Tyler O'Neal, Staff Editor APPLICATIONS

Danish Prof Allen builds a model that shows how our breathing shapes the brain

Every breath we take influences our emotions, attention and how we process the world around us. Professor Micah Allen from the Department of Clinical Medicine at Aarhus University has come a step closer to understanding how the very act of breathing shapes our brain.We breathe to survive. But a breath of fresh air does more than fill our lungs. New research from Aarhus University the second largest and second oldest university in Denmark, indicates that breathing impacts our emotions, attention and how we can process the outside world.

“Breathe in… Breathe out…” Or: “take a deep breath and count to ten.” The calming effect of breathing in stressful situations, is a concept most of us have met before. Now Professor Micah Allen from the Department of Clinical Medicine at Aarhus University has come to a step closer to understanding how the very act of breathing shapes our brain.

The researchers synthesized results from more than a dozen studies with rodent, monkey, and human brain imaging, and used it to propose a new computational model that explains how our breathing influences the brain’s expectations.

“What we found is that, across many different types of tasks and animals, brain rhythms are closely tied to the rhythm of our breath. We are more sensitive to the outside world when we are breathing in, whereas the brain tunes out more when we breathe out. This also aligns with how some extreme sports use breathing, for example, professional marksmen are trained to pull the trigger at the end of exhalation,” explains Professor Micah Allen.

The study suggests that breathing is more than just something we do to stay alive, explains Micah Allen.

“It suggests that the brain and breathing are closely intertwined in a way that goes far beyond survival, to actually impact our emotions, our attention, and how we process the outside world. Our model suggests there is a common mechanism in the brain which links the rhythm of breathing to these events.”

Breathing can affect our mental health

Understanding how breathing shapes our brain, and by extension, our mood, thoughts, and behaviors,  is an important goal in order to better prevent and treat mental illness.

“Difficulty breathing is associated with a very large increase in the risk for mood disorders such as anxiety and depression. We know that respiration, respiratory illness, and psychiatric disorders are closely linked. Our study raises the possibility that the next treatments for these disorders might be found in the development of new ways to realign the rhythms of the brain and body, rather than treating either in isolation,” explains Micah Allen.

Stabilizing our mind through breathing is a well-known and used tactic in many traditions such as yoga and meditation. The new study sheds light on how the brain makes it possible. It suggests that there are three pathways in the brain that control this interaction between breathing and brain activity. It also suggests that our pattern of breathing makes the brain more “excitable”, meaning neurons are more likely to fire during certain times of breathing

New research to come

The new study gives researchers a new target for future studies for example persons with respiratory or mood disorders, and Micah Allen and his group already have already started new projects based on the study.

“We have a variety of ongoing projects that are both building on and testing various parts of the model we have proposed. Ph.D. Student Malthe Brændholt is conducting innovative brain imaging studies in humans, to try and understand how different kinds of emotional and visual perception are influenced by breathing in the brain,” says Micah Allen.

The team is also collaborating with the Pulmonology team at Aarhus University Hospital, where tools developed in the lab are used to understand whether a person suffering from long-covid may have disruptions in the breath-brain alignment. And there are more projects coming, says Micah Allen.

”We will be using a combination of human and animal neuroimaging to better understand how breathing influences the brain, and also utilizing exploring how different drugs influence respiratory-brain interaction. We would also like to someday study how lifestyle factors like stress, sleep, and even things like winter swimming influence breath-brain interaction. We are very excited to continue this research,” says Micah Allen.

 

Spanish researchers create material for neuromorphic supercomputing

Tyler O'Neal, Staff Editor APPLICATIONS

Universitat Autònoma de Barcelona (UAB) researchers have developed a magnetic material capable of imitating the way the brain stores information. The material makes it possible to emulate the synapses of neurons and mimic, for the first time, the learning that occurs during deep sleep.

Researchers (left to right) Jordi Sort, Enric Menéndez and Zhengwei Tan in the lab at the UAB.Neuromorphic supercomputing is a new computing paradigm in which the behavior of the brain is emulated by mimicking the main synaptic functions of neurons. Among these functions is neuronal plasticity: the ability to store information or forget it depending on the duration and repetition of the electrical impulses that stimulate neurons. This plasticity would be linked to learning and memory.

Among the materials that mimic neuron synapses, memristive materials, ferroelectrics, phase change memory materials, topological insulators, and, more recently, magneto-ionic materials stand out. In the latter, changes in the magnetic properties are induced by the displacement of ions within the material caused by applying an electric field. In these materials, it is well known how the magnetism is modulated when applying the electric field, but the evolution of magnetic properties when voltage is stopped (that is, the evolution after the stimulus) is difficult to control. This makes it complicated to emulate some brain-inspired functions, such as maintaining the efficiency of learning that takes place even while the brain is in a state of deep sleep (i.e., without external stimulation).

This study, led by researchers from the UAB Department of Physics Jordi Sort and Enric Menéndez, in collaboration with the ALBA Synchrotron, the Catalan Institute of Nanoscience and Nanotechnology (ICN2), and the ICMAB, proposes a new way of controlling the evolution of magnetization both in the stimulated and in the post-stimulus states.

The researchers have developed a material based on a thin layer of cobalt mononitride (CoN) where, by applying an electric field, the accumulation of N ions at the interface between the layer and a liquid electrolyte in which the layer has been placed can be controlled. "The new material works with the movement of ions controlled by electrical voltage, in a manner analogous to our brain, and at speeds similar to those produced in neurons, of the order of milliseconds," explain ICREA research professor Jordi Sort and Serra Húnter Tenure-track Professor Enric Menéndez. "We have developed an artificial synapse that in the future may be the basis of a new computing paradigm, alternative to the one used by current computers," Sort and Menéndez point out.

By applying voltage pulses, it has been possible to emulate, in a controlled way, processes such as memory, information processing, information retrieval, and, for the first time, the controlled updating of information without applied voltage. This control has been achieved by modifying the thickness of the cobalt mononitride layers (which determines the speed of the ions' motion), and the frequency of the pulses. The arrangement of the material allows the magnetoionic properties to be controlled not only when the voltage is applied but also, for the first time, when the voltage is removed. Once the external voltage stimulus disappears, the magnetization of the system can be reduced or increased, depending on the thickness of the material and the protocol of how the voltage has been previously applied.

This new effect opens a whole range of opportunities for new neuromorphic computing functions. It offers a new logic function that allows, for example, the possibility of mimicking the neural learning that occurs after brain stimulation, when we sleep profoundly. This functionality cannot be emulated by any other type of existing neuromorphic materials.

"When the thickness of the cobalt mononitride layer is below 50 nanometers and with a voltage applied at a frequency greater than 100 cycles per second, we have managed to emulate an additional logic function: once the voltage is applied, the device can be programmed to learn or to forget, without the need for any additional input of energy, mimicking the synaptic functions that take place in the brain during deep sleep, when information processing can continue without applying any external signal", highlight Jordi Sort and Enric Menendez.

The research has been led by researchers from the UAB Department of Physics Jordi Sort, also a researcher at the Catalan Institute for Research and Advanced Studies (ICREA), and Enric Menéndez (Serra Húnter Tenure-track Professor). and with participation of Zhengwei Tan, Julius de Rojas, and Sofia Martins, researchers from the UAB Department of Physics; Aitor Lopeandia, from the Physics Department of the UAB and the Catalan Institute of Nanoscience and Nanotechnology (ICN2); Alberto Quintana, from the Barcelona Institute of Materials Science (ICMAB-CSIC); Javier Herrero-Martín, from the ALBA Synchrotron; José L. Costa-Krämer, from the Institute of Micro and Nanotechnology (IMN-CNM-CSIC); and researchers from CNR-SPIN in Italy, and IMEC and Quantum Solid State Physics (KU Leuven) in Belgium.

Japanese scientists extract data for materials databases

Tyler O'Neal, Staff Editor APPLICATIONS

Low STAM20221107 ed37dScientists in Japan have combined two computational models to extract more data on steel alloys from a single test, with implications for the discovery of new materials.

A new approach uses data from one type of test on small metal alloy samples to extract enough information for building databases that can be used to predict the properties and potentials of new materials. The details were published in the journal Science and Technology of Advanced Materials: Methods.

The test is called instrumented indentation. It involves driving an indenter tip into a material to probe some of its properties, such as hardness and elastic stiffness. Scientists have been using the data extracted from instrumented indentation to estimate the stress-strain curve of materials using computational simulations. This curve, and the data it provides, are important for understanding a material's properties. That data is also used for building massive materials databases, which can be used, in conjunction with artificial intelligence, for predicting new materials.

A problem scientists face is that this approach for estimating material properties is limited when it comes to materials called 'high work-hardening alloys': metal alloys, like steel, that are strengthened through physical processes like rolling and forging. Only so much information can be estimated from the curve of these materials. To get the necessary additional information needed to determine their properties, more experiments would need to be done, which costs time, effort, and money.

Ta-Te Chen of the University of Tsukuba and Ikumu Watanabe of the National Institute for Materials Science in Japan have developed a new computational approach to extract that additional information from instrumented indentation tests on work-hardening alloys.

"Our approach builds on an already-existing model, making it ready for use in industry. It is also applicable to existing data, including hardness," says Watanabe.

The approach involves combining the results from two computational models, the power-law and linear hardening models, which produce their own individual stress-plastic strain curves from information gathered from indentation tests. Combining the data from both curves provides the extra data that, when added to the original stress-strain curve, shows a more holistic picture of the work-hardening alloys' properties.

The scientists validated their approach by using it on high-work-hardening stainless steel.

We have extended this approach to also evaluate mechanical properties at elevated temperatures, which can contribute to the development of high-temperature alloys," says Chen.