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‘Mathematical microscope’ reveals novel, energy-efficient mechanism of working memory that works even during sleep
UCLA Health researchers have discovered a mechanism that creates memories while reducing metabolic cost, even during sleep. This efficient memory occurs in a part of the brain that is crucial for learning and memory, and where Alzheimer’s disease begins.
Does this sound familiar: You go to the kitchen to fetch something, but when you get there, you forget what you wanted. This is your working memory failing. Working memory is defined as remembering some information for a short period while you go about doing other things. We use working memory virtually all the time. Alzheimer’s and dementia patients have working memory deficits and it also shows up in mild cognitive impairment (MCI). Hence, considerable effort has been devoted to understand the mechanisms by which the vast networks of neurons in the brain create working memory.
During working memory tasks, the outermost layer of the brain, known as the neocortex, sends sensory information to deeper regions of the brain, including a central region called the entorhinal cortex, which is crucial for forming memories. Neurons in the entorhinal cortex show a complex array of responses, which have puzzled scientists for a long time and resulted in the 2014 Nobel Prize in medicine, yet the mechanisms governing this complexity are unknown. The entorhinal cortex is where Alzheimer’s disease begins forming.
“It’s therefore critical to understand what kind of magic happens in the cortico-entorhinal network, when the neocortex speaks to the entorhinal cortex which turns it into working memory. It could provide an early diagnostic of Alzheimer’s disease and related dementia, and mild cognitive impairment,” said corresponding author Mayank Mehta, a neurophysicist and head of the W. M. Keck Center for Neurophysics and the Center for Physics of Life at UCLA.
To crack this problem, Mehta and his coauthors devised a novel approach: a “mathematical microscope.”
In the world of physics, mathematical models are commonly used, from Kepler to Newton and Einstein, to reveal amazing things we have never seen or even imagined, such as the inner workings of subatomic particles and the inside of a black hole. Mathematical models are used in brain sciences too, but their predictions are not taken as seriously as in physics. The reason is that in physics, predictions of mathematical theories are tested quantitatively, not just qualitatively.
Such quantitatively precise experimental tests of mathematical theories are commonly believed to be unfeasible in biology because the brain is vastly more complex than the physical world. Mathematical theories in physics are very simple, involving very few free parameters and hence precise experimental tests. In contrast, the brain has billions of neurons and trillions of connections, a mathematical nightmare, let alone a highly precise microscope.
“To tackle this seemingly impossible challenge of devising a simple theory that can still explain the experimental of data of memory dynamics in vivo data with high precision, we hypothesized that cortico-entorhinal dialog, and memory magic, will occur even when the subjects are sleeping, or anesthetized,” said Dr. Krishna Choudhary, the lead author of the study. “Just like a car behaves like a car when it’s idling or going at 70 mph.”
UCLA researchers then made another large assumption: the dynamics of the entire cortex and the entorhinal cortex during sleep or anesthesia can be captured by just two neurons. These assumptions reduced the problem of billions of neurons’ interactions to just two only free variables — the strength of input from the neocortex to entorhinal cortex and the strength of recurrent connections within the entorhinal cortex. While this makes the problem mathematically tractable, it raises the obvious question — is it true?
“If we test our theory quantitatively on data in vivo, then these are just interesting mathematical games, not a solid understanding of memory-making magic,” said Mehta.
The crucial experimental tests of this theory required sophisticated experiments by Dr. Thomas Hahn, a coauthor who is now professor at Basel University and a clinical psychologist.
“The entorhinal cortex is a complicated circuit. To really test the theory we needed experimental techniques that can not only measure the neural activity with high precision, but also determine the precise anatomical identity of the neuron,” said Hahn.
Hahn and Dr. Sven Berberich, also a coauthor, measured the membrane potential of identified neurons from the entorhinal cortex in vivo, using whole cell patch clamp technique and then used anatomical techniques to identify the neuron. Simultaneously they measured the activity of the parietal cortex, a part of neocortex that sends inputs to the entorhinal cortex.
“A mathematical theory and sophisticated in vivo data are necessary and cool, but we had to tackle one more challenge — how does one map this simple theory onto complex neural data?” said Mehta.
“This required a protracted period of development, to generate a ‘mathematical microscope’ that can directly reveal the inner workings of neurons as they make memory,” said Choudhary. “As far as we know, this has not been done before.”
The authors observed that like an ocean wave forming and then crashing on to a shoreline, the signals from the neocortex oscillate between on and off states in intervals while a person or animal sleeps. Meanwhile, the entorhinal cortex acted like a swimmer in the water who can move up when the wave forms and then down when it recedes. The data showed this and the model captured this as well. But using this simple match the model then took a life of its own and discovered a new type of memory state known as spontaneous persistent inactivity, said Mehta.
“It’s as if a wave comes in and the entorhinal cortex said, ‘There is no wave! I’m going to remember that recently there was no wave so I am going to ignore this current wave and not respond at all’. This is persistent inactivity” Mehta said. “Alternately, persistent activity occurs when the cortical wave disappears but the entorhinal neurons remember that there was a wave very recently, and continue rolling forward.”
While many theories of working memory had shown the presence of persistent activity, which the authors found, the persistent inactivity was something that the model predicted and had never been seen before.
“The cool part about persistent inactivity is that it takes virtually no energy, unlike persistent activity, which takes a lot of energy,” said Mehta, “even better, the combination of persistent activity and inactivity more than doubles the memory capacity while cutting down the metabolic energy cost by half.”
“All this sounded too good to be true, so we really pushed our mathematical microscope to the limit, into a regime where it was not designed to work,” said Dr. Choudhary. “If the microscope was right, it would continue working perfectly even in unusual situations.”
“The math-microscope made a dozen predictions, not just about entorhinal but many other brain regions too. To our complete surprise, the mathematical microscope worked every time,” Mehta continued. “Such near perfect match between the predictions of a mathematical theory and experiments is unprecedented in neuroscience.
“This mathematical model that is perfectly matched with experiments is a new microscope,” Mehta continued. “It reveals something that no existing microscope could see without it. No matter how many neurons you have imaged, it would not have revealed any of this.
“In fact, metabolic shortcomings are a common feature of many memory disorders,” said Mehta. Mehta’s laboratory is now following up on this work to understand how complex working memory is formed, and what goes wrong in the entorhinal cortex during Alzheimer’s disease, dementia and other memory disorders.”
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Early dark energy could resolve cosmology’s two biggest puzzles
A new study by MIT physicists proposes that a mysterious force known as early dark energy could solve two of the biggest puzzles in cosmology and fill in some major gaps in our understanding of how the early universe evolved.
Now, the MIT team has found that both puzzles could be resolved if the early universe had one extra, fleeting ingredient: early dark energy. Dark energy is an unknown form of energy that physicists suspect is driving the expansion of the universe today. Early dark energy is a similar, hypothetical phenomenon that may have made only a brief appearance, influencing the expansion of the universe in its first moments before disappearing entirely.
Some physicists have suspected that early dark energy could be the key to solving the Hubble tension, as the mysterious force could accelerate the early expansion of the universe by an amount that would resolve the measurement mismatch.
The MIT researchers have now found that early dark energy could also explain the baffling number of bright galaxies that astronomers have observed in the early universe. In their new study, reported in the Monthly Notices of the Royal Astronomical Society, the team modeled the formation of galaxies in the universe’s first few hundred million years. When they incorporated a dark energy component only in that earliest sliver of time, they found the number of galaxies that arose from the primordial environment bloomed to fit astronomers’ observations.
“You have these two looming open-ended puzzles,” says study co-author Rohan Naidu, a postdoc in MIT’s Kavli Institute for Astrophysics and Space Research. “We find that in fact, early dark energy is a very elegant and sparse solution to two of the most pressing problems in cosmology.”
The study’s co-authors include lead author and Kavli postdoc Xuejian (Jacob) Shen, and MIT professor of physics Mark Vogelsberger, along with Michael Boylan-Kolchin at the University of Texas at Austin, and Sandro Tacchella at the University of Cambridge.
Big city lights
Based on standard cosmological and galaxy formation models, the universe should have taken its time spinning up the first galaxies. It would have taken billions of years for primordial gas to coalesce into galaxies as large and bright as the Milky Way.
But in 2023, NASA’s James Webb Space Telescope (JWST) made a startling observation. With an ability to peer farther back in time than any observatory to date, the telescope uncovered a surprising number of bright galaxies as large as the modern Milky Way within the first 500 million years, when the universe was just 3 percent of its current age.
“The bright galaxies that JWST saw would be like seeing a clustering of lights around big cities, whereas theory predicts something like the light around more rural settings like Yellowstone National Park,” Shen says. “And we don’t expect that clustering of light so early on.”
For physicists, the observations imply that there is either something fundamentally wrong with the physics underlying the models or a missing ingredient in the early universe that scientists have not accounted for. The MIT team explored the possibility of the latter, and whether the missing ingredient might be early dark energy.
Physicists have proposed that early dark energy is a sort of antigravitational force that is turned on only at very early times. This force would counteract gravity’s inward pull and accelerate the early expansion of the universe, in a way that would resolve the mismatch in measurements. Early dark energy, therefore, is considered the most likely solution to the Hubble tension.
Galaxy skeleton
The MIT team explored whether early dark energy could also be the key to explaining the unexpected population of large, bright galaxies detected by JWST. In their new study, the physicists considered how early dark energy might affect the early structure of the universe that gave rise to the first galaxies. They focused on the formation of dark matter halos — regions of space where gravity happens to be stronger, and where matter begins to accumulate.
“We believe that dark matter halos are the invisible skeleton of the universe,” Shen explains. “Dark matter structures form first, and then galaxies form within these structures. So, we expect the number of bright galaxies should be proportional to the number of big dark matter halos.”
The team developed an empirical framework for early galaxy formation, which predicts the number, luminosity, and size of galaxies that should form in the early universe, given some measures of “cosmological parameters.” Cosmological parameters are the basic ingredients, or mathematical terms, that describe the evolution of the universe.
Physicists have determined that there are at least six main cosmological parameters, one of which is the Hubble constant — a term that describes the universe’s rate of expansion. Other parameters describe density fluctuations in the primordial soup, immediately after the Big Bang, from which dark matter halos eventually form.
The MIT team reasoned that if early dark energy affects the universe’s early expansion rate, in a way that resolves the Hubble tension, then it could affect the balance of the other cosmological parameters, in a way that might increase the number of bright galaxies that appear at early times. To test their theory, they incorporated a model of early dark energy (the same one that happens to resolve the Hubble tension) into an empirical galaxy formation framework to see how the earliest dark matter structures evolve and give rise to the first galaxies.
“What we show is, the skeletal structure of the early universe is altered in a subtle way where the amplitude of fluctuations goes up, and you get bigger halos, and brighter galaxies that are in place at earlier times, more so than in our more vanilla models,” Naidu says. “It means things were more abundant, and more clustered in the early universe.”
“A priori, I would not have expected the abundance of JWST’s early bright galaxies to have anything to do with early dark energy, but their observation that EDE pushes cosmological parameters in a direction that boosts the early-galaxy abundance is interesting,” says Marc Kamionkowski, professor of theoretical physics at Johns Hopkins University, who was not involved with the study. “I think more work will need to be done to establish a link between early galaxies and EDE, but regardless of how things turn out, it’s a clever — and hopefully ultimately fruitful — thing to try.”
“We demonstrated the potential of early dark energy as a unified solution to the two major issues faced by cosmology. This might be an evidence for its existence if the observational findings of JWST get further consolidated,” Vogelsberger concludes. “In the future, we can incorporate this into large cosmological simulations to see what detailed predictions we get.”
This research was supported, in part, by NASA and the National Science Foundation.
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Plant-derived secondary organic aerosols can act as mediators of plant-plant interactions
A new study published in Science reveals that plant-derived secondary organic aerosols (SOAs) can act as mediators of plant-plant interactions. This research was conducted through the cooperation of chemical ecologists, plant ecophysiologists and atmospheric physicists at the University of Eastern Finland.
The study showed that Scots pine seedlings, when damaged by large pine weevils, release VOCs that activate defences in nearby plants of the same species. Interestingly, the biological activity persisted after VOCs were oxidized to form SOAs. The results indicated that the elemental composition and quantity of SOAs likely determines their biological functions.
“A key novelty of the study is the finding that plants adopt subtly different defence strategies when receiving signals as VOCs or as SOAs, yet they exhibit similar degrees of resistance to herbivore feeding,” said Professor James Blande, head of the Environmental Ecology Research Group. This observation opens up the possibility that plants have sophisticated sensing systems that enable them to tailor their defences to information derived from different types of chemical cue.
“Considering the formation rate of SOAs from their precursor VOCs, their longer lifetime compared to VOCs, and the atmospheric air mass transport, we expect that the ecologically effective distance for interactions mediated by SOAs is longer than that for plant interactions mediated by VOCs,” said Professor Annele Virtanen, head of the Aerosol Physics Research Group. This could be interpreted as plants being able to detect cues representing close versus distant threats from herbivores.
The study is expected to open up a whole new complex research area to environmental ecologists and their collaborators, which could lead to new insights on the chemical cues structuring interactions between plants.
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Folded or cut, this lithium-sulfur battery keeps going
Most rechargeable batteries that power portable devices, such as toys, handheld vacuums and e-bikes, use lithium-ion technology. But these batteries can have short lifetimes and may catch fire when damaged. To address stability and safety issues, researchers reporting in ACS Energy Letters have designed a lithium-sulfur (Li-S) battery that features an improved iron sulfide cathode. One prototype remains highly stable over 300 charge-discharge cycles, and another provides power even after being folded or cut.
The team coated iron sulfide cathodes in different polymers and found in initial electrochemical performance tests that polyacrylic acid (PAA) performed best, retaining the electrode’s discharge capacity after 300 charge-discharge cycles. Next, the researchers incorporated a PAA-coated iron sulfide cathode into a prototype battery design, which also included a carbonate-based electrolyte, a lithium metal foil as an ion source, and a graphite-based anode. They produced and then tested both pouch cell and coin cell battery prototypes.
After more than 100 charge-discharge cycles, Wang and colleagues observed no substantial capacity decay in the pouch cell. Additional experiments showed that the pouch cell still worked after being folded and cut in half. The coin cell retained 72% of its capacity after 300 charge-discharge cycles. They next applied the polymer coating to cathodes made from other metals, creating lithium-molybdenum and lithium-vanadium batteries. These cells also had stable capacity over 300 charge-discharge cycles. Overall, the results indicate that coated cathodes could produce not only safer Li-S batteries with long lifespans, but also efficient batteries with other metal sulfides, according to Wang’s team.
The authors acknowledge funding from the National Natural Science Foundation of China; the Natural Science Foundation of Sichuan, China; and the Beijing National Laboratory for Condensed Matter Physics.
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