Tag: brain activity

Concussions from American Football Slow Brain Activity of High Schoolers

Photo by Jakob Rosen on Unsplash

A new study of high school American football players found that concussions affect an often-overlooked but important brain signal. The findings are presented at the annual meeting of the Radiological Society of North America (RSNA).

Reports have emerged in recent years warning about the potential harms of youth contact sports on developing brains. Contact sports, including high school football, carry a risk of concussion. Symptoms of concussion commonly include cognitive disturbances, such as difficulty with balancing, memory or concentration.

Many concussion studies focus on periodic brain signals. These signals appear in rhythmic patterns and contribute to brain functions such as attention, movement or sensory processing. Not much is known about how concussions affect other aspects of brain function, specifically, brain signals that are not rhythmic.

“Most previous neuroscience research has focused on rhythmic brain signaling, which is also called periodic neurophysiology,” said study lead author Kevin C. Yu, BS, a neuroscience student at Wake Forest University School of Medicine. “On the other hand, aperiodic neurophysiology refers to brain signals that are not rhythmic.”

Aperiodic activity is typically treated as ‘background noise’ on brain scans, but recent studies have shown that this background noise may play a key role in how the brain functions.

“While it’s often overlooked, aperiodic activity is important because it reflects brain cortical excitability,” said study senior author Christopher T. Whitlow, MD, PhD, MHA, radiology professor at Wake Forest University School of Medicine.

Cortical excitability is a vital part of brain function. It reflects how nerve cells, or neurons, in the brain’s cortex respond to stimulation and plays a key role in cognitive functions like learning and memory, information processing, decision making, motor control, wakefulness and sleep.

To gain a better understanding of brain rhythms and trauma, the researchers sought to identify the impacts of concussions on aperiodic activity.

Pre- and post-season resting-state magnetoencephalography (MEG) data was collected from 91 high school football players, of whom 10 were diagnosed with a concussion. MEG is a neuroimaging technique that measures the magnetic fields that the brain’s electrical currents produce.

A clinical evaluation tool for concussions called the Post-Concussive Symptom Inventory was correlated with pre- and post-season physical, cognitive and behavioral symptoms.

High school football players who sustained concussions displayed slowed aperiodic activity. Aperiodic slowing was strongly associated with worse post-concussion cognitive symptoms and test scores.

Slowed aperiodic activity was present in areas of the brain that contain chemicals linked with concussion symptoms like impaired concentration and memory.

“This study is important because it provides insight into both the mechanisms and the clinical implications of concussion in the maturing adolescent brain,” said co-lead author Alex I. Wiesman, PhD, assistant professor at Simon Fraser University. “Reduced excitability is conceptually a very different brain activity change than altered rhythms and means that a clear next step for this work is to see whether these changes are related to effects of concussion on the brain’s chemistry.”

The findings from the study may also influence tracking of post-concussion symptoms and aid in finding new treatments to improve recovery.

“Our study opens the door to new ways of understanding and diagnosing concussions, using this novel type of brain activity that is associated with concussion symptoms,” Dr Whitlow said. “It highlights the importance of monitoring kids carefully after any head injury and taking concussions seriously.”

Source: Radiological Society of North America

Brain’s Structure Hangs in ‘a Delicate Balance’

Photo by Fakurian Design on Unsplash

When a magnet is heated up, it reaches a critical point where it becomes demagnetisated. Called “criticality,” this point of high complexity is reached when a physical object is transitioning smoothly from one phase into the next.

Now, a new Northwestern University study has discovered that the brain’s structural features reside in the vicinity of a similar critical point – either at or close to a structural phase transition. Surprisingly, these results are consistent across brains from humans, mice and fruit flies, which suggests the finding might be universal. Although the researchers don’t know what phases the brain’s structure is transitioning between, they say this new information could enable new designs for computational models of the brain’s complexity and emergent phenomena.

The research was published in Communications Physics.

“The human brain is one of the most complex systems known, and many properties of the details governing its structure are not yet understood,” said Northwestern’s István Kovács, the study’s senior author. “Several other researchers have studied brain criticality in terms of neuron dynamics. But we are looking at criticality at the structural level in order to ultimately understand how this underpins the complexity of brain dynamics. That has been a missing piece for how we think about the brain’s complexity. Unlike in a computer where any software can run on the same hardware, in the brain the dynamics and the hardware are strongly related.”

“The structure of the brain at the cellular level appears to be near a phase transition,” said Northwestern’s Helen Ansell, the paper’s first author. “An everyday example of this is when ice melts into water. It’s still water molecules, but they are undergoing a transition from solid to liquid. We certainly are not saying that the brain is near melting. In fact, we don’t have a way of knowing what two phases the brain could be transitioning between. Because if it were on either side of the critical point, it wouldn’t be a brain.”

While researchers have long studied brain dynamics using functional magnetic resonance imaging (fMRI) and electroencephalograms (EEG), advances in neuroscience have only recently provided massive datasets for the brain’s cellular structure. These data opened possibilities for Kovács and his team to apply statistical physics techniques to measure the physical structure of neurons.

For the new study, Kovács and Ansell analysed publicly available data from 3D brain reconstructions from humans, fruit flies and mice. By examining the brain at nanoscale resolution, the researchers found the samples showcased hallmarks of physical properties associated with criticality.

One such property is the well-known, fractal-like structure of neurons. This nontrivial fractal-dimension is an example of a set of observables, called “critical exponents,” that emerge when a system is close to a phase transition.

Brain cells are arranged in a fractal-like statistical pattern at different scales. When zoomed in, the fractal shapes are “self-similar,” meaning that smaller parts of the sample resemble the whole sample. The sizes of various neuron segments observed also are diverse, which provides another clue. According to Kovács, self-similarity, long-range correlations and broad size distributions are all signatures of a critical state, where features are neither too organised nor too random. These observations lead to a set of critical exponents that characterise these structural features.

“These are things we see in all critical systems in physics,” Kovács said. “It seems the brain is in a delicate balance between two phases.”

Kovács and Ansell were amazed to find that all brain samples studied – from humans, mice and fruit flies – have consistent critical exponents across organisms, meaning they share the same quantitative features of criticality. The underlying, compatible structures among organisms hint that a universal governing principle might be at play. Their new findings potentially could help explain why brains from different creatures share some of the same fundamental principles.

“Initially, these structures look quite different – a whole fly brain is roughly the size of a small human neuron,” Ansell said. “But then we found emerging properties that are surprisingly similar.”

“Among the many characteristics that are very different across organisms, we relied on the suggestions of statistical physics to check which measures are potentially universal, such as critical exponents. Indeed, those are consistent across organisms,” Kovács said. “As an even deeper sign of criticality, the obtained critical exponents are not independent – from any three, we can calculate the rest, as dictated by statistical physics. This finding opens the way to formulating simple physical models to capture statistical patterns of the brain structure. Such models are useful inputs for dynamical brain models and can be inspirational for artificial neural network architectures.”

Next, the researchers plan to apply their techniques to emerging new datasets, including larger sections of the brain and more organisms. They aim to find if the universality will still apply.

Source:: Northwestern University

Social Bonding Gets People on the Same Wavelength

Forming social bonds facilitates effective communication and neural synchronisation across individuals of different social status within a group

When small hierarchical groups bond, neural activity between leaders and followers aligns, promoting quicker and more frequent communication, according to a study published on March 19th in the open-access journal PLOS Biology by Jun Ni from Beijing Normal University, China, and colleagues.

Social groups are often organised hierarchically, where status differences and bonds between members shape the group’s dynamic. To better understand how bonding influences communication within hierarchical groups and which brain regions are involved in these processes, the researchers recorded 176 three-person groups of human participants (who had never met before) while they communicated with each other, sitting face-to-face in a triangle. Participants wore caps with fNIRS (functional near-infrared spectroscopy) electrodes to non-invasively measure brain activity while they communicated with their group members. Each group democratically selected a leader, so each group of three ultimately included one leader and two followers. After strategising together, groups played two economic games designed to test their willingness to make sacrifices to benefit their group (or harm other groups).

Experimenters assigned some triads to go through a bonding session, where they were grouped according to colour preferences, given uniforms, and led through an introductory chat session to build familiarity. Bonded groups spoke more freely and bounced between speakers more frequently and rapidly, relative to groups that didn’t experience this bonding session. This bonding effect was stronger between leaders and followers than between two followers. Neural activity in two brain regions linked to social interaction, the right dorsolateral prefrontal cortex (rDLPFC) and the right temporoparietal junction (rTPJ), aligned between leaders and followers if they had bonded. The authors state that this neural synchronisation suggests that leaders may be anticipating followers’ mental states during group decision-making, though they acknowledge that their findings are restricted to East Asian Chinese individuals communicating via text (without non-verbal cues), whose culture emphasises group cohesion and commitment towards group leaders.

The authors add, “Social bonding increases information exchange and prefrontal neural synchronisation selectively among individuals with different social statuses, providing a potential neurocognitive explanation for how social bonding facilitates the hierarchical structure of human groups.”

Source: PLOS

Researchers Demonstrate the Effect of Neurochemicals on fMRI Readings

Photo by Fakurian Design on Unsplash

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.

Source: UNC School of Medicine

“Movies” with Colour and Music Visualise Brain Activity Data in Beautiful Detail

Novel toolkit translates neuroimaging data into audiovisual formats to aid interpretation

Simple audiovisualisation of wide field neural activity. Adapted from Thibodeaux et al., 2024, PLOS ONE, CC-BY 4.0

Complex neuroimaging data can be explored through translation into an audiovisual format – a video with accompanying musical soundtrack – to help interpret what happens in the brain when performing certain behaviours. David Thibodeaux and colleagues at Columbia University, US, present this technique in the open-access journal PLOS ONE on February 21, 2024. Examples of these beautiful “brain movies” are included below.

Recent technological advances have made it possible for multiple components of activity in the awake brain to be recorded in real time. Scientists can now observe, for instance, what happens in a mouse’s brain when it performs specific behaviours or receives a certain stimulus. However, such research produces large quantities of data that can be difficult to intuitively explore to gain insights into the biological mechanisms behind brain activity patterns.

Prior research has shown that some brain imaging data can be translated into audible representations. Building on such approaches, Thibodeaux and colleagues developed a flexible toolkit that enables translation of different types of brain imaging data – and accompanying video recordings of lab animal behaviour – into audiovisual representations.

The researchers then demonstrated the new technique in three different experimental settings, showing how audiovisual representations can be prepared with data from various brain imaging approaches, including 2D wide-field optical mapping (WFOM) and 3D swept confocally aligned planar excitation (SCAPE) microscopy.

The toolkit was applied to previously-collected WFOM data that detected both neural activity and brain blood flow changes in mice engaging in different behaviours, such as running or grooming. Neuronal data was represented by piano sounds that struck in time with spikes in brain activity, with the volume of each note indicating magnitude of activity and its pitch indicating the location in the brain where the activity occurred. Meanwhile, blood flow data were represented by violin sounds. The piano and violin sounds, played in real time, demonstrate the coupled relationship between neuronal activity and blood flow. Viewed alongside a video of the mouse, a viewer can discern which patterns of brain activity corresponded to different behaviours.

The authors note that their toolkit is not a substitute for quantitative analysis of neuroimaging data. Nonetheless, it could help scientists screen large datasets for patterns that might otherwise have gone unnoticed and are worth further analysis.

The authors add: “Listening to and seeing representations of [brain activity] data is an immersive experience that can tap into this capacity of ours to recognise and interpret patterns (consider the online security feature that asks you to “select traffic lights in this image” – a challenge beyond most computers, but trivial for our brains)…[It] is almost impossible to watch and focus on both the time-varying [brain activity] data and the behavior video at the same time, our eyes will need to flick back and forth to see things that happen together. You generally need to continually replay clips over and over to be able to figure out what happened at a particular moment. Having an auditory representation of the data makes it much simpler to see (and hear) when things happen at the exact same time.”

  1. Audiovisualisation of neural activity from the dorsal surface of the thinned skull cortex of the awake mouse.
  2. Audiovisualisation of neural activity from the dorsal surface of the thinned skull cortex of the ketamine/xylazine anaesthetised mouse.
  3. Audiovisualisation of SCAPE microscopy data capturing calcium activity in apical dendrites in the awake mouse brain.
  4. Audiovisualisation of neural activity and blood flow from the dorsal surface of the thinned skull cortex of the awake mouse.

Video Credits: Thibodeaux et al., 2024, PLOS ONE, CC-BY 4.0