Validating maps of the brain’s resting state

Kick back and shut your eyes. Now stop thinking.

You have just put your brain into what neuroscientists call its resting state. What the brain is doing when an individual is not focused on the outside world has become the focus of considerable research in recent years. One of the potential benefits of these studies could be definitive diagnoses of mental health disorders ranging from bipolar to post-traumatic stress disorders.

A team of psychologists and imaging scientists at Vanderbilt has collaborated on a study that provides important corroboration of the validity of recent research examining the relationship of functional magnetic resonance imaging or fMRI maps of the brain’s resting state networks with it’s underlying anatomical and neurological structure. The study is published in the June 19 issue of the journal Neuron.

“Previous studies have suggested that resting state connectivity shown in brain scans is anchored by anatomical connectivity,” said co-senior author Anna Roe, professor of psychology at Vanderbilt. “But our study has confirmed this relationship at the single neuron level for the first time.”

For the last decade, neuroscientists have been using the non-invasive brain-mapping technique fMRI to examine activity patterns in human and animal brains in the resting state in order to figure out how different parts of the brain are connected and to identify the changes that occur in neurological and psychiatric diseases. For example, there are indications that Alzheimer’s may be associated with decreased connectivity; depression with increased connectivity; epilepsy with disruptions in connectivity and Parkinson’s with alterations in connectivity.

The new findings from Vanderbilt are important because fMRI doesn’t measure brain activity directly. It does so by measuring changes in blood-oxygen levels in different areas. The technique relies on the observation that when activity in an area of the brain increases, blood-oxygen levels in that region rise, which modulates the MRI signal. Neuroscientists have taken this a step further by assuming that different areas in the brain are connected if they show synchronized variations while the brain is in a resting state.

“This is an important validation,” said co-senior author John Gore, director of the Institute of Imaging Science at Vanderbilt and Hertha Ramsey Cress University Professor of Radiology and Radiological Sciences and Biomedical Engineering. “There has always been a sense of unease that we might be interpreting something incorrectly but this gives us confidence that resting state variations can be interpreted in a meaningful way and encourages us to continue the research we have been doing for a number of years. Resting state fMRI provides a uniquely powerful, non-invasive technology to look at the circuits in the human brain.”

To examine the relationship between fMRI scans, patterns of neuronal activity and anatomical structure of the brain, the researchers examined the region of the parietal lobe of squirrel monkeys devoted to monitoring touch sensations. Specifically, they looked at an area linked to the hand that consists of a series of adjacent areas each devoted to a different finger.

Using one of the strongest MRI machines available, with a field strength three to six times that of typical clinical scanners, the researchers produced brain scans that resolved millimeter-scale networks for the first time.

To compare these patterns to the actual electrical activity in the brains, the researchers inserted electrodes capable of recording the firing patterns of individual neurons. In addition, they used optical techniques to trace the anatomical connections between the neurons throughout the region.

“With all three techniques, we found the same pattern of connectivity. Connections coming from other areas in the brain tend to link to individual digits while connections that originate within the area tend to link to multiple digits,” said Roe. “Our results demonstrate that fMRI images of the resting state brain accurately reflect the brain’s anatomical and functional connectivity down to an extremely fine scale.”

Researchers Identify Emotions Based on Brain Activity

For the first time, scientists at Carnegie Mellon University have identified which emotion a person is experiencing based on brain activity.

The study, published in the June 19 issue of PLOS ONE, combines functional magnetic resonance imaging (fMRI) and machine learning to measure brain signals to accurately read emotions in individuals. Led by researchers in CMU’s Dietrich College of Humanities and Social Sciences, the findings illustrate how the brain categorizes feelings, giving researchers the first reliable process to analyze emotions. Until now, research on emotions has been long stymied by the lack of reliable methods to evaluate them, mostly because people are often reluctant to honestly report their feelings. Further complicating matters is that many emotional responses may not be consciously experienced.

Identifying emotions based on neural activity builds on previous discoveries by CMU’s Marcel Just and Tom M. Mitchell, which used similar techniques to create a computational model that identifies individuals’ thoughts of concrete objects, often dubbed “mind reading.”

“This research introduces a new method with potential to identify emotions without relying on people’s ability to self-report,” said Karim Kassam, assistant professor of social and decision sciences and lead author of the study. “It could be used to assess an individual’s emotional response to almost any kind of stimulus, for example, a flag, a brand name or a political candidate.”

One challenge for the research team was find a way to repeatedly and reliably evoke different emotional states from the participants. Traditional approaches, such as showing subjects emotion-inducing film clips, would likely have been unsuccessful because the impact of film clips diminishes with repeated display. The researchers solved the problem by recruiting actors from CMU’s School of Drama.

“Our big breakthrough was my colleague Karim Kassam’s idea of testing actors, who are experienced at cycling through emotional states. We were fortunate, in that respect, that CMU has a superb drama school,” said George Loewenstein, the Herbert A. Simon University Professor of Economics and Psychology.

For the study, 10 actors were scanned at CMU’s Scientific Imaging & Brain Research Center while viewing the words of nine emotions: anger, disgust, envy, fear, happiness, lust, pride, sadness and shame. While inside the fMRI scanner, the actors were instructed to enter each of these emotional states multiple times, in random order.

Another challenge was to ensure that the technique was measuring emotions per se, and not the act of trying to induce an emotion in oneself. To meet this challenge, a second phase of the study presented participants with pictures of neutral and disgusting photos that they had not seen before. The computer model, constructed from using statistical information to analyze the fMRI activation patterns gathered for 18 emotional words, had learned the emotion patterns from self-induced emotions. It was able to correctly identify the emotional content of photos being viewed using the brain activity of the viewers.

To identify emotions within the brain, the researchers first used the participants’ neural activation patterns in early scans to identify the emotions experienced by the same participants in later scans. The computer model achieved a rank accuracy of 0.84. Rank accuracy refers to the percentile rank of the correct emotion in an ordered list of the computer model guesses; random guessing would result in a rank accuracy of 0.50.

Next, the team took the machine learning analysis of the self-induced emotions to guess which emotion the subjects were experiencing when they were exposed to the disgusting photographs.  The computer model achieved a rank accuracy of 0.91. With nine emotions to choose from, the model listed disgust as the most likely emotion 60 percent of the time and as one of its top two guesses 80 percent of the time.

Finally, they applied machine learning analysis of neural activation patterns from all but one of the participants to predict the emotions experienced by the hold-out participant. This answers an important question: If we took a new individual, put them in the scanner and exposed them to an emotional stimulus, how accurately could we identify their emotional reaction? Here, the model achieved a rank accuracy of 0.71, once again well above the chance guessing level of 0.50.

“Despite manifest differences between people’s psychology, different people tend to neurally encode emotions in remarkably similar ways,” noted Amanda Markey, a graduate student in the Department of Social and Decision Sciences.

A surprising finding from the research was that almost equivalent accuracy levels could be achieved even when the computer model made use of activation patterns in only one of a number of different subsections of the human brain.

“This suggests that emotion signatures aren’t limited to specific brain regions, such as the amygdala, but produce characteristic patterns throughout a number of brain regions,” said Vladimir Cherkassky, senior research programmer in the Psychology Department.

The research team also found that while on average the model ranked the correct emotion highest among its guesses, it was best at identifying happiness and least accurate in identifying envy. It rarely confused positive and negative emotions, suggesting that these have distinct neural signatures. And, it was least likely to misidentify lust as any other emotion, suggesting that lust produces a pattern of neural activity that is distinct from all other emotional experiences.

Just, the D.O. Hebb University Professor of Psychology, director of the university’s Center for Cognitive Brain Imaging and leading neuroscientist, explained, “We found that three main organizing factors underpinned the emotion neural signatures, namely the positive or negative valence of the emotion, its intensity — mild or strong, and its sociality — involvement or non-involvement of another person. This is how emotions are organized in the brain.”

In the future, the researchers plan to apply this new identification method to a number of challenging problems in emotion research, including identifying emotions that individuals are actively attempting to suppress and multiple emotions experienced simultaneously, such as the combination of joy and envy one might experience upon hearing about a friend’s good fortune.

What do memories look like?

Oscar Wilde called memory “the diary that we all carry about with us.” Now a team of scientists has developed a way to see where and how that diary is written.

Led by Don Arnold and Richard Roberts of USC, the team engineered microscopic probes that light up synapses in a living neuron in real time by attaching fluorescent markers onto synaptic proteins — all without affecting the neuron’s ability to function.

The fluorescent markers allow scientists to see live excitatory and inhibitory synapses for the first time and, importantly, how they change as new memories are formed.

The synapses appear as bright spots along dendrites (the branches of a neuron that transmit electrochemical signals). As the brain processes new information, those bright spots change, visually indicating how synaptic structures in the brain have been altered by the new data.

“When you make a memory or learn something, there’s a physical change in the brain. It turns out that the thing that gets changed is the distribution of synaptic connections,” said Arnold, associate professor of molecular and computational biology at the USC Dornsife College of Letters, Arts and Sciences, and co-corresponding author of an article about the research that appears in Neuron on June 19.

The probes behave like antibodies, but they bind more tightly and are optimized to work inside the cell — something that ordinary antibodies can’t do. To make these probes, the team used a technique known as “mRNA display,” which was developed by Roberts and Nobel laureate Jack Szostak.

“Using mRNA display, we can search through more than a trillion different potential proteins simultaneously to find the one protein that binds the target the best,” said Roberts, co-corresponding author of the article and professor of chemistry and chemical engineering with joint appointments at USC Dornsife and the USC Viterbi School of Engineering.

Arnold and Roberts’ probes (called “FingRs”) are attached to green fluorescent protein (GFP), a protein isolated from jellyfish that fluoresces bright green when exposed to blue light. Because FingRs are proteins, the genes encoding them can be put into brain cells in living animals, causing the cells themselves to manufacture the probes.

The design of FingRs also includes a regulation system that cuts off the amount of FingR-GFP that is generated after 100 percent of the target protein is labeled, effectively eliminating background fluorescence — generating a sharper, clearer picture.

These probes can be put in the brains of living mice and then imaged through cranial windows using two-photon microscopy.

The new research could offer crucial insight for scientists responding to President Barack Obama’s Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative, which was announced in April.

Modeled after the Human Genome Project, the objective of the $100 million initiative is to fast-track research that maps out exactly how the brain works and “better understand how we think, learn and remember,” according to the BRAIN Initiative website.

IQ link to baby’s weight gain in first month

New research from the University of Adelaide shows that weight gain and increased head size in the first month of a baby’s life is linked to a higher IQ at early school age.

The study was led by University of Adelaide Public Health researchers, who analysed data from more than 13,800 children who were born full-term.

The results, published today in the international journal Pediatrics, show that babies who put on 40% of their birthweight in the first four weeks had an IQ 1.5 points higher by the time they were six years of age, compared with babies who only put on 15% of their birthweight.

Those with the biggest growth in head circumference also had the highest IQs.

"Head circumference is an indicator of brain volume, so a greater increase in head circumference in a newborn baby suggests more rapid brain growth," says the lead author of the study, Dr Lisa Smithers from the University of Adelaide’s School of Population Health.

"Overall, newborn children who grew faster in the first four weeks had higher IQ scores later in life," she says.

"Those children who gained the most weight scored especially high on verbal IQ at age 6. This may be because the neural structures for verbal IQ develop earlier in life, which means the rapid weight gain during that neonatal period could be having a direct cognitive benefit for the child."

Previous studies have shown the association between early postnatal diet and IQ, but this is the first study of its kind to focus on the IQ benefits of rapid weight gain in the first month of life for healthy newborn babies.

Dr Smithers says the study further highlights the need for successful feeding of newborn babies.

"We know that many mothers have difficulty establishing breastfeeding in the first weeks of their baby’s life," Dr Smithers says.

"The findings of our study suggest that if infants are having feeding problems, there needs to be early intervention in the management of that feeding."

(Image: thebabypicz.com)

It’s the way you tell em’: Study discovers how the brain controls accents and impersonations

A study, led by Royal Holloway University researcher Carolyn McGettigan, has identified the brain regions and interactions involved in impersonations and accents.

Using an fMRI scanner, the team asked participants, all non-professional impressionists, to repeatedly recite the opening lines of a familiar nursery rhyme either with their normal voice, by impersonating individuals, or by impersonating regional and foreign accents of English.

They found that when a voice is deliberately changed, it brings the left anterior insula and inferior frontal gyrus (LIFG) of the brain into play. The researchers also discovered that when comparing impersonations against accents, areas in the posterior superior temporal/inferior parietal cortex and in the right middle/anterior superior temporal sulcus showed greater responses.

“The voice is a powerful channel for the expression of our identity – it conveys information such as gender, age and place of birth, but crucially, it also expresses who we want to be,” said lead author Carolyn McGettigan from the Department of Psychology at Royal Holloway.

“Consider the difference between talking to a friend on the phone, talking to a police officer who’s cautioning you for parking violation, or speaking to a young infant. While the words we use might be different across these settings, another dramatic difference is the tone and style with which we deliver the words we say. We wanted to find out more about this process and how the brain controls it.”

While past work has found that listening to voices activates regions of the temporal lobe of the brain, no research had explored the brain regions involved in controlling vocal identity before this study.

“Our aim is to find out more about how the brain controls this very flexible communicative tool, which could potentially lead to new treatments for those looking to recover their own vocal identity following brain injury or a stroke, ” said Carolyn.

Key Protein is Linked to Circadian Clocks, Helps Regulate Metabolism

Inside each of us is our own internal timing device. It drives everything from sleep cycles to metabolism, but the inner-workings of this so-called “circadian clock” are complex, and the molecular processes behind it have long eluded scientists. But now, researchers at the Gladstone Institutes have discovered how one important protein falls under direct instructions from the body’s circadian clock. Furthermore, they uncover how this protein regulates fundamental circadian processes—and how disrupting its normal function can throw this critical system out of sync.

In the latest issue of the Journal of Neuroscience, Gladstone Investigator Katerina Akassoglou, PhD, and her team reveal in animal models how the production of the p75 neurotrophin receptor (p75NTR) protein oscillates in time with the body’s natural circadian clock—and how these rhythmic oscillations help regulate vital metabolic functions. This discovery underscores the widespread importance of p75NTR by offering insight into how the circadian clock helps maintain the body’s overall metabolic health.

Virtually every organism on the planet—from bacteria to humans—has a circadian clock, a biological timing mechanism that oscillates with a period of about 24 hours and is coordinated with the cycle of day and night. And while it runs independent of external cues, it is influenced by the rhythms of light, temperature and food availability. Intriguingly, recent studies have also found a link between circadian clocks and metabolism.

“Important metabolic functions are also heavily influenced by circadian clocks, which is why activities such as chronic night-shift work—which can cause a misalignment of this clock—increase one’s risk for metabolic and autoimmune diseases such as obesity, Type 2 diabetes, cancer and multiple sclerosis,” said Dr. Akassoglou. Dr. Akassoglou is also a professor of neurology at the University of California, San Francisco, (UCSF) with which Gladstone is affiliated. “In this study, we pinpointed p75NTR as an important molecular ‘link’ between circadian clocks and metabolic health.”

Originally, p75NTR was only thought to be active in the nervous system. Later studies found it to be active in many cell types throughout the body, suggesting that it impacts a variety of biological functions. Last year, Gladstone researchers discovered that p75NTR was present in the liver and in fat cells, and that it regulates glucose levels in the blood—an important metabolic process. Since these findings uncovered a link between p75NTR and metabolism, the research team tested—first in a petri dish and then in animal models—whether there was also a link between p75NTR and the circadian clock.

The team focused on two genes called Clock and Bmal1. These so-called “circadian regulator genes,” and others like them, are found throughout the body. Their activity controls the body’s circadian clock. The researchers wanted to see if there was a connection between these circadian genes and p75NTR.

“Our initial experiments revealed such a connection,” recalls Gladstone Postdoctoral Fellow Bernat Baeza-Raja, PhD, the paper’s lead author. “In individual cells, we saw that p75NTR production was controlled by Clock and Bmal1, which bind directly to the gene that codes for the p75NTR and start production of the protein.”

But perhaps even more important than how p75NTR was produced was when. The team found that p75NTR production, like the circadian clock genes themselves, oscillated in a 24-hour cycle—in sync with the cells’ natural circadian rhythm. Experiments in mouse models further supported these findings.

And when the team genetically modified a group of mice so that it lacked the circadian Clock gene, everything else fell out of sync. The circadian oscillation of p75NTR production was disrupted, and p75NTR levels dropped.

However, what was most fascinating, say the researchers, was how a drop in p75NTR levels then affected a variety of circadian clock systems. Specifically, the regular oscillations of other circadian genes in the brain and the liver became disrupted, as well as genes known to regulate glucose and lipid metabolism.

“The finding that a loss of p75NTR affected circadian and metabolic systems is strong evidence that this protein is intricately tied to both,” said Life Sciences Institute Director Alan Saltiel, PhD, who is also a professor at the University of Michigan and was not involved in the study. “It will be fascinating to see what additional insight Dr. Akassoglou and her team will uncover as they continue to examine the role of p75NTR in circadian clocks and metabolic function.”

“While these findings reveal p75NTR to be an important link between circadian clocks and metabolism, the system is complex, and there are likely other factors at play,” said Dr. Akassoglou. “We are currently working to identify the relationship between the circadian clock, metabolism and the immune system, so that one day we could develop therapies to treat diseases influenced by circadian clock disruption—including not only obesity and diabetes, but also potentially multiple sclerosis and even Alzheimer’s disease.”

(Image: Brain Treatment Center)

Fiber-optic pen helps see inside brains of children with learning disabilities

For less than $100, University of Washington researchers have designed a computer-interfaced drawing pad that helps scientists see inside the brains of children with learning disabilities while they read and write.

The device and research using it to study the brain patterns of children will be presented June 18 at the Organization for Human Brain Mapping meeting in Seattle. A paper describing the tool, developed by the UW’s Center on Human Development and Disability, was published this spring in Sensors, an online open-access journal. “Scientists needed a tool that allows them to see in real time what a person is writing while the scanning is going on in the brain,” said Thomas Lewis, director of the center’s Instrument Development Laboratory. “We knew that fiber optics were an appropriate tool. The question was, how can you use a fiber-optic device to track handwriting?”

To create the system, Lewis and fellow engineers Frederick Reitz and Kelvin Wu hollowed out a ballpoint pen and inserted two optical fibers that connect to a light-tight box in an adjacent control room where the pen’s movement is recorded. They also created a simple wooden square pad to hold a piece of paper printed with continuously varying color gradients. The custom pen and pad allow researchers to record handwriting during functional magnetic resonance imaging, or fMRI, to assess behavior and brain function at the same time.Other researchers have developed fMRI-compatible writing devices, but “I think it does something similar for a tenth of the cost,” Reitz said of the UW system. By using supplies already found in most labs (such as a computer), the rest of the supplies – pen, fiber optics, wooden pad and printed paper – cost less than $100.The device connects to a computer with software that records every aspect of the handwriting, from stroke order to speed, hesitations and liftoffs. Understanding how these physical patterns correlate with a child’s brain patterns can help scientists understand the neural connections involved.

Researchers studied 11- and 14-year-olds with either dyslexia or dysgraphia, a handwriting and letter-processing disorder, as well as children without learning disabilities. Subjects looked at printed directions on a screen while their heads were inside the fMRI scanner. The pen and pad were on a foam pad on their laps.

Subjects were given four-minute blocks of reading and writing tasks. Then they were asked to simply think about writing an essay (they later wrote the essay when not using the fMRI). Just thinking about writing caused many of the same brain responses as actual writing would.

“If you picture yourself writing a letter, there’s a part of the brain that lights up as if you’re writing the letter,” said Todd Richards, professor of radiology and principal investigator of the UW Integrated Brain Imaging Center. “When you imagine yourself writing, it’s almost as if you’re actually writing, minus the motion problems.”

Richards and his staff are just starting to analyze the data they’ve collected from about three dozen subjects, but they have already found some surprising results.

“There are certain centers and neural pathways that we didn’t necessarily expect” to be activated, Richards said. “There are language pathways that are very well known. Then there are other motor pathways that allow you to move your hands. But how it all connects to the hand and motion is still being understood.”

Besides learning disorders, the inexpensive pen and pad also could help researchers study diseases in adults, especially conditions that cause motor control problems, such as stroke, multiple sclerosis and Parkinson’s disease.

“There are several diseases where you cannot move your hand in a smooth way or you’re completely paralyzed,” Richards said. “The beauty is it’s all getting recorded with every stroke, and this device would help us to study these neurological diseases.”

Not all reading disabilities are dyslexia

A common reading disorder goes undiagnosed until it becomes problematic, according to the results of five years of study by researchers at Vanderbilt’s Peabody College of education and human development in collaboration with the Kennedy Krieger Institute/Johns Hopkins School of Medicine. Results of the study were recently published online by the National Institutes of Health.

Dyslexia, a reading disorder in which a child confuses letters and struggles with sounding out words, has been the focus of much reading research.

But that’s not the case with the lesser known disorder Specific Reading Comprehension Deficits or S-RCD, in which a child reads successfully but does not sufficiently comprehend the meaning of the words, according to lead investigator Laurie Cutting, Patricia and Rodes Hart Chair at Peabody.

“S-RCD is like this: I can read Spanish, because I know what sounds the letters make and how the words are pronounced, but I couldn’t tell you what the words actually mean,” Cutting said. “When a child is a good reader, it’s assumed their comprehension is on track. But 3 to 10 percent of those children don’t understand most of what they’re reading. By the time the problem is recognized, often closer to third or fourth grade, the disorder is disrupting their learning process.”

Researchers have been able to pinpoint brain activity and understand its role in dyslexia, but no functional magnetic resonance imaging or fMRI studies, until now, have examined the neurobiological profile of those who exhibit poor reading comprehension despite intact word-level abilities.

Neuroimaging of children showed that the brain function of those with S-RCD while reading is quite different and distinct from those with dyslexia. Those with dyslexia exhibited abnormalities in a specific region in the occipital-temporal cortex, a part of the brain that is associated with successfully recognizing words on a page.

But those with S-RCD did not show abnormalities in this region, instead showing specific abnormalities in regions typically associated with memory.

“It may be that these individuals have a whole different neurobiological signature associated with how they read that is not efficient for supporting comprehension,” Cutting said. “We want to understand the different systems that support reading and see which ones help different types of difficulties, and how we can target the cognitive systems that support those skills.”

The study, an ongoing 10-year effort supported by National Institutes of Health grant No. M01-RR000052, has enrolled more than 300 children to date.

The discerning fruit fly: Linking brain-cell activity and behavior in smell recognition

Behind the common expression “you can’t compare apples to oranges” lies a fundamental question of neuroscience: How does the brain recognize that apples and oranges are different? A group of neuroscientists at Cold Spring Harbor Laboratory (CSHL) has published new research that provides some answers.

In the fruit fly, the ability to distinguish smells lies in a region of the brain called the mushroom body (MB). Prior research has demonstrated that the MB is associated with learning and memory, especially in relation to the sense of smell, also known as olfaction.

CSHL Associate Professor Glenn Turner and colleagues have now mapped the activity of brain cells in the MB, in flies conditioned to have Pavlovian behavioral responses to different odors. Their results, outlined in a paper published today by the Journal of Neuroscience, suggest that the activity of a remarkably small number of neurons — as few as 25 — is required to be able to distinguish between different odors.

They also found that a similarly small number of nerve cells are involved in grouping alike odors. This means, for instance, that “if you’ve learned that oranges are good, the smell of a tangerine will also get you thinking about food,” says Robert Campbell, a postdoctoral researcher in the Turner lab and lead author on the new study.

These intriguing new findings are part of a broad effort in contemporary neuroscience to determine how the brain, easily the most complex organ in any animal, manages to make a mass of raw sensory data intelligible to the individual — whether a person or a fly — in order to serve as a basis for making vital decisions.

Looking closely at Kenyon cells

The neurons in the fly MB are known as Kenyon cells, named after their discoverer, the neuroscientist Frederick Kenyon, who was the first person to stain and visualize individual neurons in the insect brain. Kenyon cells receive sensory inputs from organs that perceive smell, taste, sight and sound. This confluence of sensory input in the MB is important for memory formation, which comes about through a linking of different types of information.

Kenyon cells make up only about 4% of the entire fly brain and are extremely sensitive to inputs triggered by odors, in which only two connections between neurons, called synapses, separate them from the receptor cells at the “front end” of the olfactory system.

But in contrast to other regions of the brain, such as the vertebrate hippocampus, the sensory responses in the MB are few in number and relatively weak. It is the sparseness of the signals in the Kenyon cell neurons that makes studying memory formation in flies so promising to Turner and his team. “We set out to learn if these signals were really informative to the animal’s learning and memory with regard to smell,” Turner says.

In particular, Turner’s group wanted to see if they could link these signals with actual behavior in flies. The team used an imaging technique that allowed them to view the responses of over 100 Kenyon cells at a time and, importantly, quantify their results. They found that even the very sparse responses in these cells that are triggered by odors provide a large amount of information about odor identity. Turner suspects the very selectiveness of the response helps in the accurate formation and recall of memories.

When the researchers used two odors blended together in a series of increasingly similar concentrations, they found that two very similar smells could be distinguished as a result of the activity of as few as 25 Kenyon cells. This correlated well with the behavior of the flies: when brain activity suggested the flies had difficulty discerning the odors, their behavior also showed they could not choose between them.

The activity of these cells also accounts for flies’ ability to discern novel odors and group them together. This was determined in a “generalization” test, in which the degree to which flies learned a generalized aversion to unfamiliar test odors could be predicted based upon the relatively similar activity patterns of Kenyon cells that the odors induced.

“Being able to do this type of ‘mind-reading’ means we really understand what signals these activity patterns are sending,” says Turner. Ultimately, he and colleagues hope to be able to relate their findings in the fly brain with the operation of the brain in mammals.