TL;DR
- The SARS-CoV-2 virus, like many viruses before it, is bad news for the brain. In a new study, researchers found that the spike protein, often depicted as the red arms of the virus, can cross the blood-brain barrier in mice. The spike proteins alone can cause brain fog. Since the spike protein enters the brain, the virus also is likely to cross into the brain.
- An international research group has discovered in the human brain a new functional coupling mechanism between neurons, which may serve as a communication channel between brain regions.
- Developing brains constantly sprout new neuronal connections called synapses as they learn and remember. Important connections — the ones that are repeatedly introduced, such as how to avoid danger — are nurtured and reinforced, while connections deemed unnecessary are pruned away. Adult brains undergo similar pruning, but it was unclear how or why synapses in the adult brain get eliminated. Now, a team of researchers has found the mechanism underlying plasticity and, potentially, neurological disorders in adult brains.
- Researchers have described how they used advanced genetic engineering techniques to transform a bacterial protein into a new research tool that may help monitor serotonin transmission with greater fidelity than current methods. Preclinical experiments, primarily in mice, showed that the sensor could detect subtle, real-time changes in brain serotonin levels during sleep, fear, and social interactions, as well as test the effectiveness of new psychoactive drugs.
- Scientists have been able to observe the way stem cells in the adult brains of mice divide over the course of months to create new nerve cells. Their study shows that brain stem cells are active over a long period, and thus provides new insights that will feed into stem cell research.
- A new device developed by engineers can recognize hand gestures based on electrical signals detected in the forearm. The system, which couples wearable biosensors with artificial intelligence (AI), could one day be used to control prosthetics or to interact with almost any type of electronic device.
- Scientists have used machine intelligence to improve the accuracy and reliability of a powerful brain-mapping technique, a new study reports. Their development gives researchers more confidence in using the technique to untangle the human brain’s wiring and to better understand the changes in this wiring that accompany neurological or mental disorders such as Parkinson’s or Alzheimer’s disease.
- For the first time, researchers have provided physiological evidence that a pervasive neuromodulation system — a group of neurons that regulate the functioning of more specialized neurons — strongly influences sound processing in an important auditory region of the brain. The neuromodulator, acetylcholine, may even help the main auditory brain circuitry distinguish speech from noise.
- Neuroscientists have made major advances in their quest to study the brain; however, there are no tools to precisely measure the brain’s principal output — behavior — in freely moving animals. Researchers present CAPTURE, a new method for long-term continuous three-dimension motion tracking in freely behaving animals. Attaching markers to rats’ head, trunk, and limbs, researchers can use CAPTURE to record the animal’s natural behavior continuously for weeks.
- A new study finds that Zika infection during the first trimester of pregnancy can impact fetal retinal development and cause congenital ocular anomalies. The virus does not appear to affect ocular growth post-natally.
- …And more!
Neuroscience market
The global neuroscience market size was valued at USD 28.4 billion in 2016 and it is expected to reach USD 38.9 billion by 2027.
Latest news and researches
The S1 protein of SARS-CoV-2 crosses the blood–brain barrier in mice
by Elizabeth M. Rhea, Aric F. Logsdon, Kim M. Hansen, Lindsey M. Williams, May J. Reed, Kristen K. Baumann, Sarah J. Holden, Jacob Raber, William A. Banks, Michelle A. Erickson in Nature Neuroscience
More and more evidence is coming out that people with COVID-19 are suffering from cognitive effects, such as brain fog and fatigue.
And researchers are discovering why. The SARS-CoV-2 virus, like many viruses before it, is bad news for the brain. In a study, researchers found that the spike protein, often depicted as the red arms of the virus, can cross the blood-brain barrier in mice.
This strongly suggests that SARS-CoV-2, the cause of COVID-19, can enter the brain.
The spike protein, often called the S1 protein, dictates which cells the virus can enter. Usually, the virus does the same thing as its binding protein, said corresponding author William A. Banks, a professor of medicine at the University of Washington School of Medicine and a Puget Sound Veterans Affairs Healthcare System physician and researcher. Banks said binding proteins like S1 usually by themselves cause damage as they detach from the virus and cause inflammation.
“The S1 protein likely causes the brain to release cytokines and inflammatory products,” he said.
In science circles, the intense inflammation caused by the COVID-19 infection is called a cytokine storm. The immune system, upon seeing the virus and its proteins, overreacts in its attempt to kill the invading virus. The infected person is left with brain fog, fatigue and other cognitive issues.
Banks and his team saw this reaction with the HIV virus and wanted to see if the same was happening with SARS CoV-2.
Banks said the S1 protein in SARS-CoV2 and the gp 120 protein in HIV-1 function similarly. They are glycoproteins — proteins that have a lot of sugars on them, hallmarks of proteins that bind to other receptors. Both these proteins function as the arms and hand for their viruses by grabbing onto other receptors. Both cross the blood-brain barrier and S1, like gp120, is likely toxic to brain tissues.
“It was like déjà vu,” said Banks, who has done extensive work on HIV-1, gp120, and the blood-brain barrier.
The Banks’ lab studies the blood-brain barrier in Alzheimer’s, obesity, diabetes, and HIV. But they put their work on hold and all 15 people in the lab started their experiments on the S1 protein in April. They enlisted long-time collaborator Jacob Raber, a professor in the departments of Behavioral Neuroscience, Neurology, and Radiation Medicine, and his teams at Oregon Health & Science University.
The study could explain many of the complications from COVID-19.
“We know that when you have the COVID infection you have trouble breathing and that’s because there’s infection in your lung, but an additional explanation is that the virus enters the respiratory centers of the brain and causes problems there as well,” said Banks.
Raber said in their experiments transport of S1 was faster in the olfactory bulb and kidney of males than females. This observation might relate to the increased susceptibility of men to more severe COVID-19 outcomes.
As for people taking the virus lightly, Banks has a message:
“You do not want to mess with this virus,” he said. “Many of the effects that the COVID virus has could be accentuated or perpetuated or even caused by virus getting in the brain and those effects could last for a very long time.”
Long-range phase synchronization of high-frequency oscillations in human cortex
by G. Arnulfo, S. H. Wang, V. Myrov, B. Toselli, J. Hirvonen, M. M. Fato, L. Nobili, F. Cardinale, A. Rubino, A. Zhigalov, S. Palva, J. M. Palva in Nature Communications
In a new study, research groups of Professor J. Matias Palva and Research Director Satu Palva at the Neuroscience Centre of the University of Helsinki and Aalto University, in collaboration with the University of Glasgow and the University of Genoa, have identified a novel coupling mechanism linking neuronal networks by using human intracerebral recordings.
Neuronal oscillations are an essential part of the functioning of the human brain. They regulate the communication between neural networks and the processing of information carried out by the brain by pacing neuronal groups and synchronising brain regions.
High-frequency oscillations with frequencies over 100 Hertz are known to indicate the activity of small neuronal populations. However, up to now, they have been considered to be exclusively a local phenomenon.
The findings of the European research project demonstrate that also high-frequency oscillations over 100 Hertz synchronize across several brain regions. This important finding reveals that strictly-timed communication between brain regions can be achieved by high-frequency oscillations.
The researchers observed that high-frequency oscillations were synchronised between neuronal groups with a similar architecture of brain structures across subjects, but occurring in individual frequency bands. Carrying out a visual task resulted in the synchronisation of high-frequency oscillations in the specific brain regions responsible for the task execution.
These observations suggest that high-frequency oscillations convey within the brain ‘information packages’ from one small neuronal group to another.
The discovery of high-frequency oscillations synchronised between brain regions is the first evidence of the transmission and reception of such information packages in a context broader than individual locations in the brain. The finding also helps to understand how the healthy brain processes information and how this processing is altered in brain diseases.
Astrocytes phagocytose adult hippocampal synapses for circuit homeostasis
by Joon-Hyuk Lee, Ji-young Kim, Seulgi Noh, Hyoeun Lee, Se Young Lee, Ji Young Mun, Hyungju Park, Won-Suk Chung in Nature
Developing brains constantly sprout new neuronal connections called synapses as they learn and remember. Important connections — the ones that are repeatedly introduced, such as how to avoid danger — are nurtured and reinforced, while connections deemed unnecessary are pruned away. Adult brains undergo similar pruning, but it was unclear how or why synapses in the adult brain get eliminated.
Now, a team of researchers based in Korea has found the mechanism underlying plasticity and, potentially, neurological disorders in adult brains.
“Our findings have profound implications for our understanding of how neural circuits change during learning and memory, as well as in diseases,” said paper author Won-Suk Chung, an assistant professor in the Department of Biological Sciences at KAIST. “Changes in synapse number have strong association with the prevalence of various neurological disorders, such as autism spectrum disorder, schizophrenia, frontotemporal dementia, and several forms of seizures.”
Gray matter in the brain contains microglia and astrocytes, two complementary cells that, among other things, support neurons and synapses. Microglial are a frontline immunity defense, responsible for eating pathogens and dead cells, and astrocytes are star-shaped cells that help structure the brain and maintain homeostasis by helping to control signaling between neurons. According to Professor Chung, it is generally thought that microglial eat synapses as part of its clean-up effort in a process known as phagocytosis.
“Using novel tools, we show that, for the first time, it is astrocytes and not microglia that constantly eliminate excessive and unnecessary adult excitatory synaptic connections in response to neuronal activity,” Professor Chung said. “Our paper challenges the general consensus in this field that microglia are the primary synapse phagocytes that control synapse numbers in the brain.”
Professor Chung and his team developed a molecular sensor to detect synapse elimination by glial cells and quantified how often and by which type of cell synapses were eliminated. They also deployed it in a mouse model without MEGF10, the gene that allows astrocytes to eliminate synapses. Adult animals with this defective astrocytic phagocytosis had unusually increased excitatory synapse numbers in the hippocampus. Through a collaboration with Dr. Hyungju Park at KBRI, they showed that these increased excitatory synapses are functionally impaired, which cause defective learning and memory formation in MEGF10 deleted animals.
“Through this process, we show that, at least in the adult hippocampal CA1 region, astrocytes are the major player in eliminating synapses, and this astrocytic function is essential for controlling synapse number and plasticity,” Chung said.
Professor Chung noted that researchers are only beginning to understand how synapse elimination affects maturation and homeostasis in the brain. In his group’s preliminary data in other brain regions, it appears that each region has different rates of synaptic elimination by astrocytes. They suspect a variety of internal and external factors are influencing how astrocytes modulate each regional circuit, and plan to elucidate these variables.
“Our long-term goal is understanding how astrocyte-mediated synapse turnover affects the initiation and progression of various neurological disorders,” Professor Chung said. “It is intriguing to postulate that modulating astrocytic phagocytosis to restore synaptic connectivity may be a novel strategy in treating various brain disorders.”
Long-term self-renewing stem cells in the adult mouse hippocampus identified by intravital imaging
by Sara Bottes, Baptiste N. Jaeger, Gregor-Alexander Pilz, David J. Jörg, John Darby Cole, Merit Kruse, Lachlan Harris, Vladislav I. Korobeynyk, Izaskun Mallona, Fritjof Helmchen, François Guillemot, Benjamin D. Simons, Sebastian Jessberger in Nature Neuroscience
Stem cells create new nerve cells in the brain over the entire life span. One of the places this happens is the hippocampus, a region of the brain that plays a significant role in many learning processes. A reduction in the number of newly formed nerve cells has been observed, for example, in the context of depression and Alzheimer’s disease, and is associated with reduced memory performance in these conditions.
From stem cell behavior to the activity of genes in individual cells
In a study, the group around Sebastian Jessberger, a professor at the University of Zurich’s Brain Research Institute, has shown that stem cells in the hippocampus of mice are active over a period of several months. The researchers, led by PhD candidate Sara Bottes and postdocs Baptiste Jaeger and Gregor Pilz, employed state-of-the-art microscopy and genetic analyses (using single-cell RNA sequencing) of stem cells and their daughter cells to analyze the formation of new nerve cells. This enabled them to observe that specific stem cell populations are active over months and can divide repeatedly. This had already been suspected in earlier studies, but this is the first time there has been direct evidence. The researchers have also been able to use single-cell RNA sequencing of stem cells and their daughter cells to demonstrate that stem cells with different division behavior (few cell divisions as opposed to long-lasting stem cell activity) can be differentiated on the basis of their molecular composition and expression of genes.
Harnessing stem cells for therapeutic purposes
“Combining two modern methods — two-photon microscopy and single-cell RNA sequencing — has enabled us to identify precisely the stem cells that can divide over the course of months,” explains Jessberger. He adds that the evidence they have now presented of long-lasting stem cell division has implications for future therapeutic approaches: “We now know that there really are stem cells that divide over a period of many months. Single-cell RNA sequencing gives us our first insight into what genes are important in terms of the division behavior of individual cells.”
The new findings will form the basis of future endeavors to investigate in detail how specific genes control the activity of stem cells. Jessberger sums up the next research objectives: “Imaging and single-cell RNA sequencing have given us completely new insights that we’ll now use to be able to systematically regulate the activity of certain genes in the future. Since we now know that there are stem cells that can divide over a longer period, going forward we want to try to increase the division activity of these cells and thus the formation of new nerve cells, for example in the context of neurodegenerative conditions such as Alzheimer’s disease.”
Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning
by Elizabeth K. Unger, Jacob P. Keller, Michael Altermatt, Ruqiang Liang, Aya Matsui, Chunyang Dong, Olivia J. Hon, Zi Yao, Junqing Sun, Samba Banala, Meghan E. Flanigan, David A. Jaffe, Samantha Hartanto, Jane Carlen, et al. in Cell
Serotonin is a neurochemical that plays a critical role in the way the brain controls our thoughts and feelings. For example, many antidepressants are designed to alter serotonin signals sent between neurons. In an article, National Institutes of Health-funded researchers described how they used advanced genetic engineering techniques to transform a bacterial protein into a new research tool that may help monitor serotonin transmission with greater fidelity than current methods. Preclinical experiments, primarily in mice, showed that the sensor could detect subtle, real-time changes in brain serotonin levels during sleep, fear, and social interactions, as well as test the effectiveness of new psychoactive drugs. The study was funded, in part, by the NIH’s Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative which aims to revolutionize our understanding of the brain under healthy and disease conditions.
The study was led by researchers in the lab of Lin Tian, Ph.D., principal investigator at the University of California Davis School of Medicine. Current methods can only detect broad changes in serotonin signaling. In this study, the researchers transformed a nutrient-grabbing, Venus flytrap-shaped bacterial protein into a highly sensitive sensor that fluorescently lights up when it captures serotonin. Previously, scientists in the lab of Loren L. Looger, Ph.D., Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia, used traditional genetic engineering techniques to convert the bacterial protein into a sensor of the neurotransmitter acetylcholine. The protein, called OpuBC, normally snags the nutrient choline, which has a similar shape to acetylcholine. For this study, the Tian lab worked with Dr. Looger’s team and the lab of Viviana Gradinaru, Ph.D., Caltech, Pasadena, California, to show that they needed the added help of artificial intelligence to completely redesign OpuBC as a serotonin catcher.
The researchers used machine learning algorithms to help a computer ‘think up’ 250,000 new designs. After three rounds of testing, the scientists settled on one. Initial experiments suggested that the new sensor reliably detected serotonin at different levels in the brain while having little or no reaction to other neurotransmitters or similarly shaped drugs. Experiments in mouse brain slices showed that the sensor responded to serotonin signals sent between neurons at synaptic communications points. Meanwhile, experiments on cells in petri dishes suggested that the sensor could effectively monitor changes in these signals caused by drugs, including cocaine, MDMA (also known as ecstasy) and several commonly used antidepressants.
Finally, experiments in mice showed that the sensor could help scientists study serotonin neurotransmission under more natural conditions. For instance, the researchers witnessed an expected rise in serotonin levels when mice were awake and a fall as mice fell asleep. They also spotted a greater drop when the mice eventually entered the deeper, R.E.M. sleep states. Traditional serotonin monitoring methods would have missed these changes. In addition, the scientists saw serotonin levels rise differently in two separate brain fear circuits when mice were warned of a foot shock by a ringing bell. In one circuit — the medial prefrontal cortex — the bell triggered serotonin levels to rise fast and high whereas in the other — the basolateral amygdala — the transmitter crept up to slightly lower levels. In the spirit of the BRAIN Initiative, the researchers plan to make the sensor readily available to other scientists. They hope that it will help researchers gain a better understanding of the critical role serotonin plays in our daily lives and in many psychiatric conditions.
Development of a Machine-Learning-Guided Library Design Paradigm, A. Schematic showing the evolution of substrate preference from D-luc to 4’Br-luc. B. Random forest (RF) modeling and generalized linear modeling (GLM) were performed on 222 variants (see Table S2). C. The combination library in (B) was generated and 276 variants were screened for their preference for D-luc or 4’Br-luc. The top and bottom 10% of variants were sequenced. D. Table showing no difference between the mutation rate of the top and bottom 10% of variants (Fisher’s Exact test). Variants with no mutations were omitted from statistical analysis. E. Table showing the frequency of different mutations predicted by statistical modeling. ∗p values were calculated by Fisher’s exact test, comparing the mutated amino acid(s) to the native amino acid, and the top 10% to either the bottom 10% or the input data as noted. F,G. Comparison between the RF (F) and GLM (G) prediction and the actual data.
Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire
by Jesse D. Marshall, Diego E. Aldarondo, Timothy W. Dunn, William L. Wang, Gordon J. Berman, Bence P. Ölveczky in Neuron
In the last decade, Neuroscientists have made major advances in their quest to study the brain. They can assemble complete wiring diagrams and catalogue the brain’s many cell types. They’ve developed electrode arrays for recording electrical activity in individual neurons and placed itty bitty microscopes on the heads of mice to visualize their brain activity. However, almost shockingly, there are no tools to precisely measure the brain’s principal output — behavior — in freely moving animals.
Animal behavior is important to a broad range of disciplines, from neuroscience and psychology to ecology and pharmacology. Carefully studying a lab animal’s behavior allows researchers to model human disease and gauge the effectiveness of new drugs. Psychologists observe it to understand how animals learn and respond to reward and punishment, while neuroscientists study it to understand how the brain produces movements. The difficulty in capturing the intricate details of an animal’s natural behavior has forced scientists to study very simple and often unnatural tasks, leaving open the question of whether the insights gained can really lead to a general understanding of brain function.
But help may be on the way! In a paper researchers from Harvard University describe a newly developed behavioral monitoring system, CAPTURE (continuous appendicular and postural tracking using retroreflector embedding), that combines motion capture and deep learning to continuously track the three-dimensional movements of freely behaving animals. In the study, lead author Jesse Marshall, postdoctoral fellow in the Department of Organismic and Evolutionary Biology, Harvard University, and senior author Bence Ölveczky, Professor in the Department of Organismic and Evolutionary Biology, Harvard University, attached markers to rats’ head, trunk, and limbs and used CAPTURE to record their natural behavior continuously for weeks.
Marshall’s fascination with the concept of CAPTURE began as a graduate student working on a mouse model of Parkinson’s. “We had developed really elaborate approaches to study how the brain is disrupted in parkinsonian mice, but our ability to measure their behavioral deficits was a far cry from the nuanced ways we can assess the impact of Parkinson’s on human behavior,” said Marshall. “It became clear to me that a major reason why so many drugs tested in mice don’t translate to humans is that our ability to measure their effects on behavior is quite limited.”
Marshall grew weary of the difficulties and constraints in relating brain activity to the animal’s behavior and the field’s emphasis on the ‘brain-first’ approach. But when he joined Ölveczky’s lab he was met with interest and support. Ölveczky too recognized the primary importance of behavior and was eager to develop new tools to measure it.
“Our lab studies how skilled movements are learned and generated by the brain,” said Ölveczky. “Traditionally, these studies are done by designing specific tasks and relating brain activity to simple behavioral readouts i.e. did the animal push this lever? Did the animal lick this port? Such observations tell us whether our rats solve the task, but says nothing about how they do it and that’s exactly what we are interested in; how the brain learns and controls skilled movements. Getting at this required more precise and sophisticated readouts of behavior.”
Marshall researched various technologies and settled on motion capture, the gold standard for measuring movements in humans and a technology perfected by Hollywood animators. He spent the first six months figuring out how to attach markers to his animals. He tried tattoos, adhesives, and hair dyes — all without luck — before settling on a somewhat unorthodox approach: body-piercings. Working with local veterinarians, the team engineered custom markers made of specialized reflective glass that are attached to the animals like tiny earrings. Marshall affixed these markers to 20 locations on the animal’s head, trunk and limbs so he could reconstruct the three-dimensional position and configuration of the animal’s major joints, so also the movements of its body.
“In contrast to traditional motion capture in humans, which is done in short bursts, we collected data continuously, 24/7,” said Marshall. “This allowed us to really quantify everything rats do in their normal lives — an atlas for behavior.”
The team then looked at how behaviors change in disease and in response to drugs. For drugs, they administered caffeine and amphetamines to the animals. While both stimulants caused the rats to move around more, they did so in different ways. After caffeine, the animals ran and explored their cage as normal animals do when highly aroused. However, when on amphetamine their behavior shifted in strange new ways; the animals ran around in repeated sequential patterns.
For disease, the team studied a rat model of Fragile X syndrome, a form of autism, and was able to identify atypical patterns of grooming that hadn’t been previously described. Scientists have long suspected that disrupted grooming could be used to model the motor stereotypies (repetitive movements or sounds) observed in autism, but before CAPTURE alterations in grooming patterns have been challenging to measure and reproduce. “For disease models, you really need to evaluate how the disease affects behavior and whether a particular compound or drug can reverse the specific deficits” said Ölveczky. “These effects can be very subtle and the more precise your behavioral measurements are, the better handle you’ll have on the disease. This is one of the uses for this technology.”
The team continues their studies by combining CAPTURE with neural recordings to describe the relationship between brain activity and behavior across the full set of natural behaviors an animal performs. They are also working with Google DeepMind to use CAPTURE to model animal behavior using deep neural networks. These studies will help model how the brain produces behavior and potentially make new advances in artificial intelligence possible.
“These technological developments mean that we can now finally open the door to understanding the organization of natural behavior and its biomechanical and neurobiological foundations,” said Ölveczky.
Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference
by Carlos Enrique Gutierrez, Henrik Skibbe, Ken Nakae, Hiromichi Tsukada, Jean Lienard, Akiya Watakabe, Junichi Hata, Marco Reisert, Alexander Woodward, Yoko Yamaguchi, Tetsuo Yamamori, Hideyuki Okano, Shin Ishii, Kenji Doya in Scientific Reports
Scientists in Japan’s brain science project have used machine intelligence to improve the accuracy and reliability of a powerful brain-mapping technique, a new study reports.
Their development, published on December 18th in Scientific Reports, gives researchers more confidence in using the technique to untangle the human brain’s wiring and to better understand the changes in this wiring that accompany neurological or mental disorders such as Parkinson’s or Alzheimer’s disease.
“Working out how all the different brain regions are connected — what we call the connectome of the brain — is vital to fully understand the brain and all the complex processes it carries out,” said Professor Kenji Doya, who leads the Neural Computation Unit at the Okinawa Institute of Science and Technology Graduate University (OIST).
To identify connectomes, researchers track nerve cell fibers that extend throughout the brain. In animal experiments, scientists can inject a fluorescent tracer into multiple points in the brain and image where the nerve fibers originating from these points extend to. But this process requires analyzing hundreds of brain slices from many animals. And because it is so invasive, it cannot be used in humans, explained Prof. Doya.
However, advances in magnetic resonance imaging (MRI) have made it possible to estimate connectomes non-invasively. This technique, called diffusion MRI-based fiber tracking, uses powerful magnetic fields to track signals from water molecules as they move — or diffuse — along nerve fibers. A computer algorithm then uses these water signals to estimate the path of the nerve fibers throughout the whole brain.
But at present, the algorithms do not produce convincing results. Just like how photographs can look different depending on the camera settings chosen by a photographer, the settings — or parameters — chosen by scientists for these algorithms can generate very different connectomes.
“There are genuine concerns with the reliability of this method,” said Dr. Carlos Gutierrez, first author and postdoctoral researcher in the OIST Neural Computation Unit. “The connectomes can be dominated by false positives, meaning they show neural connections that aren’t really there.”
Furthermore, the algorithms struggle to detect nerve fibers that stretch between remote regions of the brain. Yet these long-distance connections are some of the most important for understanding how the brain functions, Dr. Gutierrez said.
In 2013, scientists launched a Japanese government-led project called Brain/MINDS (Brain Mapping by Integrated Neurotechnologies for Disease Studies) to map the brains of marmosets — small nonhuman primates whose brains have a similar structure to human brains.
The brain/MINDS project aims to create a complete connectome of the marmoset brain by using both the non-invasive MRI imaging technique and the invasive fluorescent tracer technique.
“The data set from this project was a really unique opportunity for us to compare the results from the same brain generated by the two techniques and determine what parameters need to be set to generate the most accurate MRI-based connectome,” said Dr. Gutierrez.
In the current study, the researchers set out to fine-tune the parameters of two different widely-used algorithms so that they would reliably detect long-range fibers. They also wanted to make sure the algorithms identified as many fibers as possible while minimally pinpointing ones that were not actually present.
Instead of trying out all the different parameter combinations manually, the researchers turned to machine intelligence.
To determine the best parameters, the researchers used an evolutionary algorithm. The fiber tracking algorithm estimated the connectome from the diffusion MRI data using parameters that changed — or mutated — in each successive generation. Those parameters competed against each other and the best parameters — the ones that generated connectomes that most closely matched the neural network detected by the fluorescent tracer — advanced to the next generation.
The researchers tested the algorithms using fluorescent tracer and MRI data from ten different marmoset brains.
But choosing the best parameters wasn’t simple, even for machines, the researchers found. “Some parameters might reduce the false positive rate but make it harder to detect long-range connections. There’s conflict between the different issues we want to solve. So deciding what parameters to select each time always involves a trade-off,” said Dr. Gutierrez.
Throughout the multiple generations of this “survival-of-the-fittest” process, the algorithms running for each brain exchanged their best parameters with each other, allowing the algorithms to settle on a more similar set of parameters. At the end of the process, the researchers took the best parameters and averaged them to create one shared set.
“Combining parameters was an important step. Individual brains vary, so there will always be a unique combination of parameters that works best for one specific brain. But our aim was to come up with the best generic set of parameters that would work well for all marmoset brains,” explained Dr. Gutierrez.
The team found that the algorithm with the generic set of optimized parameters also generated a more accurate connectome in new marmoset brains that weren’t part of the original training set, compared to the default parameters used previously.
The striking difference between the images constructed by algorithms using the default and optimized parameters sends out a stark warning about MRI-based connectome research, the researchers said.
“It calls into question any research using algorithms that have not been optimized or validated,” cautioned Dr. Gutierrez.
In the future, the scientists hope to make the process of using machine intelligence to identify the best parameters faster, and to use the improved algorithm to more accurately determine the connectome of brains with neurological or mental disorders.
“Ultimately, diffusion MRI-based fiber tracking could be used to map the whole human brain and pinpoint the differences between healthy and diseased brains,” said Dr. Gutierrez. “This could bring us one step closer to learning how to treat these disorders.”
Evolution of ocular defects in infant macaques following in utero Zika virus infection
by Glenn Yiu, Sara M. Thomasy, M. Isabel Casanova, Alexander Rusakevich, Rebekah I. Keesler, Jennifer Watanabe, Jodie Usachenko, Anil Singapuri, Erin E. Ball, Eliza Bliss-Moreau, Wendi Guo, Helen Webster, Tulika Singh, Sallie Permar, Amir Ardeshir, Lark L. Coffey, Koen K.A. Van Rompay in JCI Insight
While the SARS-CoV-2 virus has dominated the news this past year, researchers continue to study the health effects of the Zika virus, which has been reported in 86 countries globally.
The Zika virus is primarily transmitted by the bite of an infected mosquito from the Aedes genus. However, it can also be passed through sexual contact, blood transfusions, organ transplants, and between mother and baby during pregnancy. The virus has been documented to cause a range of birth defects, including microcephaly and various neurological, musculoskeletal, and eye abnormalities.
A new study from Glenn Yiu, associate professor in the Department of Ophthalmology, and Koen Van Rompay, a core scientist at the California National Primate Research Center, found that Zika infection during the first trimester of pregnancy can impact fetal retinal development and cause congenital ocular anomalies. The virus does not appear to affect ocular growth postnatally, however.
“It has been known that congenital infection with the Zika virus can lead to eye defects, but it was unclear if the virus continues to replicate or affect eye development after birth,” Yiu said. “Our study in rhesus monkeys suggest that the virus primarily affects fetal development during pregnancy, but not the growth of eye after birth.”
In this collaboration between the UC Davis Eye Center and the California National Primate Research Center, two pregnant rhesus monkeys were infected with Zika virus late in the first trimester. The ocular development of the Zika-exposed infants was then studied for two years following their birth.
Ocular birth defects
The Zika-exposed infant monkeys did not display microcephaly or apparent neurological or behavioral deficits. The infants did exhibit several ocular birth defects, however. The defects included large colobomas, a missing gap in the eye due to abnormal development. The Zika-exposed infant monkeys also exhibited a loss of photoreceptors — the light-sensing cells of the retina — and retinal ganglion neuron, which helps transmit visual information to the brain.
Despite congenital ocular malformations at birth, their eyes appeared to follow normal development during their first two years.
The findings suggest that ocular defects due to Zika infection primarily occur in utero and likely do not have a continued impact on ocular development after birth.
Rhesus macaques are natural hosts of the virus and share similar immune and ocular characteristics to humans, including blood-retinal barrier characteristics and the unique presence of a macula, making them superior animal models of the infection than typical laboratory animals like mice and rats. The findings were published in JCI Insight, an open-access peer-reviewed journal dedicated to biomedical research.
History of infant macaques exposed in utero to ZIKV infection. (A) Schematic of experimental design. Pregnant macaques were inoculated by both i.v. and intra-amniotic routes between GDs 42 and 53 followed by frequent monitoring. Whereas 4 dams had fetal loss or stillbirth, the other 2 animals delivered infants that were dam-reared, subsequently weaned and then housed together until they were euthanized at approximately 2 years of age. The patterns of viral RNA levels in plasma (B) and amniotic fluid (C )of the pregnant dams that delivered live infants were similar to those for animals whose fetuses died and reflects prolonged virus replication. The dotted lines show the limit of detection. (D) The 2 ZIKV-exposed infants had normal weight gain. Green dots indicate historical control data (15,585 data points collected from n = 284 female animals over the first 2 years of life). (E) Anti-ZIKV antibodies in plasma of dams and infants measured by whole-virion ELISA, showing rapid loss of ZIKV IgG in congenitally exposed infants after birth and gradual decline of IgG in ZIKV-infected dams. Magnitude of ZIKV-specific IgG is expressed as the log of ED50. ZIKV, Zika virus; GDs, gestational days; ED50, 50% of maximal effective dilution.
A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition
by Ali Moin, Andy Zhou, Abbas Rahimi, Alisha Menon, Simone Benatti, George Alexandrov, Senam Tamakloe, Jonathan Ting, Natasha Yamamoto, Yasser Khan, Fred Burghardt, Luca Benini, Ana C. Arias, Jan M. Rabaey in Nature Electronics
A new device developed by engineers can recognize hand gestures based on electrical signals detected in the forearm. The system, which couples wearable biosensors with artificial intelligence (AI), could one day be used to control prosthetics or to interact with almost any type of electronic device.
That’s one of the goals of a new device developed by engineers at the University of California, Berkeley, that can recognize hand gestures based on electrical signals detected in the forearm. The system, which couples wearable biosensors with artificial intelligence (AI), could one day be used to control prosthetics or to interact with almost any type of electronic device.
“Prosthetics are one important application of this technology, but besides that, it also offers a very intuitive way of communicating with computers.” said Ali Moin, who helped design the device as a doctoral student in UC Berkeley’s Department of Electrical Engineering and Computer Sciences. “Reading hand gestures is one way of improving human-computer interaction. And, while there are other ways of doing that, by, for instance, using cameras and computer vision, this is a good solution that also maintains an individual’s privacy.”
To create the hand gesture recognition system, the team collaborated with Ana Arias, a professor of electrical engineering at UC Berkeley, to design a flexible armband that can read the electrical signals at 64 different points on the forearm. The electrical signals are then fed into an electrical chip, which is programmed with an AI algorithm capable of associating these signal patterns in the forearm with specific hand gestures.
The team succeeded in teaching the algorithm to recognize 21 individual hand gestures, including a thumbs-up, a fist, a flat hand, holding up individual fingers and counting numbers.
“When you want your hand muscles to contract, your brain sends electrical signals through neurons in your neck and shoulders to muscle fibers in your arms and hands,” Moin said. “Essentially, what the electrodes in the cuff are sensing is this electrical field. It’s not that precise, in the sense that we can’t pinpoint which exact fibers were triggered, but with the high density of electrodes, it can still learn to recognize certain patterns.”
Like other AI software, the algorithm has to first “learn” how electrical signals in the arm correspond with individual hand gestures. To do this, each user has to wear the cuff while making the hand gestures one by one.
However, the new device uses a type of advanced AI called a hyperdimensional computing algorithm, which is capable of updating itself with new information.
For instance, if the electrical signals associated with a specific hand gesture change because a user’s arm gets sweaty, or they raise their arm above their head, the algorithm can incorporate this new information into its model.
“In gesture recognition, your signals are going to change over time, and that can affect the performance of your model,” Moin said. “We were able to greatly improve the classification accuracy by updating the model on the device.”
Another advantage of the new device is that all of the computing occurs locally on the chip: No personal data are transmitted to a nearby computer or device. Not only does this speed up the computing time, but it also ensures that personal biological data remain private.
“When Amazon or Apple creates their algorithms, they run a bunch of software in the cloud that creates the model, and then the model gets downloaded onto your device,” said Jan Rabaey, the Donald O. Pedersen Distinguished Professor of Electrical Engineering at UC Berkeley and senior author of the paper. “The problem is that then you’re stuck with that particular model. In our approach, we implemented a process where the learning is done on the device itself. And it is extremely quick: You only have to do it one time, and it starts doing the job. But if you do it more times, it can get better. So, it is continuously learning, which is how humans do it.”
While the device is not ready to be a commercial product yet, Rabaey said that it could likely get there with a few tweaks.
“Most of these technologies already exist elsewhere, but what’s unique about this device is that it integrates the biosensing, signal processing and interpretation, and artificial intelligence into one system that is relatively small and flexible and has a low power budget,” Rabaey said.
Endogenous Cholinergic Signaling Modulates Sound-evoked Responses of Medial Nucleus of Trapezoid Body
by Chao Zhang, Nichole L. Beebe, Brett R. Schofield, Michael Pecka, R. Michael Burger in The Journal of Neuroscience
For the first time, researchers have provided physiological evidence that a pervasive neuromodulation system — a group of neurons that regulate the functioning of more specialized neurons — strongly influences sound processing in an important auditory region of the brain. The neuromodulator, acetylcholine, may even help the main auditory brain circuitry distinguish speech from noise.
“While the phenomenon of these modulators’ influence has been studied at the level of the neocortex, where the brain’s most complex computations occur, it has rarely been studied at the more fundamental levels of the brain,” says R. Michael Burger, professor of neuroscience at Lehigh University.
Burger and Lehigh Ph.D. student Chao Zhang — along with collaborators Nichole Beebe and Brett Schofield of Northeast Ohio Medical University and Michael Pecka of Ludwig-Maximilians University Munich — conducted the research.
“This study will likely bring new attention in the field to the ways in which circuits like this, widely considered a ‘simple’ one, are in fact highly complex and subject to modulatory influence like higher regions of the brain,” says Burger.
The team conducted electrophysiological experiments and data analysis to demonstrate that the input of the neurotransmitter acetylcholine, a pervasive neuromodulator in the brain, influences the encoding of acoustic information by the medial nucleus of the trapezoid body (MNTB), the most prominent source of inhibition to several key nuclei in the lower auditory system. MNTB neurons have previously been considered computationally simple, driven by a single large excitatory synapse and influenced by local inhibitory inputs. The team demonstrates that in addition to these inputs, acetylcholine modulation enhances neural discrimination of tones from noise stimuli, which may contribute to processing important acoustic signals such as speech. Additionally, they describe novel anatomical projections that provide acetylcholine input to the MNTB.
Burger studies the circuit of neurons that are “wired together” in order to carry out the specialized function of computing the locations from which sounds emanate in space. He describes neuromodulators as broader, less specific circuits that overlay the more highly-specialized ones.
“This modulation appears to help these neurons detect faint signals in noise,” says Burger. “You can think of this modulation as akin to shifting an antenna’s position to eliminate static for your favorite radio station.”
“In this paper, we show that modulatory circuits have a profound effect on neurons in the sound localization circuitry, at very low foundational level of the auditory system,” adds Zhang.
In addition, during the course of this study, the researchers identified for the first time a set of completely unknown connections in the brain between the modulatory centers and this important area of the auditory system.
Burger and Zhang think these findings could shed new light on the contribution of neuromodulation to fundamental computational processes in auditory brainstem circuitry, and that it also has implications for understanding how other sensory information is processed in the brain.
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