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NT/ A new ultrasound technique to decode the brain’s intentions

Neuroscience biweekly vol. 29, 17th March — 1st April

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Single-trial decoding of movement intentions using functional ultrasound neuroimaging

by Sumner L. Norman, David Maresca, Vassilios N. Christopoulos, Whitney S. Griggs, Charlie Demene, Mickael Tanter, Mikhail G. Shapiro, Richard A. Andersen in Neuron

What is happening in your brain as you are scrolling through this page? In other words, which areas of your brain are active, which neurons are talking to which others, and what signals are they sending to your muscles?

Mapping neural activity to corresponding behaviors is a major goal for neuroscientists developing brain-machine interfaces (BMIs): devices that read and interpret brain activity and transmit instructions to a computer or machine. Though this may seem like science fiction, existing BMIs can, for example, connect a paralyzed person with a robotic arm; the device interprets the person’s neural activity and intentions and moves the robotic arm correspondingly.

A major limitation for the development of BMIs is that the devices require invasive brain surgery to read out neural activity. But now, a collaboration at Caltech has developed a new type of minimally invasive BMI to read out brain activity corresponding to the planning of movement. Using functional ultrasound (fUS) technology, it can accurately map brain activity from precise regions deep within the brain at a resolution of 100 micrometers (the size of a single neuron is approximately 10 micrometers).

The new fUS technology is a major step in creating less invasive, yet still highly capable, BMIs.

“Invasive forms of brain-machine interfaces can already give movement back to those who have lost it due to neurological injury or disease,” says Sumner Norman, postdoctoral fellow in the Andersen lab and co-first author on the new study. “Unfortunately, only a select few with the most severe paralysis are eligible and willing to have electrodes implanted into their brain. Functional ultrasound is an incredibly exciting new method to record detailed brain activity without damaging brain tissue. We pushed the limits of ultrasound neuroimaging and were thrilled that it could predict movement. What’s most exciting is that fUS is a young technique with huge potential — this is just our first step in bringing high performance, less invasive BMI to more people.”

The new study is a collaboration between the laboratories of Richard Andersen, James G. Boswell Professor of Neuroscience and Leadership Chair and director of the Tianqiao and Chrissy Chen Brain-Machine Interface Center in the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech; and of Mikhail Shapiro, professor of chemical engineering and Heritage Medical Research Institute Investigator. Shapiro is an affiliated faculty member with the Chen Institute.

In general, all tools for measuring brain activity have drawbacks. Implanted electrodes (electrophysiology) can very precisely measure activity on the level of single neurons, but, of course, require the implantation of those electrodes into the brain. Non-invasive techniques like functional magnetic resonance imaging (fMRI) can image the entire brain but require bulky and expensive machinery. Electroencephalography (EEGs) does not require surgery but can only measure activity at low spatial resolution.

Ultrasound works by emitting pulses of high frequency sound and measuring how those sound vibrations echo throughout a substance, such as various tissues of the human body. Sound travels at different speeds through these tissue types and reflects at the boundaries between them. This technique is commonly used to take images of a fetus in utero, and for other diagnostic imaging.

Ultrasound can also “hear” the internal motion of organs. For example, red blood cells, like a passing ambulance, will increase in pitch as they approach the source of the ultrasound waves, and decrease as they flow away. Measuring this phenomenon allowed the researchers to record tiny changes in the brain’s blood flow down to 100 micrometers (on the scale of the width of a human hair).

“When a part of the brain becomes more active, there’s an increase in blood flow to the area. A key question in this work was: If we have a technique like functional ultrasound that gives us high-resolution images of the brain’s blood flow dynamics in space and over time, is there enough information from that imaging to decode something useful about behavior?” Shapiro says. “The answer is yes. This technique produced detailed images of the dynamics of neural signals in our target region that could not be seen with other non-invasive techniques like fMRI. We produced a level of detail approaching electrophysiology, but with a far less invasive procedure.”

The collaboration began when Shapiro invited Mickael Tanter, a pioneer in functional ultrasound and director of Physics for Medicine Paris (ESPCI Paris Sciences et Lettres University, Inserm, CNRS), to give a seminar at Caltech in 2015. Vasileios Christopoulos, a former Andersen lab postdoctoral scholar (now an assistant professor at UC Riverside), attended the talk and proposed a collaboration. Shapiro, Andersen, and Tanter then received an NIH BRAIN Initiative grant to pursue the research. The work at Caltech was led by Norman, former Shapiro lab postdoctoral fellow David Maresca (now assistant professor at Delft University of Technology), and Christopoulos. Along with Norman, Maresca and Christopoulos are co-first authors on the new study.

The technology was developed with the aid of non-human primates, who were taught to do simple tasks that involved moving their eyes or arms in certain directions when presented with certain cues. As the primates completed the tasks, the fUS measured brain activity in the posterior parietal cortex (PPC), a region of the brain involved in planning movement. The Andersen lab has studied the PPC for decades and has previously created maps of brain activity in the region using electrophysiology. To validate the accuracy of fUS, the researchers compared brain imaging activity from fUS to previously obtained detailed electrophysiology data.

Next, through the support of the T&C Chen Brain-Machine Interface Center at Caltech, the team aimed to see if the activity-dependent changes in the fUS images could be used to decode the intentions of the non-human primate, even before it initiated a movement. The ultrasound imaging data and the corresponding tasks were then processed by a machine-learning algorithm, which learned what patterns of brain activity correlated with which tasks. Once the algorithm was trained, it was presented with ultrasound data collected in real time from the non-human primates.

The algorithm predicted, within a few seconds, what behavior the non-human primate was going to carry out (eye movement or reach), direction of the movement (left or right), and when they planned to make the movement.

“The first milestone was to show that ultrasound could capture brain signals related to the thought of planning a physical movement,” says Maresca, who has expertise in ultrasound imaging. “Functional ultrasound imaging manages to record these signals with 10 times more sensitivity and better resolution than functional MRI. This finding is at the core of the success of brain-machine interfacing based on functional ultrasound.”

“Current high-resolution brain-machine interfaces use electrode arrays that require brain surgery, which includes opening the dura, the strong fibrous membrane between the skull and the brain, and implanting the electrodes directly into the brain. But ultrasound signals can pass through the dura and brain non-invasively. Only a small, ultrasound-transparent window needs to be implanted in the skull; this surgery is significantly less invasive than that required for implanting electrodes,” says Andersen.

Though this research was carried out in non-human primates, a collaboration is in the works with Dr. Charles Liu, a neurosurgeon at USC, to study the technology with human volunteers who, because of traumatic brain injuries, have had a piece of skull removed. Because ultrasound waves can pass unaffected through these “acoustic windows,” it will be possible to study how well functional ultrasound can measure and decode brain activity in these individuals.

Deep artificial neural networks reveal a distributed cortical network encoding propositional sentence-level meaning

by Andrew James Anderson, Douwe Kiela, Jeffrey R. Binder, Leonardo Fernandino, Colin J. Humphries, Lisa L. Conant, Rajeev D. S. Raizada, Scott Grimm, Edmund C. Lalor in The Journal of Neuroscience

Have you ever wondered why you are able to hear a sentence and understand its meaning — given that the same words in a different order would have an entirely different meaning? New research involving neuroimaging and A.I., describes the complex network within the brain that comprehends the meaning of a spoken sentence.

“It has been unclear whether the integration of this meaning is represented in a particular site in the brain, such as the anterior temporal lobes, or reflects a more network level operation that engages multiple brain regions,” said Andrew Anderson, Ph.D., research assistant professor in the University of Rochester Del Monte Institute for Neuroscience and lead author on of the study. “The meaning of a sentence is more than the sum of its parts. Take a very simple example — ‘the car ran over the cat’ and ‘the cat ran over the car’ — each sentence has exactly the same words, but those words have a totally different meaning when reordered.”

The study is an example of how the application of artificial neural networks, or A.I., are enabling researchers to unlock the extremely complex signaling in the brain that underlies functions such as processing language. The researchers gather brain activity data from study participants who read sentences while undergoing fMRI. These scans showed activity in the brain spanning across a network of different regions — anterior and posterior temporal lobes, inferior parietal cortex, and inferior frontal cortex. Using the computational model InferSent — an A.I. model developed by Facebook trained to produce unified semantic representations of sentences — the researchers were able to predict patterns of fMRI activity reflecting the encoding of sentence meaning across those brain regions.

“It’s the first time that we’ve applied this model to predict brain activity within these regions, and that provides new evidence that contextualized semantic representations are encoded throughout a distributed language network, rather than at a single site in the brain.”

Anderson and his team believe the findings could be helpful in understanding clinical conditions. “We’re deploying similar methods to try to understand how language comprehension breaks down in early Alzheimer’s disease. We are also interested in moving the models forward to predict brain activity elicited as language is produced. The current study had people read sentences, in the future we’re interested in moving forward to predict brain activity as people might speak sentences.”

An early cell shape transition drives evolutionary expansion of the human forebrain

by Silvia Benito-Kwiecinski, Stefano L. Giandomenico, Magdalena Sutcliffe, Erlend S. Riis, Paula Freire-Pritchett, Iva Kelava, Stephanie Wunderlich, Ulrich Martin, Gregory A. Wray, Kate McDole, Madeline A. Lancaster in Cell

A new study is the first to identify how human brains grow much larger, with three times as many neurons, compared with chimpanzee and gorilla brains. The study, led by researchers at the Medical Research Council (MRC) Laboratory of Molecular Biology in Cambridge, UK, identified a key molecular switch that can make ape brain organoids grow more like human organoids, and vice versa.

The study, compared ‘brain organoids’ — 3D tissues grown from stem cells which model early brain development — that were grown from human, gorilla and chimpanzee stem cells.

Similar to actual brains, the human brain organoids grew a lot larger than the organoids from other apes.

Dr Madeline Lancaster, from the MRC Laboratory of Molecular Biology, who led the study, said: “This provides some of the first insight into what is different about the developing human brain that sets us apart from our closest living relatives, the other great apes. The most striking difference between us and other apes is just how incredibly big our brains are.”

During the early stages of brain development, neurons are made by stem cells called neural progenitors. These progenitor cells initially have a cylindrical shape that makes it easy for them to split into identical daughter cells with the same shape.

The more times the neural progenitor cells multiply at this stage, the more neurons there will be later. As the cells mature and slow their multiplication, they elongate, forming a shape like a stretched ice-cream cone.

Previously, research in mice had shown that their neural progenitor cells mature into a conical shape and slow their multiplication within hours. Now, brain organoids have allowed researchers to uncover how this development happens in humans, gorillas and chimpanzees. They found that in gorillas and chimpanzees this transition takes a long time, occurring over approximately five days.

Human progenitors were even more delayed in this transition, taking around seven days. The human progenitor cells maintained their cylinder-like shape for longer than other apes and during this time they split more frequently, producing more cells.

This difference in the speed of transition from neural progenitors to neurons means that the human cells have more time to multiply. This could be largely responsible for the approximately three-fold greater number of neurons in human brains compared with gorilla or chimpanzee brains.

Dr Lancaster said: “We have found that a delayed change in the shape of cells in the early brain is enough to change the course of development, helping determine the numbers of neurons that are made. It’s remarkable that a relatively simple evolutionary change in cell shape could have major consequences in brain evolution. I feel like we’ve really learnt something fundamental about the questions I’ve been interested in for as long as I can remember — what makes us human.”

To uncover the genetic mechanism driving these differences, the researchers compared gene expression — which genes are turned on and off — in the human brain organoids versus the other apes.

They identified differences in a gene called ‘ZEB2’, which was turned on sooner in gorilla brain organoids than in the human organoids.

To test the effects of the gene in gorilla progenitor cells, they delayed the effects of ZEB2. This slowed the maturation of the progenitor cells, making the gorilla brain organoids develop more similarly to human — slower and larger.

Conversely, turning on the ZEB2 gene sooner in human progenitor cells promoted premature transition in human organoids, so that they developed more like ape organoids.

The researchers note that organoids are a model and, like all models, do not to fully replicate real brains, especially mature brain function. But for fundamental questions about our evolution, these brain tissues in a dish provide an unprecedented view into key stages of brain development that would be impossible to study otherwise.

Human telencephalic organoids are larger with extended apical lumens

(A) Schematic of the timeline for generating brain organoids from human, gorilla, and chimpanzee stem cells. Colors represent changes in media and stages of the protocol. +MG represents Matrigel embedding. Note chimpanzee organoids have an EB stage that is 2 days shorter than human and gorilla.

(B) Bright field images of human (H9, top panels), gorilla (G1, middle panels), and chimpanzee (Chmp, bottom panels) organoids at days 3, 5, and 10. Black arrows indicate neuroepithelial buds, which appear more elongated in the human beginning at day 5. Scale bar, 200 μm.

(С ) Quantification of bright field images at day 10 show human (H9) neural tissue is enlarged relative to gorilla (G1) and chimpanzee (Chmp). Left: area occupied by individual organoids. Right: visible perimeter of individual neuroepithelial buds. Mean organoid area: H9 = 168,327 μm2; G1 = 84,876 μm2; Chmp = 81,086 μm2. Mean neuroepithelial bud perimeter: H9 = 322 μm; G1 = 237 μm; Chmp = 204 μm. ∗p < 0.05, ∗∗∗∗p < 0.0001, Kruskal-Wallis and post hoc Dunn’s multiple comparisons test, n (H9) = 22 organoids and 144 neuroepithelial buds from 6 independent batches, n (G1) = 23 organoids and 148 neuroepithelial buds from 5 independent batches, n (Chmp) = 13 organoids and 107 neuroepithelial buds from 3 independent batches, error bars are SD.

(D and E) Representative immunofluorescence images of the center of whole mount human (H9), gorilla (G1), and chimpanzee (Chmp) organoids with staining for apical marker ZO1 and neural progenitor marker SOX2 at day 3 (D) and day 5 (E). Note the appearance of less rounded ZO1 positive apical lumens (arrowheads) in human organoids relative to nonhuman ape organoids at day 5. DAPI is in blue. Red background signal outside the organoid comes from nonspecific uneven staining of surrounding Matrigel. Scale bar, 100 μm.

(F) 3D MATLAB reconstructions of apical lumens of day 5 organoids showing a representative example used for quantifications in (G). Values on the axes are in μm. Luminal surface area of reconstructed examples: human (H9) = 63,394 μm2; gorilla (G1) = 15,146 μm2; chimpanzee (Chmp) = 19,730 μm2.

(G) Quantification of the surface area of the largest apical lumen per day 5 organoid reveals significantly expanded luminal surface areas in human versus nonhuman apes. Mean luminal surface area: human (H9) = 51,243 μm2; gorilla (G1) = 15,437 μm2; chimpanzee (Chmp) = 19,632 μm2. ∗p < 0.05, ∗∗p = 0.0012, ∗∗∗∗p < 0.0001, one-way ANOVA and post hoc Tukey’s multiple comparisons test, n (H9 and G1) = 11 organoids from 5 independent batches, n (Chmp) = 8 organoids from 2 independent batches, error bars are SD.

Potential differences in cleavage of the S protein and type-1 interferon together control human coronavirus infection, propagation, and neuropathology within the central nervous system

by Alain Le Coupanec, Marc Desforges, Benedikt Kaufer, Philippe Dubeau, Marceline Côté, Pierre J. Talbot in Journal of Virology

A number of studies have shown that human coronaviruses, including SARS-CoV-2 which causes COVID-19, appear to attack neurons and the nervous system in vulnerable populations. This neuroinvasion through the nasal cavity leads to the risk of neurological disorders in affected individuals. Research conducted at the Institut national de la recherche scientifique (INRS) has identified ways to prevent the spread of infection within the central nervous system (CNS).

Antiviral immunity to human coronaviruses

The research team is the first to make the demonstration of a direct link between neurovirulence, protein S cleavage by cellular proteases and innate immunity. This antiviral immunity arises from the production of interferons, frontline proteins that help to detect early the presence of the virus.

“Using a common cold coronavirus, similar to SARS-CoV-2, we were able to show that cleavage of the S protein and interferon could prevent its spread to the brain and spinal cord in mice,” says Talbot, who has been studying coronaviruses for nearly 40 years.

Two therapeutic approaches

According to Marc Desforges, currently a clinical specialist in medical biology at the CHU-Sainte-Justine virology laboratory, the cleavage of the S protein by various cellular proteases is essential for these viruses to effectively infect cells and spread to various organs and systems in the body, including the central nervous system (CNS).

“Our results demonstrate that interferon produced by different cells, including olfactory receptors and cerebrospinal fluid (CSF) producing cells in the brain, could modulate this cleavage. Thus, it could and does significantly limit the viral spread in the CNS and the severity of the associated disease,” says the specialist who worked for 16 years as a research associate at the Armand-Frappier Health Biotechnology Centre of the IRNS.

Taken together, these results point to two potential antiviral targets: protein S cleavage and effective interferon-related innate immunity. “Understanding the mechanisms of infection and viral propagation in neuronal cells is essential to better design therapeutic approaches,” says Talbot. This is especially important for vulnerable populations such as the elderly and immunocompromised.” This discovery opens the door to new therapeutic strategies.

Selective time-dependent changes in activity and cell-specific gene expression in human postmortem brain

Fabien Dachet, James B. Brown, Tibor Valyi-Nagy, Kunwar D. Narayan, Anna Serafini, Nathan Boley, Thomas R. Gingeras, Susan E. Celniker, Gayatry Mohapatra, Jeffrey A. Loeb in Scientific Reports

In the hours after we die, certain cells in the human brain are still active. Some cells even increase their activity and grow to gargantuan proportions, according to new research from the University of Illinois Chicago.

In a newly published study, the UIC researchers analyzed gene expression in fresh brain tissue — which was collected during routine brain surgery — at multiple times after removal to simulate the post-mortem interval and death. They found that gene expression in some cells actually increased after death.

These ‘zombie genes’ — those that increased expression after the post-mortem interval — were specific to one type of cell: inflammatory cells called glial cells. The researchers observed that glial cells grow and sprout long arm-like appendages for many hours after death.

“That glial cells enlarge after death isn’t too surprising given that they are inflammatory and their job is to clean things up after brain injuries like oxygen deprivation or stroke,” said Dr. Jeffrey Loeb, the John S. Garvin Professor and head of neurology and rehabilitation at the UIC College of Medicine and corresponding author on the paper.

What’s significant, Loeb said, is the implications of this discovery — most research studies that use postmortem human brain tissues to find treatments and potential cures for disorders such as autism, schizophrenia and Alzheimer’s disease, do not account for the post-mortem gene expression or cell activity.

“Most studies assume that everything in the brain stops when the heart stops beating, but this is not so,” Loeb said. “Our findings will be needed to interpret research on human brain tissues. We just haven’t quantified these changes until now.”

Loeb and his team noticed that the global pattern of gene expression in fresh human brain tissue didn’t match any of the published reports of postmortem brain gene expression from people without neurological disorders or from people with a wide variety of neurological disorders, ranging from autism to Alzheimer’s.

“We decided to run a simulated death experiment by looking at the expression of all human genes, at time points from 0 to 24 hours, from a large block of recently collected brain tissues, which were allowed to sit at room temperature to replicate the postmortem interval,” Loeb said.

Loeb and colleagues are at a particular advantage when it comes to studying brain tissue. Loeb is director of the UI NeuroRepository, a bank of human brain tissues from patients with neurological disorders who have consented to having tissue collected and stored for research either after they die, or during standard of care surgery to treat disorders such as epilepsy. For example, during certain surgeries to treat epilepsy, epileptic brain tissue is removed to help eliminate seizures. Not all of the tissue is needed for pathological diagnosis, so some can be used for research. This is the tissue that Loeb and colleagues analyzed in their research.

They found that about 80% of the genes analyzed remained relatively stable for 24 hours — their expression didn’t change much. These included genes often referred to as housekeeping genes that provide basic cellular functions and are commonly used in research studies to show the quality of the tissue. Another group of genes, known to be present in neurons and shown to be intricately involved in human brain activity such as memory, thinking and seizure activity, rapidly degraded in the hours after death. These genes are important to researchers studying disorders like schizophrenia and Alzheimer’s disease, Loeb said.

A third group of genes — the ‘zombie genes’ — increased their activity at the same time the neuronal genes were ramping down. The pattern of post-mortem changes peaked at about 12 hours.

“Our findings don’t mean that we should throw away human tissue research programs, it just means that researchers need to take into account these genetic and cellular changes, and reduce the post-mortem interval as much as possible to reduce the magnitude of these changes,” Loeb said. “The good news from our findings is that we now know which genes and cell types are stable, which degrade, and which increase over time so that results from postmortem brain studies can be better understood.”

Ultra-complex structure of the RBFOX1 gene. (A,B) Human/Fly splicing complexity analysis: for each RNA detected in the RNAseq results of patients EP158 and EP168, the lower and upper bounds of divergence between human and fly Drosophila is computed using our GRIT algorithm. The RBFOX1 gene was selected for illustration of this due to its complexity, evolutionary conservation, and relevance to human brain disease. (С )RNAseq alignment of RBFOX1 RNAs from 4 fresh samples (EP158: electrodes FP2 & FP4, EP168: electrodes SF10 & FP57) and 4 postmortem samples (SRR1747164, SRR1747173, SRR1747186, SRR1747190) show a significant departure from the reference genome (vertical bars) that reveals extensive gene editing in fresh samples that were not seen in the postmortem samples (PMI = 29 h ± 2.6 h).

Periaqueductal gray/dorsal raphe dopamine neurons contribute to sex differences in pain-related behaviors

by Waylin Yu, Dipanwita Pati, Melanie M. Pina, Karl T. Schmidt, Kristen M. Boyt, Avery C. Hunker, Larry S. Zweifel, Zoe A. McElligott, Thomas L. Kash in Neuron

Males and females, generally speaking, experience and respond to pain differently, but scientists have yet to understand all the brain circuits involved in these differences. Now, new research from the UNC School of Medicine lab of Thomas Kash, PhD, shows how neurons use dopamine to regulate pain differently in male and female mice.

The discovery could help the scientific community devise better pain management strategies, particularly for women, who are disproportionally affected by pain throughout their lifespans.

“We focused on this neural pathway because our previous work and that of others show that specific neurons release dopamine to regulate pain responses,” said Kash, the John R. Andrews Distinguished Professor of Pharmacology. “Unfortunately, that research was done only in male mice. So we decided to look at both male and female mice, and what we found was very surprising.”

Dopamine, long known as the brain’s pleasure chemical, can actually regulate a wide variety of behaviors. The dopamine neurons that Kash and his lab looked at had previously been shown to be important for both the rewarding properties and the pain-relieving properties of heroin. Beyond this, several studies have shown that these neurons can regulate attention, suggesting a link between drug abuse, pain, and attention.

Previously, using male mice, the Kash lab found that dopaminergic neurons played a key role in how opiates dampen pain, likely through the release of dopamine and glutamate. In the new experiments, his lab focused on a neural pathway starting at the midbrain region called the periaqueductal grey, including part of the dorsal raphe. That brain region is involved in behavioral adaptation — how animals learn to respond to their environment. The neurons that make dopamine in that region operate in conjunction with a brain structure called the bed nucleus of the stria terminalis, or BNST, forming a neural pathway.

“We found that activating this pathway reduced pain sensitivity in male mice, but made female mice move more, especially in the presence of something capturing their attention,” said first author Waylin Yu, PhD, a former graduate student in the Kash lab and current postdoctoral researcher at UC San Francisco. “We think this is because of the different ways males and females respond to pain.”

In particular, these experiments seem to indicate that dopamine helps males simply not feel as much pain, while in females, dopamine helps the mice focus attention elsewhere while in the presence of pain.

More research is needed, but the Kash lab research shows that activating specific neural projections to the BNST reduces acute and persistent inflammatory pain, providing further evidence that dopamine signaling can enhance the blocking of pain stimuli, thus counteracting severe pain.

“We hope to investigate how this pathway can regulate more emotional behaviors associated with chronic pain, and then also look at the dynamics of the system, such as how this pathway works in real time during behavior measurements,” Kash said. “These neurons are also implicated in the actions of opioids such as morphine, so we plan to investigate that domain, as well.”

Consciousness depends on integration between parietal cortex, striatum, and thalamus

by Mohsen Afrasiabi et al. in Cell Systems

Millions of people are administered general anesthesia each year in the United States alone, but it’s not always easy to tell whether they are actually unconscious.

A small proportion of those patients regain some awareness during medical procedures, but a new study of the brain activity that represents consciousness could prevent that potential trauma. It may also help both people in comas and scientists struggling to define which parts of the brain can claim to be key to the conscious mind.

“What has been shown for 100 years in an unconscious state like sleep are these slow waves of electrical activity in the brain,” says Yuri Saalmann, a University of Wisconsin-Madison psychology and neuroscience professor. “But those may not be the right signals to tap into. Under a number of conditions — with different anesthetic drugs, in people that are suffering from a coma or with brain damage or other clinical situations — there can be high-frequency activity as well.”

UW-Madison researchers recorded electrical activity in about 1,000 neurons surrounding each of 100 sites throughout the brains of a pair of monkeys at the Wisconsin National Primate Research Center during several states of consciousness: under drug-induced anesthesia, light sleep, resting wakefulness, and roused from anesthesia into a waking state through electrical stimulation of a spot deep in the brain (a procedure the researchers described in 2020).

“With data across multiple brain regions and different states of consciousness, we could put together all these signs traditionally associated with consciousness — including how fast or slow the rhythms of the brain are in different brain areas — with more computational metrics that describe how complex the signals are and how the signals in different areas interact,” says Michelle Redinbaugh, a graduate student in Saalman’s lab and co-lead author of the study.

To sift out the characteristics that best indicate whether the monkeys were conscious or unconscious, the researchers used machine learning. They handed their large pool of data over to a computer, told the computer which state of consciousness had produced each pattern of brain activity, and asked the computer which areas of the brain and patterns of electrical activity corresponded most strongly with consciousness.

The results pointed away from the frontal cortex, the part of the brain typically monitored to safely maintain general anesthesia in human patients and the part most likely to exhibit the slow waves of activity long considered typical of unconsciousness.

“In the clinic now, they may put electrodes on the patient’s forehead,” says Mohsen Afrasiabi, the other lead author of the study and an assistant scientist in Saalmann’s lab. “We propose that the back of the head is a more important place for those electrodes, because we’ve learned the back of the brain and the deep brain areas are more predictive of state of consciousness than the front.”

And while both low- and high-frequency activity can be present in unconscious states, it’s complexity that best indicates a waking mind.

“In an anesthetized or unconscious state, those probes in 100 different sites record a relatively small number of activity patterns,” says Saalmann, whose work is supported by the National Institutes of Health.

A larger — or more complex — range of patterns was associated with the monkey’s awake state.

“You need more complexity to convey more information, which is why it’s related to consciousness,” Redinbaugh says. “If you have less complexity across these important brain areas, they can’t convey very much information. You’re looking at an unconscious brain.”

More accurate measurements of patients undergoing anesthesia is one possible outcome of the new findings, and the researchers are part of a collaboration supported by the National Science Foundation working on applying the knowledge of key brain areas.

“Beyond just detecting the state of consciousness, these ideas could improve therapeutic outcomes from people with consciousness disorders,” Saalmann says. “We could use what we’ve learned to optimize electrical patterns through precise brain stimulation and help people who are, say, in a coma maintain a continuous level of consciousness.”

Segregated brain state during hypnosis

by Jarno Tuominen, Sakari Kallio, Valtteri Kaasinen, Henry Railo in Neuroscience of Consciousness

During a normal waking state, information is processed and shared by various parts within our brain to enable flexible responses to external stimuli. Researchers from the University of Turku, Finland, found that during hypnosis the brain shifted to a state where individual brain regions acted more independently of each other.

“In a normal waking state, different brain regions share information with each other, but during hypnosis this process is kind of fractured and the various brain regions are no longer similarly synchronised,” describes researcher Henry Railo from the Department of Clinical Neurophysiology at the University of Turku.

The finding shows that the brain may function quite differently during hypnosis when compared to a normal waking state. This is interesting because the extent to which hypnosis modifies neural processing has been hotly debated in the field. The new findings also help to better understand which types of changes and mechanisms may explain the experiential and behavioural alterations attributed to hypnosis, such as liability to suggestions.

The study focused on a single person who has been extensively studied earlier and been shown to react strongly to hypnotic suggestions. During hypnosis, this person can experience phenomena that are not typically possible in a normal waking state, such as vivid and controlled hallucinations.

“Even though these findings cannot be generalised before a replication has been conducted on a larger sample of participants, we have demonstrated what kind of changes happen in the neural activity of a person who reacts to hypnosis particularly strongly,” clarifies Jarno Tuominen, Senior Researcher at the Department of Psychology and Speech-Language Pathology.

Hypnosis Studied for the First Time with New Method

The study was conducted by tracking how a magnetically-induced electrical current spread throughout the brain during hypnosis and normal waking state. This method has been previously used to measure system-level changes in the brain in various states of consciousness, such as anaesthesia, coma, and sleep. This is the first time such a method has been used to assess hypnosis.

During the study, the participant sat still with eyes closed, alternatively either hypnotised or in a normal waking state. Hypnosis was induced via a single-word cue, and the different conditions were identical in every other respect.

“This allowed us to control the possible effects of the experimental setup or other factors, such as alertness,” Tuominen explains.

Spectral power differences (a) between hypnosis (blue) and baseline (pink) in the pre-pulse EEG data. Topographical plots indicate activation for various frequencies. Frontal and occipital electrode clusters (b) compared to a null distribution arrived at by calculating 1000 random permutations, shown individually in light grey lines. Auditory control stimuli and responses were excluded from the analyses.

Association of Loneliness and Wisdom With Gut Microbial Diversity and Composition: An Exploratory Study

by Tanya T. Nguyen, Xinlian Zhang, Tsung-Chin Wu, Jinyuan Liu, Collin Le, Xin M. Tu, Rob Knight, Dilip V. Jeste in Frontiers in Psychiatry

The evolving science of wisdom rests on the idea that wisdom’s defined traits correspond to distinct regions of the brain, and that greater wisdom translates into greater happiness and life satisfaction while being less wise results in opposite, negative consequences.

Scientists have found in multiple studies that persons deemed to be wiser are less prone to feel lonely while those who are lonelier also tend to be less wise. In a new study, researchers at University of California San Diego School of Medicine take the connection between wisdom, loneliness and biology further, reporting that wisdom and loneliness appear to influence — and/or be influenced by — microbial diversity of the gut.

The human gut microbiota is comprised of trillions of microbes — bacteria, viruses, fungi — that reside within the digestive tract. Researchers have known for a while about the “gut-brain axis,” which is a complex network that links intestinal function to the emotional and cognitive centers of the brain.

This two-way communication system is regulated by neural activity, hormones and the immune system; alterations can result in disruptions to stress response and behaviors, said the authors, from emotional arousal to higher-order cognitive abilities, such as decision-making.

Past studies have associated gut microbiota with mental health disorders including depression, bipolar disorder and schizophrenia, as well as personality and psychological traits regarded as key, biologically based components of wisdom. Recent research has connected the gut microbiome to social behavior, including findings that people with larger social networks tend to have more diverse gut microbiotas.

The new study involved 187 participants, ages 28 to 97, who completed validated self-report-based measures of loneliness, wisdom, compassion, social support and social engagement. The gut microbiota was analyzed using fecal samples. Microbial gut diversity was measured in two ways: alpha-diversity, referring to the ecological richness of microbial species within each individual and beta-diversity, referring to the differences in the microbial community composition between individuals.

“We found that lower levels of loneliness and higher levels of wisdom, compassion, social support and engagement were associated with greater phylogenetic richness and diversity of the gut microbiome,” said first author Tanya T. Nguyen, PhD, assistant professor of psychiatry at UC San Diego School of Medicine.

The authors said that the mechanisms that may link loneliness, compassion and wisdom with gut microbial diversity are not known, but observed that reduced microbial diversity typically represents worse physical and mental health, and is associated with a variety of diseases, including obesity, inflammatory bowel disease and major depressive disorder.

A more diverse gut microbiota may be less susceptible to invasion by outside pathogens, which could contribute to and help promote better resilience and stability of the community.

“It is possible that loneliness may result in decreased stability of the gut microbiome and, consequently, reduced resistance and resilience to stress-related disruptions, leading to downstream physiological effects, such as systemic inflammation,” the authors wrote. “Bacterial communities with low alpha-diversity may not manifest overt disease, but they may be less than optimal for preventing disease. Thus, lonely people may be more susceptible to developing different diseases.”

The relationship between loneliness and microbial diversity was particularly strong in older adults, suggesting that older adults may be especially vulnerable to health-related consequences of loneliness, which is consistent with prior research.

Conversely, the researchers said that social support, compassion and wisdom might confer protection against loneliness-related instability of the gut microbiome. Healthy, diverse gut microflora may buffer the negative effects of chronic stress or help shape social behaviors that promote either wisdom or loneliness. They noted that animal studies suggest that gut microbiota may influence social behaviors and interactions, though the hypothesis has not been tested in humans.

The complexity of the topic and study limitations, such as the absence of data about individuals’ social networks, diet and degree of objective social isolation versus subjective reports of loneliness, argue for larger, longer studies, wrote the authors.

“Loneliness may lead to changes in the gut microbiome or, reciprocally, alterations of the gut milieu may predispose an individual to become lonely,” said Dilip V. Jeste, MD, Distinguished Professor of Psychiatry and Neurosciences at UC San Diego School of Medicine and senior author of the paper. “We need to investigate much more thoroughly to better understand the phenomenon of the gut-brain axis.”

(A) Scatterplot depicting the relationship between loneliness and alpha-diversity (Faith’s Phylogenetic Diversity). Higher level of loneliness was associated with significantly lower alpha-diversity. (B) Bar plot illustrating differences in alpha-diversity based on loneliness severity categories. Individuals with High levels of loneliness exhibited significantly lower gut microbial alpha-diversity, compared to those with No/Low and Moderate levels of loneliness. *p or q < 0.05.

Paper Notebooks vs. Mobile Devices: Brain Activation Differences During Memory Retrieval

by Keita Umejima, Takuya Ibaraki, Takahiro Yamazaki, Kuniyoshi L. Sakai in Frontiers in Behavioral Neuroscience

A study of Japanese university students and recent graduates has revealed that writing on physical paper can lead to more brain activity when remembering the information an hour later. Researchers say that the unique, complex, spatial and tactile information associated with writing by hand on physical paper is likely what leads to improved memory.

“Actually, paper is more advanced and useful compared to electronic documents because paper contains more one-of-a-kind information for stronger memory recall,” said Professor Kuniyoshi L. Sakai, a neuroscientist at the University of Tokyo and corresponding author of the research.

Contrary to the popular belief that digital tools increase efficiency, volunteers who used paper completed the note-taking task about 25% faster than those who used digital tablets or smartphones.

Although volunteers wrote by hand both with pen and paper or stylus and digital tablet, researchers say paper notebooks contain more complex spatial information than digital paper. Physical paper allows for tangible permanence, irregular strokes, and uneven shape, like folded corners. In contrast, digital paper is uniform, has no fixed position when scrolling, and disappears when you close the app.

“Our take-home message is to use paper notebooks for information we need to learn or memorize,” said Sakai.

In the study, a total of 48 volunteers read a fictional conversation between characters discussing their plans for two months in the near future, including 14 different class times, assignment due dates and personal appointments. Researchers performed pre-test analyses to ensure that the volunteers, all 18–29 years old and recruited from university campuses or NTT offices, were equally sorted into three groups based on memory skills, personal preference for digital or analog methods, gender, age and other aspects.

Volunteers then recorded the fictional schedule using a paper datebook and pen, a calendar app on a digital tablet and a stylus, or a calendar app on a large smartphone and a touch-screen keyboard. There was no time limit and volunteers were asked to record the fictional events in the same way as they would for their real-life schedules, without spending extra time to memorize the schedule.

After one hour, including a break and an interference task to distract them from thinking about the calendar, volunteers answered a range of simple (When is the assignment due?) and complex (Which is the earlier due date for the assignments?) multiple choice questions to test their memory of the schedule. While they completed the test, volunteers were inside a magnetic resonance imaging (MRI) scanner, which measures blood flow around the brain. This is a technique called functional MRI (fMRI), and increased blood flow observed in a specific region of the brain is a sign of increased neuronal activity in that area.

Participants who used a paper datebook filled in the calendar within about 11 minutes. Tablet users took 14 minutes and smartphone users took about 16 minutes. Volunteers who used analog methods in their personal life were just as slow at using the devices as volunteers who regularly use digital tools, so researchers are confident that the difference in speed was related to memorization or associated encoding in the brain, not just differences in the habitual use of the tools.

Behavioral data. (A) The intergroup differences in the mean duration of schedule recording, together with individual data points overlapped. In addition to the three groups (Note, Tablet, and Phone), we also introduced a Device group, which consisted of participants who used mainly notebooks daily and were assigned to either the Tablet or Phone group. (B) Accuracy in the retrieval task. The broken line denotes the chance level of 25% accuracy. For the easier (i.e., more straightforward) questions, the Note group showed significantly higher accuracy than the Tablet group. (с )Response times (RTs) in the retrieval task. Error bars indicate standard errors of the mean. *p < 0.05.

Volunteers who used analog methods scored better than other volunteers only on simple test questions. However, researchers say that the brain activation data revealed significant differences.

Volunteers who used paper had more brain activity in areas associated with language, imaginary visualization, and in the hippocampus — an area known to be important for memory and navigation. Researchers say that the activation of the hippocampus indicates that analog methods contain richer spatial details that can be recalled and navigated in the mind’s eye.

“Digital tools have uniform scrolling up and down and standardized arrangement of text and picture size, like on a webpage. But if you remember a physical textbook printed on paper, you can close your eyes and visualize the photo one-third of the way down on the left-side page, as well as the notes you added in the bottom margin,” Sakai explained.

Researchers say that personalizing digital documents by highlighting, underlining, circling, drawing arrows, handwriting color-coded notes in the margins, adding virtual sticky notes, or other types of unique mark-ups can mimic analog-style spatial enrichment that may enhance memory.

Although they have no data from younger volunteers, researchers suspect that the difference in brain activation between analog and digital methods is likely to be stronger in younger people.

“High school students’ brains are still developing and are so much more sensitive than adult brains,” said Sakai.

Although the current research focused on learning and memorization, the researchers encourage using paper for creative pursuits as well.

“It is reasonable that one’s creativity will likely become more fruitful if prior knowledge is stored with stronger learning and more precisely retrieved from memory. For art, composing music, or other creative works, I would emphasize the use of paper instead of digital methods,” said Sakai.

Activated regions for the retrieval task. (A) Results of the “First 6 s — Last 4 s” contrast within the retrieval task period are shown for all participants. (B) Results of the “retrieval — 2-back” contrast are shown for all participants. The lines indicate the locations of the sections. Localized activations were observed bilaterally in the lateral premotor cortex/opercular/triangular parts of the inferior frontal gyrus (LPMC/F3op/F3t), angular/supramarginal gyri (AG/SMG), hippocampus, precuneus, and lingual gyrus/calcarine

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