neural networks Archives - Amazing Health Advances

New Technique Connects Lab-Grown “Neural Organoids” to Resemble Brain Circuits

The AHA! Team — Mon, 22 Jul 2024 08:33:29 +0000

Institute of Industrial Science, The University of Tokyo via News-Medical – Chronic kidney disease (CKD) is extremely prevalent among adults, affecting over 800 million individuals worldwide. The idea of growing a functioning human brain-like tissues in a dish has always sounded pretty far-fetched, even to researchers in the field. Towards the future goal, a Japanese and French research team has developed a technique for connecting lab-grown brain-mimicking tissue in a way that resembles circuits in our brain. It is challenging to study exact mechanisms of the brain development and functions. Animal studies are limited by differences between species in brain structure and function, and brain cells grown in the lab tend to lack the characteristic connections of cells in the human brain. What’s more, researchers are increasingly realizing that these interregional connections, and the circuits that they create, are important for many of the brain functions that define us as humans. Previous studies have tried to create brain circuits under laboratory conditions, which have been advancing the field. Researchers from The University of Tokyo have recently found a way to create more physiological connections between lab-grown “neural organoids,” an experimental model tissue in which human stem cells are grown into three-dimensional developmental brain-mimicking structures. The team did this by linking the organoids via axonal bundles, which is similar to how regions are connected in the living human brain. “In single-neural organoids grown under laboratory conditions, the cells start to display relatively simple electrical activity, when we connected two neural organoids with axonal bundles, we were able to see how these bidirectional connections contributed to generating and synchronizing activity patterns between the organoids, showing some similarity to connections between two regions within the brain.” – Tomoya Duenki, co-lead author of the study The cerebral organoids that were connected with axonal bundles showed more complex activity than single organoids or those connected using previous techniques. In addition, when the research team stimulated the axonal bundles using a technique known as optogenetics, the organoid activity was altered accordingly and the organoids were affected by these changes for some time, in a process known as plasticity. “These findings suggest that axonal bundle connections are important for developing complex networks,” explains Yoshiho Ikeuchi, senior author of the study. “Notably, complex brain networks are responsible for many profound functions, such as language, attention, and emotion.” Given that alterations in brain networks have been associated with various neurological and psychiatric conditions, a better understanding of brain networks is important. The ability to study lab-grown human neural circuits will improve our knowledge of how these networks form and change over time in different situations, and may lead to improved treatments for these conditions. Source: Institute of Industrial Science, The University of Tokyo Journal reference: Osaki, T., et al. (2024). Complex activity and short-term plasticity of human cerebral organoids reciprocally connected with axons. Nature Communications. doi.org/10.1038/s41467-024-46787-7. To read the original article click here.

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Saturated Fatty Acid Levels Increase When Making Memories

AHA Publisher — Tue, 06 Jul 2021 07:00:02 +0000

University of Queensland via EurekAlert – Saturated fatty acid levels unexpectedly rise in the brain during memory formation, according to research, opening a new avenue of investigation into how memories are made. Dr Tristan Wallis, from Professor Frederic Meunier’s laboratory at UQ’s Queensland Brain Institute (QBI), said traditionally, polyunsaturated fatty acids were considered important to health and memory, but this study highlighted the unexpected role of saturated fatty acids. “We tested the most common fatty acids to see how their levels changed as new memories were formed in the brain,” Dr Wallis said. “Unexpectedly, the changes of saturated fat levels in the brain cells were the most marked, especially that of myristic acid, which is found in coconut oil and butter. “In the kitchen, saturated fats are those which are solid at room temperature while unsaturated fats are normally liquid. “The brain is the fattiest organ in the body, being 60 per cent fat, which provides energy, structure and assists in passing messages between brain cells. “Fatty acids are the building blocks of lipids or fats and are vital for communication between nerve cells, because they help synaptic vesicles — microscopic sacs containing neurotransmitters–to fuse with the cell membrane and pass messages between the cells. “We have previously shown that when brain cells communicate with each other in a dish, the levels of saturated fatty acids increase.” Researchers have found that fatty acid levels in the rat brain, particularly saturated fatty acids, increase as memories are formed, but when they used a drug to block learning and memory formation in rats, the fatty acid levels did not change. The highest concentration of saturated fatty acids was found in the amygdala — the part of the brain involved in forming new memories specifically related to fear and strong emotions. Study contributor and QBI Director Professor Pankaj Sah said the work opened a new avenue on how memory was formed. “This research has huge implications on our understanding of synaptic plasticity — the change that occurs at the junctions between neurons that allow them to communicate, learn and build memories,” Professor Sah said. To read the original article click here.

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‘Knowing How’ Is in Your Brain

AHA Publisher — Fri, 29 May 2020 07:00:07 +0000

Carnegie Mellon University via EurekAlert – Although we often think of knowledge as “knowing that” (for example, knowing that Paris is the capital of France), each of us also knows many procedures consisting of knowing how, such as knowing how to tie a knot or start a car. Now a new study has found the brain programs that code the sequence of steps in performing a complex procedure. In a recently published paper in Psychological Science, researchers at Carnegie Mellon University have found a way to find and decode the procedural information required to tie various knots, with enough precision to identify which knot is being planned or performed. To reach this conclusion, Drs. Robert Mason and Marcel Just first trained a group of participants to tie seven different knots, and then scanned their brains while they imagined tying or actually tied the knots while they were in an MRI scanner. The main findings were that each knot had a distinctive neural signature, so the researchers could tell which knot was being tied from the sequence of brain images collected. Furthermore, the neural signatures were very similar for imagining tying a particular knot and planning to tie it. Dr. Just noted that “Tying a knot is an ancient and frequently performed human action that is the epitome of everyday procedural knowledge, making it an excellent target for investigation.” The research project was funded by the Office of Naval Research. While naval cadets frequently use nautical knots and naval doctors tie knots in surgical procedures, naval personnel are required to master many other types of procedural skills. Understanding how the brain learns and represents procedural tasks is an important part of the science of learning. The machine learning algorithm the researchers used succeeded by first processing the fMRI data from trials in which the participants mentally tied a knot like as a bowline or a sheet bend knot, and learned the association between each of the knots and their respective neural signatures. “Once we identified the neural representations of the procedures of how to tie each specific knot, we were able to predict which knot they were about to tie before they started tying it”, said Dr. Mason. That was possible because we have a plan for tying each knot, a plan that becomes activated just before the actual procedure is executed.” What distinguishes this finding from previous “brain decoding” studies is that knowing how involves a particular sequence of actions over executed over some time period, and not just a static snapshot of knowing that. In 1951, Harvard neurophysiologist Karl Lashley proposed that a complex act like playing a tune on a musical instrument requires than a linkage between the successive steps involved in the procedure; he hypothesized that it requires a higher-order mental structure or plan for organizing the sequence of steps. These new findings now confirm the existence of such higher-order mental representations of procedure, and they furthermore identify which procedure is which. The ability to identify the neural representation of a procedure may useful in developing instructional techniques that teach procedures with high efficiency and in developing brain-computer interfaces that translate a mental procedure into a robotic procedure. To read the original article click here.

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From Scaffolding to Screen Time: Understanding a Child’s Developing Brain for Reading

AHA Publisher — Tue, 05 May 2020 07:00:19 +0000

Cognitive Neuroscience Society via EurekAlert – In the debate about nature versus nurture for developing reading skills, cognitive neuroscientists have a clear message: both matter. From infancy, children have a neural scaffolding in place upon which environmental factors refine and build reading skills. In new work being presented today at the Cognitive Neuroscience Society (CNS) virtual meeting, scientists are reporting on these biological and environmental factors — including early screen time — as they uncover biomarkers that can identify children at risk for dyslexia and other reading acquisition disorders. Recycling Neural Circuits “Reading is a relatively new human invention. To read, our brains have to ‘recycle’ neural circuits originally used for other abilities such as visual and language processing, as well as attention and cognitive abilities,” says Tzipi Horowitz-Kraus of The Technion in Israel and Cincinnati Children’s Hospital, who is chairing the CNS symposium about the new work. “The fact that 5-10% of children worldwide, across cultures and genetic backgrounds, suffer from dyslexia suggests that this disability is not limited to a specific language.” Indeed, the research being presented by Horowitz-Kraus and others suggests a variety of biological precursors are present in children prior to school age across languages, and several environmental factors can help or hinder reading acquisition. The goal is to identify children at risk early, to provide the best possible interventions that will improve literacy. The Reading Brain in Infancy One of the biggest insights to come in recent years in the study of reading acquisition is that most interventions to identify and treat dyslexia in school were coming too late. Over the past decade, longitudinal studies of young children coming out of the lab of Nadine Gaab at Harvard Medical School and others at labs globally have shown that the brains of children who will develop dyslexia are already atypical even before they start into kindergarten. “We knew that the brain of someone with dyslexia was different from a control, but we didn’t know if it was something that developed before the onset of formal reading instruction or if it developed in response to a daily failure to learn to read over a significant period of time,” she says. “Our work was the first time MRI imaging could show that some of the brain characteristics predate the onset of reading development,” Gaab says. Underlying Infrastructure And in new work being presented at the CNS meeting and available via preprint, Gaab’s team has shown that, as a group, babies as young as 3 months old have an underlying infrastructure that helps predict success in reading years later. As part of the BOLD (Boston Longitudinal Dyslexia) study, Gaab’s team has scanned the brains of 140 infants who have a familial risk for dyslexia and then followed them over time to study changes in the structure and function of their brains. For the newest data, 45 of the once-infant subjects have now turned 5 or 6 years old, allowing the researchers to map their brain scans from infancy to their pre-reading skills. “What our infant data suggest is that there is a structural brain scaffold in infancy that serves as a foundation,”Gaaab explains. “Language and reading may be a process that refines this pre-existing brain scaffold.” Studying the brains of young children in an MRI machine is far from simple, Gaab explains. When they are babies, the goal is to have the participants sleep in the scanner. So her lab looks like an elaborate daycare center — with adaptable rocking chairs, swings, cribs, and other gear optimized for use with the scanner. While safely sleeping in the MRI, the babies hear stories read to them, allowing the researchers to capture both structural information about their brains but also, surprisingly, functional data. “We were very surprised to see robust language networks activated while the infants sleep,” Gaab says. Testing Pre-Reading Skills As 5- and 6-year-olds returning to the lab, the children identify word sounds in games designed to test their pre-reading skills. As they get older, the children will do increasingly more advanced tasks, such as reading in the scanner. This longitudinal work gives the researchers a big-picture view of reading development rather than just a snapshot view. Gaab’s lab is next working to understand the co-occurrence of disorders such as ADHD and dyscalculia (a math learning disorder) with dyslexia. They also want to understand techniques children successfully use to compensate for dyslexia in the brain. “We now see children are not a clean slate for reading experience,” Gaab says, and they want to not only better understand the determining factors but also inform policy-makers and the public. The Reading Brain On Screen While studying neurobiochemistry for her master’s program, Horowitz-Kraus worked on SAT preparation with her younger brother who was struggling with reading despite his high intelligence in nonverbal tasks. “Observing my brother’s frustration in executing a task that is very intuitive for individuals without dyslexia made me set the goal to seek neurobiological correlates for reading difficulties and to find ways to improve reading ability,” she says. “This way, I thought, the difficulty can be diagnosed objectively, maybe even before reading is formally acquired, and can prove without a doubt that the difficulty is real.” Fifteen years later, Horowitz-Kraus has done just that and, in new research, is seeking to understand how day-to-day conditions affect the neurobiological foundation for reading in the brain. “Although dyslexia is a genetic disorder, the environment has an impact wherein it can reduce or increase reading challenges,” she says. “The brain is extremely plastic at the pre-reading age, and hence negative stimuli, such as exposure to screens, may have an amplifying effect on a child’s outcomes.” Home Literacy Environment In a series of studies, Horowitz-Kraus and colleagues examined how the home literacy environment, including screen exposure, affects the brain circuits of children 3- to 5-years old, in particular executive functions, language and visual processing. As published recently in JAMA Pediatrics, screen-based media use beyond American Academy of Pediatrics guidelines was associated with “lower microstructural integrity of brain white matter tracts supporting language and emergent literacy skills in prekindergarten children.” Earlier work using EEG had found reduced narrative comprehension in preschool children using screens compared to in-person reading. They also have found that screen exposure engages different brain networks in children with dyslexia compared to typical readers. The results suggest, Horowitz-Kraus says, that listening to stories through screens is not similar to joint reading when seeking to nurture the developing brain. “There is no replacement for joint storytelling in engaging neuronal circuits related to future reading,” she says. What Infrastructure Is Needed? Such studies enabled by modern neuroimaging data are allowing researchers for the first time to determine what infrastructure is needed to be able to read and to track the typical and atypical development of this infrastructure — and to develop appropriate early interventions. Both Horowitz-Karus and Gaab envision moving to a more preventative model for reading disorders. “This preventive model is something we embrace a lot in medicine but for some reason, we have not yet done so in education,” Gaab says. She cites cholesterol screening to help identify those at risk for heart disease as a model that could work for dyslexia and other learning disorders. Already their research and others’ have led to new educational policies, including early dyslexia screening in 29 states to identify children at risk in kindergarten. “We and other cognitive neuroscientists hope to continue to contribute to that shift in this model,” Gaab says. To read the original article click here.

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Research Identifies Changes in Neural Circuits Underlying Self-Control During Adolescence

AHA Publisher — Sun, 19 Jan 2020 08:00:28 +0000

University of Pennsylvania School of Medicine via Science Daily – Researchers applied tools from network science to identify how anatomical connections in the brain develop to support neural activity underlying executive function. To read the original article and learn more about how these neural circuits function, click here.

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Dementia Spreads Via Connected Brain Networks

AHA Publisher — Fri, 18 Oct 2019 07:00:40 +0000

University of California – San Francisco via EurekAlert – Brain maps allow individualized predictions of frontotemporal dementia progression. In a new study, UC San Francisco scientists used maps of brain connections to predict how brain atrophy would spread in individual patients with frontotemporal dementia (FTD), adding to growing evidence that the loss of brain cells associated with dementia spreads via the synaptic connections between established brain networks. The results advance scientists’ knowledge of how neurodegeneration spreads and could lead to new clinical tools to evaluate how well novel treatments slow or block the predicted trajectory of these diseases. “Knowing how dementia spreads opens a window onto the biological mechanisms of the disease — what parts of our cells or neural circuits are most vulnerable,” said study lead author Jesse Brown, PhD, an assistant professor of neurology at the UCSF Memory and Aging Center and UCSF Weill Institute for Neurosciences. “You can’t really design a treatment until you know what you’re treating.” FTD, the most common form of dementia in people under the age of 60, comprises a group of neurodegenerative conditions with diverse linguistic and behavioral symptoms. As in Alzheimer’s disease, the diversity of FTD symptoms reflects significant differences in how the neurodegenerative disease spreads through patients’ brains. This variability makes it difficult for scientists searching for cures to pin down the biological drivers of brain atrophy and for clinical trials to evaluate whether a novel treatment is making a difference in the progression of a patient’s disease. Previous research by the study’s senior author, William Seeley, MD, a professor of neurology and pathology at the Memory and Aging Center and Weill Institute, set off a sea change in dementia research by showing that patterns of brain atrophy in many forms of dementia map closely onto well-known brain networks — groups of functionally related brain regions that work cooperatively via their synaptic connections, sometimes over long distances. In other words, Seeley’s work proposed that neurodegenerative diseases don’t spread evenly in all directions like a tumor, but can jump from one part of the brain to another along the anatomical circuits that wire these networks together. In their new study — published October 14 in Neuron — Brown, Seeley and colleagues provided further evidence supporting this idea by examining how well neural network maps based on brain scans in healthy individuals could predict the spread of brain atrophy in FTD patients over the course of a year. The researchers recruited 42 patients at the UCSF Memory and Aging Center with behavioral variant fronto-temporal dementia (bvFTD), a form of FTD that causes patients to exhibit inappropriate social behaviors, and 30 patients with semantic variant primary progressive aphasia (svPPA), a form of FTD that mainly impacts patients’ language abilities. In their first visits to UCSF, each of these patients underwent a “baseline” MRI scan to assess the extent of existing brain degeneration and then had a follow-up scan about a year later to measure how their disease had progressed. The researchers first estimated where the brain atrophy seen in each patient’s baseline scans had begun, based on the hypothesis that brain degeneration begins in some particularly vulnerable location, then spreads out to anatomically connected brain regions. To do this, the researchers built standardized maps of the main functional partners of 175 different brain regions based on functional MRI (fMRI) scans of 75 healthy adults. They then identified which of these networks best matched the pattern of brain atrophy seen in a given FTD patient’s baseline brain scans, and defined that network’s central hub as the likely epicenter of the patient’s degeneration. They then used the same standardized connectivity maps to predict where the patient’s brain atrophy was most likely to have spread in the follow-up scans done one year later, and compared the accuracy of these predictions to others that didn’t take functional network connectivity into account. They found that two particular connectivity measures significantly improved their predictions of a given brain region’s chances of developing brain atrophy between the baseline and follow-up brain scans. One, called “shortest path to the epicenter,” captured the number of synaptic “steps” that region was from the estimated disease epicenter — essentially the number of links in the neural chain connecting the two areas — while the other, called “nodal hazard,” represented how many regions connected to a given region were already experiencing significant atrophy. “It’s like with an infectious disease, where your chances of becoming infected can be predicted by how many degrees of separation you have from ‘Patient Zero’ but also by how many people in your immediate social network are already sick,” Brown said. The researchers showed that on average these two measures of network connectivity did better at predicting the spread of disease to a new brain region than its simple straight-line distance from a patient’s existing atrophy. In many cases the disease completely bypassed brain areas that were adjacent but not anatomically connected to already-atrophied regions, instead jumping to more functionally linked regions. Although this method shows great promise, the researchers emphasize that it is not yet ready for clinical use. They hope to improve the accuracy of their predictions by — among other approaches — using individualized network maps for each patient rather than using average connectivity maps, and by developing more specialized prediction models for particular subtypes of FTD. In addition to the biological insights the discovery provides about the mechanisms of spreading brain atrophy in FTD, which will inform ongoing efforts to develop treatments, the researchers also hope the findings will lead to improved metrics for evaluating therapies already entering clinical trials — for instance by giving trial scientists early insights into whether the treatment is altering a predicted course of disease progression. Researchers could also use better predictions of how atrophy will spread through the brain to help prepare patients and their families for the symptoms they are likely to experience as their disease progresses. “We are excited about this result because it represents an important first step toward a more precision medicine type of approach to predicting progression and measuring treatment effects in neurodegenerative disease,” Seeley said. In the future, Brown said, scientists might be able to develop therapies that specifically target the likely next site of disease and perhaps prevent atrophy from spreading from one region to another. “Just like epidemiologists rely on models of how infectious diseases spread to develop interventions targeted to key hubs or choke points,” Brown said. “Neurologists need to understand the underlying biological mechanisms of neurodegeneration to develop ways of slowing or halting the spread of the disease.” To read the original article click here.

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