Scientists use zebrafish to understand how the brain makes decisions!

Dr. Hannah Shoenhard was the lead author on this study. Hannah hopes to one day run a lab that connects the “small picture“ (molecules, genes, and proteins) to the “big picture“ (circuits and behaviors) in neuroscience. After earning her PhD in the Granato lab using zebrafish as a model system to study decision making, she moved to do a postdoc in the Sehgal lab studying sleep and memory in fruit flies.

or technically,

The calcium-sensing receptor (CaSR) regulates zebrafish sensorimotor decision making via a genetically defined cluster of hindbrain neurons

[See Original Abstract on Pubmed]

Authors of the study: Hannah Shoenhard, Roshan A. Jain, Michael Granato

How we make decisions is a question that scientists and philosophers have considered for ages. But did you know that there are different types of decision making? The type that we are most familiar with involves decisions that we make in our everyday lives: Should I walk to school or take the bus? Should I have pasta or salad for dinner? But the brain is actually responsible for lots of different kinds of decisions - some of which we don’t even think about! One type of decision making that is commonly studied in the field of neuroscience is called sensorimotor decision making. In this form of decision making, the brain takes in sensory information from the world, processes the information while considering past experiences, and then produces a behavioral response. 

Figure 1:

Image of a zebrafish used in scientific research. 

Source: https://news.mit.edu/2022/smarter-zebrafish-study-1118

To understand more about this type of decision making, Dr. Hannah Shoenhard, a recent Penn Neuroscience PhD graduate, and her lab used zebrafish, a common animal model that is used in neuroscience research. Her lab had previously found that when fish are presented with a sudden quiet sound, they respond with a “reorientation” response - the fish slowly turn their bodies. But if the fish are presented with a sudden loud sound, they respond with an “escape” response - the fish rapidly turn their bodies. Having learned about this fascinating behavioral phenomenon, Hannah was interested in how different proteins may be involved in this sensorimotor decision making process. Through whole-genome sequencing (a fancy way of scanning for important genes) in the zebrafish, the lab identified a protein named CaSR that is essential for sensorimotor decision. When the lab removed CaSR from the zebrafish, they found that they would produce the wrong response to a loud sound by reorienting instead of trying to escape.

Given that CaSR is important for normal sensorimotor decision making, Hannah next wanted to know which part of the zebrafish brain uses CaSR to perform this behavior. She first looked at the neurons that drive the escape response. When she reintroduced CaSR into these escape neurons, she found that it did not restore the correct escaping response. This meant that CaSR had to be acting elsewhere.

To find the location where CaSR is acting, Hannah developed a novel experimental strategy. This approach combined behavior and brain imaging. Hannah expressed CaSR in random sets of neurons in zebrafish that didn’t have any CaSR of their own. Some of these fish displayed normal decision-making, meaning CaSR had been expressed in the “correct” neurons, and some displayed impaired decision-making, meaning the “correct” neurons had been missed. Hannah then compared which neurons had CaSR in zebrafish that displayed normal decision-making or abnormal decision-making. Using this novel strategy, Hannah found a brain region in the zebrafish called DCR6, which is located in the hindbrain, near both the escape and reorientation neurons. The hindbrain controls many reflexive behaviors in both fish and humans. To validate her findings and test if this region is actually involved in sensorimotor decision making, she drove extra CaSR expression in the DCR6 and found that this was sufficient to drive escape responses in zebrafish exposed to quiet noises – in other words, the opposite of what happens when CaSR is missing.  Additionally, she used the original zebrafish strain that lacked CaSR and specifically restored CaSR only in DCR6 neurons. Hannah found that these fish performed reorientations in response to quiet sounds and escapes in response to loud sounds - just as we expect healthy zebrafish to do! 

Thus far, Hannah’s experiments have pointed to two major findings: 1) CaSR is important for normal sensorimotor decision making and 2) CaSR acts locally in DCR6 neurons, but not reorientation or escape neurons, to enable normal sensorimotor decision making. Given these findings, Hannah asked an important follow-up question - are there connections between DCR6 and reorientation or escape neurons? To answer this, she used a unique zebrafish strain that labels DCR6 neurons and escape neurons. Hannah found that DCR6 neurons do connect to escape neurons but found no connections with reorientation neurons. Nevertheless, Hannah and her colleagues were excited to find this result. 

Hannah’s amazing work in the zebrafish underscores that it is important to examine the brain both at a large scale (i.e., behavior and decision making) as well as a small scale (i.e., individual neurons and proteins, like CaSR) in order to more fully understand how it works. Secondly, her work tells us that decisions are the result of distinct parts of the brain working together to perform a behavior. When you decide to have a salad for dinner, there is one part of your brain that controls your muscles and allows you to eat the salad. There is a different part of your brain that helps in deciding to eat the salad in the first place! In the example of the zebrafish, reorientation/escape neurons allow the fish to perform the actions, but the decision making site is elsewhere - namely, in a brain region known as DCR6. On a final note, Hannah’s research reminds us of the incredible value and insight that animal models, like the zebrafish, bring to us. They allow us to study behaviors that are very seemingly very human (like decision making) in very deliberate and precise ways!

About the brief writer: Jafar Bhatti

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

Want to learn more about how these researchers study decision making in zebrafish? You can find Hannah’s 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

How do the brain’s structure and function develop together through adolescence?

or technically,

Development of structure-function coupling in human brain networks during youth

[See Original Abstract on Pubmed]

Authors of the study: Graham L. Baum, Zaixu Cui, David R. Roalf, Rastko Ciric, Richard F. Betzel, Bart Larsen, Matthew Cieslak, Philip A. Cook, Cedric H. Xia, Tyler M. Moore, Kosha Ruparel, Desmond J. Oathes, Aaron F. Alexander-Bloch, Russell T. Shinohara, Armin Raznahani, Raquel E. Gur, Ruben C. Gur, Danielle S. Bassett, and Theodore D. Satterthwaite

An important part of learning about the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. is not just understanding the way one individual brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. region works, but also how different brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. regions connect to each other. For example, our eyes receive visual information, but you only know what objects you are looking at because that visual information is also associated with your other senses and your memories. The connections between brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. regions are not completely set once a person is born, they develop as that person grows. There are several ways that these connections can develop. One way is structural, which is the physical connections between regions. These are the white matterA class of brain tissue made up of long and wire-like axons and tracts, acting as a highway of connections among the brain's cortical surface regions connections that stretch from region to region and the synapsesthe point of communication between neurons; the tiny gap between two neurons, where nerve impulses are relayed formed between cells in different regions. Another type of connection between regions is functional. This means that when one region is active and doing a task, the other region is active as well. When the structure between brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. regions also supports the functional connections between those regions, that is called structure-function coupling. Graham Baum, Neuroscience Graduate Group student and member of the Satterthwaite lab, wanted to know how structure-function coupling develops in youth.

BrainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. regions can be classified in groups based on what they process. If a brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. region only focuses on a single simple thing such as light, heat or taste, then it is called unimodal. If a brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. region associates multiple types of information that come from different brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. regions, that brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. region can be called transmodal. When children grow up, the senses are among the first parts of the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. to develop since they are more concrete; they require less abstract thought. The more evolved transmodal parts of the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. are the ones that develop later in life. It is also possible to measure how much a transmodal region is connected to other brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. regions, which is called the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. region’s participation coefficient. Graham’s study focused on the differences in development of unimodal and transmodal types of brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. regions.

To study structure-function coupling, Graham scanned people from ages 8 to 23 using MRI imaging. He made two maps for each person. The first map was a structural map, made with diffusion weighted imagingA method for imaging and measuring properties of white matter using magnetic resonance imaging.. This map showed the physical connections between regions of the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals.. The next map was a functional map. To make this map, each participant was scanned with a MRI machine while they were doing a task where they needed to remember a number of things and then repeat them back to the experimenter. That allowed Graham to make a map of the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. to see how each region of the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. communicated with every other region of the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. during this task. The last step was to correlate the structural connections with the functional connections. Each region in the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. then had a number to represent its structure-function coupling. This final value is what Graham measured and compared between participants.

The first thing Graham found was that regions that were unimodal had stronger structure-function coupling compared to transmodal areas. The more regions that one brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. region was communicating with, the weaker structure-function coupling it had. This was true across all ages. He next wanted to know how structure-function associations would change with age. To do this, he compared the structure-function coupling between younger participants and older participants. BrainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. regions that were unimodal did not change much with age. The regions that changed the most with age were the transmodal areas that support complex thought.

These age differences in structure-function coupling were exciting, but they were done across different people of different ages. Graham added to this by scanning a group of participants and then scanning those same participants about 2 years later. For most adolescents, 2 years can be a significant amount of time to change in terms of brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. development. When Graham scanned the same people twice, he confirmed that transmodal brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. regions within the same individuals developed stronger structure-function coupling over time.

The last thing Graham wanted to know is whether these structure-function connections would actually be related to behavior. He looked at how people actually did on the behavioral task that he used to make the functional maps. He found that higher structure-function coupling in a region called the rostrolateral prefrontal cortex (rlPFC) was associated with better performance on the task. The structure-function coupling of the rlPFC could also predict how two people of the same age would do on the task.

Graham’s work shows us something we didn’t know before, how the development of white matterA class of brain tissue made up of long and wire-like axons and tracts, acting as a highway of connections among the brain's cortical surface regions helps to support the way that the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. develops its cognitive abilities. This work gives us the ability to predict brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. function as humans age and develop. Additionally, this work can help us try to understand disorders that are defined by the disconnect between structural and functional development such as certain neuropsychiatric disorders.

About the brief writer: Rebecca Somach

Rebecca is a PhD Candidate in Akiva Cohen’s lab. She is interested in using electrophysiology to answer interesting and novel questions in neuroscience. Her current research focuses on how mild traumatic brain injury alters the neuronal circuitry of sleep.

Want to learn more about structure-function coupling, and how it changes as we develop? Check out Graham’s paper here.

The key to assessing Alzheimer’s disease treatments? Test them in Parkinson’s disease patients.

David Dai was the lead author on this study, and was the writer of this brief. David is a MD/PhD student in Eddie Lee's lab. He is interested in understanding the mechanisms underlying neurodegenerative diseases.

or technically,

ADNC-RS, a clinical-genetic risk score, predicts Alzheimer’s pathology in autopsy-confirmed Parkinson’s disease and Dementia with Lewy bodies

[See Original Abstract on Pubmed]

Authors of the study: David L Dai, Thomas F Tropea, John L Robinson, Eunran Suh, Howard Hurtig, Daniel Weintraub, Vivianna Van Deerlin, Edward B Lee, John Q Trojanowski, Alice S Chen-Plotkin

In a world full of uncertainty, the only thing we can be sure of is that time will go on. And with time, unfortunately, comes disease. Age is the greatest risk factor for neurodegenerativebrain diseases characterized by the progressive loss of neurons over time. diseases which are characterized by the progressive loss of neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles. Three of the most common neurodegenerativebrain diseases characterized by the progressive loss of neurons over time. diseases are AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s disease, Parkinson’s disease, and dementia with Lewy bodiesneurodegenerative disorder characterized by neuronal Lewy bodies comprised of alpha-synuclein. Differentiated from Parkinson’s disease based on the relative onset of motor versus cognitive symptoms.. Together they affect more than 6 million Americans, and that number is expected to reach over 9.8 million by 2030. To effectively diagnose and treat these diseases, it is vital to develop tools that can accurately predict who will develop disease. However, efforts to predict disease development, especially in AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s disease, have largely been unsuccessful, despite advancements in our understanding of the genetic risk factors for AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s disease.

In all three diseases, abnormal proteinsAn essential molecule found in all cells. DNA contains the recipes the cell uses to make proteins. Examples of proteins include receptors, enzymes, and antibodies. aggregate, injure brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. cells, and lead to neuronA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles death, resulting in the symptoms that patients experience -- loss of memory, cognition, and movement. Doctors do their best to diagnose patients, but diagnoses are not confirmed until after patients die, when their brainsThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. are autopsied, and the presence of abnormal proteinAn essential molecule found in all cells. DNA contains the recipes the cell uses to make proteins. Examples of proteins include receptors, enzymes, and antibodies. aggregates confirms which disease the patients experienced during life. In AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s disease, these abnormal proteinAn essential molecule found in all cells. DNA contains the recipes the cell uses to make proteins. Examples of proteins include receptors, enzymes, and antibodies. aggregates are called amyloid-beta plaques (Aβ) and tau neurofibrillary tangles. In Parkinson’s disease and dementia with Lewy bodiesneurodegenerative disorder characterized by neuronal Lewy bodies comprised of alpha-synuclein. Differentiated from Parkinson’s disease based on the relative onset of motor versus cognitive symptoms., these proteinAn essential molecule found in all cells. DNA contains the recipes the cell uses to make proteins. Examples of proteins include receptors, enzymes, and antibodies. aggregates are called Lewy bodies. Because of their similarities, Parkinson’s disease and dementia with Lewy bodiesneurodegenerative disorder characterized by neuronal Lewy bodies comprised of alpha-synuclein. Differentiated from Parkinson’s disease based on the relative onset of motor versus cognitive symptoms. are both included in a group of diseases called Lewy body diseases (LBD).

Although AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s and LBD are traditionally thought of as separate diseases, patients with LBD often have AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s. 50-80% of patients with a primary diagnosis of LBD also exhibit Aβ and tau proteinAn essential molecule found in all cells. DNA contains the recipes the cell uses to make proteins. Examples of proteins include receptors, enzymes, and antibodies. aggregates at autopsy-- hallmarks of AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s. Up to 40% of patients with a primary diagnosis of Parkinson’s have enough Aβ and tau aggregates in their brainsThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. for a secondary diagnosis of AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s. As only 10% of people age 65 and older have AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s disease in the United States, it may not just be a coincidence that Parkinson’s patients commonly also have AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s. Patients with Parkinson’s or dementia with Lewy bodiesneurodegenerative disorder characterized by neuronal Lewy bodies comprised of alpha-synuclein. Differentiated from Parkinson’s disease based on the relative onset of motor versus cognitive symptoms. may actually be at increased risk for developing AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s disease. This is important for clinicians to recognize when caring for LBD patients!

Taking advantage of LBD patients’ increased risk for developing AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s hallmarks, David Dai and the Chen-Plotkin Lab at the University of Pennsylvania asked: Can we predict which Parkinson’s/dementia with Lewy Bodiesneurodegenerative disorder characterized by neuronal Lewy bodies comprised of alpha-synuclein. Differentiated from Parkinson’s disease based on the relative onset of motor versus cognitive symptoms. patients are likely to develop AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s hallmarks using known AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s genetic risk factors? They gathered various demographic and genetic variables that could impact AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s development and used a machine learning approach to identify the specific factors important for predicting AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s status.

They found that using only 4 pieces of information (age of onset and 3 pieces of genetic information), they could predict AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s status to a modest degree. They transformed this information into a risk score, called the AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s Disease Neuropathological Change – Risk Score, that provides a continuous assessment of individuals’ risk for developing the hallmarks of AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s. A continuous risk score is useful because it reflects individuals’ incremental differences in disease development likelihood and allows researchers to set specific, numerical thresholds when predicting which patients are likely or unlikely to develop disease. They checked the effectiveness of their risk score in two additional groups and achieved comparable results. These additional validation steps were important because they showed that the risk score worked not only in the population that was used to build it but also in two other, unrelated groups. In other words, by showing that the risk score succeeded in three distinct groups, the researchers demonstrated that the AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s Disease Neuropathological Change -- Risk Score is a tool that could be successfully used in the general population.

These results have important scientific and clinical implications! By studying how the model made successful predictions, the scientists found that not all genetic information is equal when it comes to predicting AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s status in Parkinson’s/dementia with Lewy body patients. They identified three genetic locations that were particularly important for predicting AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s status in this group of individuals. They hope that further investigation will reveal why these locations are so crucial for the model’s predictions. If they are found to increase the development of AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s hallmarks in Parkinson’s and dementia with Lewy body patients, then treatments can be designed to block these genetic locations’ functions and maybe prevent AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s disease development in this subset of patients!

Given that the toxic proteinAn essential molecule found in all cells. DNA contains the recipes the cell uses to make proteins. Examples of proteins include receptors, enzymes, and antibodies. aggregates Aβ and tau accumulate in the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. decades before patients develop symptoms, the medical community is trying to remove the aggregates as early as possible in the disease development process. However, this is extremely difficult because we don’t know who will develop AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s, and we don’t know if removing the aggregates even helps patients! This new risk score could help solve that problem. On average, patients receive clinical diagnoses of AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s at ~80 years old, while Parkinson’s patients receive their diagnoses at ~60 years old. That means, using this risk score, we may be able to identify future AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s patients at least a decade before they develop symptoms, allowing us to first assess the effectiveness of Aβ and tau targeting treatments in a group of patients with increased risk of developing AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s. If those treatments are proven to work, we may finally be able to slow the progression of AlzheimerA disease (typically in older people) in which neurons die, causing people to lose their memories.’s disease!

Citations:

1. Alzheimer’s Association. 2020 Alzheimer’s Disease Facts and Figures. Alzheimer’s Dement 2020;16(3):391+. You can find the report here.

2. Parkinson’s Foundation. Statistics. Parkinson’s Foundation. 2019 September 19. You can read the article here.

Want to learn more about developing a risk score to predict disease development? You can find the group’s full paper here!

How you find what you’re looking for.

or technically,

Signals in inferotemporal and perirhinal cortex suggest an untangling of visual target information.

[See Original Abstract on Pubmed]

Authors of the study: Marino Pagan, Luke S Urban, Margot P Wohl, Nicole C Rust

Finding Your Keys
You are late and the Uber is already outside. Where are your wallet and keys? You scan the nearest table. A dirty coffee cup, excessive CVS coupons, and at last, you see your wallet and keys, poking out from under a shirt. While this process might seem effortless, quickly finding what you are looking for in a crowded scene -- a process called “visual search” -- is an ability that even sophisticated computer programs have trouble with ¹. Marino Pagan in Nicole Rust’s lab at the University of Pennsylvania spent his PhD studying exactly how our brainsThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. can quickly and flexibly find what we’re looking for out of everything we are looking at.

However, this question has been difficult for scientists to answer. Before Marino performed his experiments, it was unknown which part of the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. was responsible for visual search. In other words, scientists hadn’t yet been able to identify neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles that specifically respond when what you are looking for matches what you are looking at. Additionally, it was unknown how any brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. area would combine the information about what you are looking for and what you are looking at. How do scientists go about answering where and how the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. identifies different visual search “targets?” Before we tell you the results, we’re going to break down the approach Marino took to answering these questions.

Where in the BrainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals.?
Let’s first examine the question of where -- where are the neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles in the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. that are responsible for identifying visual search targets? We can answer this question by guessing what the activity of a brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. area that identifies search targets would look like and then looking for brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. areas with activity that matches our hypothesis. Like we mentioned before, neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles in this area would respond or “turn on” when what you are looking at matches what you are looking for. In our earlier example about getting to your Uber, we would guess that a visual search area would turn on when you were looking at your wallet, or your keys, but not the coffee cup. However, in a different situation--let’s say making coffee--what you are looking for is different. This time, the visual search area would respond when you look at the coffee cup, but not the other items. Essentially, the visual search area should turn on when you are looking at the item you were searching for.

How?
Now let’s examine how the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. figures out whether what you’re looking at matches what you’re looking for. All information in the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. is represented as different patterns of neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles turning on - or “firing.” For example, a pattern in which all neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles fire could mean something different than a pattern in which only half the neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles fire. In order for the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. to determine whether what you’re looking for matches what you’re looking at, the pattern of neuronA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles firing in the visual search area must be different for matches versus non-matches. When your brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. can differentiate - or separate - the neuronA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles firing patterns for matches and non-matches, you will be able to distinguish between the two categories in the real world! So our question of how now becomes more specific - how does the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. separate patterns of firing for matches and non-matches? It turns out, there are many ways that the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. can separate firing patterns! Learning which ones the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. uses not only teaches us about how our brainsThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. work, but can also help us build better computer algorithms to perform search tasks- not just for finding keys on a table, but for, say, identifying faces in a crowd.

Although the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. has a lot of neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles, we’re going to think about different ways to separate firing patterns by pretending there are only two neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles in the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. area responsible for visual search. In this situation, one way for the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. to determine whether a firing pattern says “match” or “no match” is to make a simple rule that divides the patterns into two groups. One example of a rule could be “If neuronA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles 1 is firing more than neuronA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles 2, you are looking at a match. Otherwise, you are not”. This rule is shown in Fig 1, on the left. Notice how the rule makes it easy to draw a straight line that perfectly puts all matches on one side of the line, and all non-matches on the other. This type of neural firing is said to be linearly separable. It is linear something that can be represented as a straight line on a graph, and directly proportional changes in two related quantities because a simple, straight line can separate the two categories. This way of separating firing patterns is both very reliable and very easy for the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. to do! Other ways of separating might require many more complicated rules (an example of this is shown in Fig 1, right). Therefore, linearly separable neural firing is good candidate for how the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. might distinguish between search targets versus other objects.

With all this in mind, Marino Pagan and his PhD advisor Nicole Rust could make hypotheses about how the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. identifies different visual search “targets” and which area of the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. is responsible for this: 1) the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals.’s “visual search area” would turn on when what you are looking for matches what you are looking at, and 2) neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles in this visual search area will have separable firing patterns for matches vs. non-matches. To test these hypotheses, they did one experiment to recreate the process we go through when looking for our keys.

Figure 1

Results
First, Marino trained monkeys to recognize specific, everyday objects (i.e. keys or wallet) in a sequence of images interspersed with other objects (i.e. the coffee cup). They then looked for (1) where in the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. there was activity specific to targets and (2) how this region separated its firing patterns for matches and non-matches (i.e. were they linearly separable). They narrowed their search to two regions of the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals.: the inferior temporal lobe and the perirhinal cortex. The inferior temporal lobe is a part of the visual system and is thought to be the first place that memory (i.e. what you are looking for) and visual information (i.e. what you are looking at) are combined². The perirhinal cortex receives information from the inferior temporal lobe and is necessary for good performance on visual search tasks³.

Marino first asked whether either brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. region had activity patterns that were selective for search targets. They found this selectivity to be much stronger in the perirhinal cortex than the inferior temporal lobe, suggesting that the where of visual search is primarily the perirhinal cortex (PRH).

To address how this selective activity arose, Marino then asked if neural firing in response to targets was more linearly separable in one brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. region compared to the other. After looking over many neuronsA nerve cell that uses electrical and chemical signals to send information to other cells including other neurons and muscles, they found that it was much easier to draw a simple line that separated targets from non-targets (similar to Fig 1, left) in perirhinal cortex compared to inferior temporal lobe. Together, Marino’s findings suggest that the perirhinal cortex codes information of the location of the search target, separated from other objects using , linearsomething that can be represented as a straight line on a graph, and directly proportional changes in two related quantities separability.

There are still many exciting questions to answer. What is the inferior temporal lobe doing to combine memory and visual information? How is the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. cell activity different in the inferior temporal lobe compared to perirhinal cortex? Do those differences contribute to the linearsomething that can be represented as a straight line on a graph, and directly proportional changes in two related quantities separability we see in the perirhinal cortex? Marino found that the answer was a bit more complicated. He found that the inferior temporal cortex may use non-linearsomething that can be represented as a straight line on a graph, and directly proportional changes in two related quantities separability, where flexible curves can separate visual and remembered information instead of rigid lines . The inferior temporal cortex then sends this information separable by flexible curves to the perirhinal cortex, which then may transform the information to again be separated by a line.

Conclusion
Like most problems in science, one experiment cannot fully and conclusively reveal everything there is to know about how we “search” with our eyes. However, the work of Marino Pagan and his mentor Nicole Rust takes important steps closer to this understanding, and adds valuable new information about where and how search targets are identified in the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals.. Not only does their work shine light on previously mysterious ways the brainThe brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. supports everyday actions like visual search but it also provides a foundation to engineer computers that can scan and find objects as quickly and flexibly as we do.

Figure 2

About the brief writer: Jeni Stiso

Jeni is a PhD Candidate in Dani Bassett’s lab. Jeni is interested in cognitive and computational neuroscience. She is interested in how changes in the electrical activity of the brain help people learn things.

Citations:

1. https://medium.com/deep-dimension/an-analysis-on-computer-vision-problems-6c68d56030c3

2. Chelazzi, L., & Desimone, R. (1993). A neural basis for visual search in IT. Nature. 363, pages 345–347.

3. Meunier, M., Bachevalier, J., Mishkin, M. & Murray, E.A. Effects on visual recognition of combined and separate ablations of the entorhinal and perirhinal cortex in rhesus monkeys. J. Neurosci. 13, 5418–5432 (1993).

To learn more about how the brain helps us quickly identify what we’re looking for, check out the full paper here.

Humans use previous experience with categories of sounds to categorize new sounds as best as they can

or, technically,
Characterizing the impact of category uncertainty on human auditory categorization behavior.[See the original abstract on PubMed]

Authors: Adam M. Gifford, Yale E. Cohen, Alan A. Stocker

Brief prepared by: Adam Gifford & Kate Christison-Lagay
Brief approved by: Peter Dong
Section Chief: Yunshu Fan
Date posted: May 3, 2016

Brief in Brief (TL;DR)

What do we know: We can group and split up sounds (and other things) into different categories, which allows us to identify and understand what is in the environment. However, choosing the best category for a sound can be difficult because sounds can belong to more than one category. For example, both dogs and wolves can howl, and incorrectly categorizing a wolf’s howl as a dog’s can be dangerous. 

What don’t we know: How we use past experience with similar sounds to categorize new, unfamiliar sounds. 

What this study shows: We use previous experience with similar sounds to make a best guess on how to categorize new sounds. Our brain isn’t perfect, though—it has a lot of activity that isn’t related to what we’re experiencing (this is called noise), so our performance isn’t perfect. But it is as good as it can be given the noise in the brain. 

What we can do in the future because of this study: Record from neurons in different parts of the brain to determine where experience with previous sounds and their categories are stored, and how and where the brain uses that information to choose a category for new sounds. 

Why you should care: Understanding how humans use prior experience to categorize new information will allow us to determine what goes wrong when we make categorization errors. This understanding could also be used to develop tools that can allow computers to perform the same kinds of categorizations, which would be useful for automated object or voice recognition.

Brief for Non-Neuroscientists

The ability to group and segregate sounds (or objects) into different categories is an important process that allows us to simplify and understand the environment. However, determining the best category for a sound can be difficult, as some sounds can belong to multiple categories. For example, both dogs and wolves can howl, and incorrectly categorizing a wolf’s howl as a dog’s can be dangerous. To solve this problem, the brain must use information learned from experience in categorizing similar sounds in the past. We expected that humans would use previous experience to decide on the best category for a new sound, minimizing the chance that they chose wrong. However, we found that humans did not seem to use the best decision strategy that minimized categorization errors. But if we assume that there is noise in the brain that limits the ability to accurately keep track of previous experience, humans’ category choices can be consistent with the best decision strategy.

Brief for Neuroscientists

Categorization is an important process that allows us to simplify, extract meaning from, and respond to sounds (or other objects) in the environment. However, categorization is complicated because a sound can belong to multiple categories. Thus, to choose the best category for a new sound, we must make use of prior information on the categories of similar sounds. Given the importance of categorization, we hypothesized that humans utilize the best decision strategy for making categorical judgments that allows us to minimize categorization errors. However, we found that humans did not minimize errors in their categorization behavior, similar to behaviors exhibited in other perceptual and cognitive tasks. We then explored the bases for this sub-optimal behavior and found that it can be consistent with the best strategy if we assume that humans have trial-by-trial noise in components of the judgment process.