Tagged: Brain Studies Toggle Comment Threads | Keyboard Shortcuts

  • richardmitnick 2:29 pm on November 4, 2017 Permalink | Reply
    Tags: , Brain Studies, Brain waves, , ,   

    From INVERSE: For All of my Friernds in Neuroscience: “Nobody Knows Where Brainwaves Come From” 



    August 7, 2017 [Just now in social media]
    Rafi Letzter

    Wub-wub-wub-wub. Brainwaves are electromagnetic proof that we are alive. Decades of research have shown that these pulses of electrical potential reflect events at the root of our impulses and thoughts. As such, they underlie one of humanity’s weightiest moral decisions: deciding whether or not a person is officially dead. If a person goes 30 minutes without producing brainwaves, even a functioning heartbeat can’t convince doctors they’re alive.

    But as much as brainwaves loom in our understanding of the brain, not a single scientist has any idea where they come from.

    At least one researcher, Michael X. Cohen, Ph.D., an assistant professor at the Donders Institute for Brain, Cognition, and Behavior in the Netherlands, thinks it’s time to fix that. In an April op-ed in the journal Trends in Neurosciences, Cohen argued that the time has come for researchers to figure out what those brainwaves they’ve been recording for decades are really all about.

    “This is maybe the most important question for neuroscience right now,” he said to Inverse, but he added that it will be a challenge to convince his colleagues that it matters at all.

    Today, as Facebook races to read your brainwaves, roboticists use them to develop mind control systems, and cybersecurity experts race to protect yours from hackers, it’s clear that Cohen’s sense of urgency is justified.

    Connecting brainwaves to neuron behavior is the next great challenge in neuroscience. No image credit

    What we do know about brainwaves is that when doctors stick silver chloride dots to a person’s scalp and hook the connected electrodes up to an electroencephalography (EEG) machine, the curves that appear on its screen represent the electrical activity inside our skulls. The German neuroscientist Hans Berger spotted the first type of brainwave — alpha waves — back in 1924.

    Researchers soon discovered more of these strange oscillations. There’s the slow, powerful delta wave, which shows up when we’re in deep sleep. There’s the low spikes of the theta wave, whose functions remain largely mysterious. Faster and even stranger is the gamma wave, which some researchers suspect plays a role in consciousness.

    These waves are at the root of our understanding of the shape and structure of human thought, as well as the methods doctors use to figure out how brains break down. It’s thought that alpha waves, for example, are a sign the brain is inhibiting certain mental systems to free up bandwidth for other tasks, like sleeping or imagining. But where does it come from in the first place?

    So far, there’s been no satisfactory answer to this question, but Cohen is determined to find it.

    An alpha brainwave resembles a sine wave. No image credit.

    As one of the world’s leading researchers on the brain’s electrical activity, he hooks people up to EEG machines to figure out how their brains behave when they see a bird, think through a complex decision, or feel sad. But Cohen is the first to admit that what’s lacking in his research is context. Not understanding how those patterns relate to the actual meat of the brain — neurons firing or not firing, getting excited, or shutting down — leaves a huge mystery right at the center of brainwave neuroscience, he says.

    “Over time it started bothering me more and more,” Cohen told Inverse. “There’s so much complexity going on at smaller spatial scales, and we have literally no fucking clue how to get from this big spatial scale to this smaller spatial scale.”

    Part of the reason why it’s so hard to understand neuroscience research in the context of the brain, Cohen explains, is because neuroscientists themselves work in discrete, isolated sub-fields based on how big a chunk of the brain they study. Researchers studying the brain at the smallest level peel open individual neurons and watch the proteins inside them fold. Microcircuit neuroscientists map out the connections between neurons. Cohen zooms out a little further, connecting electrical patterns and human thought, rarely concerning himself with single cells or small groups of neurons.

    But as we begin to fully grasp how complex the brain really is, Cohen says, it’s increasingly imperative to find a way to bridge the research that happens at the macro and micro scales. Finally understanding brainwaves, he says, could be the key to doing so.

    No image caption or credit.

    That’s because brainwaves pulse at every single level of the brain, from the tiniest neuron to the entire 3-pound organ. “If you’re recording from just one neuron, you’ll see oscillations,” Cohen says, using the scientific term for wobbling brainwaves.

    “If you’re recording from a small ensemble of neurons, you’ll see them. And if you’re recording from tens of millions of neurons, you’ll see oscillations.”

    For Cohen, brainwaves are the common thread that can unify neuroscience. But the problem is, most research deals only with the electrical activity produced from tens of millions of neurons at a time, which is the highest resolution a typical EEG machine can capture without needlessly cutting into an innocent study subject’s head. The problem is that this big, rough EEG research in humans isn’t very compatible with the intricate, neuron-scale research done in lab rats. Consequently, we have plenty of information about the brain’s parts but no understanding of how they work together as a whole.

    “It’s the difference between ‘What do Americans like?’ and ‘What does any individual American like?’” Cohen said. “And that’s a huge difference — between what any individual does and what you can say as a generality about an entire culture.”

    While we know that all that electrical activity is the result of charged chemicals sloshing around in our brains in rhythmic, patterned waves, that doesn’t tell us anything about the most important question: Why they’re generated in the first place.

    “The problem with these answers is that they’re totally meaningless from a neuroscience perspective,” Cohen says. “These answers tell you about how it’s physically possible, how the universe is constructed such that we can make these measurements. But there’s a totally different question, which is, what do these measurements mean? What do they tell us about the kinds of computations that are taking place in the brain? And that’s a huge explanatory gap.”

    Despite some puzzlement from fellow scientists, Cohen plans to collect brainwave data from rodents. No image credit.

    There are a few ways to bridge that gap. Scientists like those at the Blue Brain Project in Switzerland are trying to do so by building a computerized brain simulation that’s detailed enough to include the whole organ, as well as individual neurons, which they hope can reveal a kind of cell activity that would cause different kinds of common EEG patterns to appear. The one huge challenge to this approach, however, is that there’s no computer that can simulate a brain’s computations in real time; just a millisecond of one neuron’s time in a simulation can take 10 seconds of real-world time for a computer to figure out. It’s certainly possible, but doing so would cost billions of dollars.

    Cohen’s plan, which relies on real-world experiments, is much simpler.

    Since you can’t cut open a human brain and start sticking electrodes in there to record activity (even in “human rights-challenged places,” Cohen says), he’s relying on rodents instead. But what makes his work different is that he’s hooking those rodents up to EEG machines, which researchers don’t usually do. “They say, why are you wasting your time recording EEG from rats? EEG is for when you don’t have access to the brain, so you record from outside,” he says.

    But rodents have brainwaves, too, and their data can provide much-needed insight into how to bridge the neuron-brainwave divide. His experiments will create two huge data sets that researchers can cross-reference to figure out how neuron function and EEG behavior relate to one another. With the help of some deep-learning algorithms, they’ll then pore over that data to build a map of how individual sparks of neural activity add up to recognizable brainwaves. If Cohen’s experiments are very successful, his team will be able to look at a rodent’s EEG and predict — with what he hopes is more than 98 percent accuracy — exactly how the neural circuits are behaving in its brain.

    “I think we’re not that far away from breakthroughs. Some of these kinds of questions are not so difficult to answer, it’s just that no one has really looked,” he said. But he admits that he’s worried that the segmentation of neuroscience research will get in the way of this whole-brain approach.

    “So this is very terrifying for me and also very difficult, because I have very little experience in the techniques that i think are necessary,” he said.

    Having to admit on his grant applications that his work would employ unfamiliar techniques he has never used made it difficult to get funding, but Cohen ultimately received a grant from the European Union. Now, with the aid of a lab fully staffed with experts in rodent brains, Cohen is ready to get to work.

    Soon enough, we might finally get some answers to one of the oldest and strangest mysteries in neuroscience: where all those wub-wubs really come from and what they really mean.

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

  • richardmitnick 11:33 am on November 3, 2017 Permalink | Reply
    Tags: , Brain Studies, , For this study the researchers focused primarily on the calcium sodium potassium and other ions in cerebral fluid, LMIS4-Microsystems Laboratory 4, , Reading our brain chemistry   

    From EPFL: “Reading our brain chemistry” 

    EPFL bloc

    École Polytechnique Fédérale de Lausanne EPFL

    Clara Marc

    © Guillaume Petit-Pierre – Perfusion microdroplet allowing the extraction of interstitial liquid using the system developed by EPFL researchers.

    Researchers at EPFL have developed a new device and analysis method that let doctors measure the neurochemicals in a patient’s brain. The Microsystems Laboratory 4 (LMIS4)’s system involves collecting microdroplets of cerebral fluid and analyzing them to obtain chemical data that can help doctors diagnose and treat neurodegenerative diseases.

    Neurologists often use electrical impulses to stimulate and read brain signals. But the chemicals that neurons produce in response to these impulses are poorly understand at this point, even though they can provide valuable information for understanding the mechanisms behind neurodegenerative diseases like Alzheimer’s and Parkinson’s. “Neurons can be read two ways: electrically or chemically,” says Guillaume Petit-Pierre, a post-doc researcher at LMIS4 and one of the study’s authors. “Reading their electrical behavior can provide some limited information, such as the frequency and pace at which neurons communicate. However, reading their neurochemistry gives insight into the proteins, ions and neurotransmitters in a patient’s cerebral fluid.” By analyzing this fluid, doctors can obtain additional information – beyond that provided by neurons – and get a complete picture of a patient’s brain tissue metabolism.

    Collecting information through microchannels

    The EPFL researchers developed a system that can both collect a patient’s neurochemical feedback and form electrical connections with brain tissue. Their device is made up of electrodes and microchannels that are about half a hair in diameter. Once the device is placed inside brain tissue, the microchannels draw in cerebral fluid while the electrodes, which are located right at the fluid-collection interface, make sure that the measurements are taken at very precise locations. The microchannels subsequently create highly concentrated microdroplets of cerebral fluid. “The microdroplets form directly at the tip of the device, giving us a very high temporal resolution, which is essential if we want to accurately analyze the data,” says Petit-Pierre. The microdroplets are then placed on an analytical instrument that was also developed by scientists at the LMIS4 and the nearby University Centre of Legal Medicine which has expertise in this type of complex analysis. As a last step, the microdroplets are vaporized with a laser and the gas residue is analyzed. Both the researchers’ device and their analysis method are totally new. “Today there is only one method for performing neurochemical analyses: microdialysis. But it isn’t very effective in terms of either speed or resolution,” says Petit-Pierre. Another advantage of the researchers’ method is that it is a minimally invasive way to collect data. Currently scientists have to work directly on the brains of rats afflicted with neurodegenerative diseases, meaning the rats must be sacrificed to take the measurements. Their research was published in Nature Communications.

    Direct applications

    For this study, the researchers focused primarily on the calcium, sodium, potassium and other ions in cerebral fluid. They worked with EPFL’s Neurodegenerative Disease Laboratory to compare the measurements they took on rats with those reported in the literature – and found that the results were well correlated. The next step will be to develop a method for analyzing the proteins and neurotransmitters in cerebral fluid, so that their implications in neurodegenerative diseases can be further studied. “Doctors could measure neurochemical responses to help them make diagnoses, such as for epilepsy, when they use electricity to measure signals from a patient’s cortex,” says Guillaume, “or to improve the efficiency of treatments like deep brain stimulation (DBS) for Parkinson’s disease.” Their research could also soon find direct applications in other medical fields. Guillaume currently works on a start-up project to develop a catheter for patients affected by hemorrhagic stroke. Based on a similar technology, his catheter would let doctors treat a common yet serious complication of this condition and thereby reduce the risk of death.

    This research was carried out jointly by EPFL’s Laboratory of Microsystems 4 (LMIS4), EPFL’s Neurodegenerative Disease Laboratory (LEN), the Unit of Toxicology at the University Centre of Legal Medicine (CURML, CHUV and HUG) and the University of Lausanne’s Faculty of Biology and Medicine (FBM, UNIL).

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    EPFL campus

    EPFL is Europe’s most cosmopolitan technical university with students, professors and staff from over 120 nations. A dynamic environment, open to Switzerland and the world, EPFL is centered on its three missions: teaching, research and technology transfer. EPFL works together with an extensive network of partners including other universities and institutes of technology, developing and emerging countries, secondary schools and colleges, industry and economy, political circles and the general public, to bring about real impact for society.

  • richardmitnick 12:16 pm on November 1, 2017 Permalink | Reply
    Tags: , Brain Studies, Human chromosome 16p11.2 deletion syndrome is caused by the absence of about 27 genes on chromosome 16, , , R-Baclofen treatment   

    From MIT: “Promise seen in possible treatment for autism spectrum disorder” 

    MIT News
    MIT Widget

    MIT News

    October 31, 2017
    Picower Institute for Learning and Memory

    In searching for a potential therapeutic for autism spectrum disorder, researchers have found that R-Baclofen reverses cognitive deficits and improves social interactions in two lines of 16p11.2 deletion mice.

    Image courtesy of the Picower Institute for Learning and Memory.

    Studies in mice show improved social interaction and cognition from a potential therapeutic for a syndrome that often results in autism.

    Human chromosome 16p11.2 deletion syndrome is caused by the absence of about 27 genes on chromosome 16. This deletion is characterized by intellectual disability; impaired language, communication, and socialization skills; and autism spectrum disorder or ASD.

    Research from the laboratories of Mark Bear at MIT and Jacqueline Crawley at the University of California at Davis, has identified a potential therapeutic for ASD. Researchers found that R-Baclofen reverses cognitive deficits and improves social interactions in two lines of 16p11.2 deletion mice.

    The findings, published in the journal Neuropsychopharmacology, have the potential to treat humans with 16p11.2 deletion syndrome and ASD.

    “Our collaborative teams found that treatment with the drug R-baclofen improved scores on several learning and memory tasks, and on a standard assay of social behavior, in 16p11.2 mutant mice,” says Crawley, co-senior author of the paper along with Bear.

    “This unique corroboration of findings by two independent labs, using two distinct lines of mice with the same mutation, increases confidence that R-baclofen may be an effective pharmacological treatment for some of the symptoms of human 16p11.2 deletion syndrome, including intellectual impairment and autism,” she says.

    “These findings are particularly exciting on two fronts,” says Bear, who is the Picower Professor of Neuroscience at MIT. “First, the results show that diverse genetic causes of intellectual disability and autism may converge on a limited number of pathophysiological processes that can be ameliorated pharmacologically. Thus, a treatment for one genetically defined disorder may be beneficial for another with phenotypic overlap. Second, R-Baclofen has a well-understood safety profile and is well-tolerated in children and adults, making clinical studies feasible in the near future.”

    Growing knowledge about genetic mutations in people with autism is enabling researchers to evaluate hypothesis-driven pharmacological interventions in terms of their ability to reverse the biological and behavioral consequence of specific mutations that cause autism. One of the genes in the 16p11.2 deletion region regulates the inhibitory neurotransmitter GABA. Researchers tested the hypothesis that increasing GABA neurotransmission using R-baclofen, which binds to GABA-B receptors, could reverse analogous behavioral symptoms in a mouse model of 16p11.2 deletion syndrome.

    In the current paper, researchers report the results of animal model studies using two independently derived lines of mutant mice, each missing a chromosomal region analogous to human 16p11.2. Normal and mutant mice at both labs were tested after receiving R-baclofen in their drinking water on three tasks: novel object recognition, object location memory, and contextual recognition learning and memory. In addition, R-baclofen treated mutant mice scored better after treatment on each cognitive task than the untreated mutant mice. R-baclofen also increased scores on a standard assay of mouse social behaviors — male-female reciprocal social interactions — in the 16p11.2 mutant mice.

    This study suggests that R-baclofen should be explored for the treatment of cognitive phenotypes in affected humans.

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    MIT Seal

    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

    MIT Campus

  • richardmitnick 3:16 pm on October 27, 2017 Permalink | Reply
    Tags: , , Brain Studies, , , Trans-Tango   

    From Brown: “Novel technology provides powerful new means for studying neural circuits” 

    Brown University
    Brown University

    [This post is dedicated to E.B.M., now at Brown]

    October 26, 2017
    David Orenstein

    Choreographing trans-Tango
    Developing trans-Tango, a system that works across neural connections called synapses to trace neurons in circuits, required decades of work and a dedicated team.
    Stephen Crocker

    Motor and Sensory Regions of the Cerebral Cortex. This image was donated by Blausen Medical. Bruce Blaus

    With “trans-Tango,” a technology developed at Brown University and described in a new study in Neuron, scientists can bridge across the connections between neurons to trace — and in the future control — brain circuits.

    Finding out which neurons are connected with which others, and how they act together, is a huge challenge in neuroscience, and it’s crucial for understanding how brain circuits give rise to perception, motion, memory, and behavior. A new Brown University-developed technology called “trans-Tango” allows scientists to exploit the connections between pairs of neurons to make such discoveries in neuroscience. In a new study in Neuron, they used trans-Tango to illuminate connected neurons in fruit flies, revealing previously unmapped gustatory circuits that link the taste-sensing organs to brain regions known to govern feeding behavior and memory.

    The technology is widely applicable, the researchers say, because trans-Tango doesn’t depend on the neurotransmitters involved in a neural connection or on the types of neurons that are connected. As long as two neurons join at a synapse, trans-Tango allows scientists to label the cells connected to a starter neuron, experiments in the paper show.

    Moreover, because trans-Tango works by instigating the expression of genes in connected pairs of neurons, it also has the potential to enable scientists to control circuit functions, said senior and corresponding author Gilad Barnea, an associate professor of neuroscience at Brown who began looking for a precise, reliable and general way to visualize neural connections two decades ago. The application of trans-Tango that his team demonstrates in the new study is circuit tracing, but manipulations such as activating or shutting off connected neurons could become possible, too.

    “trans-Tango provides genetic accessibility in the context of connectivity,” Barnea said. “Our technique allows you to access the neurons that interact with the particular ‘starter’ cell you target. It therefore expands the use of molecular genetic techniques beyond the cell for which you have a marker to the ones it ‘talks’ to.”

    The team, which includes postdoctoral fellows, graduate students, research assistants and undergraduates, is now working on developing a host of other applications of trans-Tango. These include using the system to manipulate behavior, developing the equivalent technique in mice, and making it work in reverse so that it employs incoming connections from other neurons just like it does outgoing connections. That’s according to Mustafa Talay, a postdoctoral fellow who earned his Ph.D. in Barnea’s lab and is co-lead author with Ethan Richman, a former undergraduate at Brown who is now a graduate student at Stanford.

    In addition, the Barnea lab is collaborating on adapting the technology to study how cancer spreads.

    How it works

    trans-Tango works by genetically introducing an artificial signaling pathway into every neuron in the fly. The pathway acts like a switch in the neurons that can be thrown by exposure to a triggering protein. To operate trans-Tango, scientists genetically engineer the neurons of interest (starter neurons) to present this triggering protein on their synapses together with a protein that lights up the starter neurons in green. Expression of the trigger protein at the synapse causes connected neurons to light up in red, revealing the full extent of the connected neurons in the fly’s nervous system.

    In the gustatory system, for example, the team lit up connections extending all the way from peripheral taste-sensing starter neurons to connected neurons that projected into a brain region known to control feeding behavior as well as to other regions thought to regulate memory.

    trans-Tango reveals taste circuits
    Brown Unviersity scientists used trans-Tango to discover new connections linking taste-sensing organs in the fly body with specific regions in the brain.

    By design, the system stops after just one stage of connectivity because if it continued endlessly, it would eventually light up the whole nervous system, Talay said. After all, each neuron usually connects to many others, not just one or a few, and ultimately they are pretty much all connected.

    But the system is compatible with other cell imaging and targeting methods that can narrow down the number of connected neurons that respond to trans-Tango. In the new study, for example, the team combined trans-Tango with such techniques to specifically highlight individual connected neurons.

    “When we probe a circuit we have no idea about, we can first just use trans-Tango and see the totality of all the connections of a neuron,” Talay said. “After that, if we want to characterize a circuit in more detail, we can combine trans-Tango with other methods to basically dissect that circuit.”

    In many cases, revealing the full expanse that two connected neurons cover in a circuit can present deeply meaningful insights for neuroscientists. Not only did the team find novel connections in the gustatory circuitry of flies, but also they showed the different projections that various neurons in the olfactory system make, potentially clarifying how they carry out their distinct roles in connecting smell and behavior. Their experiments also highlighted connections that were already well known in the olfactory system, validating that the connections trans-Tango highlights are real.

    The technology’s triggering protein is not naturally found in the fly, and it doesn’t leave the neurons or the synapse. For this reason, the scientists said, the illumination that arises as a result of trans-Tango reveals cells that truly “talk” to each other rather than neighboring but irrelevant cells.

    How it was developed

    Barnea has sought to perform exactly this kind of circuit mapping since he joined the lab of Columbia University Professor Richard Axel as a postdoctoral researcher in 1996. They were studying the olfactory system, and Barnea wanted to map the olfactory circuits in the rodent brain.

    Tracing the connections of neurons within circuits in the brain is a fundamental but very difficult problem for neuroscientists. In all, the nervous systems of different organisms may involve many millions or billions of neurons with connections reaching into the trillions. It’s a lot to sort through.

    There are several other methods for mapping circuits, but they all suffer from drawbacks. Some are too noisy. Some are too expensive and laborious. Some are too specific to a tiny subset of connections or neurons. Some only reveal the synapses but not the full length of the cells that connect there. Some won’t work in a living organism. Barnea wanted to generate a system for circuit mapping that would be general, precise, simple to use and that would work in an organism rather than in extracted tissue.

    At Columbia, Barnea developed Tango [ PNAS], a method for studying cellular receptors that is the basis for the synthetic signaling pathway in trans-Tango. When he came to Brown in 2007, he continued this work and took on other projects. Barnea’s lab was not set for fly work, so its first fly incubator was an old egg incubator borrowed from biology professor Gary Wessel. The trans-Tango project was first supported by the Pew Charitable Trusts, then by the National Institutes of Health’s EUREKA program and subsequently by more conventional grants. The project also gained internal funding through the Innovation Award from the Brown Institute for Brain Science and Research Seed and Salomon awards from Brown’s Office of the Vice President for Research.

    Flies on the wall
    Professor Gilad Barnea surveys shelves full of research flies in his Brown University lab.
    David Orenstein

    A key feature of trans-Tango is that it employs the human hormone glucagon as the trigger that switches the synthetic pathway on. Glucagon is engineered to localize to the synapse, and it is tethered in order to prevent it from diffusing away. Barnea credits the inspiration to use that form of glucagon to co-author John Szymanski, a former undergraduate student in his lab who is now a graduate student at Columbia. Szymanski first heard about the engineered form of glucagon at a party, Barnea said.

    In 2011, Barnea met Talay while visiting Boğaziçi University in Turkey, where Talay was a master’s student. Talay was also thinking about ways to trace neural circuits and he had crucial experience working in flies, where progress could be faster than in mice.

    Richman was interested in synthetic biology so he joined the Barnea lab to advance the development of the tracing technique. Talay and Richman led the charge to develop trans-Tango and make it work in flies, continually refining it with the help of several lab mates. This collaboration continued even after Richman graduated in 2013, when he decided to delay going to Stanford to see the project through.

    “I remember very clearly the excitement of seeing the first images appear indicating a functioning technique, and the pleasure of discussing those results with Gilad,” Richman said. “That happened in January, and in the subsequent spring I had gotten accepted to graduate school and was slated to start the next fall. By the summer, Mustafa and I had made progress optimizing the technique, and the excitement in the lab was building. Having spent so long getting the technique to work, I was tantalized by the opportunity to put it into action.”

    It was indeed a long time coming. Barnea points out that one of the paper’s co-authors, former undergraduate student Cambria Chou-Freed, is younger than the original idea he envisioned 21 years ago. In all, five of the paper’s authors were undergraduates in the lab, and all stayed in the lab after graduating to continue to work on this project.

    “Everyone on the list of authors contributed something unique to the success of this project,” Barnea said. “This was driven by individuals who were committed and obsessed with it, but it was also very nice teamwork.”

    The paper’s other authors are Nathaniel Snell, Griffin Hartmann, John Fisher, Altar Sorkaç, Juan Santoyo, Nived Nair and Mark Johnson.

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition
    Welcome to Brown

    Brown U Robinson Hall
    Located in historic Providence, Rhode Island and founded in 1764, Brown University is the seventh-oldest college in the United States. Brown is an independent, coeducational Ivy League institution comprising undergraduate and graduate programs, plus the Alpert Medical School, School of Public Health, School of Engineering, and the School of Professional Studies.

    With its talented and motivated student body and accomplished faculty, Brown is a leading research university that maintains a particular commitment to exceptional undergraduate instruction.

    Brown’s vibrant, diverse community consists of 6,000 undergraduates, 2,000 graduate students, 400 medical school students, more than 5,000 summer, visiting and online students, and nearly 700 faculty members. Brown students come from all 50 states and more than 100 countries.

    Undergraduates pursue bachelor’s degrees in more than 70 concentrations, ranging from Egyptology to cognitive neuroscience. Anything’s possible at Brown—the university’s commitment to undergraduate freedom means students must take responsibility as architects of their courses of study.

  • richardmitnick 8:02 pm on October 25, 2017 Permalink | Reply
    Tags: , Brain Studies,   

    From Stanford University: “Can we better understand the language of the brain?” 

    Stanford University Name
    Stanford University

    October 18, 2017
    Nathan Collins

    Image by Guo Mong

    The answer could lead to improved brain-machine interfaces that treat neurological disease, and change the way people with paralysis interact with the world.

    Since the 19th century at least, humans have wondered what could be accomplished by linking our brains – smart and flexible but prone to disease and disarray – directly to technology in all its cold, hard precision.

    Writers of the time dreamed up intelligence enhanced by implanted clockwork and a starship controlled by a transplanted brain.

    While these remain inconceivably far-fetched, the melding of brains and machines for treating disease and improving human health is now a reality. Brain-machine interfaces that connect computers and the nervous system can now restore rudimentary vision in people who have lost the ability to see, treat the symptoms of Parkinson’s disease and prevent some epileptic seizures. And there’s more to come.

    Stanford researchers develop brain-controlled typing for people with paralysis.

    But the biggest challenge in each of those cases may not be the hardware that science-fiction writers once dwelled on. Instead, it’s trying to understand, on some level at least, what the brain is trying to tell us – and how to speak to it in return. Like linguists piecing together the first bits of an alien language, researchers must search for signals that indicate an oncoming seizure or where a person wants to move a robotic arm. Improving that communication in parallel with the hardware, researchers say, will drive advances in treating disease or even enhancing our normal capabilities.

    Listening to the language of the brain

    The scientific interest in connecting the brain with machines began in earnest in the early 1970s, when computer scientist Jacques Vidal embarked on what he called the Brain Computer Interface project. As he described in a 1973 review paper [Annual Review of Biophysics and Bioengineering], it comprised an electroencephalogram, or EEG, for recording electrical signals from the brain and a series of computers to process that information and translate it into some sort of action, such as playing a simple video game. In the long run, Vidal imagined brain-machine interfaces could control “such external apparatus as prosthetic devices or spaceships.”

    Although brain-controlled spaceships remain in the realm of science fiction, the prosthetic device is not. Stanford researchers including Krishna Shenoy, a professor of electrical engineering, and Jaimie Henderson, a professor of neurosurgery, are bringing neural prosthetics closer to clinical reality. Over the course of nearly two decades, Shenoy, the Hong Seh and Vivian W. M. Lim Professor in the School of Engineering, and Henderson, the John and Jene Blume–Robert and Ruth Halperin Professor, developed a device that, in a clinical research study, gave people paralyzed by accident or disease a way to move a pointer on a computer screen and use it to type out messages. In similar research studies, people were able to move robotic arms with signals from the brain.

    Reaching those milestones took work on many fronts, including developing the hardware and surgical techniques needed to physically connect the brain to an external computer.

    But there was always another equally important challenge, one that Vidal anticipated: taking the brain’s startlingly complex language, encoded in the electrical and chemical signals sent from one of the brain’s billions of neurons on to the next, and extracting messages a computer could understand. On top of that, researchers like Shenoy and Henderson needed to do all that in real time, so that when a subject’s brain signals the desire to move a pointer on a computer screen, the pointer moves right then, and not a second later.

    One of the people that challenge fell to was Paul Nuyujukian, now an assistant professor of bioengineering and neurosurgery. First as a graduate student with Shenoy’s research group and then a postdoctoral fellow with the lab jointly led by Henderson and Shenoy. Nuyujukian helped to build and refine the software algorithms, termed decoders, that translate brain signals into cursor movements.

    Actually, “translate” may be too strong a word – the task, as Nuyujukian put it, was a bit like listening to a hundred people speaking a hundred different languages all at once and then trying to find something, anything, in the resulting din one could correlate with a person’s intentions. Yet as daunting as that sounds, Nuyujukian and his colleagues found some ingeniously simple ways to solve the problem, first in experiments with monkeys. For example, Nuyujukian and fellow graduate student Vikash Gilja showed that they could better pick out a voice in the crowd if they paid attention to where a monkey was being asked to move the cursor.

    “Design insights like that turned out to have a huge impact on performance of the decoder,” said Nuyujukian, who is also a member of Stanford Bio-X and the Stanford Neurosciences Institute. In fact, it more than doubled the system’s performance in monkeys, and the algorithm the team developed remains the basis of the highest-performing system to date. Nuyujukian went on to adapt those insights to people in a clinical study – a significant challenge in its own right – resulting in devices that helped people with paralysis type at 12 words per minute, a record rate.

    Although there’s a lot of important work left to do on prosthetics, Nuyujukian said he believes “there are other very real and pressing needs that brain-machine interfaces can solve,” such as the treatment of epilepsy and stroke – conditions in which the brain speaks a language scientists are only beginning to understand.

    Listening for signs something’s wrong

    Indeed, if one brain-machine interface can pick up pieces of what the brain is trying to say and use that to move a cursor on a screen, others could listen for times when the brain is trying to say something’s wrong.

    One such interface, called NeuroPace and developed in part by Stanford researchers, does just that. Using electrodes implanted deep inside or lying on top of the surface of the brain, NeuroPace listens for patterns of brain activity that precede epileptic seizures and then, when it hears those patterns, stimulates the brain with soothing electrical pulses.

    Learning to listen for – and better identify – the brain’s needs could also improve deep brain stimulation, a 30-year-old technique that uses electrical impulses to treat Parkinson’s disease, tremor and dystonia, a movement disorder characterized by repetitive movements or abnormal postures brought on by involuntary muscle contractions, said Helen Bronte-Stewart, professor of neurology and neurological sciences.

    Although the method has proven successful, there is a problem: Brain stimulators are pretty much always on, much like early cardiac pacemakers. Although the consequences are less dire – the first pacemakers “often caused as many arrhythmias as they treated,” Bronte-Stewart, the John E. Cahill Family Professor, said – there are still side effects, including tingling sensations and difficulty speaking. For cardiac pacemakers, the solution was to listen to what the heart had to say and turn on only when it needed help, and the same idea applies to deep brain stimulation, Bronte-Stewart said. To that end, “we’re developing brain pacemakers that can interface with brain signaling, so they can sense what the brain is doing” and respond appropriately.

    The challenge is much the same as in Nuyujukian’s work, namely, to try to extract useful messages from the cacophony of the brain’s billions of neurons, although Bronte-Stewart’s lab takes a somewhat different approach. In one recent paper, the team focused on one of Parkinson’s more unsettling symptoms, “freezing of gait,” which affects around half of Parkinson’s patients and renders them periodically unable to lift their feet off the ground.

    Bronte-Stewart’s question was whether the brain might be saying anything unusual during freezing episodes, and indeed it appears to be. Using methods originally developed in physics and information theory, the researchers found that low-frequency brain waves were less predictable, both in those who experienced freezing compared to those who didn’t, and, in the former group, during freezing episodes compared to normal movement. In other words, although no one knows exactly what the brain is trying to say, its speech – so to speak – is noticeably more random in freezers, the more so when they freeze.

    By listening for those signs, well-timed brain stimulation may be able to prevent freezing of gait with fewer side effects than before, and one day, Bronte-Stewart said, more sophisticated feedback systems could treat the cognitive symptoms of Parkinson’s or even neuropsychiatric diseases such as obsessive compulsive disorder and major depression.

    Do we need to speak the brain’s language?

    Both Nuyujukian and Bronte-Stewart’s approaches are notable in part because they do not require researchers to understand very much of the language of brain, let alone speak that language. Indeed, learning that language and how the brain uses it, while of great interest to researchers attempting to decode the brain’s inner workings, may be beside the point for some doctors and patients whose goal is to find more effective prosthetics and treatments for neurological disease.

    But other tasks will require greater fluency, at least according to E.J. Chichilnisky, a professor of neurosurgery and of ophthalmology, who thinks speaking the brain’s language will be essential when it comes to helping the blind to see. Chichilnisky, the John R. Adler Professor, co-leads the NeuroTechnology Initiative, funded by the Stanford Neuroscience Institute, and he and his lab are working on sophisticated technologies to restore sight to people with severely damaged retinas – a task he said will require listening closely to what individual neurons have to say, and then being able to speak to each neuron in its own language.

    The problem, Chichilnisky said, is that retinas are not simply arrays of identical neurons, akin to the sensors in a modern digital camera, each of which corresponds to a single pixel. Instead, there are different types of neurons, each of which sends a different kind of information to the brain’s vision-processing system.

    “We need to talk to those neurons,” Chichilnisky said. To do that, a brain-machine interface needs to figure out, first, what types of neurons its individual electrodes are talking to and how to convert an image into a language those neurons – not us, not a computer, but individual neurons in the retina and perhaps deeper in the brain – understand. Once researchers can do that, they can begin to have a direct, two-way conversation with the brain, enabling a prosthetic retina to adapt to the brain’s needs and improve what a person can see through the prosthesis.

    “A one-way conversation sometimes doesn’t get you very far,” Chichilnisky said.

    See the full article here .

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    Leland and Jane Stanford founded the University to “promote the public welfare by exercising an influence on behalf of humanity and civilization.” Stanford opened its doors in 1891, and more than a century later, it remains dedicated to finding solutions to the great challenges of the day and to preparing our students for leadership in today’s complex world. Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto. Since 1952, more than 54 Stanford faculty, staff, and alumni have won the Nobel Prize, including 19 current faculty members

    Stanford University Seal

  • richardmitnick 5:49 am on October 12, 2017 Permalink | Reply
    Tags: , Brain Studies, , , UCLA study shows cell diversity of a key brain region   

    From UCLA Newsroom: “UCLA study shows cell diversity of a key brain region” 

    UCLA Newsroom

    October 11, 2017
    Dan Gordon

    Weizhe Hong, Courtesy of Weizhe Hong

    UCLA researchers have shown for the first time a comprehensive picture of cell diversity in the amygdala, a vital brain region involved in the regulation of emotions and social behavior, as well as in autism spectrum disorders, depression and other mental disorders.

    Brain, Wikipedia

    This almond like structure, ranging from 1-4cm , average about 1.8cm, has extensive connection with the brain. This include it’s neighboring structure hippocampus, entorhinal cortex, basal ganglia (especially the striatum), brainstem, thalamus and hypothalamus. It is also connected well with the limbic system and other associative cortex, prefrontal cortex (major role in behavior), basal forebrain and ect. Hence, it’s stimulation is predicted to bring a major effect to the entire brain! https://teddybrain.wordpress.com/2013/01/09/what-happen-if-amygdala-is-damaged/

    As part of the study, the team also reported on a new method for systematically linking the distinct types of brain cells to specific behavioral functions.

    “The level of diversity of cells within the brain has not been well understood,” said study senior author Weizhe Hong, assistant professor of biological chemistry and neurobiology at the David Geffen School of Medicine at UCLA. “By revealing the many types of cells in the amygdala and then developing a method for studying the functional role of these cells, our findings can pave the way to unraveling some of the mysteries in how this important part of the brain works and what goes wrong in mental health disorders.”

    The findings are published in the October 11 issue of the journal Neuron.

    Unlike other organs in the body, the brain is known to consist of highly heterogeneous types of cells — a heterogeneity that is at the root of cognitive functions such as learning, memory, emotional arousal and decision-making, as well as brain disorders. Using recently developed sequencing technology that allows researchers to conduct rapid analyses of individual cells, the UCLA group found that the amygdala has much greater cellular diversity than previously known — featuring 16 types of neurons and many types of non-neuronal cells.

    Describing the diversity of cells within the amygdala was only a first step. “Once we know these different cell types, we want to understand how distinct types of brain cells are linked to behavioral functions and disease conditions,” Hong said. “In the past, there was no systematic way of doing this.”

    Hong and colleagues overcame a long-standing technical hurdle to develop a method, called Act-seq, for systematically linking brain-cell types to behavioral functions. Using the new method, they pinpointed two of the 16 neuronal types in the amygdala as being involved in stress-related behaviors. The new method also facilitates the study of acute molecular and cellular changes in the brain in injury and disease. For example, the researchers found a substantial activation of glial cells, a type of supporting cells in the brain, immediately after a seizure.

    The team is continuing to use the new research tool to investigate how the amygdala controls emotional and social behaviors, as well as how this goes wrong in mental disorders, such as autism spectrum disorders and depression. “We expect to learn a great deal by breaking down the amygdala’s individual components and their functions,” Hong said.

    In addition to Hong, study authors are Ye Emily Wu, Lin Pan and Yanning Zuo of the departments of biological chemistry and neurobiology at UCLA, and Xinmin Li of the department of pathology at UCLA.

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    UC LA Campus

    For nearly 100 years, UCLA has been a pioneer, persevering through impossibility, turning the futile into the attainable.

    We doubt the critics, reject the status quo and see opportunity in dissatisfaction. Our campus, faculty and students are driven by optimism. It is not naïve; it is essential. And it has fueled every accomplishment, allowing us to redefine what’s possible, time after time.

    This can-do perspective has brought us 12 Nobel Prizes, 12 Rhodes Scholarships, more NCAA titles than any university and more Olympic medals than most nations. Our faculty and alumni helped create the Internet and pioneered reverse osmosis. And more than 100 companies have been created based on technology developed at UCLA.

  • richardmitnick 8:29 pm on October 6, 2017 Permalink | Reply
    Tags: , Barriers in sharing and accessing that data stymie progress in the field, BIDS the researchers say solves problems by providing a uniform standard, BIDS-Brain Imaging Data Structure, Brain Studies, , Neuroscience research has made incredible strides toward revealing the inner workings of our brains, Stanford psychologists are addressing those barriers through a new way of organizing brain-imaging data that simplifies data analysis and helps researchers collaborate more effectively,   

    From Stanford: “Stanford psychologists simplify brain-imaging data to foster more transparency, discoveries” 

    Stanford University Name
    Stanford University

    October 2, 2017
    Milenko Martinovich

    Researchers at the Stanford Center for Reproducible Neuroscience are championing a new way of organizing brain-imaging data that they hope will lead to more transparency, more collaboration and ultimately a better understand of the brain.

    Researchers at the Stanford Center for Reproducible Neuroscience are working to make it easier to share brain-imaging data and collaborate more effectively. (Image credit: Nomadsoul1 / Getty Images)

    Neuroscience research has made incredible strides toward revealing the inner workings of our brains – how we make decisions, plan for the future or experience emotions – thanks in part to technological advances, but barriers in sharing and accessing that data stymie progress in the field.

    Stanford psychologists are addressing those barriers through a new way of organizing brain-imaging data that simplifies data analysis and helps researchers collaborate more effectively – they call it BIDS (Brain Imaging Data Structure).

    The easier it becomes to analyze and organize data, said Russell Poldrack, a professor of psychology, the more easily that data can be shared among researchers, leading to more transparency and more progress in understanding the brain.

    “We’ve been interested for a long time in finding ways to share data between groups,” said Poldrack, director of the Stanford Center for Reproducible Neuroscience. “Sharing data is a good thing because it allows different research groups to reuse data and maximizes its potential.”

    MRI analysis today

    Thousands of research MRI studies are performed every year generating substantial amounts of data. However, there’s no consensus on how that data should be organized.

    Conceivably, you could have two neuroscience researchers working side-by-side in the same lab analyzing the same MRI scans and recording the data differently. These labs also experience significant turnover with doctoral students and postdoctoral scholars leaving for teaching and other research positions. New researchers entering the lab may need to decipher data in a format they’re not accustomed to. The dilemma gets further complicated as new data analysis methods are being developed, providing even more ways to organize the data.

    For example, Poldrack’s group is currently working on a project where participants undergo MRI scans to study their brain activity related to self-control. The data the team collects are images – up to 40 or 50 files – of the brain in various stages. But transferring these files from the MRI scanner to a format the lab’s software program can read requires transforming the files – a process that has traditionally been idiosyncratic among different researchers.

    Without a common standard, it becomes increasingly difficult for researchers to maximize these valuable data sets. It would be like if thousands of U.S. Census takers gathering demographic information on Americans all over the country sent their survey results back in different languages.

    BIDS, the researchers say, solves that problem by providing a uniform standard.

    “Basically, we constructed this language where all people collecting brain data understand each other,” said Chris Gorgolewski, co-director of the Stanford Center for Reproducible Neuroscience.

    How BIDS works

    BIDS is essentially a collection of related apps that help handle different aspects of data analysis and storage. Once a new app is tested and deployed it resides in a cloud-based service, where other scientists can download the apps directly for their own use.

    The group originally developed BIDS with support from the International Neuroinformatics Coordinating Facility, a global organization dedicated to promoting data sharing among neuroscientists. The Stanford Center for Reproducible Neuroscience has taken the lead in championing BIDS as the standard language for MRI data.

    In addition to publishing research about BIDS, the center has also hosted two annual workshops, each bringing together about 30 researchers and developers from around the world to learn about and build these apps. The lab also received a $1.4 million grant last month from the National Institutes of Health BRAIN Initiative to further the development of BIDS.

    The center’s researchers, including Gorgolewski and postdoctoral research fellow Oscar Esteban, have either built or facilitated the building of 22 BIDS apps. Their most recent innovation is the MRIQC tool (MRI Quality Control), which performs quality assessments and large-scale analysis of MRI data, which they discussed in a Sept. 25 article in PLOS ONE.

    MRI image analysis takes time and involves numerous steps, and often requires external software. The BIDS apps, conversely, are compatible with major operating systems with minimal extra work for users. They are meant to be “plug and play,” Esteban said.

    Transparency and reproducibility

    Poldrack readily admits that apps that help organize data sound “pretty boring.” He said he and his researchers sometimes see themselves as “plumbers” fixing infrastructure.

    But offering a setting of openness where scholars around the world have access to critical data is worth the work.

    “The bigger picture for us is transparency and reproducibility,” Poldrack said. “There are interesting scientific questions we want people to get at, questions about how our different psychological functions are related to each other. Part of what we want to do is to convince people to share their data when they run a study to do interesting science or reproduce the results.”

    Media Contacts
    Russell Poldrack, Department of Psychology: poldrack@stanford.edu
    Milenko Martinovich, Stanford News Service: (650) 725-9281, mmartino@stanford.edu

    See the full article here .

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    Leland and Jane Stanford founded the University to “promote the public welfare by exercising an influence on behalf of humanity and civilization.” Stanford opened its doors in 1891, and more than a century later, it remains dedicated to finding solutions to the great challenges of the day and to preparing our students for leadership in today’s complex world. Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto. Since 1952, more than 54 Stanford faculty, staff, and alumni have won the Nobel Prize, including 19 current faculty members

    Stanford University Seal

  • richardmitnick 1:40 pm on August 6, 2017 Permalink | Reply
    Tags: , Brain Studies, , ,   

    From U Michigan: “$7.75M for mapping circuits in the brain” 

    U Michigan bloc

    University of Michigan

    August 3, 2017
    Kate McAlpine

    A new NSF Tech Hub will put tools to rapidly advance our understanding of the brain into the hands of neuroscientists.

    To follow the long, winding connections among neurons, a technique called “Brainbow” labels each neuron a random color. Credit: Dawen Cai, Cai Lab, University of Michigan

    The technology exists to stimulate and map circuits in the brain, but neuroscientists have yet to tap this potential.

    Now, developers of these technologies are coming together to demonstrate and share them to drive a rapid advance in our understanding of the brain, funded by $7.75 million from the National Science Foundation.

    “We want to put our technology into the hands of people who can really use it,” said Euisik Yoon, leader of the project and professor of electrical engineering and computer science at the University of Michigan.

    By observing how mice and rats behave when certain neural circuits are stimulated, neuroscientists can better understand the function of those circuits in the brain. Then, after the rodents are euthanized, they can observe the neurons that had been activated and how they are connected. This connects the behavior that they had observed while the rodent was performing a controlled experiment with a detailed map of the relevant brain structure.

    It could lead to better understanding of disease in the brain as well as more effective treatments. In the nearer term, the details of neural circuitry could also help advance computing technologies that try to mimic the efficiency of the brain.

    Over the last decade or so, three tools have emerged that, together, can enable the mapping of circuits within the brain. The most recent, from U-M, is an implant that uses light to stimulate specific neurons in genetically modified mice or rats and then records the response from other neurons with electrodes.

    Probes like this one, which stimulate neurons with light and then record activity with electrodes, are just one facet of the technology suite that can help neuroscientists map circuits in the brain. Photo: Fan Wu, Yoon Lab, University of Michigan

    Unlike earlier methods to stimulate the brain with light, with relatively large light-emitters that activated many nearby neurons, the new probes can target fewer neurons using microscopic LEDs that are about the same size as the brain cells themselves. This control makes the individual circuits easier to pick out.

    The “pyramidal” neurons that cause action—rather than inhibit it—will be genetically modified so that they respond to the light.

    “They are just one of the neuron types we are seeking to map,” said John Seymour, one of the co-investigators and U-M assistant research scientist in electrical engineering and computer science. “If you can record from motor cortex pyramidal neurons, you can predict arm movement, for example.”

    John Seymour explains how the new grant will help neurotechnologists further research to enable a better understanding of the pathways in the brain.

    To visualize the structure of pyramidal cells and other kinds of neurons, researchers need a way to see each tree in the brain’s forest. For this, co-investigator Dawen Cai, U-M assistant professor of cell and developmental biology, has been advancing a promising approach known as Brainbow. Genetically modified brain cells produce fluorescent tags, revealing each cell as a random color.

    When it is time to examine the brain, a technique to make the brain transparent will remove all the fatty molecules from a brain and replace them with a clear gel, making it possible to see individual neurons. It was pioneered by another co-investigator, Viviana Gradinaru, who is a professor of biology and biological engineering at the California Institute of Technology.

    “Not only may we understand how the signal is processed inside the brain, we can also find out how each neuron is connected together so that we achieve structural and functional mapping at an unprecedented scale,” Yoon said.

    While these are the central tools, others at Michigan are working on methods to make the electrodes that record neuron activity even smaller and therefore more precise. In addition, a carbon wire electrode design could even pick up the chemical activity nearby, adding measurements of neurotransmitters as a new dimension of information.

    To share these new tools, the team will bring in neuroscientists for annual workshops and then provide them with the hardware and software they need to run experiments in their own labs. For the tools that prove to be most useful, they will seek commercialization opportunities so that they remain available after the project ends.


    The project is called Multimodal Integrated Neural Technologies (MINT) and has been awarded as a 5-year National Science Foundation NeuroNex Technology Hub.

    Other co-investigators include Cynthia Chestek, U-M assistant professor of biomedical engineering; James Weiland, U-M professor of biomedical engineering; Ken Wise, the William Gould Dow Distinguished University Professor Emeritus of Electrical Engineering and Computer Science at U-M; and György Buzsáki, professor of neuroscience at New York University. Seymour and Yoon are also affiliated with biomedical engineering at U-M. Cai is affiliated with Michigan Medicine.

    The neural probes with micro LEDs are made in the Lurie Nanofabrication Facility at U-M.

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    U MIchigan Campus

    The University of Michigan (U-M, UM, UMich, or U of M), frequently referred to simply as Michigan, is a public research university located in Ann Arbor, Michigan, United States. Originally, founded in 1817 in Detroit as the Catholepistemiad, or University of Michigania, 20 years before the Michigan Territory officially became a state, the University of Michigan is the state’s oldest university. The university moved to Ann Arbor in 1837 onto 40 acres (16 ha) of what is now known as Central Campus. Since its establishment in Ann Arbor, the university campus has expanded to include more than 584 major buildings with a combined area of more than 34 million gross square feet (781 acres or 3.16 km²), and has two satellite campuses located in Flint and Dearborn. The University was one of the founding members of the Association of American Universities.

    Considered one of the foremost research universities in the United States,[7] the university has very high research activity and its comprehensive graduate program offers doctoral degrees in the humanities, social sciences, and STEM fields (Science, Technology, Engineering and Mathematics) as well as professional degrees in business, medicine, law, pharmacy, nursing, social work and dentistry. Michigan’s body of living alumni (as of 2012) comprises more than 500,000. Besides academic life, Michigan’s athletic teams compete in Division I of the NCAA and are collectively known as the Wolverines. They are members of the Big Ten Conference.

  • richardmitnick 9:06 am on July 26, 2017 Permalink | Reply
    Tags: , , Brain Studies, ,   

    From UCLA Newsroom: “Brain activity test detects autism severity, UCLA study finds” 

    UCLA Newsrooom

    July 25, 2017
    Sarah C.P. Williams



    UCLA researchers have discovered that children with autism have a tell-tale difference on brain tests compared with other children. Specifically, the researchers found that the lower a child’s peak alpha frequency — a number reflecting the frequency of certain brain waves — the lower their non-verbal IQ was. This is the first study to highlight peak alpha frequency as a promising biomarker to not only differentiate children with autism from typically developing children, but also to detect the variability in cognitive function among children with autism.


    Autism spectrum disorder affects an estimated one in 68 children in the United States, causing a wide range of symptoms. While some individuals with the disorder have average or above-average reasoning, memory, attention and language skills, others have intellectual disabilities. Researchers have worked to understand the root of these cognitive differences in the brain and why autism spectrum disorder symptoms are so diverse.

    An electroencephalogram, or EEG, is a test that detects electrical activity in a person’s brain using small electrodes that are placed on the scalp. It measures different aspects of brain activity including peak alpha frequency, which can be detected using a single electrode in as little as 40 seconds and has previously been linked to cognition in healthy individuals.


    The researchers performed EEGs on 97 children ages 2 to 11; 59 had diagnoses of autism spectrum disorder and 38 did not have the disorder. The EEGs were taken while the children were awake and relaxed in dark, quiet rooms. Correlations among age, verbal IQ, non-verbal IQ and peak alpha frequency were then studied.


    The discovery that peak alpha frequency relates directly to non-verbal IQ in children with the disorder suggests a link between the brain’s functioning and the severity of the condition. Moreover, it means that researchers may be able to use the test as a biomarker in the future, to help study whether an autism treatment is effective in restoring peak alpha frequency to normal levels, for instance.

    More work is needed to understand whether peak alpha frequency can be used to predict the development of autism spectrum disorder in young children before symptoms emerge.


    The authors of the study are Shafali Spurling Jeste, UCLA associate professor in psychiatry, neurology and pediatrics and a lead investigator of the UCLA Center for Autism Research and Treatment; Abigail Dickinson and Charlotte DiStefano, postdoctoral fellows at the UCLA Center for Autism Research and Treatment; and Damla Senturk, associate professor of biostatistics at UCLA.


    The study was published online in the European Journal of Neuroscience.


    The study was funded by Autism Speaks (Meixner Postdoctoral Fellowship in Translational Research), the National Institutes of Mental Health (K23MH094517), the National Institute of General Medical Sciences (R01 GM111378-01A1) and the National Institute of Health (ACE 2P50HD055784-06).

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    UC LA Campus

    For nearly 100 years, UCLA has been a pioneer, persevering through impossibility, turning the futile into the attainable.

    We doubt the critics, reject the status quo and see opportunity in dissatisfaction. Our campus, faculty and students are driven by optimism. It is not naïve; it is essential. And it has fueled every accomplishment, allowing us to redefine what’s possible, time after time.

    This can-do perspective has brought us 12 Nobel Prizes, 12 Rhodes Scholarships, more NCAA titles than any university and more Olympic medals than most nations. Our faculty and alumni helped create the Internet and pioneered reverse osmosis. And more than 100 companies have been created based on technology developed at UCLA.

  • richardmitnick 1:29 pm on July 18, 2017 Permalink | Reply
    Tags: , Brain Studies, ,   

    From Yale: “Predicting Human Behavior Using the Brain’s Unique Signature” 

    Yale University bloc

    Yale University

    July 4, 2017
    Joshua Mathew


    Following centuries of curiosity and uncertainty about the human brain, a recent neuroimaging study [Nature Protocols] will provide us with a way to study the live human brain non-invasively. Prior to the advent of neuroimaging, neuroscientists relied solely on post-mortem, or after death, autopsies to gain insight into the workings of the brain. By contrast, neuroimaging employs a variety of techniques to structurally or functionally image the brain without surgical intervention. A multidisciplinary team of Yale researchers has developed connectome-based predictive modeling (CPM), a computational model capable of predicting human behavior based on how one’s brain is wired.

    Some commonly used brain imaging techniques include computed tomography (CT) scanning, function magnetic resonance imaging (fMRI), and electroencephalography (EEG). fMRI measures brain activity by detecting changes in oxygenated blood flow through specific areas of the brain. Specifically, the ability to detect these changes by fMRI takes advantage of the difference in the magnetic properties of oxygenated and deoxygenated blood. CPM uses fMRI to observe activity in specific regions of the brain and subsequently derive brain connectivity data for use in predicting an individual’s behavior.

    The human connectome is a network of neural connections between different regions of the brain. These connections can be determined by identifying regions with simultaneous activity in the brain. The model developed by Yale researchers can characterize these neural connections more comprehensively by utilizing a connectivity matrix acquired from fMRI data. In a nutshell, each row in this matrix represents one of 300 regions of interest in the brain, and the data within each row describe the functional relationships between this region and the remaining 299 regions. Since humans have unique brain connectivity, and thus unique connectivity matrices, your brain’s functional connectivity can be used to predict various aspects of your behavior. CPM provides a way to extract that information and interpret it in meaningful ways.

    The predictive model is constructed by gathering connectivity matrices from many people, and is then used to predict behavioral traits of a new person based on their connectivity matrix. The predictive power of CPM has immense clinical significance. Matrix data can be used to predict and analyze whether an individual has paranoia, delusions, schizophrenic symptoms, and other conditions. Additionally, psychiatric disorders could be more effectively diagnosed with the help of CPM. The current diagnostic protocol for such disorders, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), has been met with mixed results since categorization of patients is based solely on identifiable symptoms. Implementing CPM for diagnostic purposes could allow for more thorough and scientific categorization that could ultimately improve the quality of mental health care.

    Although CPM has not yet reached the stage of clinical application, future directions for this research are boundless. According to Professor Todd Constable, senior author of the study, one such direction could include identifying circuits that function aberrantly in certain diseases. Mechanistically understanding these diseases would in turn contribute to the development of more personalized and targeted treatments. “CPM has already been demonstrated to predict one’s fluid intelligence and attentive performance,” said Constable, who believes that many other traits can be similarly predicted. Another question that has yet to be answered is how the brain’s connectivity changes over time with aging and development. In contrast to DNA, our genetic code which is relatively static in comparison, brain connectivity is much more dynamic. This dynamism further challenges our efforts to study the brain.

    The novelty of CPM lies in the fact that it is the first whole-brain connectome study of its kind. Up until recently, a major limitation for connectivity research had been an inadequate amount of individual connectome data from which to develop models for predicting complex behaviors. While previously only local brain connectivity could be studied given the amount of data available, the launch of the Human Connectome Project (HCP) in 2009 has supplied a mass of connectome data that allows whole-brain connectivity studies to be done for the first time. HCP is a large-scale effort to collect and share human connectome data in order to address fundamental questions about the functional connectivity of the human brain. To further this goal, the Yale researchers have published an algorithm for implementing CPM to build predictive models. This provides researchers around the world with the tools to contribute to the ongoing study of the human brain using predictive modeling.

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    Yale University Campus

    Yale University comprises three major academic components: Yale College (the undergraduate program), the Graduate School of Arts and Sciences, and the professional schools. In addition, Yale encompasses a wide array of centers and programs, libraries, museums, and administrative support offices. Approximately 11,250 students attend Yale.

Compose new post
Next post/Next comment
Previous post/Previous comment
Show/Hide comments
Go to top
Go to login
Show/Hide help
shift + esc
%d bloggers like this: