A neural code for navigation

Neurons in the rat brain use a preexisting set of firing sequences to encode future navigational experiences

Specialized neurons called place cells, located in the hippocampus region of the brain, fire when an animal is in a particular location in its environment, and it is the linear sequence of their firing that encodes in the brain movement trajectories from one location to another. Building on previous work, George Dragoi and Susumu Tonegawa from the RIKEN–MIT Center for Neural Circuit Genetics have now shown that place cells have a preexisting inventory of firing sequences that they can use to encode multiple novel routes of exploration.

Specific sequences of place cells are known to encode spatial experiences, but it has been debated whether such sequences are formed during a new experience or preformed and adapted to specific experiences when required. Dragoi and Tonegawa recently showed that ‘future’ place cells fire in sequence while the animal is asleep, prior to experiencing a novel environment, and that animals use this preexisting neuronal firing pattern to rapidly learn how to navigate their surroundings.

To confirm and investigate this mechanism further, the researchers first recorded the neuronal activity of place cells in rats during one hour of sleep. Next, they monitored this activity during movement along a track that the rat had not previously explored, and later recorded it during movement along the same track with two additional lengths separated by right-angle turns. They then correlated the temporal pattern of place cell activity recorded during sleep with the spatial pattern of activity recorded while the animals were freely exploring the longer track.

The researchers found that the sequences of place cell activity were unique for each of the three lengths of the track and matched those recorded during sleep. “We had observed the same sequences as independent clusters of correlated temporal sequences during the preceding sleep period,” explains Dragoi. 

The results suggest that rapid encoding of particular trajectories within novel environments is achieved during exploration by selecting from a set of preexisting temporal sequences that fired during sleep. In other words, hippocampal place cells appear to be prearranged into sets of sequential firing cells that can be adapted rapidly to encode for multiple spatial trajectories that the animal could undertake in its surroundings. Based on their data, Dragoi and Tonegawa predict that the sets of hippocampal place cells could encode for at least 15 unique future spatial experiences. In addition, their findings could explain the role that the hippocampus plays in humans in imagining future encounters within our own complex environment.

Nerve Cells Can Work in Different Ways with Same Result

Epilepsy, irregular heartbeats and other conditions caused by malfunctions in the body’s nerve cells, also known as neurons, can be difficult to treat. The problem is that one medicine may help some patients but not others. Doctors’ ability to predict which drugs will work with individual patients may be influenced by recent University of Missouri research that found seemingly identical neurons can behave the same even though they are built differently under the surface.

“To paraphrase Leo Tolstoy, ‘every unhappy nervous system is unhappy in its own way,’ especially for individuals with epilepsy and other diseases,” said David Schulz, associate professor of biological sciences in MU’s College of Arts and Science. “Our study suggests that each patient’s neurons may be altered in different ways, although the resulting disease is the same. This could be a major reason why doctors have difficulty predicting which medicines will be effective with specific individuals. The same problem could affect treatment of heart arrhythmia, depression and many other neurological conditions.”

It turns out, even happy neurons may be happy in their own way.  Neurons have a natural electric activity that they are biologically programmed to maintain. If a neuron isn’t in that preferred state, the cell tries to restore it. However, contrary to some previous beliefs about neuron functioning, Schulz’s research found that two essentially identical neurons can reach the same preferred electrical activity in different ways.

In Schulz’s study, individual neurons used different combinations of cellular pores, known as ion channels, to achieve the same end goal of their preferred electrical and chemical balances. Schulz compared the situation to five people in separate rooms being given sets of blocks and told to construct a tower. Each person could devise a different method for constructing the same structure.

Schulz’s finding could inform doctor’s treatment of epilepsy. In epileptics, the neurons of the brain frequently receive too little stimulation from other neurons. Those under-stimulated epileptic neurons may overcompensate and become too sensitive. Then, when any impulses actually do reach them from other neurons, those hyper-sensitive epileptic neurons may over-react and cause a seizure.

Schulz worked with Satish Nair, professor of electrical and computer engineering in MU’s College of Engineering. The collaboration allowed their team to model nerve cell behavior in computer simulations in addition to his physical experiments using crab nervous systems.

The study, “Neurons with the same network independently achieve conserved output by differentially balancing variable conductance magnitudes,” was published in the Journal of Neuroscience. Joseph L. Ransdell, an MU doctoral student was the lead researcher of the study.

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.”

Complex brain function depends on flexibility

Over the past few decades, neuroscientists have made much progress in mapping the brain by deciphering the functions of individual neurons that perform very specific tasks, such as recognizing the location or color of an object.

However, there are many neurons, especially in brain regions that perform sophisticated functions such as thinking and planning, that don’t fit into this pattern. Instead of responding exclusively to one stimulus or task, these neurons react in different ways to a wide variety of things. MIT neuroscientist Earl Miller first noticed these unusual activity patterns about 20 years ago, while recording the electrical activity of neurons in animals that were trained to perform complex tasks.

“We started noticing early on that there are a whole bunch of neurons in the prefrontal cortex that can’t be classified in the traditional way of one message per neuron,” recalls Miller, the Picower Professor of Neuroscience at MIT and a member of MIT’s Picower Institute for Learning and Memory.

In a paper appearing in Nature on May 19, Miller and colleagues at Columbia University report that these neurons are essential for complex cognitive tasks, such as learning new behavior. The Columbia team, led by the study’s senior author, Stefano Fusi, developed a computer model showing that without these neurons, the brain can learn only a handful of behavioral tasks.

“You need a significant proportion of these neurons,” says Fusi, an associate professor of neuroscience at Columbia. “That gives the brain a huge computational advantage.”

Lead author of the paper is Mattia Rigotti, a former grad student in Fusi’s lab.

Multitasking neurons

Miller and other neuroscientists who first identified this neuronal activity observed that while the patterns were difficult to predict, they were not random. “In the same context, the neurons always behave the same way. It’s just that they may convey one message in one task, and a totally different message in another task,” Miller says.

For example, a neuron might distinguish between colors during one task, but issue a motor command under different conditions.

Miller and colleagues proposed that this type of neuronal flexibility is key to cognitive flexibility, including the brain’s ability to learn so many new things on the fly. “You have a bunch of neurons that can be recruited for a whole bunch of different things, and what they do just changes depending on the task demands,” he says.

At first, that theory encountered resistance “because it runs against the traditional idea that you can figure out the clockwork of the brain by figuring out the one thing each neuron does,” Miller says.

For the new Nature study, Fusi and colleagues at Columbia created a computer model to determine more precisely what role these flexible neurons play in cognition, using experimental data gathered by Miller and his former grad student, Melissa Warden. That data came from one of the most complex tasks that Miller has ever trained a monkey to perform: The animals looked at a sequence of two pictures and had to remember the pictures and the order in which they appeared.

During this task, the flexible neurons, known as “mixed selectivity neurons,” exhibited a great deal of nonlinear activity — meaning that their responses to a combination of factors cannot be predicted based on their response to each individual factor (such as one image).

Expanding capacity

Fusi’s computer model revealed that these mixed selectivity neurons are critical to building a brain that can perform many complex tasks. When the computer model includes only neurons that perform one function, the brain can only learn very simple tasks. However, when the flexible neurons are added to the model, “everything becomes so much easier and you can create a neural system that can perform very complex tasks,” Fusi says.

The flexible neurons also greatly expand the brain’s capacity to perform tasks. In the computer model, neural networks without mixed selectivity neurons could learn about 100 tasks before running out of capacity. That capacity greatly expanded to tens of millions of tasks as mixed selectivity neurons were added to the model. When mixed selectivity neurons reached about 30 percent of the total, the network’s capacity became “virtually unlimited,” Miller says — just like a human brain.

Mixed selectivity neurons are especially dominant in the prefrontal cortex, where most thought, learning and planning takes place. This study demonstrates how these mixed selectivity neurons greatly increase the number of tasks that this kind of neural network can perform, says John Duncan, a professor of neuroscience at Cambridge University.

“Especially for higher-order regions, the data that have often been taken as a complicating nuisance may be critical in allowing the system actually to work,” says Duncan, who was not part of the research team.

Miller is now trying to figure out how the brain sorts through all of this activity to create coherent messages. There is some evidence suggesting that these neurons communicate with the correct targets by synchronizing their activity with oscillations of a particular brainwave frequency.

“The idea is that neurons can send different messages to different targets by virtue of which other neurons they are synchronized with,” Miller says. “It provides a way of essentially opening up these special channels of communications so the preferred message gets to the preferred neurons and doesn’t go to neurons that don’t need to hear it.”

Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience

The importance of neuronal morphology in brain function has been recognized for over a century. The broad applicability of ‘‘digital reconstructions’’ of neuron morphology across neuroscience subdisciplines has stimulated the rapid development of numerous synergistic tools for data acquisition, anatomical analysis, three-dimensional rendering, electrophysiological simulation, growth models, and data sharing. Here we discuss the processes of histological labeling, microscopic imaging, and semiautomated tracing. Moreover, we provide an annotated compilation of currently available resources in this rich research ‘‘ecosystem’’ as a central reference for experimental and computational neuroscience.

Researchers image most of vertebrae brain at single cell level

Misha Ahrens and Philipp Keller, researchers with the Howard Hughes Medical Institute have succeeded in making a near real-time video of most of a zebrafish’s brain showing individual neuron cells firing. To create the video, as the team reports in their paper published in the journal Nature Methods, the two developed a type of modified light-sheet microscopy and used it in on genetically modified fish.

To create the video, the researchers turned to zebrafish in their larval state—their brains are transparent and small. To cause firing neurons to be visible they genetically altered the fish’s brains, giving them a protein that glows when responding to changes in calcium ion levels, which happen when nerve cells fire. Next, they used a microscope that was able to broadcast a sheet of light through the fish’s brain allowing for the detection of the firing neurons. The system recorded images every 1.3 seconds. The final step was stitching the images together to create a video. The result is nothing short of breathtaking—looking like something out of a science fiction movie’s special effects department.

The video marks the first visual capture of most of a living vertebrae brain at the neuron level, as it works in near real-time and offers striking evidence of the complexity of the brain—even one as small as 100,000 neurons. The researchers say their video shows approximately 80 percent of the zebrafish’s brain as it operates—though what all those firing neurons represent in particular, is still unknown.

The researchers are careful to point out that what they’ve accomplished does not portend the creation of a video of a human brain in action—our brains are much larger, have billions more neurons and perhaps more importantly, are not transparent and are covered by a thick skull. Instead they suggest that studying a simpler brain in action might help to explain how biological neural networks actually work, perhaps leading to theories that can be generalized over larger animals.

But before that can happen, the procedure the team has developed needs to be improved—neurons can fire at hundreds of times per second, which means a lot of firing in the video has been missed. Capturing at a faster rate would mean generating nearly unmanageable amounts of data—at the current rate, just one hour of capture creates a terabyte of data. Thus a new way to store and process the data must be developed.

Researchers find that alcohol consumption damages brain’s support cells

Alcohol consumption affects the brain in multiple ways, ranging from acute changes in behavior to permanent molecular and functional alterations. The general consensus is that in the brain, alcohol targets mainly neurons. However, recent research suggests that other cells of the brain known as astrocytic glial cells or astrocytes are necessary for the rewarding effects of alcohol and the development of alcohol tolerance. The study, first-authored by Dr. Leonardo Pignataro, was published in the February 6th issue of the scientific journal Brain and Behavior.

"This is a fascinating result that we could have never anticipated. We know that astrocytes are the most abundant cell type in the central nervous system and that they are crucial for neuronal growth and survival, but so far, these cells had been thought to be involved only in brain’s support functions. Our results, however, show that astrocytes have an active role in alcohol tolerance and dependence," explains Dr. Pignataro.

The team of researchers from Columbia and Yale Universities analyzed how alcohol exposure changes gene expression in astrocyte cells and identified gene sets associated with stress, immune response, cell death, and lipid metabolism, which may have profound implications for normal neuronal activity in the brain. “Our findings may explain many of the long-term inflammatory and degenerative effects observed in the brain of alcoholics,” says Dr. Pignataro. “The change in gene expression observed in alcohol-exposed astrocytes supports the idea that some of the alcohol consumed reaches the brain and that ethanol (the active component of alcoholic beverages) is locally metabolized, increasing the production free radicals that react with cell components to affect the normal function of cells. This activates a cellular stress response in the cells in an attempt to defend from this chemical damage. On the other hand, the body recognizes these oxidized molecules as “foreign objects” generating an immune response against them that leads to the death of damage cells. This mechanism can explain the inflammatory degenerative process observed in the brain of chronic alcoholics, allowing for the development of different and novel therapeutically approaches to treat this disease” added Dr. Pignataro.

The consequences of alcohol on astrocytes revealed in this study go far beyond what happens to this particular cell type. Astrocytes play a crucial role in the CNS, supporting normal neuronal activity by maintaining homeostasis. Therefore, alcohol changes in gene expression in astrocytes may have profound implications for neuronal activity in the brain.

These findings will help scientists better understand alcohol-associated disorders, such as the brain neurodegenerative damage associated with chronic alcoholism and alcohol tolerance and dependence. “We hope that this newly discovered role of astrocytes will give scientists new targets other than neurons to develop novel therapies to treat alcoholism,” Leonardo Pignataro concluded.

Neuronal activity induces tau release from healthy neurons

Researchers from King’s College London have discovered that neuronal activity can stimulate tau release from healthy neurons in the absence of cell death. The results published by Diane Hanger and her colleagues in EMBO reports show that treatment of neurons with known biological signaling molecules increases the release of tau into the culture medium. The release of tau from cortical neurons is therefore a physiological process that can be regulated by neuronal activity.

Tau proteins stabilize microtubules, the long threads of polymers that help to maintain the structure of the cell. However, in Alzheimer’s disease or certain types of dementia, tau accumulates in neurons or glial cells, where it contributes to neurodegeneration.

In addition to intracellular aggregation, recent experiments have shown that tau is released from neuronal cells and taken up by neighboring cells, which allows the spread of aggregated tau across the brain. This release could occur passively from dying neuronal cells, though some evidence suggests it might take place before neuronal cell death and neurodegeneration. The new findings indicate that tau release is an active process in healthy neurons and this could be altered in diseased brains.

“Our findings suggest that altered tau release is likely to occur in response to changes in neuronal excitability in the Alzheimer’s brain. Secreted tau could therefore be involved in the propagation of tau pathology in tauopathies, a group of neurodegenerative diseases associated with the accumulation of tau proteins in the brain,” commented Diane Hanger, Reader in the Department of Neuroscience at King’s College London. In these experiments, Amy Pooler, the lead author, revealed that molecules such as potassium chloride, glutamate or an AMPA receptor agonist could release tau from cortical neurons in an active physiological process that is, at least partially, dependent on pre-synaptic vesicle secretion.

The new findings by the scientists indicate that tau has previously unknown roles in biological signaling between cells, in addition to its well-established role in stabilizing microtubules.

“We believe that targeting the release of tau could be explored as a new therapeutic approach for the treatment of Alzheimer’s disease and related tauopathies,” said Hanger. Additional studies are needed in model organisms to test this hypothesis further.

(Image: Patrick Hoesly)