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  • 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

    1

    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 .

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    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.

     
  • richardmitnick 12:33 pm on July 15, 2017 Permalink | Reply
    Tags: , Brain Studies, , Iinspiration comes from advances in semiconductor manufacturing, , , Provide an alternate path for sight and sound to be delivered directly to the brain, Rice team developing flat microscope for the brain, Rice University, Will focus first on vision   

    From Rice: “Rice team developing flat microscope for the brain” 

    Rice U bloc

    Rice University

    July 12, 2017
    Mike Williams

    1
    Rice University engineers have built a lab prototype of a flat microscope they are developing as part of DARPA’s Neural Engineering System Design project. The microscope will sit on the surface of the brain, where it will detect optical signals from neurons in the cortex. The goal is to provide an alternate path for sight and sound to be delivered directly to the brain. (Credit: Rice University)

    Rice University engineers are building a flat microscope, called FlatScope [TM], and developing software that can decode and trigger neurons on the surface of the brain.

    Their goal as part of a new government initiative is to provide an alternate path for sight and sound to be delivered directly to the brain.

    The project is part of a $65 million effort announced this week by the federal Defense Advanced Research Projects Agency (DARPA) to develop a high-resolution neural interface. Among many long-term goals, the Neural Engineering System Design (NESD) program hopes to compensate for a person’s loss of vision or hearing by delivering digital information directly to parts of the brain that can process it.

    Members of Rice’s Electrical and Computer Engineering Department will focus first on vision. They will receive $4 million over four years to develop an optical hardware and software interface. The optical interface will detect signals from modified neurons that generate light when they are active. The project is a collaboration with the Yale University-affiliated John B. Pierce Laboratory led by neuroscientist Vincent Pieribone.

    Current probes that monitor and deliver signals to neurons — for instance, to treat Parkinson’s disease or epilepsy — are extremely limited, according to the Rice team. “State-of-the-art systems have only 16 electrodes, and that creates a real practical limit on how well we can capture and represent information from the brain,” Rice engineer Jacob Robinson said.

    Robinson and Rice colleagues Richard Baraniuk, Ashok Veeraraghavan and Caleb Kemere are charged with developing a thin interface that can monitor and stimulate hundreds of thousands and perhaps millions of neurons in the cortex, the outermost layer of the brain.

    “The inspiration comes from advances in semiconductor manufacturing,” Robinson said. “We’re able to create extremely dense processors with billions of elements on a chip for the phone in your pocket. So why not apply these advances to neural interfaces?”

    Kemere said some teams participating in the multi-institution project are investigating devices with thousands of electrodes to address individual neurons. “We’re taking an all-optical approach where the microscope might be able to visualize a million neurons,” he said.

    That requires neurons to be visible. Pieribone’s Pierce Lab is gathering expertise in bioluminescence — think fireflies and glowing jellyfish — with the goal of programming neurons with proteins that release a photon when triggered. “The idea of manipulating cells to create light when there’s an electrical impulse is not extremely far-fetched in the sense that we are already using fluorescence to measure electrical activity,” Robinson said.

    The scope under development is a cousin to Rice’s FlatCam, developed by Baraniuk and Veeraraghavan to eliminate the need for bulky lenses in cameras. The new project would make FlatCam even flatter, small enough to sit between the skull and cortex without putting additional pressure on the brain, and with enough capacity to sense and deliver signals from perhaps millions of neurons to a computer.

    Alongside the hardware, Rice is modifying FlatCam algorithms to handle data from the brain interface.

    “The microscope we’re building captures three-dimensional images, so we’ll be able to see not only the surface but also to a certain depth below,” Veeraraghavan said. “At the moment we don’t know the limit, but we hope we can see 500 microns deep in tissue.”

    “That should get us to the dense layers of cortex where we think most of the computations are actually happening, where the neurons connect to each other,” Kemere said.

    A team at Columbia University is tackling another major challenge: The ability to wirelessly power and gather data from the interface.

    In its announcement, DARPA described its goals for the implantable package. “Part of the fundamental research challenge will be developing a deep understanding of how the brain processes hearing, speech and vision simultaneously with individual neuron-level precision and at a scale sufficient to represent detailed imagery and sound,” according to the agency. “The selected teams will apply insights into those biological processes to the development of strategies for interpreting neuronal activity quickly and with minimal power and computational resources.”

    “It’s amazing,” Kemere said. “Our team is working on three crazy challenges, and each one of them is pushing the boundaries. It’s really exciting. This particular DARPA project is fun because they didn’t just pick one science-fiction challenge: They decided to let it be DARPA-hard in multiple dimensions.”

    Baraniuk is the Victor E. Cameron Professor of Electrical and Computer Engineering. Robinson, Veeraraghavan and Kemere are assistant professors of electrical and computer engineering.

    See the full article here .

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    In his 1912 inaugural address, Rice University president Edgar Odell Lovett set forth an ambitious vision for a great research university in Houston, Texas; one dedicated to excellence across the range of human endeavor. With this bold beginning in mind, and with Rice’s centennial approaching, it is time to ask again what we aspire to in a dynamic and shrinking world in which education and the production of knowledge will play an even greater role. What shall our vision be for Rice as we prepare for its second century, and how ought we to advance over the next decade?

    This was the fundamental question posed in the Call to Conversation, a document released to the Rice community in summer 2005. The Call to Conversation asked us to reexamine many aspects of our enterprise, from our fundamental mission and aspirations to the manner in which we define and achieve excellence. It identified the pressures of a constantly changing and increasingly competitive landscape; it asked us to assess honestly Rice’s comparative strengths and weaknesses; and it called on us to define strategic priorities for the future, an effort that will be a focus of the next phase of this process.

     
  • richardmitnick 12:02 pm on July 10, 2017 Permalink | Reply
    Tags: Allen Discovery Center for Human Brain Evolution at Boston Children’s Hospital and Harvard Medical School, Brain Studies,   

    From HMS: “Decoding Brain Evolution” 

    Harvard University

    Harvard University

    Harvard Medical School

    Harvard Medical School

    July 6, 2017
    NANCY FLIESLER

    1
    Image: monsitj/Getty Images

    How did our distinctive brains evolve? What genetic changes, coupled with natural selection, gave us language? What allowed modern humans to form complex societies, pursue science, create art?

    While we have some understanding of the genes that differentiate us from other primates, that knowledge cannot fully explain human brain evolution. But with a $10 million grant to some of Boston’s most highly evolved minds in genetics, genomics, neuroscience and human evolution, some answers may emerge in the coming years.

    The Seattle-based Paul G. Allen Frontiers Group has announced the creation of an Allen Discovery Center for Human Brain Evolution at Boston Children’s Hospital and Harvard Medical School. It will be led by Christopher A. Walsh, the Bullard Professor of Pediatrics and Neurology at HMS and chief of the Division of Genetics and Genomics at Boston Children’s. Michael Greenberg, the Nathan Marsh Pusey Professor of Neurobiology and head of the Department of Neurobiology at HMS, and David Reich, professor of genetics at HMS, will co-lead the center.

    “Unraveling the mysteries of the human brain will propel our understanding of brain development, brain evolution and human behavior,” said George Q. Daley, dean of HMS. “It also will help us understand what makes us unique as a species.

    “The research conducted by these three remarkable scientists spans the gamut from molecule to organism to system and underscores the cross-pollination among basic, translational and clinical discovery as well as across neurobiology, genetics, evolutionary biology and neurology,” Daley said.

    The center’s agenda is a bold one: to catalogue the key genes required for human brain evolution, to analyze their roles in human behavior and cognition and to study their functions to discover evolutionary mechanisms.

    “To understand when and how our modern brains evolved, we need to take a multi-pronged approach that will reflect how evolution works in nature and identify how experience and environment affect the genes that gave rise to modern human behavior,” Walsh said.

    “The launch of this center is a wonderful opportunity for three laboratories that have been working independently to come together and study the genetic, molecular and evolutionary forces that have given rise to the spectacular capacities of the human brain,” said Greenberg.

    The funding “will allow us to use ancient DNA analysis to track changes in the frequency of genetic mutations over time, which will in turn illuminate our understanding of the nature of human adaptation,” added Reich.

    An evolving understanding

    We already know some basics of human brain evolution. First came the enlargement of the primate brain, culminating perhaps 2 million years ago with the emergence of our genus, Homo, and the use of crude stone tools and fire. Next came a tripling of brain size during the 500,000 years before Homo sapiens arose. Finally, just over 50,000 years ago, there was a great leap forward in human behavior, with archaeological evidence of more efficient manufacturing of stone tools and a rich aesthetic and spiritual life.

    What transpired genetically? Prior research has taken a piecemeal approach to occasional genes that have different structures in humans versus non-humans. For example, Walsh’s lab has identified several genes that regulate cerebral cortical size and patterning, some of them through the study of brain abnormalities. The lab recently found a gene involved in brain folding—thanks to a brain malformation called polymicrogyria—that may have enhanced our language ability.

    But such findings only scratch the surface of the cognitive, behavioral and cultural strides humans have made over the past 50,000 years. That’s a blink of the eye in evolutionary terms. What enabled us to invent money, develop agriculture, build factories, write symphonies, tell jokes?

    Rosetta Stone(s) to decode brain evolution

    The researchers think not one but multiple mechanisms of evolution helped form the modern human brain. Such mechanisms include:

    Gene addition, duplication or deletion
    Alteration in the protein-coding sequence of genes to create new or modified biochemical functions
    Changes in noncoding DNA sequences altering patterns of gene expression, allowing an existing gene to be “re-purposed”
    Polygenic changes (changes in many genes working together)

    Accordingly, the center’s research methods will include, in varying combinations:

    Sequencing of ancient DNA recovered from bones and teeth
    Genomic studies of large populations to identify regions that correlate with human traits
    Genetic studies to test functional effects of mutations in the evolutionarily important genomic sequences
    Functional studies in neurons to determine the roles of these evolutionarily important sequences in the brain

    No genetic stone unturned

    All these approaches will be supported by powerful computational data analysis—reaching across genomes, across populations, across hundreds of thousands of years.

    The project leaders summed it up: “This group will provide the most rigorous possible examination of how, when and where the unique features of the amazing human brain came about.”

    The $10 million grant will be distributed over four years, with the potential for $30 million over eight years.

    See the full article here .

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    Established in 1782, Harvard Medical School began with a handful of students and a faculty of three. The first classes were held in Harvard Hall in Cambridge, long before the school’s iconic quadrangle was built in Boston. With each passing decade, the school’s faculty and trainees amassed knowledge and influence, shaping medicine in the United States and beyond. Some community members—and their accomplishments—have assumed the status of legend. We invite you to access the following resources to explore Harvard Medical School’s rich history.

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    Harvard is the oldest institution of higher education in the United States, established in 1636 by vote of the Great and General Court of the Massachusetts Bay Colony. It was named after the College’s first benefactor, the young minister John Harvard of Charlestown, who upon his death in 1638 left his library and half his estate to the institution. A statue of John Harvard stands today in front of University Hall in Harvard Yard, and is perhaps the University’s best known landmark.

    Harvard University has 12 degree-granting Schools in addition to the Radcliffe Institute for Advanced Study. The University has grown from nine students with a single master to an enrollment of more than 20,000 degree candidates including undergraduate, graduate, and professional students. There are more than 360,000 living alumni in the U.S. and over 190 other countries.

     
  • richardmitnick 7:31 am on July 1, 2017 Permalink | Reply
    Tags: Brain Studies, , Neural networks, Peering into neural networks   

    From MIT: “Peering into neural networks” 

    MIT News

    MIT Widget

    MIT News

    June 29, 2017
    Larry Hardesty

    1
    Neural networks learn to perform computational tasks by analyzing large sets of training data. But once they’ve been trained, even their designers rarely have any idea what data elements they’re processing. Image: Christine Daniloff/MIT

    Neural networks, which learn to perform computational tasks by analyzing large sets of training data, are responsible for today’s best-performing artificial intelligence systems, from speech recognition systems, to automatic translators, to self-driving cars.

    But neural nets are black boxes. Once they’ve been trained, even their designers rarely have any idea what they’re doing — what data elements they’re processing and how.

    Two years ago, a team of computer-vision researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) described a method for peering into the black box of a neural net trained to identify visual scenes. The method provided some interesting insights, but it required data to be sent to human reviewers recruited through Amazon’s Mechanical Turk crowdsourcing service.

    At this year’s Computer Vision and Pattern Recognition conference, CSAIL researchers will present a fully automated version of the same system. Where the previous paper reported the analysis of one type of neural network trained to perform one task, the new paper reports the analysis of four types of neural networks trained to perform more than 20 tasks, including recognizing scenes and objects, colorizing grey images, and solving puzzles. Some of the new networks are so large that analyzing any one of them would have been cost-prohibitive under the old method.

    The researchers also conducted several sets of experiments on their networks that not only shed light on the nature of several computer-vision and computational-photography algorithms, but could also provide some evidence about the organization of the human brain.

    Neural networks are so called because they loosely resemble the human nervous system, with large numbers of fairly simple but densely connected information-processing “nodes.” Like neurons, a neural net’s nodes receive information signals from their neighbors and then either “fire” — emitting their own signals — or don’t. And as with neurons, the strength of a node’s firing response can vary.

    In both the new paper and the earlier one, the MIT researchers doctored neural networks trained to perform computer vision tasks so that they disclosed the strength with which individual nodes fired in response to different input images. Then they selected the 10 input images that provoked the strongest response from each node.

    In the earlier paper, the researchers sent the images to workers recruited through Mechanical Turk, who were asked to identify what the images had in common. In the new paper, they use a computer system instead.

    “We catalogued 1,100 visual concepts — things like the color green, or a swirly texture, or wood material, or a human face, or a bicycle wheel, or a snowy mountaintop,” says David Bau, an MIT graduate student in electrical engineering and computer science and one of the paper’s two first authors. “We drew on several data sets that other people had developed, and merged them into a broadly and densely labeled data set of visual concepts. It’s got many, many labels, and for each label we know which pixels in which image correspond to that label.”

    The paper’s other authors are Bolei Zhou, co-first author and fellow graduate student; Antonio Torralba, MIT professor of electrical engineering and computer science; Aude Oliva, CSAIL principal research scientist; and Aditya Khosla, who earned his PhD as a member of Torralba’s group and is now the chief technology officer of the medical-computing company PathAI.

    The researchers also knew which pixels of which images corresponded to a given network node’s strongest responses. Today’s neural nets are organized into layers. Data are fed into the lowest layer, which processes them and passes them to the next layer, and so on. With visual data, the input images are broken into small chunks, and each chunk is fed to a separate input node.

    For every strong response from a high-level node in one of their networks, the researchers could trace back the firing patterns that led to it, and thus identify the specific image pixels it was responding to. Because their system could frequently identify labels that corresponded to the precise pixel clusters that provoked a strong response from a given node, it could characterize the node’s behavior with great specificity.

    The researchers organized the visual concepts in their database into a hierarchy. Each level of the hierarchy incorporates concepts from the level below, beginning with colors and working upward through textures, materials, parts, objects, and scenes. Typically, lower layers of a neural network would fire in response to simpler visual properties — such as colors and textures — and higher layers would fire in response to more complex properties.

    But the hierarchy also allowed the researchers to quantify the emphasis that networks trained to perform different tasks placed on different visual properties. For instance, a network trained to colorize black-and-white images devoted a large majority of its nodes to recognizing textures. Another network, when trained to track objects across several frames of video, devoted a higher percentage of its nodes to scene recognition than it did when trained to recognize scenes; in that case, many of its nodes were in fact dedicated to object detection.

    One of the researchers’ experiments could conceivably shed light on a vexed question in neuroscience. Research involving human subjects with electrodes implanted in their brains to control severe neurological disorders has seemed to suggest that individual neurons in the brain fire in response to specific visual stimuli. This hypothesis, originally called the grandmother-neuron hypothesis, is more familiar to a recent generation of neuroscientists as the Jennifer-Aniston-neuron hypothesis, after the discovery that several neurological patients had neurons that appeared to respond only to depictions of particular Hollywood celebrities.

    Many neuroscientists dispute this interpretation. They argue that shifting constellations of neurons, rather than individual neurons, anchor sensory discriminations in the brain. Thus, the so-called Jennifer Aniston neuron is merely one of many neurons that collectively fire in response to images of Jennifer Aniston. And it’s probably part of many other constellations that fire in response to stimuli that haven’t been tested yet.

    Because their new analytic technique is fully automated, the MIT researchers were able to test whether something similar takes place in a neural network trained to recognize visual scenes. In addition to identifying individual network nodes that were tuned to particular visual concepts, they also considered randomly selected combinations of nodes. Combinations of nodes, however, picked out far fewer visual concepts than individual nodes did — roughly 80 percent fewer.

    “To my eye, this is suggesting that neural networks are actually trying to approximate getting a grandmother neuron,” Bau says. “They’re not trying to just smear the idea of grandmother all over the place. They’re trying to assign it to a neuron. It’s this interesting hint of this structure that most people don’t believe is that simple.”

    See the full article here .

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  • richardmitnick 10:36 am on June 28, 2017 Permalink | Reply
    Tags: , Brain Studies, channelrhodopsin-2, , , , Purkinje cells,   

    From U Washington: “Study shines light on brain cells that control movement” 

    U Washington

    University of Washington

    06.26.2017
    Michael McCarthy
    Media contact:
    Leila Gray
    206.685.0381

    1
    In this image of neurons in the cerebellum of the brain, the yellow cells are Purkinje cells in which the channelrhodopsin-2 gene is being produced. Horwitz Lab/UW Medicine

    UW Medicine researchers have developed a technique for inserting a gene into specific cell types in the adult brain in an animal model.

    Recent work shows that the approach can be used to alter the function of brain circuits and change behavior. The study appears in the journal Neuron in the NeuroResources section.

    Gregory Horwitz, associate professor of physiology and biophysics at the University of Washington School of Medicine in Seattle, led the research team. He said that the approach will allow scientists to better understand what roles select cell types play in the brain’s complex circuitry.

    Researchers hope that the approach might someday lead to developing treatments for conditions, such as epilepsy, that might be curable by activating a small group of cells

    “The brain is made up of a mix of many cell types performing different functions. One of the big challenges for neuroscience is finding ways to study the function of specific cell types selectively without affecting the function of other cell types nearby,” Horwitz said. “Our study shows it is possible to selectively target a specific cell type in an adult brain using this technique and affect behavior nearly instantly.”

    In their study, Horowitz and his colleagues at the Washington National Primate Research Center in Seattle inserted a gene into cells in the cerebellum, a small structure located at the back of the brain and tucked under the brain’s larger cerebrum.

    The cerebellum’s primary function is controlling motor movements. Disorders of the cerebellum generally lead to often disabling loss of coordination. Recent research suggests the cerebellum may also be important in learning and may be involved in such conditions as autism and schizophrenia.

    The cells the scientists selected to study are called Purkinje cells. These cells, named after their discoverer, Czech anatomist Jan Evangelista Purkinje, are some of the largest in the human brain. They typically make connections with hundreds of other brain cells.

    “The Purkinje cell is a mysterious cell,” said Horwitz. “It’s one of the biggest and most elaborate neurons and it processes signals from hundreds of thousands of other brain cells. We know it plays a critical role in movement and coordination. We just don’t know how.”

    The gene they inserted, called channelrhodopsin-2, encodes for a light-sensitive protein that inserts itself into the brain cell’s membrane. When exposed to light, it allows ions – tiny charged particles – to pass through the membrane. This triggers the brain cell to fire.

    The technique, called optogenetics, is commonly used to study brain function in mice. But in these studies, the gene must be introduced into the embryonic mouse cell.

    “This ‘transgenic’ approach has proved invaluable in the study of the brain,” Horwitz said. “But if we are someday going to use it to treat disease, we need to find a way to introduce the gene later in life, when most neurological disorders appear.”

    The challenge for his research team was how to introduce channelrhodopsin-2 into a specific cell type in an adult animal. To achieve this, they used a modified virus that carried the gene for channelrhodopsin-2 along with segment of DNA called a promoter. The promoter stimulates the cell to start expressing the gene and make the channelrhodopsin-2 membrane protein. To make sure the gene was expressed only by Purkinje cells, the researchers used a promoter that is strongly active in Purkinje cells, called L7/Pcp2.”

    In their paper, the researchers reported that by painlessly injecting the modified virus into a small area of the cerebellum of rhesus macaque monkeys, the channelrhodopsin-2 was taken up exclusively by the targeted Purkinje cells. The researchers then showed that when they exposed the treated cells to light through a fine optical fiber, they were able stimulate the cells to fire at different rates and affect the animals’ motor control.

    Horwitz said that it was the fact that Purkinje cells express L7/Pcp2 promoter at a higher rate than other cells that made them more likely to produce the channelrhodopsin-2 membrane protein.

    “This experiment demonstrates that you can engineer a viral vector with this specific promoter sequence and target a specific cell type,” he said. “The promoter is the magic. Next, we want to use other promoters to target other cell types involved in other types of behaviors.”

    Horwitz coauthors were: lead author Yasmine El-Shamayleh, a postdoctoral fellow; Yoshiko Kojima, an acting instructor; and Robijanto Soetedjo, a UW School of Medicine research associate professor of physiology and biophysics. All are researchers at the Washington National Primate Research Center.

    This study was funded by National Institutes of Health grants to the researchers; an NIH Office of Research Infrastructure Programs grant to the Washington National Primate Research Center, and a National Eye Institute Center Core Grant for Vision Research to the University of Washington School of Medicine.

    See the full article here .

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    The University of Washington is one of the world’s preeminent public universities. Our impact on individuals, on our region, and on the world is profound — whether we are launching young people into a boundless future or confronting the grand challenges of our time through undaunted research and scholarship. Ranked number 10 in the world in Shanghai Jiao Tong University rankings and educating more than 54,000 students annually, our students and faculty work together to turn ideas into impact and in the process transform lives and our world. For more about our impact on the world, every day.
    So what defines us —the students, faculty and community members at the University of Washington? Above all, it’s our belief in possibility and our unshakable optimism. It’s a connection to others, both near and far. It’s a hunger that pushes us to tackle challenges and pursue progress. It’s the conviction that together we can create a world of good. Join us on the journey.

     
  • richardmitnick 11:10 am on June 27, 2017 Permalink | Reply
    Tags: , Brain Studies, Develop effective interventions for age-related cognitive decline or even neurodegenerative diseases such as Alzheimer's, , Reduced capacity refers to what can happen in organ systems throughout the body when they are deprived of exercise, The link between exercise and the brain is a product of our evolutionary history and our past as hunter-gatherers, , UA Research: Brains Evolved to Need Exercise   

    From U Arizona- “UA Research: Brains Evolved to Need Exercise” 

    U Arizona bloc

    University of Arizona

    6.27.17
    Alexis Blue

    1
    No image caption or credit

    Exercise significantly benefits brain structure and function, likely because of how we evolved as physically active hunter-gatherers, according to a new model proposed by UA researchers.

    In a new article published in the journal Trends in Neurosciences, University of Arizona researchers suggest that the link between exercise and the brain is a product of our evolutionary history and our past as hunter-gatherers.

    UA anthropologist David Raichlen and UA psychologist Gene Alexander, who together run a research program on exercise and the brain, propose an “adaptive capacity model” for understanding, from an evolutionary neuroscience perspective, how physical activity impacts brain structure and function.

    Their argument: As humans transitioned from a relatively sedentary apelike existence to a more physically demanding hunter-gatherer lifestyle, starting around 2 million years ago, we began to engage in complex foraging tasks that were simultaneously physically and mentally demanding, and that may explain how physical activity and the brain came to be so connected.

    “We think our physiology evolved to respond to those increases in physical activity levels, and those physiological adaptations go from your bones and your muscles, apparently all the way to your brain,” said Raichlen, an associate professor in the UA School of Anthropology in the College of Social and Behavioral Sciences.

    “It’s very odd to think that moving your body should affect your brain in this way — that exercise should have some beneficial impact on brain structure and function — but if you start thinking about it from an evolutionary perspective, you can start to piece together why that system would adaptively respond to exercise challenges and stresses,” he said.

    Having this underlying understanding of the exercise-brain connection could help researchers come up with ways to enhance the benefits of exercise even further, and to develop effective interventions for age-related cognitive decline or even neurodegenerative diseases such as Alzheimer’s.

    Notably, the parts of the brain most taxed during a complex activity such as foraging — areas that play a key role in memory and executive functions such as problem solving and planning — are the same areas that seem to benefit from exercise in studies.

    “Foraging is an incredibly complex cognitive behavior,” Raichlen said. “You’re moving on a landscape, you’re using memory not only to know where to go but also to navigate your way back, you’re paying attention to your surroundings. You’re multitasking the entire time because you’re making decisions while you’re paying attention to the environment, while you are also monitoring your motor systems over complex terrain. Putting all that together creates a very complex multitasking effort.”

    The adaptive capacity model could help explain research findings such as those published by Raichlen and Alexander last year showing that runners’ brains appear to be more connected than brains of non-runners.

    The model also could help inform interventions for the cognitive decline that often accompanies aging — in a period in life when physical activity levels tend to decline as well.

    “What we’re proposing is, if you’re not sufficiently engaged in this kind of cognitively challenging aerobic activity, then this may be responsible for what we often see as healthy brain aging, where people start to show some diminished cognitive abilities,” said Alexander, a UA professor of psychology, psychiatry, neuroscience and physiological sciences. “So the natural aging process might really be part of a reduced capacity in response to not being engaged enough.”

    Reduced capacity refers to what can happen in organ systems throughout the body when they are deprived of exercise.

    “Our organ systems adapt to the stresses they undergo,” said Raichlen, an avid runner and expert on running. “For example, if you engage in exercise, your cardiovascular system has to adapt to expand capacity, be it through enlarging your heart or increasing your vasculature, and that takes energy. So if you’re not challenging it in that way — if you’re not engaging in aerobic exercise — to save energy, your body simply reduces that capacity.”

    In the case of the brain, if it is not being stressed enough it may begin to atrophy. This may be especially concerning, considering how much more sedentary humans’ lifestyles have become.

    “Our evolutionary history suggests that we are, fundamentally, cognitively engaged endurance athletes, and that if we don’t remain active we’re going to have this loss of capacity in response to that,” said Alexander, who studies brain aging and Alzheimer’s disease as a member of the UA’s Evelyn F. McKnight Brain Institute. “So there really may be a mismatch between our relatively sedentary lifestyles of today and how we evolved.”

    Alexander and Raichlen say future research should look at how different levels of exercise intensity, as well as different types of exercise, or exercise paired specifically with cognitive tasks, affect the brain.

    For example, exercising in a novel environment that poses a new mental challenge, may prove to be especially beneficial, Raichlen said.

    “Most of the research in this area puts people in a cognitively impoverished environment. They put people in a lab and have them run on a treadmill or exercise bike, and you don’t really have to do as much, so it’s possible that we’re missing something by not increasing novelty,” he said.

    Alexander and Raichlen say they hope the adaptive capacity model will help advance research on exercise and the brain.

    “This evolutionary neuroscience perspective is something that’s been generally lacking in the field,” Alexander said. “And we think this might be helpful to advance research and help develop some new specific hypotheses and ways to identify more universally effective interventions that could be helpful to everyone.”

    See the full article here .

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    U Arizona campus

    The University of Arizona (UA) is a place without limits-where teaching, research, service and innovation merge to improve lives in Arizona and beyond. We aren’t afraid to ask big questions, and find even better answers.

    In 1885, establishing Arizona’s first university in the middle of the Sonoran Desert was a bold move. But our founders were fearless, and we have never lost that spirit. To this day, we’re revolutionizing the fields of space sciences, optics, biosciences, medicine, arts and humanities, business, technology transfer and many others. Since it was founded, the UA has grown to cover more than 380 acres in central Tucson, a rich breeding ground for discovery.

    Where else in the world can you find an astronomical observatory mirror lab under a football stadium? An entire ecosystem under a glass dome? Visit our campus, just once, and you’ll quickly understand why the UA is a university unlike any other.

     
  • richardmitnick 3:30 pm on May 27, 2017 Permalink | Reply
    Tags: , Brain Studies, ,   

    From UCSD: “New brain mapping tool produces higher resolution data during brain surgery” 

    UC San Diego bloc

    UC San Diego

    May 24, 2017
    Liezel Labios
    Jacobs School of Engineering
    Phone: 858-246-1124
    llabios@ucsd.edu

    1
    The PEDOT:PSS electrode grid is a new brain mapping device that can be used during brain surgery. Credit: David Baillot/UC San Diego Jacobs School of Engineering

    Researchers have developed a new device to map the brain during surgery and distinguish between healthy and diseased tissues. The device provides higher resolution neural readings than existing tools used in the clinic and could enable doctors to perform safer, more precise brain surgeries.

    The device is an improved version of a clinical tool called an electrode grid, which is a plastic or silicone-based grid of electrodes that is placed directly on the surface of the brain during surgery to monitor the activity of large groups of neurons. Neurosurgeons use electrode grids to identify which areas of the brain are diseased in order to avoid damaging or removing healthy, functional tissue during operations. Despite their wide use, electrode grids have remained bulky and have not experienced any major advances over the last 20 years.

    The new electrode grid, developed by a team of researchers at the University of California San Diego and Massachusetts General Hospital, is about a thousand times thinner — 6 micrometers versus several millimeters thick — than clinical electrode grids. This allows it to conform better to the intricately curved surface of the brain and obtain better readings. The new electrode grid also contains a much higher density of electrodes — spaced 25 times closer than those in clinical electrode grids — enabling it to generate higher resolution recordings.

    “Our goal is to develop a tool that can obtain more reliable information from the surface of the brain,” said electrical engineering professor Shadi Dayeh, who co-led the study with neuroscience professor Eric Halgren and electrical engineering professor Vikash Gilja, all at UC San Diego. The project was funded by the Center for Brain Activity Mapping (CBAM) at UC San Diego and brought together experts from multiple fields, including neurosurgeons, neuroscientists, electrical engineers, materials scientists and experts in systems integration. Researchers published their work on May 12 in Advanced Functional Materials.

    “By providing higher resolution views of the human brain, this technology can improve clinical practices and could lead to high performance brain machine interfaces,” Gilja said.

    To make their high resolution electrode grid, researchers had to find a way to shrink the size of the electrodes to pack them closer together. But with metal electrodes, which are typically used to make these grids, there is a tradeoff — shrinking their size increases their electrical resistance, resulting in more noisy readings.

    To overcome this problem, the team switched out the metal electrodes with ones made of a conductive polymer called PEDOT:PSS. The material is transparent, thin and flexible. Using this material enabled researchers to make smaller electrodes without sacrificing electrochemical performance. It also enhanced the richness of the information measured from the surface of the brain.

    “These electrodes occupy minuscule volumes — imagine Saran wrap, but thinner. And we demonstrate that they can capture neural activity from the human brain at least as well as conventional electrodes that are orders of magnitude larger,” Gilja said.

    Researchers worked with neurosurgeons at Jacobs Medical Center at UC San Diego Health and Brigham Women’s Hospital in Boston to test their grid on four patients. The PEDOT:PSS electrode grid and a standard clinical electrode grid were compared side by side. In standard clinical recordings, the PEDOT:PSS electrode grid either performed similarly or slightly better than the standard electrode grid, recording with lower noise and higher resolution.

    “In order to introduce a new electrode grid for clinical use, we first need to show that the device can yield the same information as that used in the clinic. Then we can build upon that work to make an even better product that can improve patient care,” Dayeh said.

    In one test, the team performed background readings of a patient’s brain waves both while the patient was awake and unconscious. The PEDOT:PSS electrode grid produced similar readings as the standard clinical electrode grid. In another test, the team monitored the brain activity of a patient undergoing epilepsy surgery. Both electrode grids identified normal functioning areas of the brain versus where the seizures were happening. The main difference is that the PEDOT:PSS electrode grid produced more detailed and higher resolution readings than the clinical electrode grid.

    Other tests monitored the brain activity of patients performing cognitive tasks. Patients were either shown a particular word or a picture illustrating that word. The word was afterwards recited to the patients. In the readings from both the PEDOT:PSS and standard electrode grids, researchers could differentiate between when the patients were hearing the word versus when they were seeing it (or a picture). “This experiment shows we can resolve functional and cognitive activity from the surface of the brain using these electrodes,” Dayeh said.

    The team’s next steps are to make higher density electrode grids for improved resolution and biocompatibility tests to see how long they can stay in the body before they experience biofouling.

    Paper title: Development and Translation of PEDOT:PSS Microelectrodes for Intraoperative Monitoring, by Mehran Ganji*, Erik Kaestner*, John Hermiz*, Nick Rogers, Atsunori Tanaka, Daniel Cleary, Sang Heon Lee, Joseph Snider, Bob S. Carter, David Barba, Vikash Gilja, Eric Halgren and Shadi A. Dayeh at UC San Diego; Milan Halgren at Massachusetts General Hospital; Garth Rees Cosgrove and Sydney S. Cash at Brigham and Women’s Hospital, Boston, Massachusetts; and Ilke Uguz and George G. Malliaras at CMP-EMSE, Gardanne, France.

    *These authors contributed equally to this work.

    This work was supported by the Center for Brain Activity Mapping (CBAM) at UC San Diego. The authors acknowledge faculty start-up support from the Department of Electrical and Computer Engineering at UC San Diego. Partial support is also acknowledged from the National Science Foundation (grant no. ECCS-1351980), the University of California Multicampus Research Programs and Initiatives (UC MRPI, grant no. MR-15-328909), and the Office of Naval Research (grant no. N00014-13-1-0672). This work was performed in part at UC San Diego’s Nano3 nanofabrication cleanroom facility, part of the San Diego Nanotechnology Infrastructure, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the National Science Foundation.

    See the full article here .

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    UC San Diego Campus

    The University of California, San Diego (also referred to as UC San Diego or UCSD), is a public research university located in the La Jolla area of San Diego, California, in the United States.[12] The university occupies 2,141 acres (866 ha) near the coast of the Pacific Ocean with the main campus resting on approximately 1,152 acres (466 ha).[13] Established in 1960 near the pre-existing Scripps Institution of Oceanography, UC San Diego is the seventh oldest of the 10 University of California campuses and offers over 200 undergraduate and graduate degree programs, enrolling about 22,700 undergraduate and 6,300 graduate students. UC San Diego is one of America’s Public Ivy universities, which recognizes top public research universities in the United States. UC San Diego was ranked 8th among public universities and 37th among all universities in the United States, and rated the 18th Top World University by U.S. News & World Report ‘s 2015 rankings.

     
  • richardmitnick 1:49 pm on May 27, 2017 Permalink | Reply
    Tags: , Brain Studies, CLARITY,   

    From Stanford- “Karl Deisseroth: How we can better understand the brain’s circuitry” 

    Stanford University Name
    Stanford University

    July 19, 2016 [Finally brought it out to play.]
    Vignesh Ramachandran

    1
    A new technique helps researchers study the connections between neurons. iStock/from2015

    How much about the human brain do we really know? Not enough, says bioengineer Karl Deisseroth. A milky, opaque tissue coats much of the organ, preventing researchers from seeing the all-important connections between neurons. So Deisseroth’s team developed a way to remove this cloud by chemically dissolving the opaque fatty tissue in a dead brain and replacing it with a transparent hydrogel.

    This technique, called CLARITY gives researchers an unprecedented ability to study the neurostructure and circuitry of the brain. “Our goal is to understand the system in its entirety but also at high resolution,” says Deisseroth, the D.H. Chen Professor in Stanford School of Medicine and a professor of bioengineering and of psychiatry and behavioral sciences.


    The accompanying video, produced by Miles O’Brien and Ann Kellan of Science Nation, and published here with permission by Stanford Engineering, shows how CLARITY could lead to new treatments for conditions like depression, and pave the way for medical advances outside the brain such as offering researchers new ways to understand electrical pathways in the heart or learn why damaged fibers in the spinal cord cause pain.

    See the full article here .

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    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 7:58 am on May 27, 2017 Permalink | Reply
    Tags: , Brain Studies,   

    From Wash U: “Researchers to model brain’s memory network” 

    Wash U Bloc

    Washington University in St.Louis

    May 11, 2017
    Gerry Everding
    gerry_everding@wustl.edu

    1
    No image caption or credit.

    Washington University in St. Louis brain scholars will join teams from four other universities in a five-year, $7.5 million research project that aims to build and test the most comprehensive model yet of how people understand and remember events.

    “The ultimate goal is a system that can watch the same movies and read the same stories that we show to our human subjects and make detailed, moment-by-moment predictions about what is going on in their minds and brains,” said Jeff Zacks, lead investigator for the Washington University team and professor of psychological and brain sciences in Arts & Sciences.

    “If we are successful, this research will enable computer systems that are better collaborators and better teachers, and it will reveal fundamental mechanisms of how people understand their complex everyday worlds.”

    See the full article here .

    Please help promote STEM in your local schools.

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    Wash U campus

    Washington University’s mission is to discover and disseminate knowledge, and protect the freedom of inquiry through research, teaching, and learning.

    Washington University creates an environment to encourage and support an ethos of wide-ranging exploration. Washington University’s faculty and staff strive to enhance the lives and livelihoods of students, the people of the greater St. Louis community, the country, and the world.

     
  • richardmitnick 2:08 pm on May 22, 2017 Permalink | Reply
    Tags: , Brain Studies, , In ‘Enormous Success’ Scientists Tie 52 Genes to Human Intelligence,   

    From NYT: “In ‘Enormous Success,’ Scientists Tie 52 Genes to Human Intelligence” 

    New York Times

    The New York Times

    MAY 22, 2017
    Carl Zimmer

    1
    Blood samples from some participants in a new study of genes linked to intelligence were held at the U.K. Biobank, above. Credit Wellcome Trust

    In a significant advance in the study of mental ability, a team of European and American scientists announced on Monday that they had identified 52 genes linked to intelligence in nearly 80,000 people.

    These genes do not determine intelligence, however. Their combined influence is minuscule, the researchers said [Nature Genetics], suggesting that thousands more are likely to be involved and still await discovery. Just as important, intelligence is profoundly shaped by the environment.

    Still, the findings could make it possible to begin new experiments into the biological basis of reasoning and problem-solving, experts said. They could even help researchers determine which interventions would be most effective for children struggling to learn.

    “This represents an enormous success,” said Paige Harden, a psychologist at the University of Texas, who was not involved in the study.

    For over a century, psychologists have studied intelligence by asking people questions. Their exams have evolved into batteries of tests, each probing a different mental ability, such as verbal reasoning or memorization.

    In a typical test, the tasks might include imagining an object rotating, picking out a shape to complete a figure, and then pressing a button as fast as possible whenever a particular type of word appears.

    Each test-taker may get varying scores for different abilities. But over all, these scores tend to hang together — people who score low on one measure tend to score low on the others, and vice versa. Psychologists sometimes refer to this similarity as general intelligence.

    It’s still not clear what in the brain accounts for intelligence. Neuroscientists have compared the brains of people with high and low test scores for clues, and they’ve found a few.

    Brain size explains a small part of the variation, for example, although there are plenty of people with small brains who score higher than others with bigger brains.

    Other studies hint that intelligence has something to do with how efficiently a brain can send signals from one region to another.

    Danielle Posthuma, a geneticist at Vrije University Amsterdam and senior author of the new paper, first became interested in the study of intelligence in the 1990s. “I’ve always been intrigued by how it works,” she said. “Is it a matter of connections in the brain, or neurotransmitters that aren’t sufficient?”

    Dr. Posthuma wanted to find the genes that influence intelligence. She started by studying identical twins who share the same DNA. Identical twins tended to have more similar intelligence test scores than fraternal twins, she and her colleagues found.

    Hundreds of other studies have come to the same conclusion, showing a clear genetic influence on intelligence [Nature Genetics]. But that doesn’t mean that intelligence is determined by genes alone.

    Our environment exerts its own effects, only some of which scientists understand well. Lead in drinking water, for instance, can drag down test scores. In places where food doesn’t contain iodine, giving supplements to children can raise scores.

    Advances in DNA sequencing technology raised the possibility that researchers could find individual genes underlying differences in intelligence test scores. Some candidates were identified in small populations, but their effects did not reappear in studies on larger groups.

    So scientists turned to what’s now called the genome-wide association study: They sequence bits of genetic material scattered across the DNA of many unrelated people, then look to see whether people who share a particular condition — say, a high intelligence test score — also share the same genetic marker.

    In 2014, Dr. Posthuma was part of a large-scale study of over 150,000 people that revealed 108 genes linked to schizophrenia. But she and her colleagues had less luck with intelligence, which has proved a hard nut to crack for a few reasons.

    Standard intelligence tests can take a long time to complete, making it hard to gather results on huge numbers of people. Scientists can try combining smaller studies, but they often have to merge different tests together, potentially masking the effects of genes.

    As a result, the first generation of genome-wide association studies on intelligence failed to find any genes. Later studies managed to turn up promising results, but when researchers turned to other groups of people, the effect of the genes again disappeared.

    But in the past couple of years, larger studies relying on new statistical methods finally have produced compelling evidence that particular genes really are involved in shaping human intelligence.

    “There’s a huge amount of real innovation going on,” said Stuart J. Ritchie, a geneticist at the University of Edinburgh who was not involved in the new study.

    Dr. Posthuma and other experts decided to merge data from 13 earlier studies, forming a vast database of genetic markers and intelligence test scores. After so many years of frustration, Dr. Posthuma was pessimistic it would work.

    “I thought, ‘Of course we’re not going to find anything,’” she said.

    She was wrong. To her surprise, 52 genes emerged with firm links to intelligence. A dozen had turned up in earlier studies, but 40 were entirely new.

    But all of these genes together account for just a small percentage of the variation in intelligence test scores, the researchers found; each variant raises or lowers I.Q. by only a small fraction of a point.

    “It means there’s a long way to go, and there are going to be a lot of other genes that are going to be important,” Dr. Posthuma said.

    Christopher F. Chabris, a co-author of the new study at Geisinger Health System in Danville, Pa., was optimistic that many of those missing genes would come to light, thanks to even larger studies involving hundreds of thousands, perhaps millions, of people.

    “It’s just like astronomy getting better with bigger telescopes,” he said.

    In the new study, Dr. Posthuma and her colleagues limited their research to people of European descent because that raised the odds of finding common genetic variants linked to intelligence.

    But other gene studies have shown that variants in one population can fail to predict what people are like in other populations. Different variants turn out to be important in different groups, and this may well be the case with intelligence.

    “If you try to predict height using the genes we’ve identified in Europeans in Africans, you’d predict all Africans are five inches shorter than Europeans, which isn’t true,” Dr. Posthuma said.

    Studies like the one published today don’t mean that intelligence is fixed by our genes, experts noted. “If we understand the biology of something, that doesn’t mean we’re putting it down to determinism,” Dr. Ritchie said.

    As an analogy, he noted that nearsightedness is strongly influenced by genes. But we can change the environment — in the form of eyeglasses — to improve people’s eyesight.

    Dr. Harden predicted that an emerging understanding of the genetics of intelligence would make it possible to find better ways to help children develop intellectually. Knowing people’s genetic variations would help scientists measure how effective different strategies are.

    Still, Dr. Harden said, we don’t have to wait for such studies to change people’s environments for the better. “We know that lead harms children’s intellectual abilities,” she said. “There’s low-hanging policy fruit here.”

    For her part, Dr. Posthuma wants to make sense of the 52 genes she and her colleagues discovered. There are intriguing overlaps between their influence on intelligence and on other traits.

    The genetic variants that raise intelligence also tend to pop up more frequently in people who have never smoked. Some of them also are found more often in people who take up smoking but quit successfully.

    As for what the genes actually do, Dr. Posthuma can’t say. Four of them are known to control the development of cells, for example, and three do an assortment of things inside neurons.

    To understand what makes these genes special, scientists may need to run experiments on brain cells. One possibility would be to take cells from people with variants that predict high and low intelligence.

    She and her colleagues might coax them to develop into neurons, which could then grow into “mini-brains” — clusters of neurons that exchange signals in the laboratory. Researchers could then see if their genetic differences made them behave differently.

    “We can’t do it overnight,” Dr. Posthuma said, “but it’s something I hope to be able to do in the future.”

    See the full article here .

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