Tag Archives: modeling

#437667 17 Teams to Take Part in DARPA’s ...

Among all of the other in-person events that have been totally wrecked by COVID-19 is the Cave Circuit of the DARPA Subterranean Challenge. DARPA has already hosted the in-person events for the Tunnel and Urban SubT circuits (see our previous coverage here), and the plan had always been for a trio of events representing three uniquely different underground environments in advance of the SubT Finals, which will somehow combine everything into one bonkers course.

While the SubT Urban Circuit event snuck in just under the lockdown wire in late February, DARPA made the difficult (but prudent) decision to cancel the in-person Cave Circuit event. What this means is that there will be no Systems Track Cave competition, which is a serious disappointment—we were very much looking forward to watching teams of robots navigating through an entirely unpredictable natural environment with a lot of verticality. Fortunately, DARPA is still running a Virtual Cave Circuit, and 17 teams will be taking part in this competition featuring a simulated cave environment that’s as dynamic and detailed as DARPA can make it.

From DARPA’s press releases:

DARPA’s Subterranean (SubT) Challenge will host its Cave Circuit Virtual Competition, which focuses on innovative solutions to map, navigate, and search complex, simulated cave environments November 17. Qualified teams have until Oct. 15 to develop and submit software-based solutions for the Cave Circuit via the SubT Virtual Portal, where their technologies will face unknown cave environments in the cloud-based SubT Simulator. Until then, teams can refine their roster of selected virtual robot models, choose sensor payloads, and continue to test autonomy approaches to maximize their score.

The Cave Circuit also introduces new simulation capabilities, including digital twins of Systems Competition robots to choose from, marsupial-style platforms combining air and ground robots, and breadcrumb nodes that can be dropped by robots to serve as communications relays. Each robot configuration has an associated cost, measured in SubT Credits – an in-simulation currency – based on performance characteristics such as speed, mobility, sensing, and battery life.

Each team’s simulated robots must navigate realistic caves, with features including natural terrain and dynamic rock falls, while they search for and locate various artifacts on the course within five meters of accuracy to score points during a 60-minute timed run. A correct report is worth one point. Each course contains 20 artifacts, which means each team has the potential for a maximum score of 20 points. Teams can leverage numerous practice worlds and even build their own worlds using the cave tiles found in the SubT Tech Repo to perfect their approach before they submit one official solution for scoring. The DARPA team will then evaluate the solution on a set of hidden competition scenarios.

Of the 17 qualified teams (you can see all of them here), there are a handful that we’ll quickly point out. Team BARCS, from Michigan Tech, was the winner of the SubT Virtual Urban Circuit, meaning that they may be the team to beat on Cave as well, although the course is likely to be unique enough that things will get interesting. Some Systems Track teams to watch include Coordinated Robotics, CTU-CRAS-NORLAB, MARBLE, NUS SEDS, and Robotika, and there are also a handful of brand new teams as well.

Now, just because there’s no dedicated Cave Circuit for the Systems Track teams, it doesn’t mean that there won’t be a Cave component (perhaps even a significant one) in the final event, which as far as we know is still scheduled to happen in fall of next year. We’ve heard that many of the Systems Track teams have been testing out their robots in caves anyway, and as the virtual event gets closer, we’ll be doing a sort of Virtual Systems Track series that highlights how different teams are doing mock Cave Circuits in caves they’ve found for themselves.

For more, we checked in with DARPA SubT program manager Dr. Timothy H. Chung.

IEEE Spectrum: Was it a difficult decision to cancel the Systems Track for Cave?

Tim Chung: The decision to go virtual only was heart wrenching, because I think DARPA’s role is to offer up opportunities that may be unimaginable for some of our competitors, like opening up a cave-type site for this competition. We crawled and climbed through a number of these sites, and I share the sense of disappointment that both our team and the competitors have that we won’t be able to share all the advances that have been made since the Urban Circuit. But what we’ve been able to do is pour a lot of our energy and the insights that we got from crawling around in those caves into what’s going to be a really great opportunity on the Virtual Competition side. And whether it’s a global pandemic, or just lack of access to physical sites like caves, virtual environments are an opportunity that we want to develop.

“The simulator offers us a chance to look at where things could be … it really allows for us to find where some of those limits are in the technology based only on our imagination.”
—Timothy H. Chung, DARPA

What kind of new features will be included in the Virtual Cave Circuit for this competition?

I’m really excited about these particular features because we’re seeing an opportunity for increased synergy between the physical and the virtual. The first I’d say is that we scanned some of the Systems Track robots using photogrammetry and combined that with some additional models that we got from the systems competitors themselves to turn their systems robots into virtual models. We often talk about the sim to real transfer and how successful we can get a simulation to transfer over to the physical world, but now we’ve taken something from the physical world and made it virtual. We’ve validated the controllers as well as the kinematics of the robots, we’ve iterated with the systems competitors themselves, and now we have these 13 robots (air and ground) in the SubT Tech Repo that now all virtual competitors can take advantage of.

We also have additional robot capability. Those comms bread crumbs are common among many of the competitors, so we’ve adopted that in the virtual world, and now you have comms relay nodes that are baked in to the SubT Simulator—you can have either six or twelve comms nodes that you can drop from a variety of our ground robot platforms. We have the marsupial deployment capability now, so now we have parent ground robots that can be mixed and matched with different child drones to become marsupial pairs.

And this is something I’ve been planning for for a while: we now have the ability to trigger things like rock falls. They still don’t quite look like Indiana Jones with the boulder coming down the corridor, but this comes really close. In addition to it just being an interesting and realistic consideration, we get to really dynamically test and stress the robots’ ability to navigate and recognize that something has changed in the environment and respond to it.

Image: DARPA

DARPA is still running a Virtual Cave Circuit, and 17 teams will be taking part in this competition featuring a simulated cave environment.

No simulation is perfect, so can you talk to us about what kinds of things aren’t being simulated right now? Where does the simulator not match up to reality?

I think that question is foundational to any conversation about simulation. I’ll give you a couple of examples:

We have the ability to represent wholesale damage to a robot, but it’s not at the actuator or component level. So there’s not a reliability model, although I think that would be really interesting to incorporate so that you could do assessments on things like mean time to failure. But if a robot falls off a ledge, it can be disabled by virtue of being too damaged to continue.

With communications, and this is one that’s near and dear not only to my heart but also to all of those that have lived through developing communication systems and robotic systems, we’ve gone through and conducted RF surveys of underground environments to get a better handle on what propagation effects are. There’s a lot of research that has gone into this, and trying to carry through some of that realism, we do have path loss models for RF communications baked into the SubT Simulator. For example, when you drop a bread crumb node, it’s using a path loss model so that it can represent the degradation of signal as you go farther into a cave. Now, we’re not modeling it at the Maxwell equations level, which I think would be awesome, but we’re not quite there yet.

We do have things like battery depletion, sensor degradation to the extent that simulators can degrade sensor inputs, and things like that. It’s just amazing how close we can get in some places, and how far away we still are in others, and I think showing where the limits are of how far you can get simulation is all part and parcel of why SubT Challenge wants to have both System and Virtual tracks. Simulation can be an accelerant, but it’s not going to be the panacea for development and innovation, and I think all the competitors are cognizant those limitations.

One of the most amazing things about the SubT Virtual Track is that all of the robots operate fully autonomously, without the human(s) in the loop that the System Track teams have when they compete. Why make the Virtual Track even more challenging in that way?

I think it’s one of the defining, delineating attributes of the Virtual Track. Our continued vision for the simulation side is that the simulator offers us a chance to look at where things could be, and allows for us to explore things like larger scales, or increased complexity, or types of environments that we can’t physically gain access to—it really allows for us to find where some of those limits are in the technology based only on our imagination, and this is one of the intrinsic values of simulation.

But I think finding a way to incorporate human input, or more generally human factors like teleoperation interfaces and the in-situ stress that you might not be able to recreate in the context of a virtual competition provided a good reason for us to delineate the two competitions, with the Virtual Competition really being about the role of fully autonomous or self-sufficient systems going off and doing their solution without human guidance, while also acknowledging that the real world has conditions that would not necessarily be represented by a fully simulated version. Having said that, I think cognitive engineering still has an incredibly important role to play in human robot interaction.

What do we have to look forward to during the Virtual Competition Showcase?

We have a number of additional features and capabilities that we’ve baked into the simulator that will allow for us to derive some additional insights into our competition runs. Those insights might involve things like the performance of one or more robots in a given scenario, or the impact of the environment on different types of robots, and what I can tease is that this will be an opportunity for us to showcase both the technology and also the excitement of the robots competing in the virtual environment. I’m trying not to give too many spoilers, but we’ll have an opportunity to really get into the details.

Check back as we get closer to the 17 November event for more on the DARPA SubT Challenge. Continue reading

Posted in Human Robots

#437608 Video Friday: Agility Robotics Raises ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

Digit is now in full commercial production and we’re excited to announce a $20M funding rounding round co-led by DCVC and Playground Global!

Digits for everyone!

[ Agility Robotics ]

A flexible rover that has both ability to travel long distances and rappel down hard-to-reach areas of scientific interest has undergone a field test in the Mojave Desert in California to showcase its versatility. Composed of two Axel robots, DuAxel is designed to explore crater walls, pits, scarps, vents and other extreme terrain on the moon, Mars and beyond.

This technology demonstration developed at NASA’s Jet Propulsion Laboratory in Southern California showcases the robot’s ability to split in two and send one of its halves — a two-wheeled Axle robot — over an otherwise inaccessible slope, using a tether as support and to supply power.

The rappelling Axel can then autonomously seek out areas to study, safely overcome slopes and rocky obstacles, and then return to dock with its other half before driving to another destination. Although the rover doesn’t yet have a mission, key technologies are being developed that might, one day, help us explore the rocky planets and moons throughout the solar system.

[ JPL ]

A rectangular robot as tiny as a few human hairs can travel throughout a colon by doing back flips, Purdue University engineers have demonstrated in live animal models. Why the back flips? Because the goal is to use these robots to transport drugs in humans, whose colons and other organs have rough terrain. Side flips work, too. Why a back-flipping robot to transport drugs? Getting a drug directly to its target site could remove side effects, such as hair loss or stomach bleeding, that the drug may otherwise cause by interacting with other organs along the way.

[ Purdue ]

This video shows the latest results in the whole-body locomotion control of the humanoid robot iCub achieved by the Dynamic Interaction Control line at IIT-Istituto Italiano di Tecnologia in Genova (Italy). In particular, the iCub now keeps the balance while walking and receiving pushes from an external user. The implemented control algorithms also ensure the robot to remain compliant during locomotion and human-robot interaction, a fundamental property to lower the possibility to harm humans that share the robot surrounding environment.

This is super impressive, considering that iCub was only able to crawl and was still tethered not too long ago. Also, it seems to be blinking properly now, so it doesn’t look like it’s always sleepy.

[ IIT ]

This video shows a set of new tests we performed on Bolt. We conducted tests on 5 different scenarios, 1) walking forward/backward 2) uneven surface 3) soft surface 4) push recovery 5) slippage recovery. Thanks to our feedback control based on Model Predictive Control, the robot can perform walking in the presence of all these uncertainties. We will open-source all the codes in a near future.

[ ODRI ]

The title of this video is “Can you throw your robot into a lake?” The title of this video should be, “Can you throw your robot into a lake and drive it out again?”

[ Norlab ]

AeroVironment Successfully Completes Sunglider Solar HAPS Stratospheric Test Flight, Surpassing 60,000 Feet Altitude and Demonstrating Broadband Mobile Connectivity.

[ AeroVironment ]

We present CoVR, a novel robotic interface providing strong kinesthetic feedback (100 N) in a room-scale VR arena. It consists of a physical column mounted on a 2D Cartesian ceiling robot (XY displacements) with the capacity of (1) resisting to body-scaled users actions such as pushing or leaning; (2) acting on the users by pulling or transporting them as well as (3) carrying multiple potentially heavy objects (up to 80kg) that users can freely manipulate or make interact with each other.

[ DeepAI ]

In a new video, personnel from Swiss energy supply company Kraftwerke Oberhasli AG (KWO) explain how they were able to keep employees out of harm’s way by using Flyability’s Elios 2 to collect visual data while building a new dam.

[ Flyability ]

Enjoy our Ascento robot fail compilation! With every failure we experience, we learn more and we can improve our robot for its next iteration, which will come soon… Stay tuned for more!

FYI posting a robot fails video will pretty much guarantee you a spot in Video Friday!

[ Ascento ]

Humans are remarkably good at using chopsticks. The Guinness World Record witnessed a person using chopsticks to pick up 65 M&Ms in just a minute. We aim to collect demonstrations from humans and to teach robot to use chopsticks.

[ UW Personal Robotics Lab ]

A surprising amount of personality from these Yaskawa assembly robots.

[ Yaskawa ]

This paper presents the system design, modeling, and control of the Aerial Robotic Chain Manipulator. This new robot design offers the potential to exert strong forces and moments to the environment, carry and lift significant payloads, and simultaneously navigate through narrow corridors. The presented experimental studies include a valve rotation task, a pick-and-release task, and the verification of load oscillation suppression to demonstrate the stability and performance of the system.

[ ARL ]

Whether animals or plants, whether in the water, on land or in the air, nature provides the model for many technical innovations and inventions. This is summed up in the term bionics, which is a combination of the words ‘biology‘ and ‘electronics’. At Festo, learning from nature has a long history, as our Bionic Learning Network is based on using nature as the source for future technologies like robots, assistance systems or drive solutions.

[ Festo ]

Dogs! Selfies! Thousands of LEGO bricks! This video has it all.

[ LEGO ]

An IROS workshop talk on “Cassie and Mini Cheetah Autonomy” by Maani Ghaffari and Jessy Grizzle from the University of Michigan.

[ Michigan Robotics ]

David Schaefer’s Cozmo robots are back with this mind-blowing dance-off!

What you just saw represents hundreds of hours of work, David tells us: “I wrote over 10,000 lines of code to create the dance performance as I had to translate the beats per minute of the song into motor rotations in order to get the right precision needed to make the moves look sharp. The most challenging move was the SpongeBob SquareDance as any misstep would send the Cozmos crashing into each other. LOL! Fortunately for me, Cozmo robots are pretty resilient.”

[ Life with Cozmo ]

Thanks David!

This week’s GRASP on Robotics seminar is by Sangbae Kim from MIT, on “Robots with Physical Intelligence.”

While industrial robots are effective in repetitive, precise kinematic tasks in factories, the design and control of these robots are not suited for physically interactive performance that humans do easily. These tasks require ‘physical intelligence’ through complex dynamic interactions with environments whereas conventional robots are designed primarily for position control. In order to develop a robot with ‘physical intelligence’, we first need a new type of machines that allow dynamic interactions. This talk will discuss how the new design paradigm allows dynamic interactive tasks. As an embodiment of such a robot design paradigm, the latest version of the MIT Cheetah robots and force-feedback teleoperation arms will be presented.

[ GRASP ]

This week’s CMU Ri Seminar is by Kevin Lynch from Northwestern, on “Robotics and Biosystems.”

Research at the Center for Robotics and Biosystems at Northwestern University encompasses bio-inspiration, neuromechanics, human-machine systems, and swarm robotics, among other topics. In this talk I will give an overview of some of our recent work on in-hand manipulation, robot locomotion on yielding ground, and human-robot systems.

[ CMU RI ] Continue reading

Posted in Human Robots

#437571 Video Friday: Snugglebot Is What We All ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

IROS 2020 – October 25-25, 2020 – [Online]
Robotica 2020 – November 10-14, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Bay Area Robotics Symposium – November 20, 2020 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.

Snugglebot is what we all need right now.

[ Snugglebot ]

In his video message on his prayer intention for November, Pope Francis emphasizes that progress in robotics and artificial intelligence (AI) be oriented “towards respecting the dignity of the person and of Creation”.

[ Vatican News ]

KaPOW!

Apparently it's supposed to do that—the disruptor flies off backwards to reduce recoil on the robot, and has its own parachute to keep it from going too far.

[ Ghost Robotics ]

Animals have many muscles, receptors, and neurons which compose feedback loops. In this study, we designed artificial muscles, receptors, and neurons without any microprocessors, or software-based controllers. We imitate the reflexive rule observed in walking experiments of cats, as a result, the Pneumatic Brainless Robot II emerged running motion (a leg trajectory and a gait pattern) through the interaction between the body, the ground, and the artificial reflexes. We envision that the simple reflex circuit we discovered will be a candidate for a minimal model for describing the principles of animal locomotion.

Find the paper, “Brainless Running: A Quasi-quadruped Robot with Decentralized Spinal Reflexes by Solely Mechanical Devices,” on IROS On-Demand.

[ IROS ]

Thanks Yoichi!

I have no idea what these guys are saying, but they're talking about robots that serve chocolate!

The world of experience of the Zotter Schokoladen Manufaktur of managing director Josef Zotter counts more than 270,000 visitors annually. Since March 2019, this world of chocolate in Bergl near Riegersburg in Austria has been enriched by a new attraction: the world's first chocolate and praline robot from KUKA delights young and old alike and serves up chocolate and pralines to guests according to their personal taste.

[ Zotter ]

This paper proposes a systematic solution that uses an unmanned aerial vehicle (UAV) to aggressively and safely track an agile target. The solution properly handles the challenging situations where the intent of the target and the dense environments are unknown to the UAV. The proposed solution is integrated into an onboard quadrotor system. We fully test the system in challenging real-world tracking missions. Moreover, benchmark comparisons validate that the proposed method surpasses the cutting-edge methods on time efficiency and tracking effectiveness.

[ FAST Lab ]

Southwest Research Institute developed a cable management system for collaborative robotics, or “cobots.” Dress packs used on cobots can create problems when cables are too tight (e-stops) or loose (tangling). SwRI developed ADDRESS, or the Adaptive DRESing System, to provide smarter cobot dress packs that address e-stops and tangling.

[ SWRI ]

A quick demonstration of the acoustic contact sensor in the RBO Hand 2. An embedded microphone records the sound inside of the pneumatic finger. Depending on which part of the finger makes contact, the sound is a little bit different. We create a sensor that recognizes these small changes and predicts the contact location from the sound. The visualization on the left shows the recorded sound (top) and which of the nine contact classes the sensor is currently predicting (bottom).

[ TU Berlin ]

The MAVLab won the prize for the “most innovative design” in the IMAV 2018 indoor competition, in which drones had to fly through windows, gates, and follow a predetermined flight path. The prize was awarded for the demonstration of a fully autonomous version of the “DelFly Nimble”, a tailless flapping wing drone.

In order to fly by itself, the DelFly Nimble was equipped with a single, small camera and a small processor allowing onboard vision processing and control. The jury of international experts in the field praised the agility and autonomous flight capabilities of the DelFly Nimble.

[ MAVLab ]

A reactive walking controller for the Open Dynamic Robot Initiative's skinny quadruped.

[ ODRI ]

Mobile service robots are already able to recognize people and objects while navigating autonomously through their operating environments. But what is the ideal position of the robot to interact with a user? To solve this problem, Fraunhofer IPA developed an approach that connects navigation, 3D environment modeling, and person detection to find the optimal goal pose for HRI.

[ Fraunhofer ]

Yaskawa has been in robotics for a very, very long time.

[ Yaskawa ]

Black in Robotics IROS launch event, featuring Carlotta Berry.

[ Black in Robotics ]

What is AI? I have no idea! But these folks have some opinions.

[ MIT ]

Aerial-based Observations of Volcanic Emissions (ABOVE) is an international collaborative project that is changing the way we sample volcanic gas emissions. Harnessing recent advances in drone technology, unoccupied aerial systems (UAS) in the ABOVE fleet are able to acquire aerial measurements of volcanic gases directly from within previously inaccessible volcanic plumes. In May 2019, a team of 30 researchers undertook an ambitious field deployment to two volcanoes – Tavurvur (Rabaul) and Manam in Papua New Guinea – both amongst the most prodigious emitters of sulphur dioxide on Earth, and yet lacking any measurements of how much carbon they emit to the atmosphere.

[ ABOVE ]

A talk from IHMC's Robert Griffin for ICCAS 2020, including a few updates on their Nadia humanoid.

[ IHMC ] Continue reading

Posted in Human Robots

#437554 Ending the COVID-19 Pandemic

Photo: F.J. Jimenez/Getty Images

The approach of a new year is always a time to take stock and be hopeful. This year, though, reflection and hope are more than de rigueur—they’re rejuvenating. We’re coming off a year in which doctors, engineers, and scientists took on the most dire public threat in decades, and in the new year we’ll see the greatest results of those global efforts. COVID-19 vaccines are just months away, and biomedical testing is being revolutionized.

At IEEE Spectrum we focus on the high-tech solutions: Can artificial intelligence (AI) be used to diagnose COVID-19 using cough recordings? Can mathematical modeling determine whether preventive measures against COVID-19 work? Can big data and AI provide accurate pandemic forecasting?

Consider our story “AI Recognizes COVID-19 in the Sound of a Cough,” reported by Megan Scudellari in our Human OS blog. Using a cellphone-recorded cough, machine-learning models can now detect coronavirus with 90 percent accuracy, even in people with no symptoms. It’s a remarkable research milestone. This AI model sifts through hundreds of factors to distinguish the COVID-19 cough from those of bronchitis, whooping cough, and asthma.

But while such high-tech triumphs give us hope, the no-tech solutions are mostly what we have to work with. Soon, as our Numbers Don’t Lie columnist, Vaclav Smil, pointed out in a recent email, we will have near-instantaneous home testing, and we will have an ability to use big data to crunch every move and every outbreak. But we are nowhere near that yet. So let’s use, as he says, some old-fashioned kindergarten epidemiology, the no-tech measures, while we work to get there:

Masks: Wear them. If we all did so, we could cut transmission by two-thirds, perhaps even 80 percent.

Hands: Wash them.

Social distancing: If we could all stay home for two weeks, we could see enormous declines in COVID-19 transmission.

These are all time-tested solutions, proven effective ages ago in countless outbreaks of diseases including typhoid and cholera. They’re inexpensive and easy to prescribe, and the regimens are easy to follow.

The conflict between public health and individual rights and privacy, however, is less easy to resolve. Even during the pandemic of 1918–19, there was widespread resistance to mask wearing and social distancing. Fifty million people died—675,000 in the United States alone. Today, we are up to 240,000 deaths in the United States, and the end is not in sight. Antiflu measures were framed in 1918 as a way to protect the troops fighting in World War I, and people who refused to wear masks were called out as “dangerous slackers.” There was a world war, and yet it was still hard to convince people of the need for even such simple measures.

Personally, I have found the resistance to these easy fixes startling. I wouldn’t want maskless, gloveless doctors taking me through a surgical procedure. Or waltzing in from lunch without washing their hands. I’m sure you wouldn’t, either.

Science-based medicine has been one of the world’s greatest and most fundamental advances. In recent years, it has been turbocharged by breakthroughs in genetics technologies, advanced materials, high-tech diagnostics, and implants and other electronics-based interventions. Such leaps have already saved untold lives, but there’s much more to be done. And there will be many more pandemics ahead for humanity.

< Back to IEEE COVID-19 Resources Continue reading

Posted in Human Robots

#437293 These Scientists Just Completed a 3D ...

Human brain maps are a dime a dozen these days. Maps that detail neurons in a certain region. Maps that draw out functional connections between those cells. Maps that dive deeper into gene expression. Or even meta-maps that combine all of the above.

But have you ever wondered: how well do those maps represent my brain? After all, no two brains are alike. And if we’re ever going to reverse-engineer the brain as a computer simulation—as Europe’s Human Brain Project is trying to do—shouldn’t we ask whose brain they’re hoping to simulate?

Enter a new kind of map: the Julich-Brain, a probabilistic map of human brains that accounts for individual differences using a computational framework. Rather than generating a static PDF of a brain map, the Julich-Brain atlas is also dynamic, in that it continuously changes to incorporate more recent brain mapping results. So far, the map has data from over 24,000 thinly sliced sections from 23 postmortem brains covering most years of adulthood at the cellular level. But the atlas can also continuously adapt to progress in mapping technologies to aid brain modeling and simulation, and link to other atlases and alternatives.

In other words, rather than “just another” human brain map, the Julich-Brain atlas is its own neuromapping API—one that could unite previous brain-mapping efforts with more modern methods.

“It is exciting to see how far the combination of brain research and digital technologies has progressed,” said Dr. Katrin Amunts of the Institute of Neuroscience and Medicine at Research Centre Jülich in Germany, who spearheaded the study.

The Old Dogma
The Julich-Brain atlas embraces traditional brain-mapping while also yanking the field into the 21st century.

First, the new atlas includes the brain’s cytoarchitecture, or how brain cells are organized. As brain maps go, these kinds of maps are the oldest and most fundamental. Rather than exploring how neurons talk to each other functionally—which is all the rage these days with connectome maps—cytoarchitecture maps draw out the physical arrangement of neurons.

Like a census, these maps literally capture how neurons are distributed in the brain, what they look like, and how they layer within and between different brain regions.

Because neurons aren’t packed together the same way between different brain regions, this provides a way to parse the brain into areas that can be further studied. When we say the brain’s “memory center,” the hippocampus, or the emotion center, the “amygdala,” these distinctions are based on cytoarchitectural maps.

Some may call this type of mapping “boring.” But cytoarchitecture maps form the very basis of any sort of neuroscience understanding. Like hand-drawn maps from early explorers sailing to the western hemisphere, these maps provide the brain’s geographical patterns from which we try to decipher functional connections. If brain regions are cities, then cytoarchitecture maps attempt to show trading or other “functional” activities that occur in the interlinking highways.

You might’ve heard of the most common cytoarchitecture map used today: the Brodmann map from 1909 (yup, that old), which divided the brain into classical regions based on the cells’ morphology and location. The map, while impactful, wasn’t able to account for brain differences between people. More recent brain-mapping technologies have allowed us to dig deeper into neuronal differences and divide the brain into more regions—180 areas in the cortex alone, compared with 43 in the original Brodmann map.

The new study took inspiration from that age-old map and transformed it into a digital ecosystem.

A Living Atlas
Work began on the Julich-Brain atlas in the mid-1990s, with a little help from the crowd.

The preparation of human tissue and its microstructural mapping, analysis, and data processing is incredibly labor-intensive, the authors lamented, making it impossible to do for the whole brain at high resolution in just one lab. To build their “Google Earth” for the brain, the team hooked up with EBRAINS, a shared computing platform set up by the Human Brain Project to promote collaboration between neuroscience labs in the EU.

First, the team acquired MRI scans of 23 postmortem brains, sliced the brains into wafer-thin sections, and scanned and digitized them. They corrected distortions from the chopping using data from the MRI scans and then lined up neurons in consecutive sections—picture putting together a 3D puzzle—to reconstruct the whole brain. Overall, the team had to analyze 24,000 brain sections, which prompted them to build a computational management system for individual brain sections—a win, because they could now track individual donor brains too.

Their method was quite clever. They first mapped their results to a brain template from a single person, called the MNI-Colin27 template. Because the reference brain was extremely detailed, this allowed the team to better figure out the location of brain cells and regions in a particular anatomical space.

However, MNI-Colin27’s brain isn’t your or my brain—or any of the brains the team analyzed. To dilute any of Colin’s potential brain quirks, the team also mapped their dataset onto an “average brain,” dubbed the ICBM2009c (catchy, I know).

This step allowed the team to “standardize” their results with everything else from the Human Connectome Project and the UK Biobank, kind of like adding their Google Maps layer to the existing map. To highlight individual brain differences, the team overlaid their dataset on existing ones, and looked for differences in the cytoarchitecture.

The microscopic architecture of neurons change between two areas (dotted line), forming the basis of different identifiable brain regions. To account for individual differences, the team also calculated a probability map (right hemisphere). Image credit: Forschungszentrum Juelich / Katrin Amunts
Based on structure alone, the brains were both remarkably different and shockingly similar at the same time. For example, the cortexes—the outermost layer of the brain—were physically different across donor brains of different age and sex. The region especially divergent between people was Broca’s region, which is traditionally linked to speech production. In contrast, parts of the visual cortex were almost identical between the brains.

The Brain-Mapping Future
Rather than relying on the brain’s visible “landmarks,” which can still differ between people, the probabilistic map is far more precise, the authors said.

What’s more, the map could also pool yet unmapped regions in the cortex—about 30 percent or so—into “gap maps,” providing neuroscientists with a better idea of what still needs to be understood.

“New maps are continuously replacing gap maps with progress in mapping while the process is captured and documented … Consequently, the atlas is not static but rather represents a ‘living map,’” the authors said.

Thanks to its structurally-sound architecture down to individual cells, the atlas can contribute to brain modeling and simulation down the line—especially for personalized brain models for neurological disorders such as seizures. Researchers can also use the framework for other species, and they can even incorporate new data-crunching processors into the workflow, such as mapping brain regions using artificial intelligence.

Fundamentally, the goal is to build shared resources to better understand the brain. “[These atlases] help us—and more and more researchers worldwide—to better understand the complex organization of the brain and to jointly uncover how things are connected,” the authors said.

Image credit: Richard Watts, PhD, University of Vermont and Fair Neuroimaging Lab, Oregon Health and Science University Continue reading

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