ETK Introduction: Argonne National Laboratory houses the National Center for Brain Mapping (NCBM) and works in cooperation with the University of Chicago, which also houses the Grossman Institute for Quantitative Biology and Human Behavior
Youtube presentations by Dr. Bobby Kasthuri, neuroscience researcher, of Argonne National Laboratory (10/25/17)
I. The Supercomputer That Could Map the Human Brain
A planned “exascale” supercomputer may be powerful enough to map the human brain.
BySamia BouzidWednesday, June 6, 2018 NOVA Next
Bobby Kasthuri has a problem.
In an effort to understand, on the finest level, what makes us human, he’s set out to create a complete map of the human brain: to chart where every neuron connects to every other neuron. The problem is, the brain has more connections than the Milky Way has stars. Just one millionth of the organ contains more information than all the written works in the Library of Congress. A map of the brain would represent the single largest dataset ever collected about anything in the history of the world.
A supercomputer, called Mira, at Argonne National Laboratory
Making that map seems like a task that could consume not just one lifetime , but dozens. Yet in just three years, it might just be possible.
Kasthuri, a neuroscientist at Argonne National Laboratory, is one of many scientists whose research will use a new supercomputer the lab is building, which is scheduled to be deployed by 2021. The computer, called Aurora 21 , will run one quintillion operations in parallel—a billion billion calculations—putting it on par with the processing power of the human brain. For the U.S., which has lagged behind China in an intensifying supercomputing race since 2013, this milestone—exascale computing power—is both a national status symbol and a scientific game-changer.
The demands for a simulation of the brain are immense, and just building a computer like Aurora 21 is a massive undertaking. The finished computer is expected to cost hundreds of millions of dollars. It will occupy around a quarter-acre, have thousands of miles of wiring, and, if supercomputer trends continue, draw as much electricity as a medium-sized city.
Aurora 21 is designed for more than just simulating our brains . It will be able to perform computationally demanding simulations for tasks as diverse as predicting the weather, tracing the evolution of the cosmos, and understanding how new medicines will interact with the human body.
“The metric of success is what kind of science you enable.”
Every computing milestone brings new possibilities for research, says Rick Stevens, Argonne’s associate laboratory director for computing, environment, and life sciences. But this one holds particular promise for neuroscience. In providing the capacity to simulate the brain, the supercomputer could illuminate the largely mysterious processes that underpin human learning, behavior, and even psychiatric disorders.
The promise of breakthroughs like these drive this arms race, says Michela Taufer, professor of high-performance computing at the University of Tennessee, Knoxville. The victor won’t necessarily have the biggest computer or the most medals, she says. “The metric of success is what kind of science you enable.”
Mapping the Brain
Early in his career, Kasthuri was struck by the fact that a fruit fly hatches from its egg fully competent, already knowing how to fly, while a human baby is born so utterly helpless that it’ll die of starvation without someone to feed it. He knew that this helplessness, the years we spend developing into functional adults, had to be key to what makes us human.
“We trade off being competent for being able to learn almost anything,” Kasthuri says. We’re born with a blank slate of some 100 billion neurons that get arranged and rearranged over time to create the hardware we run on.
But no one really understands how, exactly, that hardware is wired. So it’s at the level of these neurons that Kasthuri has set out to explore the brain.
With the help of Aurora 21, he’ll be able to piece together millions of two-dimensional images, reconstructing the brain in three dimensions—essentially creating a map, known as a connectome. In many ways, it’s like a city map. “That map of the streets in the city is going to tell you something about what that dynamic city is like,” Kasthuri says, like which parts sustain the most traffic and which parts are directly connected to others.
An exascale computer would be the first machine capable of crunching through such a massive amount of data at an efficient pace—in theory, letting scientists like Kasthuri map multiple brains. “There’s no way we want to do just one brain,” Kasthuri says. The most interesting findings, he expects, will come from comparisons. How does the connectome vary between two adults with different skills? Between an adult and a baby?
Kasthuri even hopes to compare a human brain with that of an octopus. Our last common ancestor was probably some worm-like creature that lived 600 million years ago , meaning that the octopus, which can learn and solve problems much like humans, evolved independently. But Kasthuri wonders what structural principles our brains share and what those principles reveal about how we think and learn. “Is there only one plan for a brain that can problem-solve?” Kasthuri wonders. “Or is there more than one way to skin that cat?”
The Aurora 21 supercomputer will perform computationally demanding simulations for tasks as diverse as predicting the weather, tracing the evolution of the cosmos, and understanding how new medicines will interact with the human body.
That’s not something an unaided human could discern. It would be like looking at New York City’s roads, subway lines, air traffic, and shipping lanes and trying to understand where everyone is going. Fortunately, it’s just the job for a high-performance computer. Stevens, who has been working on plans for Aurora 21 since 2007, jokes that it helps that supercomputers don’t get bored poring over millions of images. “We need this kind of idiot-savant brain to understand the real brain,” he says.
Beyond the Structural Map
The structural map is just one part of the story, though. With just a street map, Kasthuri says, you never really know where traffic might build up or why. Likewise, the structure of the brain is just a starting point.
Kasthuri hopes to combine a structural map with collaborators’ maps of the brain’s electrical activity, or “traffic,” to see how the two together influence a person’s learning and behavior.
Many disorders, such as autism and schizophrenia, are likely rooted in anomalies in the structure of neurons.
If successful, the technology could make waves in the medical field. Susie Huang, a radiologist at Massachusetts General Hospital and researcher with the Human Connectome Project , says that many disorders, such as autism and schizophrenia, are likely rooted in anomalies in the structure of neurons.
“A lot of how we diagnose disease is looking under a microscope and saying, ‘OK, these cells are altered, so therefore you have this kind of disease,’ ” she says. But MRIs and other current brain-imaging methods are too coarse and can’t easily suss out such anomalies. They can’t tie together cause and effect.
A fine-grained map of the brain could change that, she says, and help doctors diagnose psychiatric disorders or possibly even predict them.
A Changing Field
For other neuroscientists, like Columbia University’s Rafael Yuste, the most exciting part is not the map itself but how a national lab for neuroscience could transform the field. “Neuroscience has operated always a little bit like a ma-and-pa store,” he says, with small labs working within the limits of their budgets and the tools they can develop. But more recently, it’s begun to outgrow that model.
Kasthuri says that neuroscience has quietly evolved into a big-data field—“and we didn’t realize it as a community till five or ten years ago.”
Other fields have had to cope with similar growing pains. It’s a phase that the field of physics outgrew decades ago as researchers around the world started getting their data from large observatories and particle accelerators. Now, Yuste and Kasthuri believe, neuroscience needs to scale up, too.
Aurora 21 will help catalyze that transformation. It’s happened in other fields like physics, where massive, expensive tools push scientists to work together more by sharing time on the machines to gain access to their potential for discovery. In the process, those collaborations advance the field in a way that a lone machine or hundreds of independent scientists never could. Yuste hopes that this is the beginning of more collaborative and ambitious neuroscience.
Yuste led the team that first proposed a detailed map of the brain’s activity in the summer of 2011 at a meeting discussing the future of the field. He argued that the holy grail of neuroscience was to break the neural code—that is, to read the activity of every neuron in the brain and map that activity to a behavior. It was a goal separate from Kasthuri’s connectome but similar in scope. “Many people were very critical,” Yuste recalls. “They said that you couldn’t do it—it was impossible.”
Then, he says, George Church, one of the pioneers of the Human Genome Project , stood up from the seat next to him. Church said he’d heard these criticisms before. People had said similar things about the Human Genome Project, and they’d been wrong. Yuste says that’s when the conversation shifted.
Church is more modest about his role. “I’m not one to twist arms,” Church says. “There are a lot of things that I’ve run up the flagpole that, if nobody salutes, I just slink away with my tail between my legs.” But he says the moment was right for this one. The technology, the excitement, the ambition—they were all there.
Despite the stiff competition between the U.S. and China, ultimately the supercomputer age is not about any one country or any one project, Taufer says, but about the diversity of science being made possible. “I look at this machine and I see a key to a solution.”
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Yuste and Church supported what ended up being the multi-billion-dollar BRAIN Initiative, an Obama-era grand challenge which funds research that attempts to understand how the brain works. Both Yuste’s and Kasthuri’s work on mapping the brain are just two of the “impossible” projects that the initiative has set in motion.
“It’s not exactly analogous, but I often think of the moonshot,” Kasthuri says. He thinks about how the average age of a NASA scientist was only 28 when the first crew landed on the moon in 1969 and about how the challenge fascinated a generation of scientists.
Kasthuri can’t be sure how his project will play out. In some respect, it’ll probably fail, he says with a laugh. “It seems enormous and monumental. A lot of those things don’t work,” he adds. “That’s just the nature of trying to do something incredibly hard.” But he’s inspired by having a challenge that captures his imagination.
For Taufer, the high-performance computing scientist, supercomputers like Aurora 21 swing open the door to possibilities that don’t exist in real life—the ability to test medicine, infrastructure, even weaponry free of the cost, safety, and ethical concerns that constrain real-life experiments.
But as grandiose as the possibilities are, Taufer emphasizes that the applications will work their way into our everyday lives, from predicting the weather to assessing the safety of our aging bridges and fighting common diseases like Alzheimer’s.
A journey to map the mind
UChicago-Argonne project blends science, computation to chart billions of brain cells
By Rob Mitchum | Photo by Mark Lopez/Argonne National Laboratory
If you want to know how a machine works, it helps to look inside. Crack open the case and look at how it’s wired together; you might need an engineering degree, a microscope and a lot of time, but eventually you can puzzle out what makes any given device tick.
But can that same approach work for the most amazing machine we know—one capable of making complex calculations in a fraction of a second, while using less energy than a common light bulb?
Reverse engineering the human brain is one of the great scientific challenges of our time, and scientists at the University of Chicago and Argonne National Laboratory are combining new techniques in microscopy, neurobiology and computing to reveal the brain’s inner mechanisms in unprecedented detail.
Neuroscientists discuss what mapping the brain means for the advancement in understanding human behavior, mental illness, energy efficiency and artificial intelligence.
(Video by UChicago Medicine)
Treating the brain as a machine is not a far-fetched metaphor. In the abstract, the brain is an electrochemical computer, operating on electrical impulses and chemical signals sent between cells. Though the individual pieces may be small, on the scale of mere nanometers, drawing the wiring diagram for this machinery is theoretically possible, and has been done for very simple organisms such as the roundworm C. elegans.
But the size and complexity of the human brain create far bigger challenges. Scientists estimate that the brain contains nearly 100 billion neurons, the basic type of brain cell. Each of those neurons makes tens of thousands of contacts with other cells, bringing the number of connections into the quadrillions, or a million billion.
A complete map of these connections—sometimes called the connectome—would be nothing less than the largest dataset ever created. But within that massive inventory could lie answers to some of the most elusive scientific questions: the fundamental rules of cognition, explanations for many mental illnesses, even the biological factors that separate humans from other animals.
“It’s a huge theory of neuroscience that all of our behaviors, all of our pathologies, all of our illnesses, all of the learning that we do, is all due to changes in the connections between brain cells,” said Narayanan “Bobby” Kasthuri, assistant professor of neurobiology at the University and neuroscience researcher at Argonne. “It’s probably the equivalent of the standard model in physics, but in neuroscience.”
“We just needed somebody crazy enough to imagine this was a real possibility.”
—Prof. Peter Littlewood on neuroscientist Bobby Kasthuri
‘Soft, squishy things’
Since the time of Hippocrates and Herophilus, scientists have placed the location of the mind, emotions and intelligence in the brain. For centuries, this theory was explored through anatomical dissection, as the early neuroscientists named and proposed functions for the various sections of this unusual organ. It wasn’t until the late 19th century that Camillo Golgi and Santiago Ramón y Cajal developed the methods to look deeper into the brain, using a silver stain to detect the long, stringy cells now known as neurons and their connections, called synapses.
Today, neuroanatomy involves the most powerful microscopes and computers on the planet. Viewing synapses, which are only nanometers in length, requires an electron microscope imaging a slice of brain thousands of times thinner than a sheet of paper. To map an entire human brain would require 300,000 of these images, and even reconstructing a small three-dimensional brain region from these snapshots requires roughly the same supercomputing power it takes to run an astronomy simulation of the universe.
Fortunately, both of these resources exist at Argonne, where, in 2015, Kasthuri was the first neuroscientist ever hired by the U.S. Department of Energy laboratory. Peter Littlewood, the former director of Argonne who brought him in, recognized that connectome research was going to be one of the great big data challenges of the coming decades, one that UChicago and Argonne were perfectly poised to tackle.
“All real advances in science are advances in technology,” said Littlewood, professor of physics at the University. “What we were doing at Argonne with X-rays and electron microscopy was going to produce a straightforward change in the way we could process data in high resolution. We just needed somebody crazy enough to imagine this was a real possibility and who also owned the technology and understanding to do it.”
Two neighboring neurons (blue and green) form multiple synaptic connections (orange) with each other.
(Illustration courtesy of Kasthuri et. al.)
Kasthuri brought with him automated methods he developed for efficiently mapping the brain. A diamond knife with an edge only five atoms thick cuts 50-nanometer-thin slices of human, mouse or even octopus brain, which float away on water to a conveyer belt that takes them sequentially beneath the gaze of an electron microscope.
“You look at Bobby’s setup, it’s like somebody slicing cheese at the deli,” said Michael Papka, SM’02, PhD’09, director of the Argonne Leadership Computing Facility and professor of computer science at Northern Illinois University. “It’s not the world that computer scientists normally work with, it’s soft squishy things. I find it a fascinating pipeline.”
That’s the easy part. The Theta supercomputer at Argonne clocks out at 11.69 petaflops—between 11,000 and 12,000 million million operations per second. It’s typically used for processing particle physics data from the Large Hadron Collider at CERN or running models of universal expansion that assist the search for dark matter. Kasthuri’s data, Papka said, is beyond the capabilities of this world-class machine; the data has to be simplified, or downscaled, before it can be analyzed.
“Bobby talks about the number of neurons and number of galaxies, how complexity-wise they’re roughly the same,” Papka said. “Actually, the brain’s probably even more complex.”
But the close relationship between the University and Argonne provides a unique location to untangle this knot.
“At most other universities, I’d just have to give up this idea,” Kasthuri said. “Even a small part of a brain I could never map, because even 1 percent of a mouse brain is something like 1,000 terabytes of data. No other university in the world, I think, could conceivably handle that.”
Neuroscientist Bobby Kasthuri discusses the possibility of mapping the entire human brain.
(Video by UChicago Medicine)
To reduce this mind-boggling complexity into more practical science, Kasthuri is starting (relatively) small. Where other high-profile connectome projects have focused on building a complete map of the human brain, Kasthuri is focusing first on comparisons: between young brains and old, between animal brains and human, between “normal” brains and the brains of people with mental disorders.
“I think the only way we’re going to understand the brain is by comparing it to other things,” he said. “As far as we know, a neuron in the mouse looks like a neuron in the human. The ion channels in a mouse neuron are the same; the genes are the same. We’re left with this idea that the difference between a human brain and a mouse brain is in the pattern of connections, the number of neurons and, therefore, the number of connections in those two brains.”
One approach is to compare a common segment of brain from two very different organisms, such as the mouse and the octopus. The largest invertebrate brain belongs to the octopus, and cephalopod species have been well studied with neuropsychological and genetic methods by scientists such as Clifton Ragsdale, professor of neurobiology at the University.
Asst. Prof. Bobby Kasthuri and Prof. Clifton Ragsdale have proposed mapping the brain of the octopus.
(Photo by Vlad Tchompalov)
In a proposed project with Ragsdale, Kasthuri will map and compare the visual brain areas of the mouse and octopus—the latter an extremely visual species, yet one that views the world differently from most mammals, focusing on form instead of movement.
“If you’re going to apply connectomic techniques to a particular system in octopus, then you should pick something like vision,” Ragsdale said. “What’s striking about it is the eye looks vertebrate-like, but as soon as you hit the photoreceptors and the optic lobe, those are invertebrate structures. So we might get insight at a circuit level into how cephalopod mollusks carry out their visual processing, and knowing the key elements of the circuitry is essential to begin to have any chance of understanding how neural circuits in invertebrates and vertebrates underlie behavior.”
Another comparison, this one within species, could offer answers to a classic dilemma: Can you teach an old dog, or human, new tricks? Kasthuri, fascinated by the ability of children to easily acquire new skills or assimilate culturally compared to adults’ difficulty with the same tasks, wants to compare young and old brains.
Beyond the age curve of learning, such a study could also address fundamental questions about how the adult brain is built—one connection at a time like a mosaic or pruned from a surplus of neurons and connections like a topiary.
“There’s some deep trade-off in our brains between having a young brain capable of learning anything but not really being good at any of it, and having an adult brain being good at a few things, but having no capacity to learn,” Kasthuri said. “There has to be a physical basis to this phenomenon at some level, and I want to know what that is.”
John Maunsell, director of the Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, studies how brain signals generate perceptions and guide behavior.
(Photo by Rob Kozloff)
Unlocking the possibilities
Extending the applications of connectome research to medicine may be a longer road. Though scientists have found evidence of neuroanatomical differences in people suffering from schizophrenia and behavioral disorders, the link remains controversial.
Instead, the near-term benefits of brain mapping will be to equip scientists studying more elemental links between brain and behavior with a deeper understanding of the organ’s complex mechanical structure.
“You won’t understand the brain with just the wiring diagram, but you also probably won’t understand the brain without that wiring diagram,” said John Maunsell, the Albert D. Lasker Professor of neurobiology and director of the Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior. He is co-chair of a working group, assembled by the NIH Director’s Advisory Committee, to review progress on the U.S. BRAIN initiative and advise on future directions.
“On the basic science side, there’s lots of different questions that haven’t been approachable, things I think are just sitting there waiting to be done as soon as we can get some really solid data on what’s changing synaptically,” Maunsell said.
“At most other universities, I’d just have to give up this idea.”
—Asst. Prof. Bobby Kasthuri
Another potential application spills outside of neurobiology itself into the world of computer engineering. Computer scientists already take inspiration from the brain in how they design both hardware and algorithms — one popular machine learning technique used to make predictions on data is called a “neural network” and works in similar fashion to today’s understanding of how neuronal connections strengthen and weaken.
The connectome’s higher-resolution view of how the brain stores information and learns new functions could lead to even more advanced artificial intelligence approaches. And the incredible energy efficiency of the brain—running at only about 20 watts—could hold lessons for designing less power-hungry supercomputers.
“Our brains can compute at an energy scale that’s impossible to currently imagine with these kinds of computers, and [people] can still do operations that these computers cannot do,” Kasthuri said. “There’s already an effort here—at Argonne and at the UChicago Institute of Molecular Engineering—to think about the next generation of computing hardware. If that next generation is modeled on the energy efficiency of our brains, that is going to be a game changer.”
For the computers inside our skulls, mapping the connectome also unlocks myriad new science and engineering possibilities. Like the Human Genome Project, its potential is equaled only by its challenges, and the path to the finish line is steep, but reaching it and understanding how the brain is wired can make great strides in teaching us who we are.
“I have to categorically dismiss the claim that it’s beyond our understanding,” Maunsell said. “It is complex, it’s fantastically complex. But just because we’re not there yet doesn’t mean we’re not going to get there. And you know the whole history of science is just breaking down these walls one after the next.”
Feature Story: Small Brain, Big Data
By John Spizzirri
| September 11, 2017
A neuroscientist and a computational scientist walk into a synchrotron facility to study a mouse brain… Sounds like a great set-up for a comedy bit, but there is no punchline.
Argonne Neuroscientist Bobby Kasthuri is using Argonne’s supercomputer to map the intricacies of brain function at the deepest levels. (Image by Argonne National Laboratory.)
The result is cutting-edge science that can only be accomplished in a facility as scientifically integrated as the U.S. Department of Energy’s (DOE) Argonne National Laboratory.
At a casual, or even a more attentive glance, Doga Gursoy and Bobby Kasthuri would seem at opposite ends of the research spectrum. Gursoy is an assistant computational scientist at Argonne’s Advanced Photon Source (APS), a DOE Office of Science User Facility; Kasthuri, an Argonne neuroscientist.
But together, they are using Argonne’s vast arsenal of innovative technologies to map the intricacies of brain function at the deepest levels, and describing them in greater detail than ever before through advanced data analysis techniques.
“The basic goal is simple — would like to be able to image all of the neurons in the brain — but the datasets from X-rays and electron microscopes are extremely large. They are at the tera- and petabyte scales. So we would like to use Theta to build the software and codebase infrastructure in order to analyze that data.” – Doga Gursoy, assistant computational scientist in the X-Ray Science Division of Argonne’s Advanced Photon Source
Doga Gursoy, an assistant computational scientist
Doga Gursoy, an assistant computational scientist, is using Argonne’s Advanced Photon Source to help map all of the neurons in the brain and is also using Argonne’s supercomputer to analyze the reams of data from this project. (Image by Argonne National Laboratory.)
Gursoy and Kasthuri are among the first group of researchers to access Theta, the new 9.65 petaflops Intel-Cray supercomputer housed at the Argonne Leadership Computing Facility (ALCF), also a DOE Office of Science User Facility. Theta’s advanced and flexible software platform supports the ALCF Data Science Program (ADSP), a new initiative targeted at big data problems, like Gursoy and Kasthuri’s brain connectome project.
ADSP projects explore and improve a variety of computational methods that will enable data-driven discoveries across all scientific disciplines.
“By developing and demonstrating rapid analysis techniques, such as data mining, graph analytics and machine learning, together with workflows that will facilitate productive usage on our systems for applications, we will pave the way for more and more science communities to use supercomputers for their big data challenges in the future,” said Venkat Vishwanath, ALCF Data Sciences Group Lead.
All about the connections
This new ADSP study of connectomes maps the connections of every neuron in the brain, whether human or mouse. Determining the location of every cell in the brain and how they communicate with each other is a daunting task, as each cell makes thousands of connections. The human brain, for example, has some 100 billion neurons, creating 100 trillion connections. Even the average mouse brain has 75 million neurons.
“This ALCF award targets big data problems and our application of brain imaging does just that,” said Gursoy, assistant computational scientist in the X-Ray Science Division of Argonne’s Advanced Photon Source. “The basic goal is simple — we would like to be able to image all of the neurons in the brain — but the datasets from X-rays and electron microscopes are extremely large. They are at the tera- and petabyte scales. So we would like to use Theta to build the software and codebase infrastructure in order to analyze that data.”
This research was supported by the U.S. Department of Energy’s Office of Science. A portion of the work was also supported by Argonne’s Laboratory-Directed Research and Development (LDRD) program.
The process begins with two imaging techniques that will provide the massive sets of data for analysis by Theta. One is at the APS, where full brains can be analyzed at submicron resolution — in this case, the brain of a petite shrewmouse — through X-ray microtomography, a high-resolution 3-D imaging technique. Argonne’s X-ray Sciences Division of the APS provides the expertise for the microtomography research. Much like a CT scanner, it produces images as micro-thin slices of a material whose structure can be meticulously scrutinized.
“At the APS, this technique is performed with an ultra-high brightness synchrotron source, enabling full brain acquisitions with high resolution in an hour or so,” said Vincent De Andrade from sector 32-ID at the APS. While this resolution provides a detailed picture of blood vessels and cell bodies, the researchers aim to go still deeper.
That depth of detail requires the use of an electron microscope, which transmits a short-wavelength electron beam to deliver resolution at the nanometer scale. This will allow for the capture of all the synaptic connections between individual neurons at small targeted regions guided by the X-ray microtomography.
“For years, scientists at the APS have used these techniques to deepen our understanding of a wide variety of materials, from soil samples to new materials to biological matter,” said Kamel Fezzaa from sector 32-ID at the APS. “By coordinating our efforts with Argonne high-speed computing capabilities for this project, we are able to provide some truly revolutionary images that could provide details about brain functions that we have never before been able to observe.”
Both techniques can produce petabytes of information a day and, according to the researchers, the next generations of both microscopes will increase that amount dramatically.
Images produced by these datasets have to be processed, reconstructed and analyzed. Through the ADSP, Gursoy and Kasthuri are developing a series of large-scale data and computational steps — a pipeline — that integrates exascale computational approaches into an entirely new set of tools for brain research.
Taming of the shrew
The first case study for this pipeline is the reconstruction of an entire adult shrewmouse brain, which, they estimate, will produce one exabyte of data, or one billion gigabytes. And the studies only get bigger from there.
“Machine learning will go through these datasets and help come up with predictive models. For this project, it can help with segmentation or reconstruction of the brain and help classify or identify features of interest,” said Vishwanath.
Lessons learned from the smaller shrewmouse brain will be applied to a large mouse brain, which constitutes a 10-fold increase in volume. Comparisons between the two will reveal how organizational structures form during development, from embryo to adult, and how they evolve. The reconstruction of a non-human primate brain, with a volume 100 times larger than a mouse brain, is being considered for a later study.
A neuroscientist and a computational scientist leave a synchrotron facility with studies from a mouse brain … armed with new techniques to analyze this data. The images produced by their work will provide a clearer understanding of how even the smallest changes to the brain play a role in the onset and evolution of neurological diseases, such as Alzheimer’s and autism, and perhaps lead to improved treatments or even a cure.
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