Links for Keyword: Brain imaging

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Maria Temming Getting robots to do what we want would be a lot easier if they could read our minds. That sci-fi dream might not be so far off. With a new robot control system, a human can stop a bot from making a mistake and get the machine back on track using brain waves and simple hand gestures. People who oversee robots in factories, homes or hospitals could use this setup, to be presented at the Robotics: Science and Systems conference on June 28, to ensure bots operate safely and efficiently. Electrodes worn on the head and forearm allow a person to control the robot. The head-worn electrodes detect electrical signals called error-related potentials — which people’s brains unconsciously generate when they see someone goof up — and send an alert to the robot. When the robot receives an error signal, it stops what it is doing. The person can then make hand gestures — detected by arm-worn electrodes that monitor electrical muscle signals — to show the bot what it should do instead. MIT roboticist Daniela Rus and colleagues tested the system with seven volunteers. Each user supervised a robot that moved a drill toward one of three possible targets, each marked by an LED bulb, on a mock airplane fuselage. Whenever the robot zeroed in on the wrong target, the user’s mental error-alert halted the bot. And when the user flicked his or her wrist left or right to redirect the robot, the machine moved toward the proper target. In more than 1,000 trials, the robot initially aimed for the correct target about 70 percent of the time, and with human intervention chose the right target more than 97 percent of the time. The team plans to build a system version that recognizes a wider variety of user movements. That way, “you can gesture how the robot should move, and your motion can be more fluidly interpreted,” says study coauthor Joseph DelPreto, also a roboticist at MIT. |© Society for Science & the Public 2000 - 2018

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior; Chapter 17: Learning and Memory
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System; Chapter 13: Memory, Learning, and Development
Link ID: 25111 - Posted: 06.20.2018

By Matt Warren Scientists regularly comb through 3D data, from medical images to maps of the moon, yet they are often stuck using flat computer screens that can’t fully represent 3D data sets. Now, researchers have developed a method of 3D printing that lets scientists produce stunning, high-definition 3D copies of their data. Conventional 3D-printing converts data into a computer model made up of tiny, connected triangles. But this process can create awkward images: The fine lines of the brain’s white matter, for example, show up as bulky tubes. Conventional printing also has problems creating objects where solid parts (or data points) are separated by empty space. The new process is far more direct. Instead of transforming into a computer model, the data set is sliced up into thousands of horizontal images, each consisting of hundreds of thousands of voxels, or 3D pixels. Each voxel is printed with droplets of colored resin hardened by ultraviolet light. Different colors can be combined to create new ones, and transparent resin is used to represent empty space. Each layer is printed, one on top of another, to gradually build up a 3D structure. So far, the researchers have used the voxel printing process to produce high-definition models of brain scans, topographical maps, and laser-scanned statues. And although it may take some time to get there, the team sees a day when anyone will be able to print off a copy of their data at the press of a button, from archaeologists reproducing important artifacts for conservation to doctors creating models of body parts to plan surgical procedures. Posted in: © 2018 American Association for the Advancement of Science.

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 25050 - Posted: 06.02.2018

By Dennis Normile SHANGHAI, CHINA—The nascent China Brain Project took another step toward reality last week with the launch of the Shanghai Research Center for Brain Science and Brain-Inspired Intelligence. The new center and its Beijing counterpart, launched 2 months ago, are expected to become part of an ambitious national effort to bring China to the forefront of neuroscience. But details of that 15-year project—expected to rival similar U.S. and EU efforts in scale and ambition—are still being worked out, 2 years after the government made it a priority. Preparation for the national effort “was taking quite a long time,” says Zhang Xu, a neuroscientist and executive director of the new center here. So Beijing and Shanghai got started on their own plans, he says. China’s growing research prowess and an increasing societal interest in neuroscience—triggered in part by an aging population—as well as commercial opportunities and government support are all coming together to make this “a good time for China’s brain science efforts,” Zhang says. Government planners called for brain research to be a key science and technology project in the nation’s 13th Five-Year Plan, adopted in spring 2016. The effort would have three main pillars, according to a November 2016 Neuron paper from a group that included Poo Mu-ming, director of Shanghai’s Institute of Neuroscience (ION), part of the Chinese Academy of Sciences (CAS). It would focus on basic research on neural mechanisms underlying cognition, translational studies of neurological diseases with an emphasis on early diagnosis and intervention, and brain simulations to advance artificial intelligence and robotics. Support under the 5-year plan was just the start of a 15-year program, the group wrote. © 2018 American Association for the Advancement of Science.

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 25013 - Posted: 05.23.2018

In 2007, I spent the summer before my junior year of college removing little bits of brain from rats, growing them in tiny plastic dishes, and poring over the neurons in each one. For three months, I spent three or four hours a day, five or six days a week, in a small room, peering through a microscope and snapping photos of the brain cells. The room was pitch black, save for the green glow emitted by the neurons. I was looking to see whether a certain growth factor could protect the neurons from degenerating the way they do in patients with Parkinson's disease. This kind of work, which is common in neuroscience research, requires time and a borderline pathological attention to detail. Which is precisely why my PI trained me, a lowly undergrad, to do it—just as, decades earlier, someone had trained him. Now, researchers think they can train machines to do that grunt work. In a study described in the latest issue of the journal Cell, scientists led by Gladstone Institutes and UC San Francisco neuroscientist Steven Finkbeiner collaborated with researchers at Google to train a machine learning algorithm to analyze neuronal cells in culture. The researchers used a method called deep learning, the machine learning technique driving advancements not just at Google, but Amazon, Facebook, Microsoft. You know, the usual suspects. It relies on pattern recognition: Feed the system enough training data—whether it's pictures of animals, moves from expert players of the board game Go, or photographs of cultured brain cells—and it can learn to identify cats, trounce the world's best board-game players, or suss out the morphological features of neurons.

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior; Chapter 17: Learning and Memory
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System; Chapter 13: Memory, Learning, and Development
Link ID: 24862 - Posted: 04.13.2018

In a small room tucked away at the University of Toronto, Professor Dan Nemrodov is pulling thoughts right out of people's brains. He straps a hat with electrodes on someone's head and then shows them pictures of faces. By reading brain activity with an electroencephalography (EEG) machine, he's then able to reconstruct faces with almost perfect accuracy. Student participants wearing the cap look at a collection of faces for two hours. At the same time, the EEG software recognizes patterns relating to certain facial features found in the photos. Machine-learning algorithms are then used to recreate the images based on the EEG data, in some cases within 98-per-cent accuracy. Nemrodov and his colleague, Professor Adrian Nestor say this is a big thing. "Ultimately we are involved in a form of mind reading," he says. The technology has huge ramifications for medicine, law, government and business. But the ethical questions are just as huge. Here are some key questions: What can be the benefits of this research? If developed, it can help patients with serious neurological damage. People who are incapacitated to the point that they cannot express themselves or ask a question. According to clinical ethicist Prof. Kerry Bowman and his students at the University of Toronto, this technology can get inside someone's mind and provide a link of communication. It may give that person a chance to exercise their autonomy, especially in regard to informed consent to either continue treatment or stop. ©2018 CBC/Radio-Canada.

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24810 - Posted: 04.02.2018

By Liz Tormes When I first started working as a photo researcher for Scientific American MIND in 2013, a large part of my day was spent looking at brains. Lots of them. They appeared on my computer screen in various forms—from black-and-white CT scans featured in dense journals to sad-looking, grey brains sitting on the bottom of glass laboratory jars. At times they were boring, and often they could be downright disturbing. But every now and then I would come across a beautiful 3D image of strange, rainbow-colored pathways in various formations that looked like nothing I had ever seen before. I was sure it had been miscategorized somehow—no way was I looking at a brain! Through my work I have encountered countless images of multi-colored Brainbows, prismatic Diffusion Tensor Imaging (DTI), and even tiny and intricate neon mini-brains grown from actual stem cells in labs. Increasingly I have found myself dazzled, not just by the pictures themselves, but by the scientific and technological advances that have made this type of imaging possible in only the past few years. It was through my photo research that I happened upon the Netherlands Institute for Neuroscience’s (NIN) annual Art of Neuroscience contest. This exciting opportunity for neurologists, fine artists, videographers and illustrators, whose work is inspired by human and animal brains, was something I wanted to share with our readers. © 2018 Scientific American

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24803 - Posted: 03.31.2018

By Simon Makin Neuroscientists today know a lot about how individual neurons operate but remarkably little about how large numbers of them work together to produce thoughts, feelings and behavior. What is needed is a wiring diagram for the brain—known as a connectome—to identify the circuits that underlie brain functions. The challenge is dizzying: There are around 100 billion neurons in the human brain, which can each make thousands of connections, or synapses, making potentially hundreds of trillions of connections. So far, researchers have typically used microscopes to visualize neural connections, but this is laborious and expensive work. Now in a paper published March 28 in Nature, an innovative brain-mapping technique developed at Cold Spring Harbor Laboratory (CSHL) has been used to trace the connections emanating from hundreds of neurons in the main visual area of the mouse cortex, the brain’s outer layer. The technique, which exploits the advancing speed and plummeting cost of genetic sequencing, is more efficient than current methods, allowing the team to produce a more detailed picture than previously possible at unprecedented speed. Once the technology matures it could be used to provide clues to the nature of neuro-developmental disorders such as autism that are thought to involve differences in brain wiring. The team, led by Anthony Zador at CSHL and neuroscientist Thomas Mrsic-Flogel of the University of Basel in Switzerland, verified their method by comparing it with a previous gold-standard means of identifying connections among nerve cells—a technique called fluorescent single neuron tracing. This involves introducing into cells genes that produce proteins that fluoresce with a greenish glow, so they and their axons (neurons’ output wires) can be visualized with light microscopy. © 2018 Scientific American

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior; Chapter 7: Life-Span Development of the Brain and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System; Chapter 13: Memory, Learning, and Development
Link ID: 24802 - Posted: 03.30.2018

Juliette Jowit The world’s first brain scanner that can be worn as people move around has been invented, by a team who hope the contraption can help children with neurological and mental disorders and reveal how the brain handles social situations. The new scalp caps – made on 3D printers – fit closely to the head, so can record the electromagnetic field produced by electrical currents between brain cells in much finer detail than previously. This design means the scanner can work in ways never possible before: subjects can move about, for example, and even play games with the equipment on, while medics can use it on groups such as babies, children and those with illnesses which cause them to move involuntarily. “This has the potential to revolutionise the brain imaging field, and transform the scientific and clinical questions that can be addressed with human brain imaging,” said Prof Gareth Barnes at University College London, one of three partners in the project. The other two are the University of Nottingham and the Wellcome Trust. The brain imaging technique known as magnetoencephalography, or MEG, has been helping scientists for decades, but in many cases has involved using huge contraptions that look like vintage hair salon driers. The scanners operated further from the head than the new devices, reducing the detail they recorded, and users had to remain incredibly still. © 2018 Guardian News and Media Limited

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24780 - Posted: 03.22.2018

By Ruth Williams When optogenetics burst onto the scene a little over a decade ago, it added a powerful tool to neuroscientists’ arsenal. Instead of merely correlating recorded brain activity with behaviors, researchers could control the cell types of their choosing to produce specific outcomes. Light-sensitive ion channels (opsins) inserted into the cells allow neuronal activity to be controlled by the flick of a switch. Nevertheless, MIT’s Edward Boyden says more precision is needed. Previous approaches achieved temporal resolution in the tens of milliseconds, making them a somewhat blunt instrument for controlling neurons’ millisecond-fast firings. In addition, most optogenetics experiments have involved “activation or silencing of a whole set of neurons,” he says. “But the problem is the brain doesn’t work that way.” When a cell is performing a given function—initiating a muscle movement, recalling a memory—“neighboring neurons can be doing completely different things,” Boyden explains. “So there is a quest now to do single-cell optogenetics.” Illumination techniques such as two-photon excitation with computer-generated holography (a way to precisely sculpt light in 3D) allow light to be focused tightly enough to hit one cell. But even so, Boyden says, if the targeted cell body lies close to the axons or dendrites of neighboring opsin-expressing cells, those will be activated too. © 1986-2018 The Scientist

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior; Chapter 3: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System; Chapter 3: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals
Link ID: 24732 - Posted: 03.08.2018

By Diana Kwon When optogenetics debuted over a decade ago, it quickly became the method of choice for many neuroscientists. By using light to selectively control ion channels on neurons in living animal brains, researchers could see how manipulating specific neural circuits altered behavior in real time. Since then, scientists have used the technique to study brain circuity and function across a variety of species, from fruit flies to monkeys—the method is even being tested in a clinical trial to restore vision in patients with a rare genetic disorder. Today (February 8) in Science, researchers report successfully conducting optogenetics experiments using injected nanoparticles in mice, inching the field closer to a noninvasive method of stimulating the brain with light that could one day have therapeutic uses. “Optogenetics revolutionized how we all do experimental neuroscience in terms of exploring circuits,” says Thomas McHugh, a neuroscientist at the RIKEN Brain Science Institute in Japan. However, this technique currently requires a permanently implanted fiber—so over the last few years, researchers have started to develop ways to stimulate the brain in less invasive ways. A number of groups devised such techniques using magnetic fields, electric currents, and sound. McHugh and his colleagues decided to try another approach: They chose near-infrared light, which can more easily penetrate tissue than the blue-green light typically used for optogenetics. “What we saw as an advantage was a kind of chemistry-based approach in which we can harness the power of near-infrared light to penetrate tissue, but still use this existing toolbox that's been developed over the last decade of optogenetic channels that respond to visible light,” McHugh says. © 1986-2018 The Scientist

Related chapters from BN8e: Chapter 3: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals; Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 3: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals; Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24637 - Posted: 02.09.2018

By Giorgia Guglielmi ENIGMA, the world’s largest brain mapping project, was “born out of frustration,” says neuroscientist Paul Thompson of the University of Southern California in Los Angeles. In 2009, he and geneticist Nicholas Martin of the Queensland Institute of Medical Research in Brisbane, Australia, were chafing at the limits of brain imaging studies. The cost of MRI scans limited most efforts to a few dozen subjects—too few to draw robust connections about how brain structure is linked to genetic variations and disease. The answer, they realized over a meal at a Los Angeles shopping mall, was to pool images and genetic data from multiple studies across the world. After a slow start, the consortium has brought together nearly 900 researchers across 39 countries to analyze brain scans and genetic data on more than 30,000 people. In an accelerating series of publications, ENIGMA’s crowdsourcing approach is opening windows on how genes and structure relate in the normal brain—and in disease. This week, for example, an ENIGMA study published in the journal Brain compared scans from nearly 4000 people across Europe, the Americas, Asia, and Australia to pinpoint unexpected brain abnormalities associated with common epilepsies. ENIGMA is “an outstanding effort. We should all be doing more of this,” says Mohammed Milad, a neuroscientist at the University of Illinois in Chicago who is not a member of the consortium. ENIGMA’s founders crafted the consortium’s name—Enhancing NeuroImaging Genetics through Meta-Analysis—so that its acronym would honor U.K. mathematician Alan Turing’s code-breaking effort targeting Germany’s Enigma cipher machines during World War II. Like Turing’s project, ENIGMA aims to crack a mystery. Small brain-scanning studies of twins or close relatives done in the 2000s showed that differences in some cognitive and structural brain measures have a genetic basis. © 2018 American Association for the Advancement of Science.

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior; Chapter 7: Life-Span Development of the Brain and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System; Chapter 13: Memory, Learning, and Development
Link ID: 24560 - Posted: 01.24.2018

Harriet Dempsey-Jones Nobody really believes that the shape of our heads are a window into our personalities anymore. This idea, known as “phrenonolgy”, was developed by the German physician Franz Joseph Gall in 1796 and was hugely popular in the 19th century. Today it is often remembered for its dark history – being misused in its later days to back racist and sexist stereoptypes, and its links with Nazi “eugenics”. But despite the fact that it has fallen into disrepute, phrenology as a science has never really been subjected to rigorous, neuroscientific testing. That is, until now. Researchers at the University of Oxford have hacked their own brain scanning software to explore – for the first time – whether there truly is any correspondence between the bumps and contours of your head and aspects of your personality. The results have recently been published in an open science archive, but have also been submitted to the journal Cortex. But why did phrenologists think that bumps on your head might be so informative? Their enigmatic claims were based around a few general principles. Phrenologists believed the brain was comprised of separate “organs” responsible for different aspects of the mind, such as for self-esteem, cautiousness and benevolence. They also thought of the brain like a muscle – the more you used a particular organ the more it would grow in size (hypertrophy), and less used faculties would shrink. The skull would then mould to accommodate these peaks and troughs in the brain’s surface – providing an indirect reflection of the brain, and thus, the dominant features of an person’s character. © 2010–2018, The Conversation US, Inc.

Related chapters from BN8e: Chapter 1: Biological Psychology: Scope and Outlook; Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 1: An Introduction to Brain and Behavior; Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24554 - Posted: 01.23.2018

By DENISE GRADY One blue surgical drape at a time, the patient disappeared, until all that showed was a triangle of her shaved scalp. “Ten seconds of quiet in the room, please,” said Dr. David J. Langer, the chairman of neurosurgery at Lenox Hill Hospital in Manhattan, part of Northwell Health. Silence fell, until he said, “O.K., I’ll take the scissors.” His patient, Anita Roy, 66, had impaired blood flow to the left side of her brain, and Dr. Langer was about to perform bypass surgery on slender, delicate arteries to restore the circulation and prevent a stroke. The operating room was dark, and everyone was wearing 3-D glasses. Lenox Hill is the first hospital in the United States to buy a device known as a videomicroscope, which turns neurosurgery into an immersive and sometimes dizzying expedition into the human brain. Enlarged on a 55-inch monitor, the stubble on Ms. Roy’s shaved scalp spiked up like rebar. The scissors and scalpel seemed big as hockey sticks, and popped out of the screen so vividly that observers felt an urge to duck. “This is like landing on the moon,” said a neurosurgeon who was visiting to watch and learn. The equipment produces magnified, high-resolution, three-dimensional digital images of surgical sites, and lets everyone in the room see exactly what the surgeon is seeing. The videomicroscope has a unique ability to capture “the brilliance and the beauty of the neurosurgical anatomy,” Dr. Langer said. He and other surgeons who have tested it predict it will change the way many brain and spine operations are performed and taught. “The first time I used it, I told students that this gives them an understanding of why I went into neurosurgery in the first place,” Dr. Langer said. © 2018 The New York Times Company

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24504 - Posted: 01.09.2018

Tina Hesman Saey In movies, exploring the body up close often involves shrinking to microscopic sizes and taking harrowing rides through the blood. Thanks to a new virtual model, you can journey through a three-dimensional brain. No shrink ray required. The Society for Neuroscience and other organizations have long sponsored the website BrainFacts.org, which has basic information about how the human brain functions. Recently, the site launched an interactive 3-D brain. A translucent, light pink brain initially rotates in the middle of the screen. With a click of a mouse or a tap of a finger on a mobile device, you can highlight and isolate different parts of the organ. A brief text box then pops up to provide a structure’s name and details about the structure’s function. For instance, the globus pallidus — dual almond-shaped structures deep in the brain — puts a brake on muscle contractions to keep movements smooth. Some blurbs tell how a structure got its name or how researchers figured out what it does. Scientists, for example, have learned a lot about brain function by studying people who have localized brain damage. But the precuneus, a region in the cerebral cortex along the brain’s midline, isn’t usually damaged by strokes or head injuries, so scientists weren’t sure what the region did. Modern brain-imaging techniques that track blood flow and cell activity indicate the precuneus is involved in imagination, self-consciousness and reflecting on memories. |© Society for Science & the Public 2000 - 2018

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24502 - Posted: 01.09.2018

by Emilie Reas Functional MRI (fMRI) is one of the most celebrated tools in neuroscience. Because of their unique ability to peer directly into the living brain while an organism thinks, feels and behaves, fMRI studies are often devoted disproportionate media attention, replete with flashy headlines and often grandiose claims. However, the technique has come under a fair amount of criticism from researchers questioning the validity of the statistical methods used to analyze fMRI data, and hence the reliability of fMRI findings. Can we trust those flashy headlines claiming that “scientists have discovered the area of the brain,” or are the masses of fMRI studies plagued by statistical shortcomings? To explore why these studies can be vulnerable to experimental failure, in their new PLOS One study coauthors Henk Cremers, Tor Wager and Tal Yarkoni investigated common statistical issues encountered in typical fMRI studies, and proposed how to avert them moving forward. The reliability of any experiment depends on adequate power to detect real effects and reject spurious ones, which can be influenced by various factors including the sample size (or number of “subjects” in fMRI), how strong the real effect is (“effect size”), whether comparisons are within or between subjects, and the statistical threshold used. To characterize common statistical culprits of fMRI studies, Cremers and colleagues first simulated typical fMRI scenarios before validating these simulations on a real dataset. One scenario simulated weak but diffusely distributed brain activity, and the other scenario simulated strong but localized brain activity (Figure 1). The simulation revealed that effect sizes are generally inflated for weak diffuse, compared to strong localized, activations, especially when the sample size is small. In contrast, effect sizes can actually be underestimated for strong localized scenarios when the sample size is large. Thus, more isn’t always better when it comes to fMRI; the optimal sample size likely depends on the specific brain-behavior relationship under investigation.

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24501 - Posted: 01.09.2018

By Sharon Begley Technologies to detect brain activity — fine, we’ll come right out and call it mind reading — as well as to change it are moving along so quickly that “a bit of a gold rush is happening, both on the academic side and the corporate side,” Michel Maharbiz of the University of California, Berkeley, told a recent conference at the Massachusetts Institute of Technology. Here are three fast-moving areas of neuroscience we’ll be watching in 2018: Neural dust/neurograins Whatever you call these electronics, they’re really, really tiny. We’re eagerly awaiting results from DARPA’s $65 million neural engineering program, which aims to develop a brain implant that can communicate digitally with the outside world. The first step is detecting neurons’ electrochemical signaling (DARPA, the Pentagon’s Defense Advanced Research Projects Agency, says 1 million neurons at a time would be nice). To do that, scientists at Brown University are developing salt-grain-sized “neurograins” containing an electrode to detect neural firing as well as to zap neurons to fire, all via a radio frequency antenna. Advertisement Maharbiz’s “neural dust” is already able to do the first part. The tiny wireless devices can detect what neurons are doing, he and his colleagues reported in a 2016 rat study. (The study’s lead scientist recently moved to Elon Musk’s startup Neuralink, one of a growing number of brain-tech companies.) Now Maharbiz and team are also working on making neural dust receive outside signals and cause neurons to fire in certain ways. Such “stimdust” would be “the smallest [nerve] stimulator ever built,” Maharbiz said. Eventually, scientists hope, they’ll know the neural code for, say, walking, letting them transmit the precise code needed to let a paralyzed patient walk. They’re also deciphering the neural code for understanding spoken language, which raises the specter of outside signals making people hear voices — raising ethical issues that, experts said, neurotech will generate in abundance. © 2017 Scientific American

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior; Chapter 11: Motor Control and Plasticity
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System; Chapter 5: The Sensorimotor System
Link ID: 24468 - Posted: 12.29.2017

by Bethany Brookshire An astonishing number of things that scientists know about brains and behavior are based on small groups of highly educated, mostly white people between the ages of 18 and 21. In other words, those conclusions are based on college students. College students make a convenient study population when you’re a researcher at a university. It makes for a biased sample, but one that’s still useful for some types of studies. It would be easy to think that for studies of, say, how the typical brain develops, a brain is just a brain, no matter who’s skull its resting in. A biased sample shouldn’t really matter, right? Wrong. Studies heavy in rich, well-educated brains may provide a picture of brain development that’s inaccurate for the American population at large, a recent study found. The results provide a strong argument for scientists to pay more attention to who, exactly, they’re studying in their brain imaging experiments. It’s “a solid piece of evidence showing that those of us in neuroimaging need to do a better job thinking about our sample, where it’s coming from and who we can generalize our findings to,” says Christopher Monk, who studies psychology and neuroscience at the University of Michigan in Ann Arbor. The new study is an example of what happens when epidemiology experiments — studies of patterns in health and disease — crash into studies of brain imaging. “In epidemiology we think about sample composition a lot,” notes Kaja LeWinn, an epidemiologist at the University of California in San Francisco. Who is in the study, where they live and what they do is crucial to finding out how disease patterns spread and what contributes to good health. But in conversations with her colleagues in psychiatry about brain imaging, LeWinn realized they weren’t thinking very much about whose brains they were looking at. Particularly when studying healthy populations, she says, there was an idea that “a brain is a brain is a brain.” |© Society for Science & the Public 2000 - 2017. All rights reserved.

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior; Chapter 7: Life-Span Development of the Brain and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System; Chapter 13: Memory, Learning, and Development
Link ID: 24432 - Posted: 12.16.2017

By Neuroskeptic Sometimes, scientific misconduct is so blatant as to be comical. I recently came across an example of this on Twitter. The following is an image from a paper published in the Journal of Materials Chemistry C: As pointed out on PubPeer, this image – which is supposed to be an electron microscope image of some carbon dot (CD) nanoparticles – is an obvious fake. The “dots” are identical, and have clearly been cut-and-pasted. Where one copy has been placed over the top of another, the overlap is quite visible. It would be charitable to even call this ‘scientific’ fraud. The Journal of Materials Chemistry editors said on Twitter that they are “urgently” looking into this paper; I’ve no doubt it will be retracted soon, although the fact that it was published at all raises questions about the peer-review standards of this journal. To me as a neuroscientist, cases like this from chemistry get me worried. In a field like materials chemistry, or any field in which results take the form of images or photographs (such as Western blots), low-effort fraud is easy to spot because the manipulation of an image can, at least in unsubtle cases, be easily proven from the image itself. But what of fields like psychology or neuroscience where data don’t take the form of images? Perhaps low-effort frauds occur in these fields as well, but it is much more difficult to detect them when the results are statistical rather than pictorial in nature.

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24383 - Posted: 12.01.2017

By Mary Beth Aberlin Like the entomologist in search of colorful butterflies, my attention has chased in the gardens of the grey matter cells with delicate and elegant shapes, the mysterious butterflies of the soul, whose beating of wings may one day reveal to us the secrets of the mind. —Santiago Ramón y Cajal, Recollections of My Life Based on this quote, I am pretty certain that Santiago Ramón y Cajal, a founding father of modern neuroscience, would approve of this month’s cover. The Spaniard had wanted to become an artist, but, goaded by his domineering father into the study of medicine, Ramón y Cajal concentrated on brain anatomy, using his artistic talent to render stunningly beautiful and detailed maps of neuron placement throughout the brain. Based on his meticulous anatomical studies of individual neurons, he proposed that nerve cells did not form a mesh—the going theory at the time—but were separated from each other by microscopic gaps now called synapses. Fast-forward from the early 20th century to the present day, when technical advances in imaging have revealed any number of the brain’s secrets. Ramón y Cajal would no doubt have marveled at the technicolor neuron maps revealed by the Brainbow labeling technique. (Compare Ramón y Cajal’s drawings of black-stained Purkinje neurons to a Brainbow micrograph of the type of neuron.) But the technical marvels have gotten even more revelatory. © 1986-2017 The Scientist

Related chapters from BN8e: Chapter 7: Life-Span Development of the Brain and Behavior; Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 13: Memory, Learning, and Development; Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24348 - Posted: 11.24.2017

By Bahar Gholipour, The same techniques that generate images of smoke, clouds and fantastic beasts in movies can render neurons and brain structures in fine-grained detail. Two projects presented yesterday at the 2017 Society for Neuroscience annual meeting in Washington, D.C., gave attendees a sampling of what these powerful technologies can do. “These are the same rendering techniques that are used to make graphics for ‘Harry Potter’ movies,” says Tyler Ard, a neuroscientist in Arthur Toga’s lab at the University of Southern California in Los Angeles. Ard presented the results of applying these techniques to magnetic resonance imaging (MRI) scans. The methods can turn massive amounts of data into images, making them ideally suited to generate brain scans. Ard and his colleagues develop code that enables them to easily enter data into the software. They plan to make the code freely available to other researchers. The team is also combining the visualization software with virtual reality to enable scientists to explore the brain in three dimensions, and even perform virtual dissections of the brain. In one demo, the user can pick at a colored, segmented brain that can be pulled apart like pieces of Lego. “This can be useful when learning neuroanatomy,” Ard says. “The way that I learned it, we had to look at slices, and that’s real hard. This is a way that allows you to understand 3-D structure better.” The team plans to release the program, called Neuro Imaging in Virtual Reality, online next year. © 2017 Scientific American

Related chapters from BN8e: Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 24340 - Posted: 11.20.2017