The image represents a statistical average of how different types of love light up different regions of the brain. Photo: Pärttyli Rinne et al 2024, Aalto University.
We use the word ‘love’ in a bewildering range of contexts, from sexual adoration to parental love or the love of nature. Now, more comprehensive imaging of the brain may shed light on why we use the same word for such a diverse collection of human experiences.
“You see your newborn child for the first time. The baby is soft, healthy and hearty – your life’s greatest wonder. You feel love for the little one.”
The above statement was one of many simple scenarios presented to 55 parents, self-described as being in a loving relationship. Researchers from Aalto University utilised functional magnetic resonance imaging (fMRI) to measure brain activity while subjects mulled brief stories related to six different types of love.
“We now provide a more comprehensive picture of the brain activity associated with different types of love than previous research,” says Pärttyli Rinne, the philosopher and researcher who coordinated the study. “The activation pattern of love is generated in social situations in the basal ganglia, the midline of the forehead, the precuneus and the temporoparietal junction at the sides of the back of the head.”
Love for one’s children generated the most intense brain activity, closely followed by romantic love.
“In parental love, there was activation deep in the brain’s reward system in the striatum area while imagining love, and this was not seen for any other kind of love,” says Rinne. Love for romantic partners, friends, strangers, pets and nature were also part of the study, which was published in the journal Cerebral Cortex.
According to the research, brain activity is influenced not only by the closeness of the object of love, but also by whether it is a human being, another species or nature.
Unsurprisingly, compassionate love for strangers was less rewarding and caused less brain activation than love in close relationships. Meanwhile, love of nature activated the reward system and visual areas of the brain, but not the social brain areas.
Pet-owners identifiable by brain activity
The biggest surprise for the researchers was that the brain areas associated with love between people ended up being very similar, with differences lying primarily in the intensity of activation. All types of interpersonal love activated areas of the brain associated with social cognition, in contrast to love for pets or nature – with one exception.
Subjects’ brain responses to a statement like the following, on average, revealed whether or not they shared their life with a furry friend:
“You are home lolling on the couch and your pet cat pads over to you. The cat curls up next to you and purrs sleepily. You love your pet.”
“When looking at love for pets and the brain activity associated with it, brain areas associated with sociality statistically reveal whether or not the person is a pet owner. When it comes to the pet owners, these areas are more activated than with non-pet owners,” says Rinne.
Love activations were controlled for in the study with neutral stories in which very little happened. For example, looking out the bus window or absent-mindedly brushing your teeth. After hearing a professional actor’s rendition of each ‘love story’, participants were asked to imagine each emotion for 10 seconds.
This is not the first effort at finding love for Rinne and his team, which includes researchers Juha Lahnakoski, Heini Saarimäki, Mikke Tavast, Mikko Sams and Linda Henriksson. They have undertaken several studies seeking to deepen our scientific knowledge of human emotions. The group released research mapping subjects’ bodily experiences of love a year ago, with the earlier study also linking the strongest physical experiences of love with close interpersonal relationships.
Not only can understanding the neural mechanisms of love help guide philosophical discussions about the nature of love, consciousness, and human connection, but also, the researchers hope that their work will enhance mental health interventions in conditions like attachment disorders, depression or relationship issues.
The brain is an incredibly complex and active organ that uses electricity and chemicals to transmit and receive signals between its sub-regions. Researchers have explored various technologies to directly or indirectly measure these signals to learn more about the brain. Functional magnetic resonance imaging (fMRI), for example, allows them to detect brain activity via changes related to blood flow.
Yen-Yu Ian Shih, PhD, professor of neurology and associate director of UNC’s Biomedical Research Imaging Center, and his fellow lab members have long been curious about how neurochemicals in the brain regulate and influence neural activity, blood flow, and subsequently, fMRI measurement in the brain.
A new study by the lab has confirmed their suspicions that fMRI interpretation is not as straightforward as it seems.
“Neurochemical signalling to blood vessels is less frequently considered when interpreting fMRI data,” said Shih, who also leads the Center for Animal MRI. “In our study on rodent models, we showed that neurochemicals, aside from their well-known signalling actions to typical brain cells, also signal to blood vessels, and this could have significant contributions to fMRI measurements.”
Their findings, published in Nature Communications, stem from the installation and upgrade of two 9.4-Tesla animal MRI systems and a 7-Tesla human MRI system at the Biomedical Research Imaging Center.
When activity in neurons increases in a specific brain region, blood flow and oxygen levels increase in the area, usually proportionate to the strength of neural activity. Researchers decided to use this phenomenon to their advantage and eventually developed fMRI techniques to detect these changes in the brain.
For years, this method has helped researchers better understand brain function and influenced their knowledge about human cognition and behaviour. The new study from Shih’s lab, however, demonstrates that this well-established neuro-vascular relationship does not apply across the entire brain because cell types and neurochemicals vary across brain areas.
Shih’s team focused on the striatum, a region deep in the brain involved in cognition, motivation, reward, and sensorimotor function, to identify the ways in which certain neurochemicals and cell types in the brain region may be influencing fMRI signals.
For their study, Shih’s lab controlled neural activity in rodent brains using a light-based technique, while measuring electrical, optical, chemical, and vascular signals to help interpret fMRI data. The researchers then manipulated the brain’s chemical signalling by injecting different drugs into the brain and evaluated how the drugs influenced the fMRI responses.
They found that in some cases, neural activity in the striatum went up, but the blood vessels constricted, causing negative fMRI signals. This is related to internal opioid signaling in the striatum. Conversely, when another neurochemical, dopamine, predominated signaling in striatum, the fMRI signals were positive.
“We identified several instances where fMRI signals in the striatum can look quite different from expected,” said Shih. “It’s important to be mindful of underlying neurochemical signaling that can influence blood vessels or perivascular cells in parallel, potentially overshadowing the fMRI signal changes triggered by neural activity.”
Members of Shih’s lab, including first- and co-authors Dominic Cerri, PhD, and Lindsey Walton, PhD, travelled to the University of Sussex in the United Kingdom, where they were able to perform experiments and further demonstrate the opioid’s vascular effects.
They also collected human fMRI data at UNC’s 7-Tesla MRI system and collaborated with researchers at Stanford University to explore possible findings using transcranial magnetic stimulation, a procedure that uses magnetic fields to stimulate the human brain.
By better understanding fMRI signaling, basic science researchers and physician scientists will be able to provide more precise insights into neural activity changes in healthy brains, as well as in cases of neurological and neuropsychiatric disorders.
Cross-sectional diagram of the NexGen 7T scanner, showing the new Impulse head-only gradient coil (green) and receiver-transmit coil (white) resting on a movable bed (brown) and connected to an electronic interface (blue) containing nearly a thousand wires (blue) that extend out of the magnet. Credit: Bernhard Gruber, MGH Harvard
An intense international effort to improve the resolution of magnetic resonance imaging (MRI) for studying the human brain has culminated in an ultra-high resolution 7 Tesla scanner that records up to 10 times more detail than current 7T scanners and over 50 times more detail than current 3T scanners, the mainstay of most hospitals.
This next generation or NexGen 7T functional MRI (fMRI) scanner can resolve features 0.4mm across, compared to the 2–3mm typical of today’s standard 3T fMRIs. It is described in a paper published in Nature Methods.
“The NexGen 7T scanner is a new tool that allows us to look at the brain circuitry underlying different diseases of the brain with higher spatial resolution in fMRI, diffusion and structural imaging, and therefore to perform human neuroscience research at higher granularity,” said David Feinberg, the director of the project to build the scanner. “The ultra-high resolution scanner will allow research on underlying changes in brain circuitry in a multitude of brain disorders, including degenerative diseases, schizophrenia and developmental disorders, including autism spectrum disorder.”
The improved resolution will help neuroscientists probe the neuronal circuits in different regions of the brain’s neocortex and allow researchers to track signals propagating from one area of the cortex, and perhaps discover underlying causes of developmental disorders. This could lead to better ways of diagnosing brain disorders, perhaps by identifying new biomarkers that would allow diagnosis of mental disorders earlier or, more specifically, in order to choose the best therapy.
“Normally, MRI is not fast enough at all to see the direction of the information being passed from one area of the brain to another,” Feinberg said. “The scanner’s higher spatial resolution can identify activity at different depths in the brain’s cortex to indirectly reveal brain circuitry by differentiating activity in different cell layers of the cortex.”
This is possible because neuroscientists have found in vision brain areas that the superficial and deepest cortex layers incorporate ‘top-down’ circuits, that is, they receive information from higher cortical brain areas, whereas the middle cortex involves ‘bottom-up’ circuitry, receiving sensory input. Pinpointing the fMRI activity to a specific depth in the cortex lets neuroscientists track the flow of information throughout the brain and cortex.
With the higher spatial resolution, neuroscientists will be able to home in on the activity of something on the order of 850 individual neurons within a single voxel – a 3D pixel – instead of the 600 000 recorded with standard hospital MRIs, said Silvia Bunge, a UC Berkeley professor of psychology who is one of the first to use the NexGen 7T to conduct research on a human brain.
“We were able to look at the layer profile of the prefrontal cortex, and it’s beautiful,” said Bunge, who studies abstract reasoning. “It’s so exciting to have this state-of-the-art, world-class machine.”
For William Jagust, a UC Berkeley professor of public health who studies the brain changes associated with Alzheimer’s disease, the improved resolution could finally help connect the dots between observed changes due to Alzheimer’s that occur in the brain – abnormal clumps of protein called beta amyloid and tau – and changes in memory.
“We know that part of the memory system in the brain degenerates as we get older, but we know little about the actual changes to the memory system – we can only go so far because of the resolution of our current MRI systems,” said Jagust. “With this new scanner, we think we’re going to be able to take apart a lot more carefully exactly where things have gone wrong. This could help with diagnosis or predicting outcomes in normal people.”
Jack Gallant, a UC Berkeley professor of psychology, hopes the scanner will help neuroscientists discover how functional changes in the brain lead to developmental and mental disorders such as dyslexia, autism and schizophrenia, or that result from neurological disorders, such as dementia and stroke.
“Mental disorders have an enormous impact on individuals, families and society. Together they represent about 10% of the US GDP. Mental disorders are fundamentally disorders of brain function, but functional measures are not used currently to diagnose most brain disorders or to look to see if a treatment’s working. Instead, these disorders are diagnosed behaviourally. This is a weak approach, because there are a lot of different mental brain states that can lead to exactly the same behaviour,” Gallant said. “What we need is more powerful MRI machines like this so that we can map, at high resolution, how information is represented in the brain. To me this is the big potential clinical benefit of ultra-high resolution MRI.”
Funding initiatives lead to ‘quantum leap’
The breakthrough came about through $22 million of funding from various government and private sector sources.
Incorporating newly developed hardware technology from those groups, Siemens collaborated with Feinberg’s team to rebuild a conventional 7 Tesla MRI scanner delivered to UC Berkeley in 2000 to improve the spatial resolution in pictures captured during brain scans.
“There’s been a large increase throughout the world of sites that use 7T MRI scanners, but they were mostly for development and were difficult to use,” said Nicolas Boulant, a physicist visiting from the NeuroSpin project at the University of Paris in Saclay, where he leads the team that operates the world’s only 11.7 Tesla MRI scanner, the strongest magnetic field employed to date. “David’s team really put together many ingredients to make a quantum leap at 7 Tesla, to go beyond what was achievable before and gain performance.”
Boulant hopes to adapt some of the new ingredients in the NexGen 7T – in particular, redesigned gradient coils – to eventually achieve even better resolution with the 11.7 Tesla MRI scanner. The gradient coils generate a rising magnetic field across the brain so that each part of the brain sees a different field strength, which helps to precisely map brain activity.
“The higher the magnetic field, the more difficult it is to really grab the potential promised by these higher-field MRI scanners to see finer details in the human brain,” he said. “You need all this peripheral equipment, which needs to be on steroids to meet those promises. The NexGen 7T is really a game-changer when you want to do neuro MRI.”
To reach higher spatial resolution, the NexGen 7T scanner had to be designed with a greatly improved gradient coil and with larger receiver array coils – which pick up the brain signals – using from 64 to 128 channels to achieve a higher signal-to-noise ratio (SNR) in the cortex and faster data acquisition. All these improvements were combined with a higher signal from the ultra-high field 7T magnet to achieve cumulative gains in the scanner performance.
The extremely powerful gradient coil is the first to be made with three layers of wire windings. Designed by Peter Dietz at Siemens in Erlangen, Germany, the “Impulse” gradient has 10 times the performance of gradient systems in current 7T scanners. Mathias Davids, then a physics graduate student at Heidelberg University in Mannheim, Germany, and a member of Feinberg’s team, collaborated with Dietz in performing physiologic modelling to allow a faster gradient slew rate – a measure of how quickly the magnetic field changes across the brain – while remaining under the neuronal stimulation thresholds of the human body.
“It’s designed so that the gradient pulses can be turned on and off much quicker – in microseconds – to record the signals much quicker, and also so the much higher amplitude gradients can be utilised without stimulating the peripheral nerves in the body or stimulating the heart, which are physiologic limitations,” Feinberg said.
A second key development in the scanner, Feinberg said, is the 128-channel receiver system that replaces the standard 32 channels. The large receiver coil arrays built by Shajan Gunamony of MR CoilTech in Glasgow, UK, gave a higher signal-to-noise ratio in the cerebral cortex and also provided higher parallel imaging acceleration for faster data acquisition to encode large image matrices for ultra high resolution fMRI and structural MRI.
To take advantage of the new hardware technology, Suhyung Park, Rüdiger Stirnberg, Renzo Huber, Xiaozhi Cao and Feinberg designed new pulse sequences of precisely timed gradient pulses to rapidly achieve ultra high resolution. The smaller voxels, measured in units of cubic millimetres and less than 0.1 microlitre, provide a 3D image resolution that is 10 times higher than that of previous 7T fMRIs and 125 times higher than the typical hospital 3T MRI scanners used for medical diagnosis.
Voxel-perfect resolution
The most common MRI scanners employ superconducting magnets that produce a steady magnetic field of 3 Tesla – 90 000 times stronger than Earth’s magnetic field and 3000 times stronger than a fridge magnet.
“A 3T fMRI scanner can resolve spatial details with a resolution of about 2 to 3mm. The cortical circuits that underpin thought and behaviour are about 0.5mm across, so standard research scanners cannot resolve these important structures,” Gallant said.
In contrast, fMRI focuses on blood flow in arteries and veins and can vividly distinguish oxygenated haemoglobin funnelling into working areas of the brain from deoxygenated haemoglobin in less active areas. This allows neuroscientists to determine which areas of the brain are engaged during a specific task.
But again, the 3mm resolution of a 3T fMRI can distinguish only large veins, not the small ones that could indicate activity within microcircuits.
The NexGen 7T will allow neuroscientists to pinpoint activity within the thin cortical layers in the grey matter, as well as within the narrow column circuits that are organised perpendicular to the layers. These columns are of special interest to Gallant, who studies how the world we see is represented in the visual cortex. He has actually been able to reconstruct what a person is seeing based solely on recordings from the brain’s visual cortex.
“The machine that David has built, in theory, should get down to 500 microns, or something like that, which is way better than anything else – we’re very near the scale you would want if you’re getting signals from a single column, for example,” Gallant said. “It’s fantastic. The whole thing about MRI is how big is the little volumetric unit, the voxel […] that’s the only thing that matters.”
For the moment, NexGen 7T brain scanners must be custom-built from regular 7T scanners but should be a lot cheaper than the $22 million required to build the first one.
Feinberg said that UC Berkeley’s NexGen 7T scanner technology will be disseminated by Siemens and MR CoilTech Ltd.
“My view is that we may never be able to understand the human brain on the cellular synaptic circuitry level, where there are more connections than there are stars in the universe,” Feinberg said. ” But we are now able to see signal patterns of brain circuits and begin to tease apart feedback and feed forward circuitry in different depths of the cerebral cortex. And in that sense, we will soon be able to understand the human brain organisation better, which will give us a new view into disease processes and ultimately allow us to test new therapies. We are seeking a better understanding and view of brain function that we can reliably test and reproducibly use noninvasively.”
Scientists have concentrated on the grey matter of the cortex, composed of nerve cell bodies , while ignoring white matter, composed of axons, even though it makes up half the brain. Now, in the Proceedings of the National Academy of Sciences, Vanderbilt University researchers report strong signs of brain activity when performing certain tasks.
For several years, John Gore, PhD, director of the Vanderbilt University Institute of Imaging Science, and his colleagues have used functional magnetic resonance imaging (fMRI) to detect blood oxygenation-level dependent (BOLD) signals, a key marker of brain activity, in white matter.
In this latest paper, the researchers report that when people who are having their brains scanned by fMRI perform a task, like wiggling their fingers, BOLD signals increase in white matter throughout the brain.
“We don’t know what this means,” said the paper’s first author, Kurt Schilling, PhD, research assistant professor of Radiology and Radiological Sciences at VUMC. “We just know that something is happening. There truly is a powerful signal in the white matter.”
It is important to pursue this because disorders as diverse as epilepsy and multiple sclerosis disrupt the “connectivity” of the brain, Schilling said. This suggests that something is going on in white matter.
To find out, the researchers will continue to study changes in white matter signals they’ve previously detected in schizophrenia, Alzheimer’s disease and other brain disorders. Through animal studies and tissue analysis, they also hope to determine the biological basis for these changes.
In grey matter, BOLD signals reflect a rise in blood flow (and oxygen) in response to increased nerve cell activity.
Perhaps the axons, or the glial cells that maintain the protective myelin sheath around them, also use more oxygen when the brain is ‘working’. Or perhaps these signals are somehow related to what’s going on in the grey matter.
But even if nothing biological is going on in white matter, “there’s still something happening here,” Schilling said. “The signal is changing. It’s changing differently in different white matter pathways and it’s in all white matter pathways, which is a unique finding.”
One reason that white matter signals have been understudied is that they have lower energy than grey matter signals, and thus are more difficult to distinguish from the brain’s background “noise.”
The VUMC researchers boosted the signal-to-noise ratio by having the person whose brain was being scanned repeat a visual, verbal or motor task many times to establish a trend and by averaging the signal over many different white matter fibre pathways.
“For 25 or 30 years, we’ve neglected the other half of the brain,” Schilling said. Some researchers not only have ignored white matter signals but have removed them from their reports of brain function.
The Vanderbilt findings suggest that many fMRI studies thus “may not only underestimate the true extent of brain activation, but also … may miss crucial information from the MRI signal,” the researchers concluded.
In a study in Nature, researchers reported being able to identify words and phrases in volunteers undergoing fMRI imaging reasonable accuracy. The process is non-invasive, unlike implanted electrodes, but requires hours of preparation and scanning.
This technology would be a significant breakthrough for people suffering debilitating conditions that prevent them from speaking or otherwise communicating. Previously, decoding language required the use of extensive electrode implants.
The participants, two male and one female, listened to recordings of radio shows. This was used to train a language model which was based on an early version of ChatGPT. By looking at the brain’s responses, the language model was able to capture the gist of what the participants were thinking, sometimes replicating exact words or entire phrases.
Marked safe from ‘Big Brother’… for now
At this stage, the technology used requires the subject to cooperate, the researchers wrote, allaying concerns over any malicious use of this technology to tap into people’s private thoughts. Testing the decoding model on people who it hadn’t been trained on produced unintelligible results, as was the case when the trained participants put up resistance.
While the technology cannot be used for nefarious mind-reading, the march of progress means that one day such concerns will become real.
Nita Farahany, JD, PhD, of Duke University in Durham, North Carolina, told MedPage Today that the technology could one day be used against people. “This research illustrates the rapid advances being made toward an age of much greater brain transparency, where even continuous language and semantic meaning can be decoded from the brain.
“While people can employ effective countermeasures to prevent decoding their brains using fMRI, as brain wearables become widespread that may not be an effective way to protect us from interception, manipulation, or even punishment for our thoughts.”
While lugging around a massive MRI machine would be a challenge for future thought police, smaller, more portable means of measuring brain activity remotely. Senior author Alexander Huth, PhD, of the University of Texas at Austin, says that one such technology could be functional near-infrared spectroscopy (fNIRS).
“fNIRS measures where there’s more or less blood flow in the brain at different points in time, which, it turns out, is exactly the same kind of signal that fMRI is measuring,” Huth said. “So, our exact kind of approach should translate to fNIRS,” but the resolution with fNIRS would be lower.
A team of researchers at Kyoto University’s Graduate School of Medicine has now found that mindfulness meditation does reduce anxieties associated with anorexia nervosa. Results from the study, published in BHPsych Open, show changes in the activity of brain regions involved in anxiety.
Anorexia nervosa (AN) is a severe psychiatric illness associated with intense anxieties concerning weight, shape, and self-esteem. AN is characterised by food restriction, voluntary vomiting, and extreme emaciation. Mindfulness meditation has already become a globally recognised method for addressing AN but its effectiveness in clinically treating neurogenic emaciation has not been studied yet.
The team’s mindfulness meditation program has seen a significant decrease in obsessive thoughts about test subject’s self-image and brain activity associated with related emotions.
“Our results suggest that the participants in the study became better at accepting their anxiety as it is,” says lead author Tomomi Noda.
Mindfulness and meditation work hand-in-hand. The former teaches practitioners to hone their awareness of their present experience and their ability to not judge and rather accept their circumstances. The latter is the medium by which mindfulness can be approached.
“We focused on the possibility that patients with AN try to avoid their crippling anxiety about weight gain and self-image by restricting food or vomiting,” adds co-author Masanori Isobe.
A 4-week mindfulness intervention program examined neural changes using tasks designed to induce weight-related anxiety. The researchers then regulated this anxiety by helping patients accept their current situations and experiences at face value, instead of avoiding them.
The researchers used functional magnetic resonance imaging (fMRI) to analyse attention regulation in relation to eating disorders. The study’s results support the subjective experiences of the researchers, although it was unexpected to them that several global events, such as the COVID pandemic and the Russo-Ukrainian war, were significant factors in patients’ anxieties.
“We anticipate practical implications of our results in clinical psychiatry and psychology and broader research into mitigating suffering through mindfulness, using the strategy of self-acceptance to regulate attention,” concludes group leader Toshiya Murai.
Observations of the brain when the body is immersed in cold water reveal changes in connectivity between areas which process emotion, which could explain why people often feel more upbeat and alert after swimming outside or taking cold baths.
During a research trial, published in the journal Biology, 33 healthy volunteers were given a functional MRI (fMRI) scan immediately after bathing in cold water. The team included imaging experts from Bournemouth University and University Hospitals Dorset (UHD), and extreme environments researcher, Dr Heather Massey, from the University of Portsmouth.
Dr Massey, from the School of Sport, Health and Exercise Science, said: “It has been a really pleasing experience to work with this interdisciplinary team to develop a method and publish this piece of research that could only be completed by a group with such a diverse skill set.
“With the growing popularity of outdoor swimming and cold water immersion, which many now use to support improved mood, it is long overdue that we study how it may affect us. We know so much about the impact cold water immersion can have on the body, but the brain has had little focus, primarily as it has been more challenging to study. It is only now that technology is developing, can we start to get some insight.”
Dr Ala Yankouskaya, Senior Lecturer in Psychology at Bournemouth University, led the study. She said: “The benefits of cold-water immersion are widely known from previous studies where participants were questioned on how they feel afterwards, but we wanted to see how the shock of going into the cold water actually affects the brain.”
Each participant was given an initial fMRI scan and then immersed in a pool of water at 20°C for five minutes whilst an ECG and respiratory equipment measured their bodies’ physiological responses. After being quickly dried they were given a second fMRI scan so the team could look for any changes in their brains’ activity.
“All tiny parts of the brain are connected to each other in a certain pattern when we carry out activities in our day-to-day lives, so the brain works as a whole.” said Dr Yankouskaya. “After our participants went in the cold water, we saw the physiological effects – such as shivering and heavy breathing. The MRI scans then showed us how the brain rewires its connectivity to help the person cope with the shock.”
Comparing the scans showed that changes had occurred in the connectivity between specific parts of the brain, in particular, the medial prefrontal cortex and the parietal cortex.
“These are the parts of the brain that control our emotions, and help us stay attentive and make decisions,” Dr Yankouskaya said. “So when the participants told us that they felt more alert, excited and generally better after their cold bath, we expected to see changes to the connectivity between those parts. And that is exactly what we found.”
The team are now planning to use their findings to understand more about the wiring and interactions between parts of the brain for people with mental health conditions.
“The medial prefrontal cortex and parietal cortex have different wiring when people have conditions such as depression and anxiety,” Dr Yankouskaya explained.
“Learning how cold water can rewire these parts of the brain could help us understand why the connectivity is so different for people with these conditions, and hopefully, in the long-term, lead to alternative treatments,” she concluded.
Neuroimaging technologies hold great promise in helping clinicians link specific symptoms of mental health disorders to abnormal patterns of brain activity. But a new study published in the American Journal of Psychiatry shows there are still kinks to be ironed out before doctors can translate images of the brain to psychiatric disorders such as post-traumatic stress disorder (PTSD).
Several years ago, The National Institutes of Mental Health launched a multi-billion-dollar research effort to locate biomarkers of brain activity that point to the biological roots of a host of mental health diseases, which today are typically identified by clinical evaluation of a constellation of often overlapping symptoms reported by patients.
“The idea is to forget classification of disease by symptoms and find underlying biological causes,” said Yale’s Ilan Harpaz-Rotem, professor of psychiatry and psychology and senior author of the study.
For the new study, the Yale-led team attempted to replicate the findings of an earlier nationwide neuroimaging study, in which scientists linked clusters of brain activity to a variety of outcomes among patients who had arrived at US emergency departments following traumatic events. Specifically, when researchers measured patients’ brain activity during the performance of simple tasks such as mapping responses to threats and rewards, they detected a cluster of brain activity that showed high reactivity to both threat and reward signals and seemed to predict more severe symptoms of PTSD later on.
However, when Yale researchers analysed similar neuroimaging data collected from recent trauma survivors in Israel, they were not able to replicate these findings. While they did identify the different clusters of brain activity observed in the earlier study, they found no association with prospective PTSD symptoms.
“That is not to say one set of data is right and the other is wrong, just that there is a lot of fundamental work that needs to be done to develop reliable models that could generalise across different studies,” said Yale’s Ziv Ben-Zion, a postdoctoral associate at Yale School of Medicine and the corresponding author of the study.
In fact, Yale researchers are currently working with the investigators of the original study to merge datasets “to search for common underlying patterns of brain activity associated with different responses to trauma,” Ben-Zion said.
“It took about 100 years to come up with current classifications of mental illness, but we’ve only been exploring refining psychiatric diagnoses using biomarkers for the last 10 years,” said Harpaz-Rotem. “We still have a long way to go.”
A study published in eLife reveals how the brains of humans and other primates under anaesthesia differ from mammals such as mice, with the visual cortex in primates being isolated from certain effects.
Anaesthesia still holds mysteries for modern science. Electroencephalography (EEG) studies show that, during anaesthesia, the brain is put into a deep sleep-like state in which periods of rhythmic electrical activity alternate with periods of complete inactivity. This state is called burst-suppression. Until now, it was unclear where exactly this state happens in the brain and which brain areas are involved.
Shedding light on the phenomenon would help better understand how the brain functions under anaesthesia. To this end, researchers used functional magnetic resonance imaging (fMRI) to study the precise spatial distribution of synchronously working brain regions in anaesthetised humans, long-tailed macaques, common marmosets and rats. They were able to show for the first time that the areas where burst-suppression is evident differ significantly in primates and rodents. While in rats large parts of the cerebral cortex synchronously show the burst-suppression pattern, in primates individual sensory regions, such as the visual cortex, are excluded from it.
“Our brain can be thought of as a full soccer stadium when we are awake,” explained Nikoloz Sirmpilatze, lead author of the study. “Our active neurons are like tens of thousands of spectators all talking at once. Under anaesthesia, however, neuronal activity is synchronised. You can measure this activity using EEG as uniform waves, as if all the spectators in the stadium were singing the same song. In deep anaesthesia, this song is repeatedly interrupted by periods of silence. This is called burst-suppression. The deeper the anaesthesia, the shorter the phases of uniform activity, the bursts, and the longer the periodically recurring inactive phases, the so-called suppressions.”
The phenomenon is caused by many different anaesthetics, some of which vary in their mechanisms of action. And burst-suppression is also detectable in coma patients. However, it is not known whether this condition is a protective reaction of the brain or a sign of impaired functioning. It has also been unclear where in the brain burst-suppression occurs and which brain areas are involved, as localisation by EEG alone is not possible.
To answer this question, the researchers fMRI. In the first part of the study, the researchers established a system to evaluate fMRI data in humans, monkeys and rodents in a standardised manner using the same method. To do this, they used simultaneously-measured EEG and fMRI data from anaesthetised patients that had been generated in a previous study. “We first looked to see whether the burst-suppression detected in the EEG was also visible in the fMRI data and whether it showed a certain pattern,” says Nikoloz Sirmpilatze. “Based on that, we developed a new algorithm that allowed detecting burst-suppression events in the experimental animals using fMRI, without additional EEG measurement.”
The researchers then performed fMRI measurements in anaesthetised long-tailed macaques, common marmosets and rats. In all animals, they were able to detect and precisely localise burst-suppression as a function of anesthetic concentration. The spatial distribution of burst-suppression showed that in both humans and monkey species, certain sensory areas, such as the visual cortex, were excluded from it. In contrast, in the rats, the entire cerebral cortex was affected by burst-suppression.
“At the moment, we can only speculate about the reasons,” said Nikoloz Sirmpilatze, who was awarded the German Primate Center’s 2021 PhD Thesis Award for his work. “Primates orient themselves mainly through their sense of sight. Therefore, the visual cortex is a highly specialised region that differs from other brain areas by special cell types and structures. In rats, this is not the case. In future studies, we will investigate what exactly happens in these regions during anaesthesia to ultimately understand why burst-suppression is not detectable there with fMRI.”
Susann Boretius, senior author of the study adds: “The study not only raises the question of the extent to which rodents are suitable models for many areas of human brain research, especially when it comes to anaesthesia, but the results also have many implications for neuroscience and the evolution of neural networks in general.”
