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!

Does the size of your social network predict how big certain parts of your brain are?

Camille was the lead author on this study. She is a 5th year graduate student at the University of Pennsylvania with Dr. Michael Platt. She wants to understand the evolution and neurobiological mechanisms of social relationships in primates using approaches from behavioral ecology of primates in the wild to single cell electrophysiology in the lab!

or technically,

Social Connections predict brain structure in a multidimensional free-ranging primate society

[See original abstract on PubMed]

Authors of the study: Camille Testard, Lauren J. N. Brent, Jesper Andersson, Kenneth L. Chiou, Josue E. Negron-Del Valle, Alex R. DeCasien, Arianna Acevedo-Ithier, Michala K. Stock, Susan C. Antón, Olga Gonzalez, Christopher S. Walker, Sean Foxley, Nicole R. Compo, Samuel Bauman, Angelina V. Ruiz-Lambides, Melween I. Martinez, J. H. Pate Skene, Julie E. Horvath, Cayo Biobank Research Unit, James P. Higham, Karla L. Miller, Noah Snyder-Mackler, Michael J. Montague, Michael L. Platt, Jérôme Sallet

When I think of neuroscience, I think of scientists in white lab coats examining brains under a microscope. While it’s true that neuroscience these days typically takes place in a laboratory environment, some would argue that this isn’t the best way to study the brain. If we want to study how the brain works naturally, why would we study it in an artificial environment, such as a lab?

While of course there are some topics that are better suited to be studied in labs like how individual neurons in the brain function and work together, topics like social behavior, which is what Camille and her colleagues were interested in, may benefit from more naturalistic experimental conditions. In particular, Camille and her colleagues wanted to know how the size of an individual’s social network can affect their brain structure and function. To do this they studied the behavior and brains of rhesus macaque monkeys living in a semi-free range colony on Cayo Santiago Island in Puerto Rico. 

In their paper, the researchers examined the behavior of a single social group composed of 103 individual monkeys of which 39 were male and 64 were female. For each monkey in the colony, the researchers looked at two measures of social behavior. The first measure was the monkey’s social network, which was based on the number of grooming interactions a given monkey had with other monkeys. The more grooming partners a monkey had, the larger its network was. The second measure they looked at was the monkey’s social status, which was based on aggressive interactions given and received that a given monkey encountered with others (threats, chases, submissions, etc.). 

Camille and her team observed each monkey’s behavior for 3 months prior to  measuring their brain structure using a technique known as MRI, or magnetic resonance imaging. With this technique, they were able to determine the size of different brain areas in each monkey. Then, they wanted to see if there was a relationship between a given monkey’s social behavior and any part of the monkey’s brain. 

Interestingly, the researchers found that there was a positive correlation between the social network size (i.e, number of grooming partners) of a monkey and the size of two specific brain regions (see Figure 1). The first brain region is called the mid superior temporal sulcus (mid-STS, for short). In previous studies, the mid-STS has been found to be involved in responding to social scenes. This region is also thought to be involved in deciding whether to cooperate versus compete with a partner. The second brain region is called the ventral dysgranular insula (vd-insula, for short). In previous studies, this region has been found to be involved in grooming behavior in macaques and empathy in humans!

Because social interactions between monkeys are multi-faceted, just as in humans, Camille also looked at several other nuances of the monkeys’ social network to see if they predicted the size of these brain regions. For example, they looked at “betweenness” (was a given monkey able to bridge connections between distant members of the colony?) and “closeness” (how close was a given monkey to every other monkey in the colony?). These other measures did not correlate with any brain region in these monkeys. Because of this, the researchers took a closer look at social network size, which did show a correlation with brain size. Since this measure was determined by grooming interactions, they were curious if the direction of the grooming mattered: whether the monkey actively groomed other individuals or was being groomed. When they looked at the data this way, they found that how many individuals in the colony that groomed a given monkey more closely predicted its brain size. 

Finally, the researchers wondered if the relationship that they found between social network size and brain size in adult monkeys was also true for infant monkeys. These monkeys are too young to form complex social networks so the researchers instead used the social network of the mothers of these infants. They reasoned that they might still see a relationship because previous studies showed that an infant macaque’s social network mimick the social network of his/her mother. However they found no clear relationship between a mother’s social network and her infant’s brain size. The authors suggested that the infants were perhaps too young for their brains to have fully developed and any size differences to be observable. These results led the researchers to believe that the brain-size differences that they see in adult macaques are due to the increased sociability that occurs during development. 

In summary, Camille’s research offers incredible insight into how the size of specific brain regions is related to the ability of mammals to form large social networks in their natural environment. Her team determined the social network size of each monkey in the colony and found a significant correlation with two socialization-related brain regions, the mid-STS and the vd-Insula. Furthermore, this relationship could not be found in infant monkeys, leading them to believe that increased sociability during development leads to the observed differences in brain structure seen in adult monkeys. Camille’s work is important because her discoveries in wild, free-ranging monkeys emphasize that complex social forces, for instance in human societies, can powerfully drive the physical expansion of socially related areas in the brain.

About the brief writer: Jafar Bhatti

Jafar is a PhD Candidate in Maria Geffen’s lab. I’m broadly interested in brain networks involved in auditory processing and decision-making.

Want to learn more about how these researchers study the social behavior and brains of free ranging monkeys? You can find Camille’s full paper here!

Can a single neuron in the brain really solve complicated problems all by itself?

Ilenna Jones was the lead author on these studies. She is a Neuroscience Ph.D. candidate in Dr. Konrad Kording’s lab at Penn.

or technically,

Might a single neuron solve interesting machine learning problems through successive computations on its dendritic tree? & Do biological constraints impair dendritic computation?

See Original Abstracts on Pubmed: Paper 1 Paper 2

Authors of the studies: Ilenna Simone Jones & Konrad Kording

Figure 1: A Purkinje neuron found exclusively in the cerebellum. Illustration by Ramon y Cajal.

In the late 1800s, a scientist named Ramon y Cajal turned his microscope to the brain and discovered neurons, the cells of the brain. At the time, cameras had not yet been invented, so instead he drew what he saw. He compiled a collection of beautiful illustrations of the many different shapes and variations of neurons, which are still cited and referenced to this day (see Figure 1). In doing so he gave birth to the field of modern neuroscience.

Cajal’s drawings demonstrated the anatomical complexity and variety of neurons throughout the brain. He observed that neurons are composed of several parts, including branched fibers called dendrites that converge onto a cell body, and a single thin fiber that departs the cell body called an axon. Since Cajal’s time, neuroscientists have learned that neurons receive electrical activity from other neurons through their dendrites and send electrical activity through their axons. These electrical signals form the basis of brain activity and allow us to sense, interpret, and respond to cues in our environment.  

Much of neuroscience research has focused on the activity of populations and networks of neurons, but how much can a single neuron do? Does a neuron’s extensive tree of dendrites allow it to perform complex calculations and send new information to other neurons? Or does a neuron simply act like a relay station that transfers the signals it receives without analyzing it? These are the questions that Neuroscience Graduate Group student Ilenna Jones wanted to answer. 

In her first paper, Ilenna used a computerized version of a neuron and asked it to perform various complex tasks. By modifying the number and organization of dendrites on her “virtual neuron,” she found that neurons with complex branching patterns performed tasks better than neurons with simpler branching patterns. This finding suggests that the shape of a neuron actually influences how much it can do! Neurons with densely layered, tree-like dendritic structures can perform sophisticated calculations, as opposed to neurons with more simple dendritic structures which cannot. 

In her second paper, Ilenna next wondered whether making her “virtual neuron” more realistic would change how they performed the same tasks. To do this she included even more of the biological properties found in real neurons, including how dendrites receive and respond to electrical signals from other neurons. She expected that by ‘humanizing’ her virtual neuron it would impair its ability to perform complex calculations, leading to worse task performance. This is a reasonable prediction because in many cases adding more rules for a computer model to follow can push it farther from the ‘idealized case' where it performs very well. But to her surprise, adding these new, realistic characteristics to her neuron actually improved performance in many cases! 

Thanks to Ilenna, we now know that dendritic complexity can allow individual neurons to act as mini-computers that receive information, perform calculations on it, and send new information to many other neurons. Moreover, because neurons come in many shapes and sizes across the brain, it’s likely that different types of neurons can perform completely different calculations depending on their shape. Her findings are significant because it opens up a whole new perspective as to how neurons process information. Understanding what individual neurons are capable of will help neuroscientists study the brain more closely and ultimately help us understand how the brain works!

Want to learn more about the details of Ilenna’s computational modeling of neurons? You can check out the full papers here and here!

About the brief writer: Joe Stucynski

Joe is a graduate student in Dr. Franz Weber’s and Dr. Shinjae Chung’s labs at Penn. He is broadly interested in what makes us sleep how the brain transitions between states.

Citations:

1. Purkinje Neuron Picture: https://upload.wikimedia.org/wikipedia/commons/b/bb/PurkinjeCellCajal.gif