Finding the patterns of white matter growth that support children’s cognitive development

Joëlle Bagautdinova was the lead author on this study. Joëlle is broadly interested in brain development and how this may go awry in psychiatric disorders. For her PhD in Dr. Ted Satterthwaite’s lab, Joëlle is using neuroimaging to study the mechanisms underlying brain development, cognition and psychiatric disorders. She is particularly interested in understanding the potential role of sleep as a risk factor in the emergence of mental illness.

or technically,

Development of white matter fiber covariance networks supports executive function in youth

[See Original Abstract on Pubmed]

Authors of the study: Joëlle Bagautdinova, Josiane Bourque, Valerie J. Sydnor, Matthew Cieslak,Aaron F. Alexander-Bloch, Maxwell A. Bertolero, Philip A. Cook, Raquel E. Gur, Ruben C. Gur, Fengling Hu, Bart Larsen, Tyler M. Moore, Hamsanandini Radhakrishnan, David R. Roalf, Russel T. Shinohara, Tinashe M. Tapera, Chenying Zhao, Aristeidis Sotiras, Christos Davatzikos, and Theodore D. Satterthwaite

Recently, many neuroscientists have been trying to uncover the developmental “blueprint” of the brain’s gray matter, or the specific ways in which brain regions grow and change over the course of adolescence. However, less attention has been paid to the brain’s white matter, which is the insulated, wire-like “tracts” that connect one brain region to another. NGG student Joëlle Bagautdinova and her colleagues in the Satterthwaite lab filled this gap by investigating white matter’s structural development in MRI scans from almost 1000 people ages 8 to 22 years.

While it famously does NOT imply causation, correlation can show parts of the brain have similar structures and, therefore, might be following the same developmental blueprint. So, Joëlle and her colleagues decided to cluster every point along the brain’s white matter tracts (Figure 1) into groups with similar structures (Figure 2). Specifically, they grouped points with similar fiber density, or how many “wires” are packed together to make the tract, and cross-section, or how thick the tract is (Figure 1); they refer to the combination of these measurements as “FDC”. She also tested to see how each group’s FDC values changed across adolescence.

Figure 1. White matter tracts can be measured by their density and cross-section.

Figure 2. Points of white matter can be grouped by how similar their FDC (fiber density and cross-section) values are.

Usually, researchers assume that all points along a tract will develop similarly; however, because Joëlle determined her groups based on how similar the points are, different points along the same tract could be put into different groups, while points from more than one tract could be lumped together. This allowed her to uncover brand new relationships between different white matter tracts and unique subsections that develop differently than the white matter tract. For instance, she found that FDC in the lower part of the corticospinal tract, which connects the brain and spinal cord, was different than the FDC in the upper corticospinal tract, and each portion had its own unique growth trajectory. All in all, the researchers found 14 different groups of similarly-structured white matter regions, 12 of which showed significant structural changes across this period of adolescent development.

The age at which each white matter group developed most also seems to follow a pattern. Specifically, they found that the white matter in the lower back area of the brain matures earlier in adolescence while the white matter in the upper front area of the brain doesn’t mature until a bit later. These early-maturing white matter tracts tend to connect parts of the brain that do what scientists call “lower-order functions” like vision processing, basic movement, and emotions – all things that children can do pretty well. Meanwhile, the later-maturing white matter tracts tend to connect brain regions that do “higher order” functions like complex reasoning. Overall, the fact that white matter maturation seems to progress “basic” to “complex” tracts suggests that white matter may play a big role in the brain’s development across adolescence.

Finally, Joëlle and her colleagues wanted to see if these white matter structures helped kids’ executive function, which is one of these “higher order” cognitive functions that includes planning, organizing, and impulse control. They found that if you remove the effects of age, kids with better executive function tend to have higher FDC in all but one white matter group. This means that white matter tracts that are thicker and/or more tightly packed do a better job of sending signals between brain regions, especially those in the front of the brain that are responsible for cognition, and that this enhanced signaling may allow children to have stronger executive functions.

By using new, cutting-edge analyses, Joëlle and her collaborators were able to: uncover brand-new, biologically-based relationships between white matter areas; chart how these areas develop over adolescence; and show which white matter structures seem to help with cognitive function. All in all, this work fills in important gaps in our understanding how the brains we’re born with mature into the brains of capable, full-grown adults.

About the brief writer: Margaret Gardner

Margaret is a PhD student in the Brain-Gene-Development Lab working with Dr. Aaron Alexander-Bloch. She is interested in studying how different biological and demographic factors influence people’s brain development and their risk for mental illnesses.

Want to learn more about this exciting research? Check out Joëlle’s paper here!

Understanding the brain during mindfulness

Dale Zhou was the lead author on this study. Dale is interested in how the brain network compresses and reconstructs information as network structure changes across the lifespan. He aims to account for computations of memory and reward as network functions of dimensionality reduction and expansion using experimental, naturalistic, and clinical data.

or technically,

Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present

[See Original Abstract on Pubmed]

Authors of the study: Dale Zhou, Yoona Kang, Danielle Cosme, Mia Jovanova, Xiaosong He, Arun Mahadevan, Jeesung Ahn, Ovidia Stanoi, Julia K. Brynildsen, Nicole Cooper, Eli J. Cornblath, Linden Parkes, Peter J. Mucha, Kevin N. Ochsner , David M. Lydon-Staley, Emily B. Falk, and Dani S. Bassett

In recent years, the practice of meditation has received a lot of attention for its health benefits, both physically and mentally. One popular form of meditation, mindfulness meditation, teaches individuals to focus on, and attend to the present moment. The ability to shift focus depends on the ability to orchestrate shifts in neural activity, and has been previously called executive function. While the benefits of mindfulness meditation are widely recognized, what’s going on in the brain is much less clear.

In order to understand how mindfulness is represented in the brain, Dale Zhou, a recent NGG graduate, and his collaborators recruited healthy college students who identified as social drinkers and asked them to perform a task rating from 1 to 5 how much they would crave an alcoholic drink, presented to them on a computer screen. Dale simultaneously measured the activity patterns in participants’ brains using functional magnetic resonance imaging, or fMRI, while they completed this task. One group of participants was instructed to practice mindfulness while rating their cravings by “mentally distancing themselves by observing the situation and their response to it with a more impartial, nonjudgmental, or curious mindset, and without getting caught up in the situation or response”. The other group was instructed to rate their cravings with their natural gut reaction to the drink.  For some trials, participants in the mindful group were asked to switch to their gut reaction instead, allowing Dale and his colleagues to compare which brain areas were simultaneously active or quiet during the different reactions. This allowed them to draw some interesting conclusions about how the brain represents mindfulness.

Figure 1: Simplified representation of brain states. In this example, the brain has only two areas and the brain state is defined by the activity of region 1 and region 2.

Before Dale analyzed the results from the experiment, he first asked how mindfulness can be measured in the brain and if the “amount” of mindfulness in our brains impacts our day-to-day behaviors. To answer this question, he used average brain activity from the participants’ scans to calculate a measure of the executive function called controllability. To understand controllability, it is helpful to think of the brain as having different “brain states” (Figure 1). When a person is doing some activity, like walking, the brain exists in a particular brain state - some brain areas are very active and some are quiet. When the same person is doing a different activity, like eating, the brain exists in a different brain state - a different set of brain areas are active and quiet. Dale and his colleagues defined controllability as how readily the brain can switch into any possible brain state. By calculating controllability for each participant, and tracking their drinking behavior weeks after the brain scan, Dale found that the participants with higher controllability tended to have fewer drinks than those with lower controllability, suggesting that perhaps mindfulness does impact our day to day behaviors in a positive manner. 

Now back to the experiment. Dale asked whether there were differences in controllability, and therefore brain activity, between the two groups.  To do this, he calculated the amount of effort, or control, it took for participants in each group to enter either a mindful state or gut reaction state while reacting to the alcohol cue. He found that participants instructed to react mindfully took more effort to enter this brain state after being prompted than participants instructed to react naturally took to enter their gut reaction brain state. This was exactly what they expected to see, since it is known that achieving a state of mindfulness initially requires more thought and brain activity. However, he also found that when participants from both groups were instructed to react naturally, those who had previously reacted mindfully still required more effort to enter this gut reaction brain state than those who had not. This suggests that practicing mindfulness might make us more effortful in attention, even when we are not actively trying or instructed to. 

Finally, Dale found that brain areas that use more effort had shorter episodes of neural activity. These shorter episodes suggested that there was less influence of the past in these areas. Furthermore, these quick episodes were typically found in brain areas that help us sense the world around us rather than areas that help us think about past experiences or plan for the future. Practicing mindfulness, therefore, may put us in a more effortful state of attention which is more focused on the present moment rather than on the past or future. 

In conclusion, Dale’s hard work on this project has allowed us to take a glimpse at the brain during mindfulness and how it might be benefiting our behavior. His work reminds us that, although the brain is composed of many different brain areas, human behavior is a product of these various areas interacting with one another, producing unique states of mind such as mindfulness. Work similar to his will hopefully lead the way to a better understanding of some of the brain’s other complex functions.

About the brief writer: Jafar Bhatti

Jafar is a PhD Candidate in Long Ding and Josh Gold’s lab. He is broadly interested in brain systems involved in sensory decision-making. 

Want to learn more about how these researchers study mindfulness? You can find Dale’s paper here!

Little kids, big insights: What childhood can teach us about how the brain supports cognition

Ursula Tooley was the lead author on this study. Ursula is a postdoctoral research scholar at Washington University in St. Louis. Her research examines functional brain network development in neonates and toddlers, with a focus on the pace of brain maturation and how neuroplasticity changes across development. She received her Ph.D. in Neuroscience in 2022 from the University of Pennsylvania, under the direction of Dr. Allyson Mackey and Dr. Dani Bassett, where she studied functional brain network development in children and adolescents. She received her B.S. in Neuroscience from the University of Arizona, where she conducted research on sleep disruption in children with Down syndrome.

or technically,

The age of reason: Functional brain network development during childhood

[See Original Abstract on Pubmed]

Authors of the study: Ursula A. Tooley, Anne T. Park, Julia A. Leonard, Austin L. Boroshok, Cassidy L. McDermott, Matthew A. Tisdall, Dani S. Bassett, and Allyson P. Mackey.

Early and middle childhood (4-10 years old) are full of developmental milestones. How children speak, move, learn, and play is constantly evolving and improving. Kids build social networks, become better able to control their attention, and begin to develop cognitive skills, like reasoning. However, despite the rapid cognitive development happening during this early childhood period, neuroscientists have very little information about how brain function is changing. 

This is because getting a clear picture of brain activity, like getting a clear picture of anything, requires that the subject stays almost perfectly still. If you’ve ever watched a 4-year-old sit at the dinner table, it comes as no surprise that they don’t make the best neuroimaging subjects. Functional magnetic resonance imaging (fMRI) scanners, which take many consecutive snapshots of brain activity, are even more sensitive to motion than cameras. The tiniest movements, even just a few millimeters, can blur the images and make it impossible for neuroscientists to tell what brain activity belongs to which brain region. So, most neuroimaging work to date uses subjects over 7 years old, which means that while researchers work to understand how the brain develops to support cognition, they’re missing many of the first pieces of the puzzle. 

Here’s where recent Neuroscience Graduate Group alumn Ursula Tooley and collaborators from the Robust Methods for Magnetic Resonance group stepped in. The team engineered a way to monitor and correct for head motion inside the scanner, allowing them to collect high-quality neuroimaging data from wiggly subjects during this critical early childhood period. Specifically, this motion-tracking technology gave the researchers a way to record exactly how much and in which direction kids were moving at any given point during the scan. Ursula could then use this information to correct (think: realign) the images of brain activity or exclude the child from the study if they moved too much. The ability to precisely monitor head position in real time also created an opportunity for kids to practice the correct behavior. Before the scanning session, children came to the lab to watch a movie while laying in a mock scanner that made the same whirring noises and beeps as the real deal. Each time they moved their head more than 1 millimeter, the movie paused. Incorporating this period of exploring the scanner and the scanning expectations meant that most of the kids who enrolled in the study stayed still enough for usable images of brain activity to be collected. This is a huge feat for Ursula and the team as well as a huge win for neuroscience, making it possible to take an earlier look at the developing brain.

Over the course of the study, Ursula and her colleagues scanned a diverse group of 92 children ages 4-10 from the Philadelphia community. Each child completed an fMRI scan as well as a series of cognitive tests (which they did outside of the scanner) designed to measure the strength of their cognitive reasoning abilities. What is cognitive reasoning? Reasoning is an umbrella term describing the ability to process information, problem solve, and make predictions based on pattern recognition (Fig. 1). Successful cognitive reasoning involves much of the brain and improves dramatically during early and middle childhood. Research suggests that how kids perform on cognitive reasoning tasks is predictive of their academic achievement — even years down the road! By combining a child's cognitive reasoning ability with information about their brain activity, Ursula was able to ask whether and how changes in brain function might support this shift in cognitive performance.

Figure 1

An example question from the cognitive reasoning test, which was administered at different difficulty levels to children in the study depending on their age. Here, we see the red rectangle switches from the background (left) to the foreground (right). To answer the question correctly, the child has to understand this spatial relationship for the rectangles and extend it to the pentagons.

Ursula used resting-state fMRI data (data collected while the kids laid “at rest” in the scanner) to explore the brain’s functional organization. In other words, she inferred how much different brain regions talk to each other based on how their activity fluctuates together over time. As such, regions with activity that rises and falls together are likely functionally connected. These groups of connected brain regions are called “systems.” The brain has many of these functionally-connected systems, and neuroscience research shows that they can rewire and reconfigure themselves. For example, another neuroimaging study of older kids and young adults (ages 8-22) from Philadelphia showed that the organization of these brain systems changes with age [1]. Specifically, our brain systems become more segregated and more modular as we move towards adulthood, with weaker connections between systems and stronger connections within systems (Fig. 2). Ursula found the same trends in her data with older kids tending to have more segregated brain systems than younger kids, suggesting that our brain’s functional architecture is flexible and continues to refine as we age.

Figure 2

As we age and develop, our brain systems (red, green, and blue ovals) reorganize, moving from more integrated (e.g., many connections between systems) to more modular (e.g., more connections within systems and fewer connections between systems). Ursula’s work shows that this brain system separation supports the development of cognitive skills, like reasoning.

Do some systems remodel more than others? Ursula found that changes in connectivity were largest in brain systems involved in abstract cognition, visual processing, and attention. As it turns out, these are the same systems involved in cognitive reasoning. For instance, reasoning is supported by the brain’s visual areas taking in information from the world while attention systems focus the brain’s resources on what’s important to the task at hand while ignoring distractors. Given what we know about the blossoming of cognitive reasoning during childhood, Ursula wondered if there could be a relationship between these changes in brain connectivity and cognitive ability. To test this, Ursula compared the patterns of brain system connectivity for each child with their scores on the cognitive reasoning test (Fig. 1). She found that the remodeling of cognition, visual processing, and attention systems was associated with increased cognitive ability! In other words, kids who had more mature patterns of brain system connectivity were better equipped to reason about the world and their place in it.

Taken together, Ursula’s work suggests that the massive restructuring of brain systems as kids age might be happening to support the rapid development of cognitive abilities emerging during these early and middle childhood years. Beyond offering a new perspective on healthy brain development, this relationship between brain organization and brain function offers new ways to think about -- and potentially treat -- various neurodevelopmental or neurological disorders.

About the brief writer: Kara McGaughey

Kara is a PhD candidate in Josh Gold’s lab studying how we make decisions in the face of uncertainty and instability. Combining electrophysiology and computational modeling, she’s investigating the neural mechanisms that may underlie this adaptive behavior.

Citations:

1. Baum, G.L., Ciric R., Roalf, D.R., Betzel, R.F., Moore, T.M., Shinohara, R.T., … & Satterthwaite, T.D. (2017). Modular segregation of structural brain networks supports the development of executive function in youth. Current Biology, 27(11). doi: 10.1016/j.cub.2017.04.051.

Want to learn more about how brain function supports the development of cognitive reasoning during childhood? You can find Ursula’s full paper here!

Keeping your brain's symphony in sync

Dr. Brenna Shortal was one of the two lead authors of this publication. Her graduate and undergraduate research focused on understanding the neurological mechanisms of consciousness, and she has published a number of papers on the topic. While she was a student at UPenn, Dr. Shortal was the director of Brains in Briefs, and her passion for science communication led her to pursue a career as a medical writer for Red Nucleus following her graduation in 2021. She hopes to continue working to communicate and advocate for scientific research to broad audiences.

or technically,

Weakly correlated local cortical state switches under anesthesia lead to strongly correlated global states

[See Original Abstract on Pubmed]

Authors of the study: Ethan B Blackwood, Brenna P Shortal, Alex Proekt

The most complicated piece of machinery you will ever encounter is sitting right between your ears: your brain. Our ability to move, sense, and think is thanks to billions of individual neurons that interact in varied and complicated ways. With this level of complexity, it’s miraculous that our brains work at all, let alone as well or as long as they do. Even more impressively, when our brain gets knocked off track, like from a seizure or anesthesia, it can quickly go back to typical patterns of activity. How does such a complex thing keep itself in sync?

Ethan Blackwood is a fifth-year neuroscience graduate student in the lab of Dr. Alex Proekt. Before coming to Penn and as a rotation student with Dr. Proekt, his research focused on how neural oscillations ("brain waves") change over time or with stimulation and what this means for behavior. More recently, he has been zooming in to the individual neuron level and studying how the firing of large groups of neurons changes during learning.

Neuroscience PhD student Ethan Blackwood and Drs. Brenna Shortal and Alex Proekt at the University of Pennsylvania sought to answer this question by studying brain activity in rats under anesthesia. Anesthesia is a useful way to study the coordination of brain activity because it is easy to put animals under anesthesia in the lab and because researchers already know a lot about the patterns of brain activity that occur when people are under anesthesia. The team studied this phenomenon in rats because they were able to directly record the activity of the neurons in the rat’s brain, something that is rarely possible in the human brain.  

The team had two ideas about how the brain keeps itself in sync. Their ideas are easiest to understand if we think of the brain as a symphony with your neurons as the musicians. Just like an orchestral piece comes together because the musicians move in sync from one part of the music to the next, so too do the groups of neurons in your brain. The researchers’ first idea about how the brain might keep its symphony together was that there is a conductor who dictates how all the groups of neurons behave. The second possibility was that there is no conductor, but nearby neurons listen to each other so that the whole orchestra stays together.

To distinguish between these two possibilities, the team recorded a kind of brain signal called a local field potential in two parts of the rat brain. They did this by placing electrodes in the rat’s brain and listening to the activity of nearby neurons. This is like listening to a few microphones placed in the cello and violin sections to understand how the whole orchestra works. Each microphone captures sound produced by several nearby musicians, but it can’t capture the whole orchestra’s sound.

The team started by identifying what musical melodies, which they call brain states, each electrode recorded and noting when the nearby neurons switched from one state to the next. By doing this for all the electrodes, they showed that there were only a small number of brain states that the neurons played, and the same states appeared in different rats. The relatively small number of brain states they found is something other neuroscientists have observed, and it’s key to how the brain keeps itself in sync. If every musician in the orchestra played their own tune, it would be hard to make sense of what was going on. However, by moving through different sections of the same piece of music in sync, the instruments create a beautiful piece of music together. The same is true of your brain’s symphony. Rather than coordinating billions of songs, each sung by different neurons, your brain’s symphony sings just a few, transitioning between a small number of brain states over time.

Now that they had their brain recordings, the team could see which of their two proposals about how the neural symphony stays in sync was true. If their first prediction, that there is a conductor that signals when to transition from one state to another, was true, the team expected to see all the groups of neurons transitioning between states at similar times. On the other hand, if their second prediction was true, that the neural symphony stays in sync by listening to nearby neurons, the researchers would expect to see groups of nearby neurons transitioning between themes mostly together, with nearby neurons more likely to move together than neurons that are further apart. When they measured the neurons’ activity, they found that transitions between states measured on different electrodes corresponded only weakly to each other, but that the closer the electrodes were, the more the state transitions were related. This supported their second prediction, that neurons listen to their neighbors to decide when to transition from one state to another.

This is an exciting step toward understanding how the brain coordinates the movements between states that help keep our complex brains in sync. Understanding this process is important because it can help us develop therapies that mimic it for patients whose brain activity can’t always keep up healthy patterns, such as seizure patients. Beyond medical uses, understanding nature’s elegant solution to managing the complexity of brain signaling can teach us how to build computer systems and models that can handle increasingly more complexity to do things like power robots. And if none of these applications excite you, hopefully you can appreciate the wonder of understanding a little more about what makes us tick and how our neural symphonies stay in sync.

About the brief writer: Catrina Hacker

Catrina Hacker is a PhD candidate working in Dr. Nicole Rust’s lab. She is broadly interested in the neural correlates of cognitive processes and is currently studying how we remember what we see. She also co-directs PennNeuroKnow.

Want to learn more about how our brain activity changes during anesthesia? Read this paper to learn more!

How brain waves might help us see

Dr. Adeeti Aggarwal was the lead author on this study. Her ultimate career goal is to become an academic ophthalmologist whose clinical insights motivate her research in visual processing, and whose research also translates back to patient care. She is fascinated by how cortical networks transform visual sensory information into perception and how defects in sensory processing may alter or abolish perception such as in hallucinations or blindness. This interest has driven her research in graduate school and she hopes to continue studying how visual processing pathways participate in perceptual experience as her career progresses. 

or technically,

Visual evoked feedforward-feedback traveling waves organize neural activity across the cortical hierarchy in mice

[See Original Abstract on Pubmed]

Authors of the study: Adeeti Aggarwal, Connor Brennan, Jennifer Luo, Helen Chung, Diego Contreras, Max B. Kelz, Alex Proekt

Modern cameras do an amazing job of turning the photons of light in the world into pixels on our phone or laptop screen that faithfully capture that moment in time. The fact that we all walk around with the technology to do this sitting in our pockets is the result of decades of innovation and technological advancement. But even with everything that your smartphone’s camera can capture, we have an even more elegant piece of machinery doing all that and more sitting between our ears all day: our brains.

How is our ability to see different than a camera? To start, there’s the obvious difference in materials. Cameras are made of hard, man-made materials, whereas your brain is filled with comparatively squishier biological material. But even more importantly, a camera and your brain are trying to accomplish two different things. The goal of a camera is to recreate the world exactly as it is. The goal of your visual system is to use what you see to interact with the world. Unlike cameras, you need to do things like pay attention to one thing over another, predict what’s coming next, or change your behavior according to what you see.

We can think of the brain as needing to accomplish two things: 1) build up a representation of what is in the world, and 2) integrate that into our current understanding of the world and intended actions to accomplish something. One popular idea, or hypothesis, is that the brain accomplishes the first goal of building up a representation of the world by sending neural signals through several brain regions moving from the back of your head toward the front, termed feedforward communication. The second goal is then accomplished by integrating those signals with neural activity in other brain regions and then passing a signal backwards through the same regions from front to back, which is called feedback. These “traveling waves” of brain activity could coordinate brain activity across different parts of the brain and integrate the two goals of the visual system.

Figure 1

Illustration of the hypothesized direction of the flow of brain activity for feedforward waves (yellow) and feedback waves (blue). Figure made with biorender.com.

Testing this hypothesis has been difficult, because it requires the ability to look at brain activity across large portions of the brain as it changes very quickly and the tools to do this were only recently developed. Until recently, several scientists had used what tools were available to study feedforward and feedback activity, but they could only look for small snapshots of evidence of feedforward and feedback waves. However, last year a team of researchers at the University of Pennsylvania led by Dr. Adeeti Aggarwal, a former PhD student in the Neuroscience Graduate Group, used new technology to visualize these waves of activity across the mouse brain for the first time.

To do this, Dr. Aggarwal and her team recorded brain activity across several areas of the mouse brain while they flashed a green light in front of the mouse’s eye. By using a special kind of analysis that allowed them to get a cleaner look at the data, they were able to see the two kinds of brain waves that the hypothesis predicted. The first feedforward wave fluctuated quickly and moved from the back to the front of the brain, while the second feedback wave fluctuated more slowly and moved from the front to the back of the brain. Importantly, the team found that both waves of activity spread equally far across the brain, despite the feedforward wave fluctuating faster than the feedback wave. Through this and other observations the team concluded that the two waves of brain activity interact and integrate to form a cohesive wave of brain activity that could be combining the information about what the mouse is seeing with other brain signals.

This was exciting evidence that the kinds of feedforward and feedback waves that neuroscientists thought could coordinate visual information are actually present in the brain, but how might they help a mouse to see?  Your brain cells, called neurons, communicate with each other by sending a kind of signal called an action potential, or spike. Whether and how a neuron produces spikes is what ultimately influences what you see and how you behave. To demonstrate that these waves of brain activity could shape these important brain signals, Dr. Aggarwal and her team looked at whether the waves of brain activity had an impact on whether and how neurons produced spikes.  They found that neurons were more likely to produce spikes at the peaks of the slow oscillation than at the lower points. This links the waves of brain activity that they observed directly to spikes, which suggests that these waves are capable of coordinating brain information about what the mouse is seeing with other kinds of signals.

Dr. Aggarwal and her team’s paper provides exciting new evidence for how different parts of the brain can be coordinated through waves of activity, and future work will continue to determine how these waves can be linked to behavior and whether they can be seen in human brains as well. Understanding how the brain coordinates activity across brain regions to turn sight into action could be helpful in many ways. For one, this information could help to engineer better visual prosthetics for people who are blind. If these waves are necessary to coordinate brain activity across parts of the brain, it may be necessary for visual prosthetics to produce signals that work in the same way. Beyond direct human applications, incorporating similar principles into the design of robotic systems that need to coordinate information about the world with a set of goals or actions could produce robots that can better interact with the world to accomplish their goals. As with all scientific advancements, Dr. Aggarwal’s study is one exciting piece in many bigger puzzles.

About the brief writer: Catrina Hacker

Catrina Hacker is a PhD candidate working in Dr. Nicole Rust’s Lab. She is broadly interested in the neural correlates of cognitive processes and is currently studying how we remember what we see. She also co-directs PennNeuroKnow.

Interested in learning more about Adeeti’s work? Check out the full paper here!

How different levels of brain development help adolescent cognition - or don’t

Adam Pines was the lead author on this study. Adam is a postdoctoral fellow in the Stanford PanLab for Precision Psychiatry and Translational Neuroscience. He completed his Ph.D. in Neuroscience at UPenn in 2022. His other research interests include developmental neuroscience, brain-environment interactions, and adaptive plasticity in the brain.

or technically,

Dissociable multi-scale patterns of development in personalized brain networks

[See Original Abstract on Pubmed]

Authors of the study: Adam R. Pines, Bart Larsen, Zaixu Cui, Valerie J. Sydnor, Maxwell A. Bertolero, Azeez Adebimpe, Aaron F. Alexander-Bloch, Christos Davatzikos, Damien A. Fair, Ruben C. Gur, Raquel E. Gur, Hongming Li, Michael P. Milham, Tyler M. Moore, Kristin Murtha, Linden Parkes, Sharon L. Thompson-Schill, Sheila Shanmugan, Russell T. Shinohara, Sarah M. Weinstein, Danielle S. Bassett, Yong Fan & Theodore D. Satterthwaite

You don’t need to be a scientist to know that kids get smarter as they grow up - they get better at things like problem-solving, thinking flexibly, and remembering information. But what exactly is changing in the brain to make these cognitive skills, which researchers call “executive function” easier?

Like instruments in a band, different areas of the human brain have different roles and will perform together in different combinations to everything from processing what your eyes see, to controlling your muscles, to solving a crossword, to feeling emotions. A group of brain regions that work together is called a functional brain network. Some functional brain networks perform easier, or “lower-order”  tasks, like sensing pain when you get a cut. Others perform harder, more complex tasks, like solving physics equations or learning a language, which are considered “higher-order”. 

Dr. Adam Pines, who recently graduated from the Neuroscience Graduate Group, wanted to know how all these functional networks mature as kids age and how this pattern of development relates to kids’ improving executive function. To study this, Adam had two challenges. First, we don’t know how many functional networks there “really” are in the brain; you can divide the brain up into different numbers of chunks and still do a good job of grouping regions that activate together and separating those that don’t (Figure 1, Columns). Second, the layout of everyone’s functional networks is a tiny bit different: one network may take up a little more space in one person, for instance, or the parts of the brain that do a certain task on one person may be just a little bit more to the left on another (Figure 1, Rows). Therefore, Adam made personalized functional networks (PFNs), which are maps of a person’s unique functional network layout, for every subject in the study. He also tried grouping the brain into different numbers of networks to see whether this would change his results.

Figure 1: Illustration of personalized functional networks mapped for varying numbers of networks.

Adam mapped the unique functional networks of each person in the study (PFNs), as shown in the rows. He also divided the brain’s activity into different numbers of networks, with maps of 4, 7, and 13 networks pictured. Different colors show that the brain regions are part of different functional networks.

To make personalized functional networks (PFNs) for each subject (Figure 1, Rows), Adam and his colleagues mapped the layout of every functional network in the average person and mathematically tweaked the layout to fit each participant’s unique pattern of brain activation. Then, they repeated this step using different numbers of networks in their baseline map (Figure 1, Columns) and labeled whether each network did lower- or higher-order functions. In the end, they had 29 brain maps for each person (each dividing brain activity into 2 to 30 functional networks), that they could compare to each participant’s age and score on a test of executive function.

First, Adam compared PFNs across participants ages 8 through 23 and found that lower- and higher-order networks tended to develop differently. Lower-order networks (each of which does an easier task) became more interconnected over the course of adolescence, while higher-order networks (each of which does a harder task) became less interconnected. Next, he tested how these PFN patterns were related to kids’ executive function. Interestingly, he found executive function tends to be better when very low-order and very high-order networks are distinct, but networks that fall in the middle (ones that do medium-complexity tasks) are more interconnected. Dividing the brain into a greater number of PFNs, Adam saw this effect grow stronger, especially in lower-order networks.

Taken together, Adam’s results are surprising because, while aging makes higher-order networks more distinct (which is better for executive function), lower-order networks actually become more interconnected (which is worse for executive function)! This may mean that while increasingly distinct higher-order networks allow kids’ executive function to improve as they grow up, their brains’ lower-order networks are already starting to decline. These findings will be important for future scientists studying how kids’ executive function develops and may help uncover why some kids struggle with cognitive development.

About the brief writer: Margaret Gardner

Margaret is a PhD student in the Brain-Gene-Development Lab working with Dr. Aaron Alexander-Bloch. She is interested in studying how different biological and demographic factors influence people’s brain development and their risk for mental illnesses.

Want to read Adam’s work for yourself? You can find the full article (complete with equations and pretty brain pictures) here!