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Science, Neuroscience, Neurotechnology, Brain, Paradigm.
studies to facilitate further research in this area. As compared to conventional gait training, REAER allows participants to walk at volumes needed to realize functional adaptations — via vigorous neurophysiological demands — that lead to improved cognition and mobility. Effects on brain activity | medium | 4,282 |
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patterns were studied using the functional MRI capabilities of the Rocco Ortenzio Neuroimaging Center at Kessler Foundation. Investigators compared participants’ improvement after four weeks of REAER vs four weeks of conventional gait training, looking at functional mobility, walking endurance, | medium | 4,283 |
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cognitive processing speed, and brain connectivity. The results were positive: Relative to conventional gait training, four weeks of REAER was associated with large improvements in functional mobility (?p2=.38), cognitive processing speed (?p2=.53), and brain connectivity outcomes, most | medium | 4,284 |
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significantly between the thalamus and ventromedial prefrontal cortex (?p2=.72). “Four weeks is relatively short for an exercise training study,” noted Dr. Sandroff, senior research scientist at Kessler Foundation and director of the Exercise Neurorehabilitation Research Laboratory. “Seeing | medium | 4,285 |
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improvements within this timeframe shows the potential for exercise to change how we treat MS. Exercise is really powerful behavior that involves many brain regions and networks that can improve over time and result in improved function.” “This is particularly exciting because therapy using robotic | medium | 4,286 |
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exoskeletons shows such promise for improving the lives of people with co-occurring mobility and cognitive disability, a cohort that likely has the greatest potential to benefit from this new technology,” said Dr. Androwis, lead author and research scientist in the Center for Mobility and | medium | 4,287 |
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Rehabilitation Engineering Research at Kessler Foundation. “We’re eager to design a larger trial to further study these effects. Based on our initial results, we’re optimistic that this approach may be superior to the current standard of care.” (A) Participant with MS engaging in REAER (Ekso-GT, | medium | 4,288 |
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Ekso Bionics, Inc.); (B) Participant with MS engaging in CGT. The “cognitive clock”: A novel indicator of brain health by Patricia A. Boyle, Tianhao Wang, Lei Yu, Robert S. Wilson, Robert Dawe, Konstantinos Arfanakis, Julie A. Schneider, Todd Beck, Kumar B. Rajan, Denis Evans, David A. Bennett in | medium | 4,289 |
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Alzheimer’s & Dementia How old is your brain compared to your chronological age? A new measure of brain health developed by researchers at Rush University Medical Center may offer a novel approach to identifying individuals at risk of memory and thinking problems, according to research results | medium | 4,290 |
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published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. Dubbed the “cognitive clock” by the researchers, the tool is a measure of brain health based on cognitive performance. It may be used in the future to predict the likelihood of memory and thinking problems that develop | medium | 4,291 |
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as a person ages. “Alzheimer’s disease, which is of the most common cause of dementia, and other diseases of the brain accumulate slowly over time as people get older. Age is widely recognized as the main risk factor for Alzheimer’s disease, but it’s a very imperfect predictor, since not everyone | medium | 4,292 |
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develops dementia as they age,” said Patricia Boyle, PhD, professor in Rush Medical College’s Division of Behavioral Sciences neuropsychologist in the Rush Alzheimer’s Disease Center (RADC), and lead author of the study. “Our new cognitive clock provides a measure of brain health that tells us more | medium | 4,293 |
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about how well a person’s brain is functioning than chorological age. In this way, the clock can help us detect who is at highest risk of developing cognitive impairment in the coming years. “For some people, cognition remains fairly stable as they age,” Boyle added. “But, for others, cognition | medium | 4,294 |
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declines slowly over time, and still others show steep declines.” The researchers believed that cognitive performance data, even using a simple cognitive screening test, could be used to distinguish people exhibiting normal cognitive aging from those who are on their way to developing memory and | medium | 4,295 |
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thinking problems that are often coupled with aging. This thesis led the Rush researchers to look at data they acquired from several long-term studies conducted by the RADC, including the Rush Memory and Aging Project (MAP) which included people living in the community in greater Chicago; the | medium | 4,296 |
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Religious Orders Study (ROS), which included older Catholic clergy from across the United States; and the Chicago Health and Aging Project (CHAP), a biracial population-based study. “We used long-term cognitive testing data from our participants to develop a profile of cognitive aging, what we call | medium | 4,297 |
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the cognitive clock” Boyle said. “The cognitive clock reflects the general pattern of age-related cognitive decline and allows us to see who is doing better than average and who is doing worse at a given point in time. This helps us identify who might be at high risk of developing memory and | medium | 4,298 |
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thinking problems.” The cognitive clock was first developed working with data from 1057 participants from the MAP and the ROS, who began without cognitive impairment and underwent yearly cognitive assessments for up to 24 years. The cognitive assessment included the Mini-Mental State Exam, a widely | medium | 4,299 |
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used test of cognitive function among the elderly that measures orientation, attention, memory, language and visual-spatial skills. In addition to the MMSE, detailed evaluations also included a structured medical history, neurologic examinations, and a set of neurocognitive tests. The researchers | medium | 4,300 |
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examined how cognitive performance changes over time with advancing age using a novel statistical approach to identify the typical profile of cognitive aging. Using this cognitive clock, researchers can estimate an individual’s cognitive age — their position on the clock — at any given point in | medium | 4,301 |
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time. Cognitive age is an indicator of brain health. “We found that, on average, cognition remains stable until a cognitive age of around 80 years of age, then declines moderately until 90, then declines more rapidly until death,” Boyle said. “Further, we found that cognitive age is a much better | medium | 4,302 |
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predictor than chronological age of dementia, mild cognitive impairment and mortality. It also is more strongly associated with other aspects of brain health.” The researchers then applied the clock to an independent sample of 2,592 participants from CHAP to confirm its accuracy for predicting | medium | 4,303 |
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outcomes such as Alzheimer’s dementia, mild cognitive impairment, and mortality. Again, they found that cognitive age was a better predictor of these outcomes than chronological age. “Essentially, what we did is use cognitive data collected over many years to create a single, easy-to-understand | medium | 4,304 |
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metric that may be used to predict health outcomes with good accuracy,” Boyle said. This tool may serve as an aid in aging research moving forward and may offer a new tool to identify at risk individuals. “It is very difficult to develop a test or biomarker that accurately predicts health outcomes | medium | 4,305 |
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on an individual level. This has been a longstanding challenge in aging research. However, we are hoping that with additional research and validation, we may be able extend the approach applied here to clinical settings,” Boyle said. “Ideally, we could have a patient come into a clinic or hospital | medium | 4,306 |
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and complete a brief cognitive screen that gives us information to plug into a formula to estimate their cognitive age. That will provide important information about their brain health, and from there, we can estimate likelihood of developing Alzheimer’s disease or dementia in the coming years. | medium | 4,307 |
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That would be an exciting advance.” Randomized Manipulation of Early Cognitive Experience Impacts Adult Brain Structure by Martha J. Farah, Saul Sternberg, Thomas A. Nichols, Jeffrey T. Duda, Terry Lohrenz, Yi Luo, Libbie Sonnier, Sharon L. Ramey, Read Montague, Craig T. Ramey in Journal of | medium | 4,308 |
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Cognitive Neuroscience An enhanced learning environment during the first five years of life shapes the brain in ways that are apparent four decades later, say Virginia Tech and University of Pennsylvania scientists writing in the June edition of the Journal of Cognitive Neuroscience. The | medium | 4,309 |
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researchers used structural brain imaging to detect the developmental effects of linguistic and cognitive stimulation starting at six weeks of age in infants. The influence of an enriched environment on brain structure had formerly been demonstrated in animal studies, but this is the first | medium | 4,310 |
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experimental study to find a similar result in humans. “Our research shows a relationship between brain structure and five years of high-quality, educational and social experiences,” said Craig Ramey, professor and distinguished research scholar with Fralin Biomedical Research Institute at VTC and | medium | 4,311 |
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principal investigator of the study. “We have demonstrated that in vulnerable children who received stimulating and emotionally supportive learning experiences, statistically significant changes in brain structure appear in middle age.” The results support the idea that early environment influences | medium | 4,312 |
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the brain structure of individuals growing up with multi-risk socioeconomic challenges, said Martha Farah, director of the Center for Neuroscience and Society at Penn and first author of the study. “This has exciting implications for the basic science of brain development, as well as for theories | medium | 4,313 |
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of social stratification and social policy,” Farah said. The study follows children who have continuously participated in the Abecedarian Project, an early intervention program initiated by Ramey in Chapel Hill, North Carolina, in 1971 to study the effects of educational, social, health, and family | medium | 4,314 |
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support services on high-risk infants. Both the comparison and treatment groups received extra health care, nutrition, and family support services; however, beginning at six weeks of age, the treatment group also received five years of high quality educational support, five days a week, 50 weeks a | medium | 4,315 |
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year. When scanned, the Abecedarian study participants were in their late 30s to early 40s, offering the researchers a unique look at how childhood factors affect the adult brain. “People generally know about the potentially large benefits of early education for children from very low resource | medium | 4,316 |
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circumstances,” said co-author Sharon Landesman Ramey, professor and distinguished research scholar at Fralin Biomedical Research Institute. “The new results reveal that biological effects accompany the many behavioral, social, health, and economic benefits reported in the Abecedarian Project. This | medium | 4,317 |
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affirms the idea that positive early life experiences contribute to later positive adjustment through a combination of behavioral, social, and brain pathways.” During follow-up examinations, structural MRI scans of the brains of 47 study participants were conducted at the Fralin Biomedical Research | medium | 4,318 |
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Institute Human Neuroimaging Lab. Of those, 29 individuals had been in the group that received the educational enrichment focused on promoting language, cognition, and interactive learning. The other 18 individuals received the same robust health, nutritional, and social services supports provided | medium | 4,319 |
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to the educational treatment group, and whatever community childcare or other learning their parents provided. The two groups were well matched on a variety of factors such as maternal education, head circumference at birth and age at scanning. Analyzing the scans, the researchers looked at brain | medium | 4,320 |
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size as a whole, including the cortex, the brain’s outermost layer, as well as five regions selected for their expected connection to the intervention’s stimulation of children’s language and cognitive development. Those included the left inferior frontal gyrus and left superior temporal gyrus, | medium | 4,321 |
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which may be relevant to language, and the right inferior frontal gyrus and bilateral anterior cingulate cortex, relevant to cognitive control. A fifth, the bilateral hippocampus, was added because its volume is frequently associated with early life adversity and socioeconomic status. The | medium | 4,322 |
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researchers determined that those in the early education treatment group had increased size of the whole brain, including the cortex. Several specific cortical regions also appeared larger, according to study co-authors Read Montague, professor and director of the Human Neuroimaging Lab and | medium | 4,323 |
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Computational Psychiatry Unit at the Fralin Biomedical Research Institute, and Terry Lohrenz, research assistant professor and member of the institute’s Human Neuroimaging Laboratory. The scientists noted the group intervention treatment results for the brain were substantially greater for males | medium | 4,324 |
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than for females. The reasons for this are not known, and were surprising, since both the boys and girls showed generally comparable positive behavioral and educational effects from their early enriched education. The current study cannot adequately explain the sex differences. Percent differences | medium | 4,325 |
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in volume of individual ROIs resulting from treatment in male (top) and female (bottom) participants, with 95% confidence intervals. “When we launched this project in the 1970s, the field knew more about how to assess behavior than it knew about how to assess brain structure,” Craig Ramey said. | medium | 4,326 |
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“Because of advances in neuroimaging technology and through strong interdisciplinary collaborations, we were able to measure structural features of the brain. The prefrontal cortex and areas associated with language were definitely affected; and to our knowledge, this is the first experimental | medium | 4,327 |
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evidence on a link between known early educational experiences and long-term changes in humans.” “We believe that these findings warrant careful consideration and lend further support to the value of ensuring positive learning and social-emotional support for all children — particularly to improve | medium | 4,328 |
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outcomes for children who are vulnerable to inadequate stimulation and care in the early years of life,” Craig Ramey said. Predicting language treatment response in bilingual aphasia using neural network-based patient models by Grasemann U, Peñaloza C, Dekhtyar M, Miikkulainen R, Kiran S. in Sci | medium | 4,329 |
Science, Neuroscience, Neurotechnology, Brain, Paradigm.
Rep At Boston University, a team of researchers is working to better understand how language and speech is processed in the brain, and how to best rehabilitate people who have lost their ability to communicate due to brain damage caused by a stroke, trauma, or another type of brain injury. This | medium | 4,330 |
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type of language loss is called aphasia, a long-term neurological disorder caused by damage to the part of the brain responsible for language production and processing that impacts over a million people in the US. “It’s a huge problem,” says Swathi Kiran, director of BU’s Aphasia Research Lab, and | medium | 4,331 |
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College of Health & Rehabilitation Sciences: Sargent College associate dean for research and James and Cecilia Tse Ying Professor in Neurorehabilitation. “It’s something our lab is working to tackle at multiple levels.” For the last decade, Kiran and her team have studied the brain to see how it | medium | 4,332 |
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changes as people’s language skills improve with speech therapy. More recently, they’ve developed new methods to predict a person’s ability to improve even before they start therapy. In a new paper published in Scientific Reports, Kiran and collaborators at BU and the University of Texas at Austin | medium | 4,333 |
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report they can predict language recovery in Hispanic patients who speak both English and Spanish fluently — a group of aphasia patients particularly at risk of long-term language loss — using sophisticated computer models of the brain. They say the breakthrough could be a game changer for the | medium | 4,334 |
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field of speech therapy and for stroke survivors impacted by aphasia. “This [paper] uses computational modeling to predict rehabilitation outcomes in a population of neurological disorders that are really underserved,” Kiran says. In the US, Hispanic stroke survivors are nearly two times less | medium | 4,335 |
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likely to be insured than all other racial or ethnic groups, Kiran says, and therefore they experience greater difficulties in accessing language rehabilitation. On top of that, oftentimes speech therapy is only available in one language, even though patients may speak multiple languages at home, | medium | 4,336 |
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making it difficult for clinicians to prioritize which language a patient should receive therapy in. “This work started with the question, ‘If someone had a stroke in this country and [the patient] speaks two languages, which language should they receive therapy in?’” says Kiran. “Are they more | medium | 4,337 |
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likely to improve if they receive therapy in English? Or in Spanish?” This first-of-its-kind technology addresses that need by using sophisticated neural network models that simulate the brain of a bilingual person that is language impaired, and their brain’s response to therapy in English and | medium | 4,338 |
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Spanish. The model can then identify the optimal language to target during treatment, and predict the outcome after therapy to forecast how well a person will recover their language skills. They found that the models predicted treatment effects accurately in the treated language, meaning these | medium | 4,339 |
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computational tools could guide healthcare providers to prescribe the best possible rehabilitation plan. “There is more recognition with the pandemic that people from different populations — whether [those be differences of] race, ethnicity, different disability, socioeconomic status — don’t | medium | 4,340 |
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receive the same level of [healthcare],” says Kiran. “The problem we’re trying to solve here is, for our patients, health disparities at their worst; they are from a population that, the data shows, does not have great access to care, and they have communication problems [due to aphasia].” As part | medium | 4,341 |
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of this work, the team is examining how recovery in one language impacts recovery of the other — will learning the word “dog” in English lead to a patient recalling the word “perro,” the word for dog in Spanish? “If you’re bilingual you may go back and forth between languages, and what we’re trying | medium | 4,342 |
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to do [in our lab] is use that as a therapy piece,” says Kiran. Clinical trials using this technology are already underway, which will soon provide an even clearer picture of how the models can potentially be implemented in hospital and clinical settings. “We are trying to develop effective therapy | medium | 4,343 |
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programs, but we also try to deal with the patient as a whole,” Kiran says. “This is why we care deeply about these health disparities and the patient’s overall well-being.” (a) The BiLex model consists of three interconnected SOMs, one for word meanings shared across languages, and two for their | medium | 4,344 |
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phonetic representations in L1 and L2. Bidirectional associative connections transfer activation between maps. (b) The semantic map organizes the model’s vocabulary according to word meanings, such that similar words are close together on the map. Plot c shows a detail of this map. Phonetic maps | medium | 4,345 |
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are organized in the same way, but reflect phonetic similarity. Neurovascular coupling and oxygenation are decreased in hippocampus compared to neocortex because of microvascular differences by Shaw K, Bell L, Boyd K, et al. in Nature Communications In a world first, scientists from the University | medium | 4,346 |
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of Sussex have recorded blood oxygen levels in the hippocampus and provided experimental proof for why the area, commonly referred to as ‘the brain’s memory centre’, is vulnerable to damage and degeneration, a precursor to Alzheimer’s disease. To understand why this region is so sensitive, the | medium | 4,347 |
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University of Sussex researchers, headed up by Dr Catherine Hall from the School of Psychology and Sussex Neuroscience, studied brain activity and blood flow in the hippocampus of mice. The researchers then used simulations to predict that the amount of oxygen supplied to hippocampal neurons | medium | 4,348 |
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furthest from blood vessels is only just enough for the cells to keep working normally. Dr Catherine Hall, Senior Lecturer in Psychology at the University of Sussex says: “These findings are an important step in the search for preventative measures and treatments for Alzheimer’s, because they | medium | 4,349 |
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suggest that increasing blood flow in the hippocampus might be really effective at preventing damage from happening. If it’s right that increasing blood flow in the hippocampus is important in protecting the brain from diseases like Alzheimer’s, then it will throw further weight behind the | medium | 4,350 |
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importance of regular exercise and a low-cholesterol diet to long-term brain health. We think that the hippocampus exists at a watershed. It’s just about OK normally, but when anything else happens to decrease brain blood flow, oxygen levels in the hippocampus reduce to levels that stop neurons | medium | 4,351 |
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working. We think that’s probably why Alzheimer’s disease first causes memory problems — because the early decrease in blood flow stops the hippocampus from working properly. The same factors that put you at risk of having a heart attack make you more likely to develop dementia. That’s because our | medium | 4,352 |
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brains need enough blood flow to provide energy — in the form of oxygen and glucose — so brain cells can work properly, and because blood flow can clear away waste products such as the beta amyloid proteins that build up in Alzheimer’s disease. Now we want to discover whether the lower blood flow | medium | 4,353 |
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and oxygen levels in the hippocampus are what causes beta amyloid to start to build up in Alzheimer’s disease. Understanding what causes early damage will be really important to help us learn how to treat or prevent disease.” Dr Kira Shaw, a psychology researcher at the University of Sussex who | medium | 4,354 |
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undertook the main experiments, said: “We found that blood flow and oxygen levels in the hippocampus were lower than those in the visual cortex. Also, when neurons are active, there is a large increase in blood flow and oxygen levels in the visual cortex. This provides energy to hungry neurons. But | medium | 4,355 |
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in the hippocampus, these responses were much smaller.” The scientists also found that blood vessels in the hippocampus contained fewer mRNA transcripts (codes for making proteins) for proteins that shape blood vessel dilation. Additionally, the cells that dilate small blood vessels, called | medium | 4,356 |
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pericytes, were a different shape in the hippocampus than in the visual cortex. Dr Shaw concluded: “We think blood vessels in the hippocampus are less able to dilate than in the visual cortex”. Representative schematic showing the GCaMP6f-positive pyramidal neurons (green) and blood vessels (red) | medium | 4,357 |
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accessible for two-photon imaging after a visual cortical or b hippocampal surgery, with example maximum-projected images across each layer. Scale bars represent 100 µm, and similar z-stack images across layers were taken for nine animals in HC and 11 animals in V1. c Schematic of the imaging | medium | 4,358 |
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set-up. Either the two-photon objective or oxy-CBF probe was used to collect data while the mouse was head-fixed but awake and able to run on the cylinder. d Representative locomotion recorded by the rotary encoder during one imaging session (centimetres per second). Distinct periods of running are | medium | 4,359 |
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indicated by the black bars. A virtual reality maze e or drifting gratings f were presented on the screens in c. Locomotion advanced the mice through the virtual reality maze. The arrows beneath the drifting gratings display show the direction the gratings travelled. g Example, haemodynamic | medium | 4,360 |
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recordings from visual cortex using the oxy-CBF probe during visual stimulation (grey bar represents stimulation, N = 4 animals, 10 sessions, 202 trials). h The cerebral metabolic rate of oxygen consumption (CMRO2) is calculated from the haemodynamic parameters collected using the oxy-CBF probe for | medium | 4,361 |
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the data in g. All data traces are unsmoothed averages, and error bands represent mean ± SEM. MISC Subscribe to Paradigm! Medium. Twitter. Telegram. Telegram Chat. Reddit. LinkedIn. Main sources Research articles Nature Neuroscience Science Daily Technology Networks Neuroscience News Frontiers Cell | medium | 4,362 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
Image generated using Imgflip In my previous blog talking about TextGenie, I mentioned the issues I faced while collecting text data from scratch and using paraphrases generated from T5(Text-To-Text Transfer Transformer) as one of the methods to augment text data. Having seen the model in action, | medium | 4,363 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
let’s get our hands dirty with the training process😉 If you wish to walk along with me throughout, you can find the notebook for training here on my Github repo. Tip: If you do not have a GPU, I suggest using Google Colaboratory for training the model. Installing dependencies Before proceeding, | medium | 4,364 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
let’s get all the required packages handy using: pip install simpletransformers datasets tqdm pandas Dataset We shall use the TaPaCo dataset for our task. The dataset consists of a total of 1.9 million sentences in 73 languages from which, we shall take sentences in English language. Preprocessing | medium | 4,365 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
the dataset(optional) Before feeding the dataset to the model, it needs to be converted to pairs of input sentences and target sentences. The code for preprocessing can be found here as well as in the notebook. Downloading already preprocessed dataset If you do not wish to preprocess the data, I’ve | medium | 4,366 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
already done the task for you. You can directly download the preprocessed version of the dataset from here. Loading the dataset Once done, you can load the dataset as: import pandas as pd dataset_df = pd.read_csv("tapaco_paraphrases_dataset.csv",sep="\t") Once loaded, the columns of the data need | medium | 4,367 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
to be renamed. Also, we need to add a prefix to each sentence. Here, the prefix can be any text added as a column with same value for each row. # Renaming the columns dataset_df.columns = ["input_text","target_text"] # Adding a prefix. Here we shall keep "paraphrase" as a prefix. | medium | 4,368 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
dataset_df["prefix"] = "paraphrase" Splitting the dataset We shall split the dataset in a ratio of 90%-10% from sklearn.model_selection import train_test_split train_data,test_data = train_test_split(dataset_df,test_size=0.1) Training the model The model needs certain parameters to be tweaked, | medium | 4,369 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
which can be found as: Initializing the T5Model class object from simpletransformers: from simpletransformers.t5 import T5Model import sklearn model = T5Model("t5","t5-small", args=args) We shall go with the t5-small model for now. Let’s proceed with the training: model.train_model(train_data, | medium | 4,370 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
eval_data=test_data, use_cuda=True,acc=sklearn.metrics.accuracy_score) Loading and predicting using the trained model It might take a few hours for the model to train. Once the training is complete, you may find the final model in the outputs directory. Which can be loaded as: Loading the trained | medium | 4,371 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
model from simpletransformers.t5 import T5Model import os root_dir = os.getcwd() trained_model_path = os.path.join(root_dir,"outputs") args = { "overwrite_output_dir": True, "max_seq_length": 256, "max_length": 50, "top_k": 50, "top_p": 0.95, "num_return_sequences": 5 } trained_model = | medium | 4,372 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
T5Model("t5",trained_model_path,args=args) Generating paraphrases using the trained model Let’s see how the model performs with our custom input: prefix = "paraphrase" pred = trained_model.predict([f"{prefix}: The house will be cleaned by me every Saturday."]) print(pred) #Output: [['My home will | medium | 4,373 |
Deep Learning, Transformers, NLP, Machine Learning, Artificial Intelligence.
be cleaned on Saturdays.', 'I will clean the house every Saturday.', 'The house is going to be clean every Saturday.', "I'll clean the house every Saturday.", 'I will clean the house every Saturday.']] And it works!! Yay! That’s all with the T5 model training. I’ve open sourced pretrained models | medium | 4,374 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
Maggie in search (discovery/binding/dissemination) of smells (or my IoT based remote dog feeder) Unless you are living off the grid, you are surrounded by smart devices. This will expand to 40–50 Billion in the next decade. Without becoming slaves to our devices, and without giving up (or going off | medium | 4,376 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
the grid) new automation approaches are needed to simplify life without worrying about these new unbounded complexities. You want use of these devices to be fun and rewarding, and you want the opportunity to finally build those applications you just dreamt of before. You want the calm feeling that | medium | 4,377 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
the cable, and by definition, simplicity, is back in your life. Well the truth is that cat’s out of the bag, but putting the cable back, like Gene Wilder in Young Frankenstein putting the “candle” back — is a goal our Software Architectures should strive for: See my Put the Cable Back writeup: | medium | 4,378 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
https://medium.com/@knowledgeshark/put-the-cable-candle-back-a313ef74ce68 How will this be accomplished when 40–50 billion devices are active? Imagine walking down a random isle of a quaint shopping boutique, when your smart phone beeps or vibrates. It’s letting you know that a rare item you have | medium | 4,379 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
been searching for is nearly. Although you had forgotten about it; now using iBeacons to get you closer, like a geocache, the item is grabbed and you are onto other adventures. This binding flexibility augmented with deployed computers in the environment helps realize this vision. Entering a room, | medium | 4,380 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
like a museum or smart house, and now just discovering and absorbing all the available information in actionably form is possible. You might be in front of the Mona Lisa, and the tools maybe on the wall, disseminate interesting and relevant tidbits. It might even relate this painting with the Van | medium | 4,381 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
Gough you just saw; the tools learned the unique routing you might have taken. This new computing freedom does come with interesting social changes, what might be called stockerish or big-brother. Communication over a Distance, or Telegraph, was an approach that delivered accurate messages faster | medium | 4,382 |
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