Noninvasive brain implant could someday translate thoughts into movement

ScienceDaily (June 17, 2011) — A brain implant developed at the University of Michigan uses the body's skin like a conductor to wirelessly transmit the brain's neural signals to control a computer, and may eventually be used to reactivate paralyzed limbs.

The implant is called the BioBolt, and unlike other neural interface technologies that establish a connection from the brain to an external device such as a computer, it's minimally invasive and low power, said principal investigator Euisik Yoon, a professor in the U-M College of Engineering, Department of Electrical Engineering and Computer Science.

Currently, the skull must remain open while neural implants are in the head, which makes using them in a patient's daily life unrealistic, said Kensall Wise, the William Gould Dow Distinguished University professor emeritus in engineering.

BioBolt does not penetrate the cortex and is completely covered by the skin to greatly reduce risk of infection. Researchers believe it's a critical step toward the Holy Grail of brain-computer interfacing: allowing a paralyzed person to "think" a movement.

"The ultimate goal is to be able to reactivate paralyzed limbs," by picking the neural signals from the brain cortex and transmitting those signals directly to muscles, said Wise, who is also founding director of the NSF Engineering Research Center for Wireless Integrated MicroSystems (WIMS ERC). That technology is years away, the researchers say.

Another promising application for the BioBolt is controlling epilepsy, and diagnosing certain diseases like Parkinson's.

The concept of BioBolt is filed for patent and was presented on June 16 at the 2011 Symposium on VLSI Circuits in Kyoto, Japan. Sun-Il Chang, a PhD student in Yoon's research group, is lead author on the presentation.

The BioBolt looks like a bolt and is about the circumference of a dime, with a thumbnail-sized film of microcircuits attached to the bottom. The BioBolt is implanted in the skull beneath the skin and the film of microcircuits sits on the brain. The microcircuits act as microphones to 'listen' to the overall pattern of firing neurons and associate them with a specific command from the brain. Those signals are amplified and filtered, then converted to digital signals and transmitted through the skin to a computer, Yoon said.

Another hurdle to brain interfaces is the high power requirement for transmitting data wirelessly from the brain to an outside source. BioBolt keeps the power consumption low by using the skin as a conductor or a signal pathway, which is analogous to downloading a video into your computer simply by touching the video.

Eventually, the hope is that the signals can be transmitted through the skin to something on the body, such as a watch or a pair of earrings, to collect the signals, said Yoon, eliminating the need for an off-site computer to process the signals.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Michigan, via EurekAlert!, a service of AAAS.

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Noninvasive brain implant could someday translate thoughts into movement

ScienceDaily (June 17, 2011) — A brain implant developed at the University of Michigan uses the body's skin like a conductor to wirelessly transmit the brain's neural signals to control a computer, and may eventually be used to reactivate paralyzed limbs.

The implant is called the BioBolt, and unlike other neural interface technologies that establish a connection from the brain to an external device such as a computer, it's minimally invasive and low power, said principal investigator Euisik Yoon, a professor in the U-M College of Engineering, Department of Electrical Engineering and Computer Science.

Currently, the skull must remain open while neural implants are in the head, which makes using them in a patient's daily life unrealistic, said Kensall Wise, the William Gould Dow Distinguished University professor emeritus in engineering.

BioBolt does not penetrate the cortex and is completely covered by the skin to greatly reduce risk of infection. Researchers believe it's a critical step toward the Holy Grail of brain-computer interfacing: allowing a paralyzed person to "think" a movement.

"The ultimate goal is to be able to reactivate paralyzed limbs," by picking the neural signals from the brain cortex and transmitting those signals directly to muscles, said Wise, who is also founding director of the NSF Engineering Research Center for Wireless Integrated MicroSystems (WIMS ERC). That technology is years away, the researchers say.

Another promising application for the BioBolt is controlling epilepsy, and diagnosing certain diseases like Parkinson's.

The concept of BioBolt is filed for patent and was presented on June 16 at the 2011 Symposium on VLSI Circuits in Kyoto, Japan. Sun-Il Chang, a PhD student in Yoon's research group, is lead author on the presentation.

The BioBolt looks like a bolt and is about the circumference of a dime, with a thumbnail-sized film of microcircuits attached to the bottom. The BioBolt is implanted in the skull beneath the skin and the film of microcircuits sits on the brain. The microcircuits act as microphones to 'listen' to the overall pattern of firing neurons and associate them with a specific command from the brain. Those signals are amplified and filtered, then converted to digital signals and transmitted through the skin to a computer, Yoon said.

Another hurdle to brain interfaces is the high power requirement for transmitting data wirelessly from the brain to an outside source. BioBolt keeps the power consumption low by using the skin as a conductor or a signal pathway, which is analogous to downloading a video into your computer simply by touching the video.

Eventually, the hope is that the signals can be transmitted through the skin to something on the body, such as a watch or a pair of earrings, to collect the signals, said Yoon, eliminating the need for an off-site computer to process the signals.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Michigan, via EurekAlert!, a service of AAAS.

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

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New technology could inspire brain implant for detecting and treating seizures

ScienceDaily (June 2, 2011) — Tiny electrodes have been coated with a drug-loaded polymer in an attempt to design an implant capable of detecting a number of neurological symptoms, such as those associated with an epileptic seizure, and treating them simultaneously.

In a study published June 2, 2011 in IOP Publishing's Journal of Neural Engineering, researchers have developed a novel technology to precisely modulate individual neurons in rats, allowing the molecular, neuronal, and circuit functions to be analysed with unprecedented precision.

Based on the electrical conducting properties of the polymer Polypyrrole (PPy), the researchers, from the University of Pittsburgh, have demonstrated a novel way of loading specific drugs onto an array of electrodes and triggering their release into cultured neurons, allowing for a more precise insight into the cellular mechanisms of neuronal networks.

On top of this, the researchers have also demonstrated how the release of drugs could be informed, in real-time, by the recording of activity in neurons, a step essential for creating a closed-loop system that both diagnoses and treats symptoms simultaneously, creating several potential applications.

Co-author Professor X Tracy Cui said, "We envision an implanted device in the future that will monitor the brain activity, detect or predict an onset of epileptic seizure, and send the command to the electrode at the most appropriate location, releasing an anti-convulsive drug to prevent the seizure."

Multielectrode arrays (MEAs) -- small devices that can control or record the electrical circuitry in neurons -- have long been used as a way of measuring neuronal activity and transforming this into an action; technologies such as ear implants and cardiac pacemakers have benefited from them.

Recent advances, however, have allowed MEAs to be coupled with devices that release specific drugs in order to test how neural circuits function, as well as investigating the underlying mechanisms within neuronal cells.

The researchers coated PPy, containing all of the necessary neurochemicals, onto an MEA. Whilst positioned on the cultured rat brain, the polymer was electrically stimulated, causing the neurochemicals to dissociate and diffuse away to the necessary locations.

Results showed that the drugs retained their activity and function with spatial and temporal precision.

Current state-of-the-art drug delivery methods, such as picospritzer and ionotopheriesis, give researchers a greater understanding of cellular mechanisms of neural dynamics; however, both of these techniques are limited to a few sites and face the risk of drug leakage.

By having the required neurochemicals dissociate from the polymer, this technique avoids the need for an external reservoir containing the drug, which would greatly increase the size of a potential implant and could cause tissue damage.

Professor Cui continues, "By directly loading a drug of interest onto an individual electrode site and using an electrical signal to trigger its release, we can precisely control the drug delivery site with ease. Additionally, our technology can be used for a combination of exogenous chemicals such as subtype-specific receptor antagonists, thus potentially allowing for more precise dissection of neural circuit function at the molecular level."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Institute of Physics, via EurekAlert!, a service of AAAS.

Journal References:

Stauffer et al. Rapid modulation of local neural activity by controlled drug release from polymer-coated recording microelectrodes. J. Neural Eng., 2011 [link]Stauffer et al. Rapid modulation of local neural activity by controlled drug release from polymer-coated recording microelectrodes. Journal of Neural Engineering, 2011; [link]

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

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Scientists afflict computers with 'schizophrenia' to better understand the human brain

ScienceDaily (May 6, 2011) — Computer networks that can't forget fast enough can show symptoms of a kind of virtual schizophrenia, giving researchers further clues to the inner workings of schizophrenic brains, researchers at The University of Texas at Austin and Yale University have found.

The researchers used a virtual computer model, or "neural network," to simulate the excessive release of dopamine in the brain. They found that the network recalled memories in a distinctly schizophrenic-like fashion.

Their results were published in April in Biological Psychiatry.

"The hypothesis is that dopamine encodes the importance-the salience-of experience," says Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin. "When there's too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn't be learning from."

The results bolster a hypothesis known in schizophrenia circles as the hyperlearning hypothesis, which posits that people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would. Without forgetting, they lose the ability to extract what's meaningful out of the immensity of stimuli the brain encounters. They start making connections that aren't real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.

The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language. In this study it was used to simulate what happens to language as the result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.

In order to model the process, Grasemann and Miikkulainen began by teaching a series of simple stories to DISCERN. The stories were assimilated into DISCERN's memory in much the way the human brain stores information-not as distinct units, but as statistical relationships of words, sentences, scripts and stories.

"With neural networks, you basically train them by showing them examples, over and over and over again," says Grasemann. "Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output. You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned."

In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered. They simulated an excessive release of dopamine by increasing the system's learning rate-essentially telling it to stop forgetting so much.

"It's an important mechanism to be able to ignore things," says Grasemann. "What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia."

After being re-trained with the elevated learning rate, DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.

In another instance, DISCERN began showing evidence of "derailment"-replying to requests for a specific memory with a jumble of dissociated sentences, abrupt digressions and constant leaps from the first- to the third-person and back again.

"Information processing in neural networks tends to be like information processing in the human brain in many ways," says Grasemann. "So the hope was that it would also break down in similar ways. And it did."

The parallel between their modified neural network and human schizophrenia isn't absolute proof the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.

"We have so much more control over neural networks than we could ever have over human subjects," he says. "The hope is that this kind of modeling will help clinical research."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

Ralph E. Hoffman, Uli Grasemann, Ralitza Gueorguieva, Donald Quinlan, Douglas Lane, Risto Miikkulainen. Using Computational Patients to Evaluate Illness Mechanisms in Schizophrenia. Biological Psychiatry, 2011; DOI: 10.1016/j.biopsych.2010.12.036

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Scientists afflict computers with 'schizophrenia' to better understand the human brain

ScienceDaily (May 6, 2011) — Computer networks that can't forget fast enough can show symptoms of a kind of virtual schizophrenia, giving researchers further clues to the inner workings of schizophrenic brains, researchers at The University of Texas at Austin and Yale University have found.

The researchers used a virtual computer model, or "neural network," to simulate the excessive release of dopamine in the brain. They found that the network recalled memories in a distinctly schizophrenic-like fashion.

Their results were published in April in Biological Psychiatry.

"The hypothesis is that dopamine encodes the importance-the salience-of experience," says Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin. "When there's too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn't be learning from."

The results bolster a hypothesis known in schizophrenia circles as the hyperlearning hypothesis, which posits that people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would. Without forgetting, they lose the ability to extract what's meaningful out of the immensity of stimuli the brain encounters. They start making connections that aren't real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.

The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language. In this study it was used to simulate what happens to language as the result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.

In order to model the process, Grasemann and Miikkulainen began by teaching a series of simple stories to DISCERN. The stories were assimilated into DISCERN's memory in much the way the human brain stores information-not as distinct units, but as statistical relationships of words, sentences, scripts and stories.

"With neural networks, you basically train them by showing them examples, over and over and over again," says Grasemann. "Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output. You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned."

In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered. They simulated an excessive release of dopamine by increasing the system's learning rate-essentially telling it to stop forgetting so much.

"It's an important mechanism to be able to ignore things," says Grasemann. "What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia."

After being re-trained with the elevated learning rate, DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.

In another instance, DISCERN began showing evidence of "derailment"-replying to requests for a specific memory with a jumble of dissociated sentences, abrupt digressions and constant leaps from the first- to the third-person and back again.

"Information processing in neural networks tends to be like information processing in the human brain in many ways," says Grasemann. "So the hope was that it would also break down in similar ways. And it did."

The parallel between their modified neural network and human schizophrenia isn't absolute proof the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.

"We have so much more control over neural networks than we could ever have over human subjects," he says. "The hope is that this kind of modeling will help clinical research."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

Ralph E. Hoffman, Uli Grasemann, Ralitza Gueorguieva, Donald Quinlan, Douglas Lane, Risto Miikkulainen. Using Computational Patients to Evaluate Illness Mechanisms in Schizophrenia. Biological Psychiatry, 2011; DOI: 10.1016/j.biopsych.2010.12.036

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Researchers see a 'picture' of threat in the brain: Work may lead to new model of neuroinflammation

ScienceDaily (May 3, 2011) — A team of researchers is beginning to see exactly what the response to threats looks like in the brain at the cellular and molecular levels.

This new information, including the discovery that a model of social stress can increase inflammation among brain cells, should provide new insight into how the stress response affects inflammatory and behavioral responses.

It may also provide new targets for drugs treatments in the continuing struggle to curtail depression and anxiety.

Scientists from Ohio State University's Institute of Behavioral Medicine Research reported their results in the latest issue of the Journal of Neuroscience.

John Sheridan, professor of oral biology, and Jonathan Godbout, an assistant professor of molecular virology, immunology and medical genetics, turned to colonies of mice to make their discoveries.

Groups of mice living together quickly adopt a hierarchy ranging from dominant to subordinate. This vaguely political system controls the interaction among the animals. Once these patterns had been established, the researchers then added an additional, highly aggressive mouse to the mix for a two-hour period each day to disrupt the social hierarchy.

With no place to retreat, the mice were forced into conflicts with the new aggressor. After as few as three episodes with the aggressor, the original mice showed distinct signs of what the researchers considered "anxiety-like behaviors." This kind of experiment creates a "social disruption" within the group of mice and is widely used to mimic psychological stress.

"These animals can't flee, so they have to stand and fight," Sheridan explained. "In doing so, they're repeatedly defeated, creating a condition called "learned helplessness," a condition closely linked to depression.

What Sheridan and Godbout saw was that the animals' anxiety continued for a long time after the termination of the stressful episodes of defeat. "For two weeks or more after we stopped the stressor, we could still see this anxiety-like behavior," Sheridan said.

The real discoveries came when the researchers analyzed what was happening in the animals' brains and in their immune response.

"We found that in the stressed animals, a certain type of immune cell (myeloid progenitor cell, or MPC), produced in the bone marrow, entered the circulatory system and migrated to the brain," explained Godbout.

These MPCs might normally relocate in this way to deal with an infection or an injury in the brain, but in this case, they moved solely because of the response to a social stressor, he said. The experiments showed that the number of these cells more than tripled in the brain following the stress.

Other immune cells called microglia, normally residing in the brain, also triggered an inflammatory response because of the stress. The researchers also noted that the stressor caused a particular activation pattern of neurons, or nerve cells, within the brain.

The response to social stress also caused an increase in the amounts of some inflammatory cytokines in the brain, including interleukin-1 (IL-1) and tumor necrosis factor-alpha (TNF-a) which are linked to inflammation. These cytokine responses correlated with an insensitivity of MPCs to glucocorticoids, hormones that normally inhibit inflammation in the body.

So the research team saw these and other cellular changes occurring in the brain following the stress, at the same time they were seeing the behavioral changes -- the anxiety-like behavior.

The findings are evidence of a two-way communication that's existing between the body and the brain in times of stress, Sheridan said.

To test that apparent connection, they gave the mice injections of propranolol -- a so-called "beta-blocker" drug often used for cardiac conditions -- before they encountered the more aggressive mouse. In this case, the researchers saw no increase in IL-1 or TNF-a, no glucocorticoid insensitivity, and no long-lasting anxiety-like behavior in the test animals.

"If we treated the animal with a beta-blocker each night before we put the intruder in, it completely blocked the signal. The anxiety-like behavior never developed," Godbout said.

"What this basically argues is that we may now have a new target for individuals who have extended anxiety-like behavior," Sheridan said. "We may have a new target cell to think about in terms of new therapies.

"And since that cell (the MPCs traveling from the bone marrow) is coming from the periphery of the body, we might not need to resort to psychoactive drugs that can have adverse effects on the brain."

Proving that, however, will take more animal studies and subsequent large studies using humans before this approach could be used clinically, he said.

The research was supported in part by the National Institute of Mental Health, the National Institute on Aging and the National Institute of Dental and Craniofacial Research.

Working with Sheridan and Godbout on the research were Eric Wohleb, Mark Hanke, Angela Corona, Nicole Powell, LaTonia Stiner, Michael Bailey and Randy Nelson, all of Ohio State.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Ohio State University. The original article was written by Earle Holland.

Journal Reference:

E. S. Wohleb, M. L. Hanke, A. W. Corona, N. D. Powell, L. M. Stiner, M. T. Bailey, R. J. Nelson, J. P. Godbout, J. F. Sheridan.  -Adrenergic Receptor Antagonism Prevents Anxiety-Like Behavior and Microglial Reactivity Induced by Repeated Social Defeat. Journal of Neuroscience, 2011; 31 (17): 6277 DOI: 10.1523/JNEUROSCI.0450-11.2011

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Ecstasy associated with chronic change in brain function

ScienceDaily (May 3, 2011) — Ecstasy -- the illegal "rave" drug that produces feelings of euphoria and emotional warmth -- has been in the news recently as a potential therapeutic. Clinical trials are testing Ecstasy in the treatment of post-traumatic stress disorder.

But headlines like one in Time magazine's health section in February -- "Ecstasy as therapy: have some of its negative effects been overblown?" -- concern Ronald Cowan, M.D., Ph.D., associate professor of Psychiatry.

His team reports in the May issue of Neuropsychopharmacology that recreational Ecstasy use is associated with a chronic change in brain function.

"There's tension in the fields of psychiatry and psychotherapy between those who think Ecstasy could be a valuable therapeutic that's not being tested because of overblown fears, and those who are concerned about the drug's potentially harmful effects," Cowan said.

"We're not on one side or the other; we're just trying to find out what's going on in the brain -- is there any evidence for long-lasting changes in the brain?"

The message in news reports needs to be accurate, Cowan said. His team's studies suggest that the current message should be: "If you use Ecstasy recreationally, the more you use, the more brain changes you get."

Cowan and his colleagues examined brain activation during visual stimulation, using functional magnetic resonance imaging (fMRI), in subjects who had previously used Ecstasy (but not in the two weeks prior to imaging) and in subjects who had not previously used Ecstasy.

They found increased brain activation in three brain areas associated with visual processing in Ecstasy users with the highest lifetime exposure to the drug. The findings were consistent with the investigators' predictions based on results from animal models: that Ecstasy use is associated with a loss of serotonin signaling, which leads to hyper-excitability (increased activation) in the brain.

The hyper-excitability suggests a loss in brain efficiency, Cowan said, "meaning that it takes more brain area to process information or perform a task."

The investigators found that this shift in brain excitability did not return to normal in subjects who had not used Ecstasy in more than a year.

"We think this shift in cortical excitability may be chronic, long-lasting, and even permanent, which is a real worry," Cowan said, noting that the Ecstasy users in the study are young (18 to 35 years old). "The question is what will happen to their brains as they age over the next 60 years."

Cowan said that the pattern of hyper-excitability is similar to that observed in fMRI studies of individuals at risk for, or with early, Alzheimer's disease.

"I'm not saying that these people are at increased risk for dementia, but that there's a loss of brain efficiency in both recreational Ecstasy use and early Alzheimer's."

The findings suggest that brain hyper-excitability (increased activation in fMRI scans) may be a useful biomarker for Ecstasy-induced neurotoxicity, which the investigators will continue to study.

"Our goal is to be able to let people know whether or not the drug is causing long-term brain damage," Cowan said. "That's really critical because millions of people are using it."

The 2009 National Survey on Drug Use and Health estimated that 14.2 million individuals 12 years or older in the United States had used Ecstasy in their lifetime; 760,000 people had used Ecstasy in the month prior to being surveyed.

Cowan is also interested in determining the doses of Ecstasy that are toxic, and whether there are genetic vulnerabilities to toxicity. If clinical trials show that the drug has therapeutic benefits, it's critical to know the risks, he said.

The research was supported by the National Science Foundation, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Center for Research Resources.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Vanderbilt University Medical Center, via EurekAlert!, a service of AAAS.

Journal Reference:

Amy L Bauernfeind, Mary S Dietrich, Jennifer U Blackford, Evonne J Charboneau, James G Lillevig, Christopher J Cannistraci, Neil D Woodward, Aize Cao, Tristan Watkins, Christina R Di Iorio, Carissa Cascio, Ronald M Salomon, Ronald L Cowan. Human Ecstasy Use is Associated with Increased Cortical Excitability: An fMRI Study. Neuropsychopharmacology, 2011; 36 (6): 1127 DOI: 10.1038/npp.2010.244

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

New route to map brain fat

ScienceDaily (May 4, 2011) — Mapping the fat distribution of the healthy human brain is a key step in understanding neurological diseases, in general, and the neurodegeneration that accompanies Alzheimer's disease in particular. Antonio Veloso and colleagues, from the University of the Basque Country in Leioa, Spain, find a new technique to reveal the fat distribution of three different areas of the healthy human brain.

Their work is published online in Springer's journal, Analytical & Bioanalytical Chemistry.

The human central nervous system has an abundance of lipid molecules -- some are structural and energetic components of cells; others play a role in neurotransmission and are known as neurolipids. Mapping these neurolipids can increase neurologists' knowledge of the precise metabolic routes that produce them and where this production takes place. Knowing the fat composition of the healthy brain and the distribution of the different lipid species can give them clues about how neurodegenerative diseases develop.

The multi-disciplinary team used a combination of MALDI-TOF imaging mass spectrometry (a technique used to visualize the spatial distribution of compounds by their molecular mass) and functional autoradiography (an image recorded on a photographic film that shows the distribution of the activity induced by a drug) to scan healthy brain tissue slices. They mapped out, in detail, the lipid distribution of 43 types of lipids in three distinct areas of the human brain: the frontal cortex, hippocampus and striatum. (The frontal cortex, or frontal lobe, is the front part of the brain; the hippocampus is a part of the brain located above the temples, which plays a role in memory and emotion; the striatum is the striated mass of white and grey matter in the forebrain.) What is unique about their method, is its ability to identify the lipid species as well as locate them. Indeed, localization of lipid species is lost with the use of traditional techniques.

The authors conclude: "The application of imaging mass spectrometry to the localization of lipid species in the brain will be especially helpful to elucidate the specific functions of each type of lipid. Moreover, during the last few years the modulation of the signaling by neurolipids has been found to be implicated in neurodegenerative diseases. In this context, Alzheimer's disease is especially interesting since the familial mutation of some proteins that transport lipids, as the Apolipoprotein E, is a risk factor in this disease. The imaging mass spectrometry technique is still in an early stage. It is expected that in the near future, new hardware developments will allow a precise determination of an increasing number of lipid species, aiming at producing a three-dimensional map of the lipid distribution in the brain."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Springer Science+Business Media, via AlphaGalileo.

Journal Reference:

Antonio Veloso, Roberto Fernández, Egoitz Astigarraga, Gabriel Barreda-Gómez, Iván Manuel, M. Teresa Giralt, Isidro Ferrer, Begoña Ochoa, Rafael Rodríguez-Puertas, José A. Fernández. Distribution of lipids in human brain. Analytical and Bioanalytical Chemistry, 2011; DOI: 10.1007/s00216-011-4882-x

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Functioning synapse created using carbon nanotubes: Devices might be used in brain prostheses or synthetic brains

ScienceDaily (Apr. 22, 2011) — Engineering researchers the University of Southern California have made a significant breakthrough in the use of nanotechnologies for the construction of a synthetic brain. They have built a carbon nanotube synapse circuit whose behavior in tests reproduces the function of a neuron, the building block of the brain.

The team, which was led by Professor Alice Parker and Professor Chongwu Zhou in the USC Viterbi School of Engineering Ming Hsieh Department of Electrical Engineering, used an interdisciplinary approach combining circuit design with nanotechnology to address the complex problem of capturing brain function.

In a paper published in the proceedings of the IEEE/NIH 2011 Life Science Systems and Applications Workshop in April 2011, the Viterbi team detailed how they were able to use carbon nanotubes to create a synapse.

Carbon nanotubes are molecular carbon structures that are extremely small, with a diameter a million times smaller than a pencil point. These nanotubes can be used in electronic circuits, acting as metallic conductors or semiconductors.

"This is a necessary first step in the process," said Parker, who began the looking at the possibility of developing a synthetic brain in 2006. "We wanted to answer the question: Can you build a circuit that would act like a neuron? The next step is even more complex. How can we build structures out of these circuits that mimic the function of the brain, which has 100 billion neurons and 10,000 synapses per neuron?"

Parker emphasized that the actual development of a synthetic brain, or even a functional brain area is decades away, and she said the next hurdle for the research centers on reproducing brain plasticity in the circuits.

The human brain continually produces new neurons, makes new connections and adapts throughout life, and creating this process through analog circuits will be a monumental task, according to Parker.

She believes the ongoing research of understanding the process of human intelligence could have long-term implications for everything from developing prosthetic nanotechnology that would heal traumatic brain injuries to developing intelligent, safe cars that would protect drivers in bold new ways.

For Jonathan Joshi, a USC Viterbi Ph.D. student who is a co-author of the paper, the interdisciplinary approach to the problem was key to the initial progress. Joshi said that working with Zhou and his group of nanotechnology researchers provided the ideal dynamic of circuit technology and nanotechnology.

"The interdisciplinary approach is the only approach that will lead to a solution. We need more than one type of engineer working on this solution," said Joshi. "We should constantly be in search of new technologies to solve this problem."

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