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“Cthonic Self” [Experimental Photography, 2020]
George Bataille’s Acéphale by André Masson
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During Sleep, Brain Regions Synchronize to Create Motor Memory
When the Golden State Warriors’ Steph Curry makes a free throw, his brain draws on motor memory. Now researchers at UC San Francisco have shown how this type of memory is consolidated during sleep, when the brain processes the day’s learning to make the physical act of doing something subconscious.
The study, published in Nature, shows the brain does this by reviewing the trials and errors of a given action. In the analogy, that means sorting through all the free throws Curry has ever thrown, weeding out the memory of all the actions except those that hit the mark, or that the brain decided were “good enough.” The result is the ability to make the free throw with a high degree of accuracy without having to think about the physical movements involved.
“Even elite athletes makes errors, and that’s what makes the game interesting,” said Karunesh Ganguly, MD, PhD, a professor of neurology and member of the UCSF Weill Institute for Neurosciences. “Motor memory isn’t about perfect performance. It’s about predictable errors and predictable successes. As long as the errors are stable from day to day, the brain says, ‘Let's just lock this memory in.’”
Ganguly and his team found that the “locking in” process involves some surprisingly complex communication between different parts of the brain and takes place during the deep restorative slumber known as non-REM sleep.
Sleep is important because our conscious brains tend to focus on the failures, said Ganguly, who previously identified the sleep-associated brain waves that influence skill retention.
“During sleep, the brain is able to sift through all the instances it’s taken in and bring forward the patterns that were successful,” he said.
Earthbound Motor Skills Wouldn’t Work on Avatar’s Planet Pandora
It was once thought that learning motor skills only required the motor cortex. But in recent years a more complex picture has emerged.
To look into this process more closely, Ganguly set rats on a task to reach for pellets. Then, the team looked at their brain activity in three regions during NREM sleep: the hippocampus, which is the region responsible for memory and navigation, the motor cortex and the prefrontal cortex (PFC).
Over the course of 13 days, a pattern emerged.
First, in a process called “fast learning,” the PFC coordinated with the hippocampus, likely enabling the animal to perceive its motion with respect to the space around it and its location in that space. In this phase, the brain seemed to be exploring and comparing all the actions and patterns created while practicing the task.
Second, in a process called slow learning, the PFC appeared to make value judgements, likely driven by reward centers that were activated when the task was successful. It engaged in crosstalk with the motor cortex and the hippocampus, turning down the signals related to failures and turning up the ones related to successes.
Finally, as the electrical activity of the regions became synchronized, the role of the hippocampus diminished and the instances the brain had noted as rewarding came to the fore, where they were stored in what we call “motor memory.”
While the rats were initially learning the task, their brain signals were noisy and disorganized. As time went on, Ganguly could see the signals synchronizing, until the rats were succeeding about 70 percent of the time. After that point, the brain seemed to ignore mistakes and maintained the motor memory as long as the level of success was stable. In other words, the brain starts to expect a certain level of error and does not update the motor memory.
Just like NBA players, the rats mastered a skill based on a mental model of how the world works, which they created from their physical experience with gravity, space and other cues. But this kind of motor learning wouldn’t easily transfer to a situation where the cues and physical environment were different.
“If all that changed, for example, if Steph Curry was in the world of Avatar, he might not look as skilled initially,” Ganguly said.
The Best Way to Break a Habit
What if Curry hurt a finger and had to learn to shoot baskets a little differently? The study offered an answer.
“It’s possible to unlearn a task, but to do that, you have to stress the situation to a point where you’re making mistakes,” Ganguly said.
When the researchers made a slight change to the rats’ pellet procurement task, the rats would make more mistakes and the researchers saw more noise in the rats’ brain activity.
The change was small enough that the rats didn’t have to go all the way back to the beginning of their learning, only to the “breaking point,” and relearn the task from there.
But because motor memory gets ingrained as a set of motions that follow each other in time, Ganguly said, changing motor memory in a complex motion like free throwing a basketball might require changing a motion that is used to initiate the whole sequence.
If Curry usually bounces a basketball twice before he throws, Ganguly said, “It might be best to retrain the brain by bouncing it only once, or three times. That way, you’d start with a clean slate.”
New Research Investigates How the Brain Processes Language
Humans accomplish a phenomenal amount of tasks by combining pieces of information. We perceive objects by combining edges, categorize scenes by combining objects, interpret events by combining actions, and understand sentences by combining words. But researchers don't yet have a clear understanding of how the brain forms and maintains the meaning of the whole — such as a sentence — from its parts. School of Computer Science (SCS) researchers in the Machine Learning Department (MLD) have shed new light on the brain processes that support the emergent meaning of combined words.
Mariya Toneva, a former MLD Ph.D. student now faculty at the Max Planck Institute for Software Systems, worked with Leila Wehbe, an assistant professor in MLD, and Tom Mitchell, the Founders University Professor in SCS, to study which regions of the brain processed the meaning of combined words and how the brain maintained and updated the meaning of words. This work could contribute to a more complete understanding of how the brain processes, maintains and updates the meaning of words, and could redirect research focus to areas of the brain suitable for future wearable neurotechnology, such as devices that can decode what a person is trying to say directly from brain activity. These devices can help people with diseases like Parkinson's or multiple sclerosis that limit muscle control.
Toneva, Mitchell and Wehbe used neural networks to build computational models that could predict the areas of the brain that process the new meaning of words when they are combined. They tested this model by recording the brain activity of eight people as they read a chapter of "Harry Potter and the Sorcerer's Stone." The results suggest that some regions of the brain process both the meaning of individual words and the meaning of combined words, while others process only the meanings of individual words. Crucially, the authors also found that one of the neural activity recording tools they used, magnetoencephalography (MEG), did not capture a signal that reflected the meaning of combined words. Since future wearable neurotechnology devices might use recording tools similar to MEG, one potential limitation is their inability to detect the meaning of combined words, which could affect their capacity to help users produce language.
The team's work builds on past research from Wehbe and Mitchell that used functional magnetic resonance imaging to identify the parts of the brain engaged as people read a chapter of the same Potter book. The result was the first integrated computational model of reading, identifying which parts of the brain are responsible for such subprocesses as parsing sentences, determining the meaning of words and understanding relationships between characters.
Braingeneers develop novel method to automate the growth of brain tissue organoids on a chip
A team of engineers at UC Santa Cruz has developed a new method for remote automation of the growth of cerebral organoids – miniature, three-dimensional models of brain tissue grown from stem cells. Cerebral organoids allow researchers to study and engineer key functions of the human brain with a level of accuracy not possible with other models. This has implications for understanding brain development and the effects of pharmaceutical drugs for treating cancer or other diseases.
In a new study published in the journal Nature Scientific Reports, researchers from the UCSC Braingeneers group detail their automated, internet-connected microfluidics system, called “Autoculture.” The system precisely delivers feeding liquid to individual cerebral organoids in order to optimize their growth without the need for human interference with the tissue culture.
Cerebral organoids require a high level of expertise and consistency to maintain the precise conditions for cell growth over weeks or months. Using an automated system, as demonstrated in this study, can eliminate disturbance to cell culture growth caused by human interference or error, provide more robust results, and allow more scientists access to opportunities to conduct research with human brain models.
Autoculture also addresses variation that arises in organoid growth due to “batch effect” issues, where organoids grown at different times or at different labs under similar conditions may vary just because of the complexity of their growth. Using this uniform, automated system can reduce variation and allow researchers to better compare and validate their results.
“One of the big challenges is that these cultures are not very reproducible, and in part it's not surprising because these are months-long experiments. You have to change media every couple of days and try to treat these cultures uniformly, which is extremely challenging,” said Sofie Salama, an acting professor of molecular, cellular and developmental biology at UCSC and an author on the study.
Unique design
Autoculture uses a microfluidic chip designed by the researchers, spearheaded by Associate Professor of Electrical and Computer Engineering Mircea Teodorescu and Biomolecular Engineering Ph.D. student Spencer Seiler. Their novel chips, created from a unique bi-layer mold, have tiny wells and channels for delivering minute amounts of liquid to the organoid, which allow the scientists to have a high level of control over nutrient concentrations and byproducts. Overall, the system uses mostly off-the-shelf, low-cost components, making it accessible and modular.
“A novel and important feature of this machine is that on one hand, it streamlines the process and makes sure that everything is very consistent,” Teodorescu said. “On the other hand, it's very modular because the system is controlled by the computer, so there are different parts of the chip that are interchangeable and have their own advantages – it's very much a modern agent.”
Because the system delivers a non-stop flow of liquid to the organoids, it more closely resembles the real conditions of the brain, which is constantly fed nutrients through the blood.
Unlike other methods for organoid growth which grow the cultures together in one dish, the Autoculture system contains a culture plate with 24 individual wells, so each well can be its own experiment in which cultures can be grown independently and fed liquids at varying, programmable concentrations and times. An in-incubator imaging system lets the researchers constantly remotely monitor organoid growth and morphology.
“The prize of the system is that every organoid has its own, personal micro-environment for which fluid flows in and out of,” Seiler said. “Now we’ve separated them – this would be too laborious to do by hand, but it's fine for a machine.”
Additionally, a unique feature of the system is that feeding media for each individual culture can be pulled out for analysis at any point during an experiment. This allows researchers to non-invasively measure data such as pH and glucose levels which can be important for monitoring cell growth.
The microfluidics system is connected to the internet to allow scientists to remotely operate and retrieve real-time data from the system at any point, without disrupting the culture. Another paper from the Braingeneers group, published in the journal Internet of Things, shows how the Autoculture system is one example of the power of extending the internet-of-things to enable remote-controlled experiments – a need which the pandemic made more urgent.
When measuring their cerebral organoids, the researchers found that the stem cells grown using the Autoculture system not only differentiated into various cell types normally, but actually looked healthier than those grown using standard methods. RNA sequencing found lower levels of glycolytic and endoplasmic reticulum stress, showing a first promising set of data for addressing cellular stress identified in a Nature paper by collaborating researchers at UCSF, evidence that the group plans to expand on in ongoing research.
Why ketamine is a speedster antidepressant
Ketamine is the speedster of antidepressants, working within hours compared to more common antidepressants that can take several weeks. But ketamine can only be given for a limited amount of time because of its many side effects.
Now, a new Northwestern Medicine study identifies for the first time exactly how ketamine works so quickly, and how it might be adapted for use as a drug without the side effects.
The study in mice shows ketamine works as a rapid antidepressant by increasing the activity of the very small number of newborn neurons, which are part of an ongoing neurogenesis in the brain.
New neurons are always being made at a slow rate. It’s been known that increasing the number of neurons leads to behavioral changes. Other antidepressants work by increasing the rate of neurogenesis, in other words, increasing the number of neurons. But this takes weeks to happen.
By contrast, ketamine produces behavioral changes simply by increasing the activity of the existing new neurons. This can happen immediately when the cells are activated by ketamine.
“We narrowed down the population of cells to a small window that is involved,” said lead study author Dr. John Kessler, a professor of neurology at Northwestern University Feinberg School of Medicine and the Ken and Ruth Davee Professor of Stem Cell Biology. “That’s important because when you give ketamine to patients now, it affects multiple regions of the brain and causes a lot of adverse side effects. But since we now know exactly which cells we want to target, we can design drugs to focus only on those cells.”
The side effects of ketamine include blurred or double vision, nausea, vomiting, insomnia, drowsiness and addiction.
The study was published recently in Nature Communications.
Goal to develop faster-working antidepressant
“The goal is to develop an antidepressant that doesn’t take three to four weeks to work because people don’t do well during that period of time,” Kessler said. “If you are badly depressed and start taking your drug and nothing is happening, that is depressing in itself. To have something that works right away would make a huge difference.”
Newborn neurons act like a match to ignite activity in neurons
“We prove neurogenesis is responsible for the behavioral effects of ketamine,” Kessler said. “The reason is these newborn neurons form synapses (connections) that activate the other cells in the hippocampus. This small population of cells acts like a match, starting a fire that ignites a bunch of activity in a lot of other cells that produce the behavioral effects.”
“However, it has not been understood that the same behavioral changes can be accomplished by increasing the activity of the new neurons without increasing the rate at which they are born,” Kessler said. “This obviously is a much more rapid effect.”
For the study, Northwestern scientists created a mouse in which only the very small population of newborn neurons had a receptor that allowed these cells to be silenced or activated by a drug that did not affect any other cells in the brain. Scientists showed if they silenced the activity of these cells, ketamine didn’t work anymore. But if they used the drug to activate this population of cells, the results mirrored those of ketamine. This showed conclusively that it is the activity of these cells that is responsible for the effects of ketamine, Kessler said.
Neurobiologists Reveal How Value Decisions are Coded into Our Brains
In 2019 University of California San Diego researchers discovered the area of the brain where “value decisions” are made.
An area within the cerebrum known as the retrosplenial cortex (RSC), they found, is the site that we use to make value choices such as which restaurant we decide to visit for tonight’s dinner. We then update the RSC with fresh information based on the new impressions of how much we enjoyed the evening’s soup and pasta.
New research led by Division of Biological Sciences postdoctoral scholar Ryoma Hattori and Professor Takaki Komiyama is now revealing details about how such dynamic information is processed. The results, published in the journal Neuron, show that persistency allows value signals to be most effectively represented, or “coded,” across different areas of the brain, especially the RSC.
To investigate the details of how brain activity represents value-based decision making, a core animal behavior that is impaired in neurological conditions such as schizophrenia, dementia and addiction, the researchers set up reinforcement learning experiments in which mice were presented with options and their choices were rewarded with certain probabilities. They recorded corresponding brain activities during the reinforcement learning. The resulting data and network simulations pointed to the significance of persistent coding in how the mice and their value decisions were represented and the RSC as a nexus for this activity.
“These results suggest that, although information coding is highly distributed, not all of the information represented in neural activity may be used in each area,” the authors explain in the paper. “These results reveal that context-dependent, untangled persistency facilitates reliable signal coding and its distribution across the brain.”
According to Hattori, neurons are known to cycle through different activity patterns, with some neurons spiking in activity and others remaining silent. These brain activity patterns have been shown to correlate to certain task-related information such as value information for decision making. Because the RSC plays a central role in connecting several brain networks and functions, the new findings reinforce ideas about the site’s fundamental importance.
“We think that in the mouse brain the RSC functions as a stable reservoir for value information,” said Hattori. “The RSC appears to distribute value information to other brain areas that are vital for further processing of the value signals when mice perform reinforcement learning and decision making.”
To further test their findings, Hattori and Komiyama tapped into their “big data” trove of more than 100,000 mouse decisions recorded during the experiments. They programmed artificial intelligence (AI) networks to imitate behavioral strategies in computer-based reinforcement trials and found remarkably similar results to the real-world experiments.
“When we trained the artificial intelligence network to do the same behavior, it adopted the same strategy and the same way of representing the information in neural activity,” said Komiyama, who is a professor of neurobiology (Division of Biological Sciences) and neurosciences (Department of Neurosciences, School of Medicine), with affiliations in UC San Diego’s Center for Neural Circuits and Behavior and Halıcıoğlu Data Science Institute. “This suggests that this is an evolutionarily selected strategy for neural circuits to perform this behavior. This parallel between the biological brain and the AI that Ryoma trained is really interesting.”
‘Untitled’/2020
Proper synaptic joint will get you good night’s sleep
PTPδ, a synaptic adhesion molecule, regulates synaptic development and sleep behavior in mouse models.
Insight into the synapses The direct contact of mGluR4 receptors with other key proteins plays a significant role in the regulation of synaptic activity.
Ion channel VRAC enhances immune response against viruses Findings suggest cell-to-cell transmission of cGAMP via LRRC8/VRAC ion channels is central to effective antiviral immunity.
COVID-19 should be wake-up call for robotics research
Robots could help perform the “dull, dirty, and dangerous” pandemic response jobs, limiting human exposure to COVID-19.
‘Zombie’ brain cells develop into working neurons
Genetically preventing apoptosis during brain growth allows ‘zombie’ cells to develop into functioning neurons.
Brain imprints on cranial bones from humans and great apes refute the long-standing belief that the human pattern of brain asymmetry is unique. Researchers noted similar patterns of asymmetry in our great ape ancestors. However, there was more variability in this pattern in humans. Findings suggest lateralized and uniquely human cognitive abilities, such as language, evolved by adapting an ancestral brain asymmetry pattern.