience, researchers show that the level of theta brainwave activity in the prefrontal cortex predicts whether people will be able to overcome these ingrained biases when doing so is required to achieve a goal.

The study helps explain a distinctly human mechanism of cognition, said the lead researchers at Brown University, and could be applied to studying and treating reward-seeking or punishment-avoidance conditions such as addiction or obsessive-compulsive disorder.

Despite how we have evolved, life doesn’t always encourage acting to gain reward or freezing to avoid punishment. Sometimes we must restrain ourselves to gain a reward (baseball batters can get on base by not swinging at bad pitches) or take action to avoid a penalty (tax cheaters can come forward during amnesties). Acting counter to our ingrained Pavlovian biases is a matter of the brain recognizing the conflict between the rational course of action and the instinct.

“We have suggested that more advanced brain mechanisms in the prefrontal are needed to exert cognitive control over behavior in these circumstances,” said Michael Frank, associate professor of cognitive, linguistic and psychological sciences and the paper’s senior author. “This study provides evidence that temporally specific brain activity within the prefrontal cortex is related to this ability, both between and within individuals.”

Human vs. bias

That brain activity could be measured and quantified as theta brainwaves. Brown postdoctoral researcher James Cavanagh led the research in which he recruited 34 people to play a custom-designed computer game while wearing EEG scalp monitors.

The game involved four scenarios, all reinforced by putting a little real money on the line: the instinctual scenarios of clicking for a reward and not clicking to avoid a penalty, and the trickier scenarios of clicking to avoid penalty and not clicking to gain a reward.

Over many rounds, players tried to learn what to do when presented with one of four distinct symbols, each of which corresponded to a different scenario.

Cavanagh programmed the scenarios usually, but not always, to reward the proper behavior. For this reason, people had to pay attention to what was likely, rather than merely memorize a simple reliable pattern.

Cavanagh and his co-authors measured how well people learned the proper action for each scenario. With the advantage of instinct, almost everyone learned to click for a reward. Most people also managed to learn not to click to avoid penalty and even managed in similar numbers to click to avoid penalty. Like the chickens, however, significantly fewer people could restrain themselves in order to gain a reward.

Those who were bad at overcoming one Pavlovian bias were much more likely to fail at the other.

While the subjects were playing the game, the experimenters also measured theta brainwave activity in each subject’s prefrontal cortex — for instance at the exact moment they saw the distinct symbols of the tasks.

The main idea of the study was to correlate the subjects’ theta brain activity during the tasks with their ability to overcome ingrained bias when appropriate. Sure enough, the subject’s ability to repress Pavlovian bias was predicted by the enhancement of theta during the trials when the bias was unwanted, compared to when it provided proper guidance.

“Some people are really good at it and some are not, and we were able to predict that from their brain activity,” Cavanagh said.

This was not only true when comparing individual subjects, but also when comparing the subjects to themselves at different times (e.g., some subjects’ abilities wavered from task to task and the theta varied right along).

Many psychological factors could have confounded the results — differential sensitivity to gains and losses, for example – but Cavanagh and Frank controlled for those with the help of a sophisticated computer model that accounts for and statistically disentangles the relationship of bias and theta from those other influences.

Our better nature

All of the study subjects were screened to ensure they were psychiatrically healthy. In these subjects, the study results not only confirmed that people harbor the ingrained biases, but that they differ in their ability to overcome them. Frank said the variations likely come from innate and situational factors. Evidence suggests that the degree of ingrained bias may have genetic and neurological roots, he said, but can also vary within the same individual based on factors such as fatigue or stress.

For people with psychiatric disorders, Cavanagh said, the predictive value of measurable theta activity for behavioral patterns could become an important tool for diagnosis and predicting treatment outcomes.

Frank, who is affiliated with the Brown Institute for Brain Science, added that the lab has begun studying whether people can improve behavior by purposely modulating theta activity. If so, that could lead to a therapy for addiction.

“We are beginning studies that allow us to safely manipulate activity in specific frequencies like theta in the frontal cortex which will allow us to assess the causal role these signals may be playing,” he said.

It’s not easy to work against primal intuition, but people have that ability and now researchers know how that ability is reflected in brains.

“This tells us a lot about the neurobiology of why we’re special,” Cavanagh said.

Filed under prefrontal cortex brain activity brainwaves cognitive control neuroscience science

643 notes

Scientists probe the source of a pulsing signal in the sleeping brain New findings clarify where and how the brain’s “slow waves” originate. These rhythmic signal pulses, which sweep through the brain during deep sleep at the rate of about one cycle per second, are assumed to play a role in processes such as consolidation of memory. For the first time, researchers have shown conclusively that slow waves start in the cerebral cortex, the part of the brain responsible for cognitive functions. They also found that such a wave can be set in motion by a tiny cluster of neurons. "The brain is a rhythm machine, producing all kinds of rhythms all the time," says Prof. Arthur Konnerth of the Technische Universitaet Muenchen (TUM). "These are clocks that help to keep many parts of the brain on the same page." One such timekeeper produces the so-called slow waves of deep sleep, which are thought to be involved in transmuting fragments of a day’s experience and learning into lasting memory. They can be observed in very early stages of development, and they may be disrupted in diseases such as Alzheimer’s. Previous studies, relying mainly on electrical measurements, have lacked the spatial resolution to map the initiation and propagation of slow waves precisely. But using light, Konnerth’s Munich-based team – in collaboration with researchers at Stanford and the University of Mainz – could both stimulate slow waves and observe them in unprecedented detail. One key result confirmed that the slow waves originate only in the cortex, ruling out other long-standing hypotheses. “The second major finding,” Konnerth says, “was that out of the billions of cells in the brain, it takes not more than a local cluster of fifty to one hundred neurons in a deep layer of the cortex, called layer 5, to make a wave that extends over the entire brain.” New light on a fundamental neural mechanism Despite considerable investigation of the brain’s slow waves, definitive answers about the underlying circuit mechanism have remained elusive. Where is the pacemaker for this rhythm? Where do the waves start, and where do they stop? This study – based on optical probing of intact brains of live mice under anesthesia – now provides the basis for a detailed, comprehensive view. "We implemented an optogenetic approach combined with optical detection of neuronal activity to explore causal features of these slow oscillations, or Up-Down state transitions, that represent the dominating network rhythm in sleep," explains Prof. Albrecht Stroh of the Johannes Gutenberg University Mainz. Optogenetics is a novel technique that enabled the researchers to insert light-sensitive channels into specific kinds of neurons, to make them responsive to light stimulation. This allowed for selective and spatially defined stimulation of small numbers of cortical and thalamic neurons. Access to the brain via optical fibers allowed for both microscopic recording and direct stimulation of neurons. Flashes of light near the mouse’s eyes were also used to stimulate neurons in the visual cortex. By recording the flux of calcium ions, a chemical signal that can serve as a more spatially precise readout of the electric activity, the researchers made the slow waves visible. They also correlated optical recordings with more conventional electrical measurements. As a result, it was possible to watch individual wave fronts spread – like ripples from a rock thrown into a quiet lake – first through the cortex and then through other brain structures. A new picture begins to emerge: Not only is it possible for a tiny local cluster of neurons to initiate a slow wave that will spread far and wide, recruiting multiple regions of the brain into a single event – this appears to be typical. “In spontaneous conditions,” Konnerth says, “as it happens with you and me and everyone else every night in deep sleep, every part of the cortex can be an initiation site.” Furthermore, a surprisingly simple communication protocol can be seen in the slow wave rhythm. During each one-second cycle a single neuron cluster sends its signal and all others are silenced, as if they are taking turns bathing the brain in fragments of experience or learning, building blocks of memory. The researchers view these findings as a step toward a better understanding of learning and memory formation, a topic Konnerth’s group is investigating with funding from the European Research Council. They also are testing how the slow waves behave during disease.

Scientists probe the source of a pulsing signal in the sleeping brain

New findings clarify where and how the brain’s “slow waves” originate. These rhythmic signal pulses, which sweep through the brain during deep sleep at the rate of about one cycle per second, are assumed to play a role in processes such as consolidation of memory. For the first time, researchers have shown conclusively that slow waves start in the cerebral cortex, the part of the brain responsible for cognitive functions. They also found that such a wave can be set in motion by a tiny cluster of neurons.

"The brain is a rhythm machine, producing all kinds of rhythms all the time," says Prof. Arthur Konnerth of the Technische Universitaet Muenchen (TUM). "These are clocks that help to keep many parts of the brain on the same page." One such timekeeper produces the so-called slow waves of deep sleep, which are thought to be involved in transmuting fragments of a day’s experience and learning into lasting memory. They can be observed in very early stages of development, and they may be disrupted in diseases such as Alzheimer’s.

Previous studies, relying mainly on electrical measurements, have lacked the spatial resolution to map the initiation and propagation of slow waves precisely. But using light, Konnerth’s Munich-based team – in collaboration with researchers at Stanford and the University of Mainz – could both stimulate slow waves and observe them in unprecedented detail. One key result confirmed that the slow waves originate only in the cortex, ruling out other long-standing hypotheses. “The second major finding,” Konnerth says, “was that out of the billions of cells in the brain, it takes not more than a local cluster of fifty to one hundred neurons in a deep layer of the cortex, called layer 5, to make a wave that extends over the entire brain.”

New light on a fundamental neural mechanism

Despite considerable investigation of the brain’s slow waves, definitive answers about the underlying circuit mechanism have remained elusive. Where is the pacemaker for this rhythm? Where do the waves start, and where do they stop? This study – based on optical probing of intact brains of live mice under anesthesia – now provides the basis for a detailed, comprehensive view.

"We implemented an optogenetic approach combined with optical detection of neuronal activity to explore causal features of these slow oscillations, or Up-Down state transitions, that represent the dominating network rhythm in sleep," explains Prof. Albrecht Stroh of the Johannes Gutenberg University Mainz. Optogenetics is a novel technique that enabled the researchers to insert light-sensitive channels into specific kinds of neurons, to make them responsive to light stimulation. This allowed for selective and spatially defined stimulation of small numbers of cortical and thalamic neurons.

Access to the brain via optical fibers allowed for both microscopic recording and direct stimulation of neurons. Flashes of light near the mouse’s eyes were also used to stimulate neurons in the visual cortex. By recording the flux of calcium ions, a chemical signal that can serve as a more spatially precise readout of the electric activity, the researchers made the slow waves visible. They also correlated optical recordings with more conventional electrical measurements. As a result, it was possible to watch individual wave fronts spread – like ripples from a rock thrown into a quiet lake – first through the cortex and then through other brain structures.

A new picture begins to emerge: Not only is it possible for a tiny local cluster of neurons to initiate a slow wave that will spread far and wide, recruiting multiple regions of the brain into a single event – this appears to be typical. “In spontaneous conditions,” Konnerth says, “as it happens with you and me and everyone else every night in deep sleep, every part of the cortex can be an initiation site.” Furthermore, a surprisingly simple communication protocol can be seen in the slow wave rhythm. During each one-second cycle a single neuron cluster sends its signal and all others are silenced, as if they are taking turns bathing the brain in fragments of experience or learning, building blocks of memory. The researchers view these findings as a step toward a better understanding of learning and memory formation, a topic Konnerth’s group is investigating with funding from the European Research Council. They also are testing how the slow waves behave during disease.

Filed under sleep deep sleep brainwaves cerebral cortex optogenetics neurons neuroscience science

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State science fair winner creates robot The winner of this year’s State Science and Engineering Fair is from South Florida, and her project can someday make life easier for the physically challenged. "It captures the brain waves of electrochemical activity. Basically, the nerve impulse produced by the brain, and it sends it over to the robot," said Daniela Rodriguez. Steve is an award winning robot controlled by brain waves. He was invented by 13-year-old Daniela Rodriguez, who loves math and science. “I’ve always been interested in robotics; it’s my passion,” she said. This year, Rodriguez won first place in the Annual State Science and Engineering Fair against 900 other finalists. Rodriguez’ goal is to help people. “If the person is disabled, they can sit in their wheelchair, and they can use their thoughts and brain waves to control its movements, so they don’t have to move,” she said. Her science project comes from the heart. Her mother was diagnosed with multiple sclerosis in 1996, and she is trying to find a way to keep her mom independent. “I work really hard to try to stay mobile, but the fact that she wants to help patients dealing with this illness is just a Godsend” said Rodriguez’ mom Jeannie. Rodriguez’ wants to one day use her technology to help paralyzed people. Steve’s technology can even give wounded veterans the ability to use their brains to move the robot. “To help them move around in their wheelchairs or move their prosthetics because usually prosthetics now is just the muscle movement, but now it can be used and be more natural. It’s moving by your brain,” said Rodriguez. Not only is Rodriguez winning awards, prosthetic companies have expressed interest in her program.

State science fair winner creates robot

The winner of this year’s State Science and Engineering Fair is from South Florida, and her project can someday make life easier for the physically challenged.

"It captures the brain waves of electrochemical activity. Basically, the nerve impulse produced by the brain, and it sends it over to the robot," said Daniela Rodriguez.

Steve is an award winning robot controlled by brain waves. He was invented by 13-year-old Daniela Rodriguez, who loves math and science. “I’ve always been interested in robotics; it’s my passion,” she said.

This year, Rodriguez won first place in the Annual State Science and Engineering Fair against 900 other finalists.

Rodriguez’ goal is to help people. “If the person is disabled, they can sit in their wheelchair, and they can use their thoughts and brain waves to control its movements, so they don’t have to move,” she said.

Her science project comes from the heart. Her mother was diagnosed with multiple sclerosis in 1996, and she is trying to find a way to keep her mom independent. “I work really hard to try to stay mobile, but the fact that she wants to help patients dealing with this illness is just a Godsend” said Rodriguez’ mom Jeannie.

Rodriguez’ wants to one day use her technology to help paralyzed people. Steve’s technology can even give wounded veterans the ability to use their brains to move the robot. “To help them move around in their wheelchairs or move their prosthetics because usually prosthetics now is just the muscle movement, but now it can be used and be more natural. It’s moving by your brain,” said Rodriguez.

Not only is Rodriguez winning awards, prosthetic companies have expressed interest in her program.

Filed under brain brainwaves robots robotics Steve prosthetics neuroscience science

187 notes

Sleep study reveals how the adolescent brain makes the transition to mature thinking A new study conducted by monitoring the brain waves of sleeping adolescents has found that remarkable changes occur in the brain as it prunes away neuronal connections and makes the major transition from childhood to adulthood. “We’ve provided the first long-term, longitudinal description of developmental changes that take place in the brains of youngsters as they sleep,” said Irwin Feinberg, professor emeritus of psychiatry and behavioral sciences and director of the UC Davis Sleep Laboratory. “Our outcome confirms that the brain goes through a remarkable amount of reorganization during puberty that is necessary for complex thinking.” The research, published in the February 15 issue of American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, also confirms that electroencephalogram, or EEG, is a powerful tool for tracking brain changes during different phases of life, and that it could potentially be used to help diagnose age-related mental illnesses. It is the final component in a three-part series of studies carried out over 10 years and involving more than 3,500 all-night EEG recordings. The data provide an overall picture of the brain’s electrical behavior during the first two decades of life. Feinberg explained that scientists have generally assumed that a vast number of synapses are needed early in life to recover from injury and adapt to changing environments. These multiple connections, however, impair the efficient problem solving and logical thinking required later in life. His study is the first to show how this shift can be detected by measuring the brain’s electrical activity in the same children over the course of time. Two earlier studies by Feinberg and his colleagues showed that EEG fluctuations during the deepest (delta or slow wave) phase of sleep, when the brain is most recuperative, consistently declined for 9- to 18-year-olds. The most rapid decline occurred between the ages of 12 and 16-1/2. This led the team to conclude that the streamlining of brain activity — or “neuronal pruning” — required for adult cognition occurs together with the timing of reproductive maturity. Questions remained, though, about electrical activity patterns in the brains of younger children. For the current study, Feinberg and his research team monitored 28 healthy, sleeping children between the ages of 6 and 10 for two nights every six months. The new findings show that synaptic density in the cerebral cortex reaches its peak at age 8 and then begins a slow decline. The recent findings also confirm that the period of greatest and most accelerated decline occurs between the ages of 12 and 16-1/2 years, at which point the drop markedly slows. “Discovering that such extensive neuronal remodeling occurs within this 4-1/2 year timeframe during late adolescence and the early teen years confirms our view that the sleep EEG indexes a crucial aspect of the timing of brain development,” said Feinberg. The latest study also confirms that EEG sleep analysis is a powerful approach for evaluating adolescent brain maturation, according to Feinberg. Besides being a relatively simple, accessible technology for measuring the brain’s electrical activity, it is more accurate than more cumbersome and expensive options. “Structural MRI, for instance, has not been able to identify the adolescent accelerations and decelerations that are easily and reliably captured by sleep EEG,” said Feinberg. “We hope our data can aid the search for the unknown genetic and hormonal biomarkers that drive those fluctuations. Our data also provide a baseline for seeking errors in brain development that signify the onset of diseases such as schizophrenia, which typically first become apparent during adolescence. Once these underlying processes have been identified, it may become possible to influence adolescent brain changes in ways that promote normal development and correct emerging abnormalities.” (Image: iStockphoto)

Sleep study reveals how the adolescent brain makes the transition to mature thinking

A new study conducted by monitoring the brain waves of sleeping adolescents has found that remarkable changes occur in the brain as it prunes away neuronal connections and makes the major transition from childhood to adulthood.

“We’ve provided the first long-term, longitudinal description of developmental changes that take place in the brains of youngsters as they sleep,” said Irwin Feinberg, professor emeritus of psychiatry and behavioral sciences and director of the UC Davis Sleep Laboratory. “Our outcome confirms that the brain goes through a remarkable amount of reorganization during puberty that is necessary for complex thinking.”

The research, published in the February 15 issue of American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, also confirms that electroencephalogram, or EEG, is a powerful tool for tracking brain changes during different phases of life, and that it could potentially be used to help diagnose age-related mental illnesses. It is the final component in a three-part series of studies carried out over 10 years and involving more than 3,500 all-night EEG recordings. The data provide an overall picture of the brain’s electrical behavior during the first two decades of life.

Feinberg explained that scientists have generally assumed that a vast number of synapses are needed early in life to recover from injury and adapt to changing environments. These multiple connections, however, impair the efficient problem solving and logical thinking required later in life. His study is the first to show how this shift can be detected by measuring the brain’s electrical activity in the same children over the course of time.

Two earlier studies by Feinberg and his colleagues showed that EEG fluctuations during the deepest (delta or slow wave) phase of sleep, when the brain is most recuperative, consistently declined for 9- to 18-year-olds. The most rapid decline occurred between the ages of 12 and 16-1/2. This led the team to conclude that the streamlining of brain activity — or “neuronal pruning” — required for adult cognition occurs together with the timing of reproductive maturity.

Questions remained, though, about electrical activity patterns in the brains of younger children.

For the current study, Feinberg and his research team monitored 28 healthy, sleeping children between the ages of 6 and 10 for two nights every six months. The new findings show that synaptic density in the cerebral cortex reaches its peak at age 8 and then begins a slow decline. The recent findings also confirm that the period of greatest and most accelerated decline occurs between the ages of 12 and 16-1/2 years, at which point the drop markedly slows.

“Discovering that such extensive neuronal remodeling occurs within this 4-1/2 year timeframe during late adolescence and the early teen years confirms our view that the sleep EEG indexes a crucial aspect of the timing of brain development,” said Feinberg.

The latest study also confirms that EEG sleep analysis is a powerful approach for evaluating adolescent brain maturation, according to Feinberg. Besides being a relatively simple, accessible technology for measuring the brain’s electrical activity, it is more accurate than more cumbersome and expensive options.

“Structural MRI, for instance, has not been able to identify the adolescent accelerations and decelerations that are easily and reliably captured by sleep EEG,” said Feinberg. “We hope our data can aid the search for the unknown genetic and hormonal biomarkers that drive those fluctuations. Our data also provide a baseline for seeking errors in brain development that signify the onset of diseases such as schizophrenia, which typically first become apparent during adolescence. Once these underlying processes have been identified, it may become possible to influence adolescent brain changes in ways that promote normal development and correct emerging abnormalities.”

(Image: iStockphoto)

Filed under adolescent brain brainwaves brain development developmental changes EEG neuroscience psychology science

531 notes

Mico from Neurowear analyses brainwaves, plays music that fits your mood The always creative Neurowear company, creator of the overly successful brain-controlled Necomimi cat ears and the wearable tail accessory Shippo, has announced its newest invention, Mico, a system consisting of a pair of headphones, a brainwave sensor and an iOS app, aiming to free users from having to manually select songs ever again. Mico -short for Music Inspiration from your Subconsciousness- is made up of two parts: the headphones with a sensor and an iPhone application. The headphones read the user’s brain signals and determines whether the person is focused, drowsy or stressed. The device sends this information to the iPhone app which searches for and plays music that matches the user’s mood. As a unique touch, LED signs on the side of the headphones light up, which also lets people know just what kind of state the user is in. Neurowear recently revealed Zen Tunes, an application that analyses a user’s brainwaves when listening to music and then produces a recommended playlist based on their state of mind. Mico, takes this idea a step further. According to Neurowear, “Mico frees the user from having to select songs and artists and allows users to encounter new music just by wearing the device. The device detects brainwaves through the sensor on your forehead. Our app then automatically plays music that fits your mood.” If you like Necomimi, you will probably like Mico just as much. To learn more about the product check out the official Mico website where you can also find a recently posted photo gallery with j-pop star Julie Watai wearing the new device. If you look close enough (search for the indicator signs) you might be even able to tell in what mood Julie was during the photo session. Release date or price not known at this point but Neurowear will demonstrate the device for the first time at the SXSW Trade Show in Austin, Texas from March 8-13.

Mico from Neurowear analyses brainwaves, plays music that fits your mood

The always creative Neurowear company, creator of the overly successful brain-controlled Necomimi cat ears and the wearable tail accessory Shippo, has announced its newest invention, Mico, a system consisting of a pair of headphones, a brainwave sensor and an iOS app, aiming to free users from having to manually select songs ever again.

Mico -short for Music Inspiration from your Subconsciousness- is made up of two parts: the headphones with a sensor and an iPhone application. The headphones read the user’s brain signals and determines whether the person is focused, drowsy or stressed. The device sends this information to the iPhone app which searches for and plays music that matches the user’s mood. As a unique touch, LED signs on the side of the headphones light up, which also lets people know just what kind of state the user is in.

Neurowear recently revealed Zen Tunes, an application that analyses a user’s brainwaves when listening to music and then produces a recommended playlist based on their state of mind. Mico, takes this idea a step further.

According to Neurowear, “Mico frees the user from having to select songs and artists and allows users to encounter new music just by wearing the device. The device detects brainwaves through the sensor on your forehead. Our app then automatically plays music that fits your mood.”

If you like Necomimi, you will probably like Mico just as much. To learn more about the product check out the official Mico website where you can also find a recently posted photo gallery with j-pop star Julie Watai wearing the new device. If you look close enough (search for the indicator signs) you might be even able to tell in what mood Julie was during the photo session.

Release date or price not known at this point but Neurowear will demonstrate the device for the first time at the SXSW Trade Show in Austin, Texas from March 8-13.

Filed under brain brainwaves Mico Neurowear technology neuroscience science

186 notes

Study reveals potential target to better treat, cure anxiety disorders

Researchers at Boston University School of Medicine (BUSM) have, for the first time, identified a specific group of cells in the brainstem whose activation during rapid eye movement (REM) sleep is critical for the regulation of emotional memory processing. The findings, published in the Journal of Neuroscience, could help lead to the development of effective behavioral and pharmacological therapies to treat anxiety disorders, such as post-traumatic stress disorder, phobias and panic attacks.

There are two main stages of sleep – REM and non-REM – and both are necessary to maintain health and to regulate multiple memory systems, including emotional memory. During non-REM sleep, the body repairs tissue, regenerates cells and improves the function of the body’s immune system. During REM sleep, the brain becomes more active and the muscles of the body become paralyzed. Additionally, dreaming generally occurs during REM sleep, as well as physiological events including saccadic eye movements and rapid fluctuations of respiration, heart rate and body temperature. One particular physiological event, which is a hallmark sign of REM sleep, is the appearance of phasic pontine waves (P-waves). The P-wave is a unique brain wave generated by the activation of a group of glutamatergic cells in a specific region within the brainstem called the pons.

Memories of fearful experiences can lead to enduring alterations in emotion and behavior and sleep plays a natural emotional regulatory role after stressful and traumatic events. Persistence of sleep disturbances, particularly of REM sleep, is predictive of developing symptoms of anxiety disorders. A core symptom of these disorders frequently reported by patients is the persistence of fear-provoking memories that they are unable to extinguish. Presently, exposure therapy, which involves controlled re-exposure to the original fearful experience, is considered one of the most effective evidence-based treatments for anxiety disorders. Exposure therapy produces a new memory, called an extinction memory, to coexist and compete with the fearful memory when the fearful cue/context is re-encountered.

The strength of the extinction memory determines the efficacy of exposure therapy. A demonstrated prerequisite for the successful development of an extinction memory is adequate sleep, particularly REM sleep, after exposure therapy. However, adequate or increased sleep alone does not universally guarantee its therapeutic efficacy.

"Given the inconsistency and unpredictability of exposure therapy, we are working to identify which process(es) during REM sleep dictate the success or failure of exposure therapy," said Subimal Datta, PhD, director and principle investigator at the Laboratory of Sleep and Cognitive Neuroscience at BUSM who served as the study’s lead author.

The researchers used contextual fear extinction training, which works to turn off the conditioned fear, to study which brain mechanisms play a role in the success of exposure therapy. The study results showed that fear extinction training increased REM sleep. Surprisingly, however, only 57 percent of subjects retained fear extinction memory, meaning that they did not experience the fear, after 24 hours. There was a tremendous increase of phasic P-wave activity among those subjects. In 43 percent of subjects, however, the wave activity was absent and they failed to retain fear extinction memory, meaning that they re-experienced fear.

"The study results provide direct evidence that the activation of phasic P-wave activity within the brainstem, in conjunction with exposure therapy, is critical for the development of long-term retention of fear extinction memory," said Datta, who also is a professor of psychiatry and neurology at BUSM. In addition, the study indicates the important role that the brainstem plays in regulating emotional memory.

Future research will explore how to activate this mechanism in order to help facilitate the development of new potential pharmacological treatments that will complement exposure therapy to better treat anxiety and other psychological disorders.

According to the National Institute of Mental Health, anxiety disorders affect approximately 40 million American adults each year. While anxiety can sometimes be a normal and beneficial reaction to stress, some people experience excessive anxiety that they are unable to control, which can negatively impact their day to day life.

(Source: eurekalert.org)

Filed under anxiety memory eye movements saccadic eye movements brainwaves sleep fear extinction neuroscience science

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Research shows why not everyone learns from their mistakes Some people do not learn from their mistakes because of the way their brain works, according to research led by an academic at Goldsmiths, University of London. The research, led by Professor Joydeep Bhattacharya in the Department of Psychology at Goldsmiths, examined what it is about the brain that defines someone as a ‘good learner’ from those who do not learn from their mistakes. Professor Bhattacharya said: “We are always told how important it is to learn from our errors, our experiences, but is this true? If so, then why do we all not learn from our experiences in the same way? It seems some people rarely do, even when they were informed of their errors in repeated attempts. "This study presents a first tantalising insight into how our brain processes the performance feedback and what it does with this information, whether to learn from it or to brush it aside." The study, published in a recent issue of the Journal of Neuroscience, investigated brainwave patterns of 36 healthy human volunteers performing a simple time estimation task. Researchers asked the participants to estimate a time interval of 1.7 seconds and provided feedback on their errors. The participants were then measured to see whether they incorporated the feedback to improve their future performances. 'Good learners', who were successful in incorporating the feedback information in adjusting their future performance, presented increased brain responses as fast as 200 milliseconds after the feedback on their performance was presented on a computer screen. This brain response was weaker in the poor learners who did not learn the task well and who showed decreased responses to their performance errors. The researchers further found that the good learners showed increased communication between brain areas involved with performance monitoring and sensorimotor processes. Caroline Di Bernardi Luft, one of the research paper’s co-authors from the Federal University of Santa Catarina, commented: “Good learners used the feedback not only to check their past performance, but also to adjust their next performance accordingly.” The brain responses correlated highly with how well the volunteers learned this simple task over the course of the experiment, and how good they were at maintaining the learned skill without any guiding feedback. "Though these results are very encouraging in establishing a correlation between brains responses and learning performance, future studies are needed to identify a causal role of these effects," Professor Bhattacharya added.

Research shows why not everyone learns from their mistakes

Some people do not learn from their mistakes because of the way their brain works, according to research led by an academic at Goldsmiths, University of London.

The research, led by Professor Joydeep Bhattacharya in the Department of Psychology at Goldsmiths, examined what it is about the brain that defines someone as a ‘good learner’ from those who do not learn from their mistakes.

Professor Bhattacharya said: “We are always told how important it is to learn from our errors, our experiences, but is this true? If so, then why do we all not learn from our experiences in the same way? It seems some people rarely do, even when they were informed of their errors in repeated attempts.

"This study presents a first tantalising insight into how our brain processes the performance feedback and what it does with this information, whether to learn from it or to brush it aside."

The study, published in a recent issue of the Journal of Neuroscience, investigated brainwave patterns of 36 healthy human volunteers performing a simple time estimation task. Researchers asked the participants to estimate a time interval of 1.7 seconds and provided feedback on their errors. The participants were then measured to see whether they incorporated the feedback to improve their future performances.

'Good learners', who were successful in incorporating the feedback information in adjusting their future performance, presented increased brain responses as fast as 200 milliseconds after the feedback on their performance was presented on a computer screen.

This brain response was weaker in the poor learners who did not learn the task well and who showed decreased responses to their performance errors. The researchers further found that the good learners showed increased communication between brain areas involved with performance monitoring and sensorimotor processes.

Caroline Di Bernardi Luft, one of the research paper’s co-authors from the Federal University of Santa Catarina, commented: “Good learners used the feedback not only to check their past performance, but also to adjust their next performance accordingly.”

The brain responses correlated highly with how well the volunteers learned this simple task over the course of the experiment, and how good they were at maintaining the learned skill without any guiding feedback.

"Though these results are very encouraging in establishing a correlation between brains responses and learning performance, future studies are needed to identify a causal role of these effects," Professor Bhattacharya added.

Filed under brain brain responses learning performance brainwaves feedback neuroscience science

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Researchers discover a biological marker of dyslexia Though learning to read proceeds smoothly for most children, as many as one in 10 is estimated to suffer from dyslexia, a constellation of impairments unrelated to intelligence, hearing or vision that make learning to read a struggle. Now, Northwestern University researchers report they have found a biological mechanism that appears to play an important role in the reading process. "We discovered a systematic relationship between reading ability and the consistency with which the brain encodes sounds," says Nina Kraus, Hugh Knowles Professor of Neurobiology, Physiology and Communication. "Unstable Representation of Sound: A Biological Marker of Dyslexia," co-authored by Jane Hornickel, will appear in the Feb. 20 issue of The Journal of Neuroscience. Recording the automatic brain wave responses of 100 school-aged children to speech sounds, the Northwestern researchers found that the very best readers encoded the sound most consistently while the poorest readers encoded it with the greatest inconsistency. Presumably, the brain’s response to sound stabilizes when children learn to successfully connect sounds with their meanings. Happily biology is not destiny. In prior work in Northwestern’s Auditory Neuroscience Laboratory, Kraus and her colleagues found that the inconsistency with which the poorest readers encode sound could be “fixed” through training. In that study, children with reading impairments were fitted for a year with assistive listening devices that transmitted their teacher’s voice directly into their ears. After a year, the children showed improvement not only in reading but also in the consistency with which their brains encoded speech sounds, particularly consonants. "Use of the devices focused youngsters’ brains on the "meaningful" sounds coming from their teacher, diminishing other, extraneous distractions," said Kraus. "After a year of use, the students had honed their auditory systems and no longer required the assistive devices to keep their reading and encoding advantage." People rarely have difficulty encoding vowel sounds, which are relatively simple and long, according to Kraus. It is consonant sounds — sounds which are shorter and more acoustically complex — that are most likely to be incorrectly categorized by the brain. "Understanding the biological mechanisms of reading puts us in a better position to both understand how normal reading works and to ameliorate it where it goes awry," says Kraus. "Our results suggest that good readers profit from a stable neural representation of sound, and that children with inconsistent neural responses are likely at a disadvantage when learning to read," Kraus adds. "The good news is that response consistency can be improved with auditory training." Decades of research from laboratories worldwide have shown that reading ability is associated with auditory skills, including auditory memory and attention, the ability to rhyme sounds and the ability to categorize rapidly occurring sounds. (Image: Michael Pettigrew)

Researchers discover a biological marker of dyslexia

Though learning to read proceeds smoothly for most children, as many as one in 10 is estimated to suffer from dyslexia, a constellation of impairments unrelated to intelligence, hearing or vision that make learning to read a struggle. Now, Northwestern University researchers report they have found a biological mechanism that appears to play an important role in the reading process.

"We discovered a systematic relationship between reading ability and the consistency with which the brain encodes sounds," says Nina Kraus, Hugh Knowles Professor of Neurobiology, Physiology and Communication. "Unstable Representation of Sound: A Biological Marker of Dyslexia," co-authored by Jane Hornickel, will appear in the Feb. 20 issue of The Journal of Neuroscience.

Recording the automatic brain wave responses of 100 school-aged children to speech sounds, the Northwestern researchers found that the very best readers encoded the sound most consistently while the poorest readers encoded it with the greatest inconsistency. Presumably, the brain’s response to sound stabilizes when children learn to successfully connect sounds with their meanings.

Happily biology is not destiny. In prior work in Northwestern’s Auditory Neuroscience Laboratory, Kraus and her colleagues found that the inconsistency with which the poorest readers encode sound could be “fixed” through training.

In that study, children with reading impairments were fitted for a year with assistive listening devices that transmitted their teacher’s voice directly into their ears. After a year, the children showed improvement not only in reading but also in the consistency with which their brains encoded speech sounds, particularly consonants.

"Use of the devices focused youngsters’ brains on the "meaningful" sounds coming from their teacher, diminishing other, extraneous distractions," said Kraus. "After a year of use, the students had honed their auditory systems and no longer required the assistive devices to keep their reading and encoding advantage."

People rarely have difficulty encoding vowel sounds, which are relatively simple and long, according to Kraus. It is consonant sounds — sounds which are shorter and more acoustically complex — that are most likely to be incorrectly categorized by the brain.

"Understanding the biological mechanisms of reading puts us in a better position to both understand how normal reading works and to ameliorate it where it goes awry," says Kraus.

"Our results suggest that good readers profit from a stable neural representation of sound, and that children with inconsistent neural responses are likely at a disadvantage when learning to read," Kraus adds. "The good news is that response consistency can be improved with auditory training."

Decades of research from laboratories worldwide have shown that reading ability is associated with auditory skills, including auditory memory and attention, the ability to rhyme sounds and the ability to categorize rapidly occurring sounds.

(Image: Michael Pettigrew)

Filed under dyslexia brainwaves biological marker reading ability neuroscience science

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Fear factor: Study shows brain’s response to scary stimuli

Driving through his hometown, a war veteran with post-traumatic stress disorder may see roadside debris and feel afraid, believing it to be a bomb. He’s ignoring his safe, familiar surroundings and only focusing on the debris; yet, when it comes to the visual cortex, a recent study at the University of Florida suggests this is completely normal.

The findings, published last month in the Journal of Neuroscience, show that even people who don’t have anxiety disorders respond visually at the sight of something scary while ignoring signs that indicate safety. This contradicts a common belief that only people with anxiety disorders have difficulty processing comforting visual stimuli, or safety cues, said Andreas Keil, a professor of psychology in UF’s College of Liberal Arts and Sciences.

“We’ve established that, in terms of visual responding, it’s not a disorder to not respond to a safety cue,” Keil said. “We all do that. So now we can study at what stage in the processing stream, with given patients, is the problem occurring.”

Co-authors Keil and Vladimir Miskovic, both members of the UF Center for the Study of Emotion and Attention, examined the effect of competing danger and safety cues within the visual cortex. The study results could help distinguish between normal and abnormal processes within the visual cortex and identify what parts of the brain are targets for the treatment of anxiety disorders.

“You’d think the visual cortex would just faithfully code for visual information,” said Shmuel Lissek, an assistant professor of psychology at the University of Minnesota not involved in the study. “This kind of work is testing the idea that activations in the visual cortex are actually different if the stimulus has an emotional value than if it doesn’t.”

(Source: news.ufl.edu)

Filed under visual cortex visual stimuli PTSD brainwaves anxiety anxiety disorders neuroscience psychology science

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Fluctuations in the size of brain waves contribute to information processing Cyclical variations in the size of brain wave rhythms may participate in the encoding of information by the brain, according to a new study led by Colin Molter of the Neuroinformatics Japan Center, RIKEN Brain Science Institute, Wako. Brain waves are produced by the synchronized activity of large populations of neurons. Low frequency brain waves called theta oscillations are known to support memory formation. Researchers typically examine the frequency of oscillations in a given part of the brain and the timing of oscillations in different brain regions, but know very little about how variations in the size of these oscillations contribute to information processing. Molter and his colleagues used electrode arrays to record brain waves from the rat hippocampus, a structure known to be critical for memory formation and spatial navigation, while the animals performed various behaviors, such as exploring open spaces, running through a maze and in a wheel, and sleeping. They observed fluctuations in the size of theta oscillations during all the behaviors—the brain waves did not remain the same size, but rather waxed and waned second by second. During spatial navigation for example, individual hippocampal neurons called place cells become more active when the animal is in one or a few specific locations compared to the rest of the explored environment. The researchers found that the time of firing of many of the place cells correlated with the fluctuations in the size of the theta waves. During sleep, the activity of most of the cells was timed with the largest theta oscillations. Even though the size of theta waves is correlated with motor behavior, their cyclic fluctuations at this time scale, observed while the rats ran and explored, were not correlated with the animals’ speed or acceleration. The fluctuations are instead likely to be generated by the brain itself, as their presence during sleep also suggests they are intrinsic. The researchers speculate that this phenomenon could be helpful for the neuronal representation of space, resolving the ambiguity of space coding by place cells that become active in multiple preferred locations. “We are currently working on several new experiments to understand how the spatial location may affect the slow modulation and how the timing of the slow modulation affects behavior,” says Molter. “We are also trying to provide a model that incorporates the theta slow modulation to help propagation of activity between cell assemblies.”

Fluctuations in the size of brain waves contribute to information processing

Cyclical variations in the size of brain wave rhythms may participate in the encoding of information by the brain, according to a new study led by Colin Molter of the Neuroinformatics Japan Center, RIKEN Brain Science Institute, Wako.

Brain waves are produced by the synchronized activity of large populations of neurons. Low frequency brain waves called theta oscillations are known to support memory formation. Researchers typically examine the frequency of oscillations in a given part of the brain and the timing of oscillations in different brain regions, but know very little about how variations in the size of these oscillations contribute to information processing.

Molter and his colleagues used electrode arrays to record brain waves from the rat hippocampus, a structure known to be critical for memory formation and spatial navigation, while the animals performed various behaviors, such as exploring open spaces, running through a maze and in a wheel, and sleeping. They observed fluctuations in the size of theta oscillations during all the behaviors—the brain waves did not remain the same size, but rather waxed and waned second by second.

During spatial navigation for example, individual hippocampal neurons called place cells become more active when the animal is in one or a few specific locations compared to the rest of the explored environment. The researchers found that the time of firing of many of the place cells correlated with the fluctuations in the size of the theta waves. During sleep, the activity of most of the cells was timed with the largest theta oscillations.

Even though the size of theta waves is correlated with motor behavior, their cyclic fluctuations at this time scale, observed while the rats ran and explored, were not correlated with the animals’ speed or acceleration. The fluctuations are instead likely to be generated by the brain itself, as their presence during sleep also suggests they are intrinsic.

The researchers speculate that this phenomenon could be helpful for the neuronal representation of space, resolving the ambiguity of space coding by place cells that become active in multiple preferred locations. “We are currently working on several new experiments to understand how the spatial location may affect the slow modulation and how the timing of the slow modulation affects behavior,” says Molter. “We are also trying to provide a model that incorporates the theta slow modulation to help propagation of activity between cell assemblies.”

Filed under brainwaves memory formation spatial navigation motor behavior neuroscience science

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