Neuroscience

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Comparing mouse and human immune systems
It is a familiar note struck when authors conclude their reports on experiments conducted in mouse models: They suggest caution when translating their findings from mouse to human. A variation of this refrain can be heard when a small molecule that works in mice fails in human clinical trials.
There may be myriad reasons why results differ, and some challenges to the relevance of mouse models to human disease and therapy may be more anecdotal than evidence-driven, scientists say. But the need for better understanding the differences and similarities between human and mouse is clear. Genomic tools and analysis have opened the door to making comprehensive comparisons at a basic level that can inform future research in both mice and humans.
Scientists studying cell differentiation and function in the immune system set out to chart how the mouse and human compare in this area. Tal Shay, a postdoctoral associate in Aviv Regev’s lab at the Broad Institute of Harvard and MIT, led a team from Harvard Medical School, the Broad and Stanford University who compared two large compendia containing transcriptional profiles—how genes are expressed—in human and mouse immune cell types.
The researchers found remarkable consistency between gene expression profiles in the mouse and human immune systems but also some instances of divergence. The majority of gene expression patterns—conservatively estimated at 80 percent—were the same in mouse and human. In addition, they suggest a role for transcriptional regulators that may guide some of the similarities.
Shay and her colleagues reported their findings in PNAS and also deposited their data and analysis in a web portal, which they hope will serve as a reference map for other investigators. Their work is part of the ImmGen Consortium, a collaboration of immunologists and computational biologists generating a complete compendium of gene expression and its regulation in the mouse immune system.
“We wanted to pinpoint where immune system genes and gene expression are different and where you should be very suspicious if something is found in mouse and likely to be translated to human,” said Shay, who is a lead author of the paper. “We thought we might be able to map those places where the comparison is less robust, but we had a very hard time pinpointing convincing differences.”
The researchers had to take extraordinary pains to make sure they were comparing only what was comparable—apples to apples. Not all mouse genes had a corresponding gene in the human data set, or they had more than one: There might be one gene in humans versus five in mice for smell receptors, for example. Sometimes differences were a matter of timing: Genes were activated earlier or later, depending on the species, said David Puyraimond-Zemmour, an HMS graduate student in immunology in the lab of Christophe Benoist and Diane Mathis and a co-author of the PNAS paper.
In all, they found several dozen genes in seven immune cell types that have different expression in 80 human and 137 mouse samples. Their conclusions are based on comparing data from the Differentiation Map—which measures gene expression in about 40 human cell types—and data from ImmGen, which does the same for about 200 mouse cell types. They did further analyses of gene expression when cells were activated in different states, such as responding to infection, based on a data set produced by Ei Wakamatsu and Ting Feng, postdoctoral fellows in the Benoist-Mathis lab. Shay also worked with the Differentiation Map data from the lab of Benjamin Ebert, HMS associate professor of medicine at Brigham and Women’s Hospital and Dana-Farber Cancer Institute and an associate member of the Broad Institute, as well as from the ImmGen Project.
“What we assume most people will be interested in knowing is, if they are working on gene X, whether gene X has the same expression pattern in human and mouse immune systems,” Shay said. “Most lineages have the same expression signature but some genes behave differently and we think it’s important for why some things work in mice but not humans and the other way around.”
Benoist, Morton Grove-Rasmussen Professor of Immunohematology at Harvard Medical School, said the continuing debate about the usefulness of mouse models in understanding humans “is often at the level of the emotional and not necessarily very informed.” Wildly different experimental conditions—hugely varying doses or duration in clinical trials—make comparisons suspect, he said.
Having clear data that scientists can freely access will be useful, said Benoist, who is also a co-author of the PNAS paper.
“The value here is putting up signposts, signaling when the function of a gene in mice may not be relevant to humans,” he said, referring to data and analysis from the work published in PNAS. “Because the differentiation and function of human and mouse lineages are highly related, there is the expectation of conservation, so it is important to know when inter-species inferences may be an issue. Mouse models are far too valuable to be jettisoned for pre-clinical exploration, but it is important to know when caution is needed.”

Comparing mouse and human immune systems

It is a familiar note struck when authors conclude their reports on experiments conducted in mouse models: They suggest caution when translating their findings from mouse to human. A variation of this refrain can be heard when a small molecule that works in mice fails in human clinical trials.

There may be myriad reasons why results differ, and some challenges to the relevance of mouse models to human disease and therapy may be more anecdotal than evidence-driven, scientists say. But the need for better understanding the differences and similarities between human and mouse is clear. Genomic tools and analysis have opened the door to making comprehensive comparisons at a basic level that can inform future research in both mice and humans.

Scientists studying cell differentiation and function in the immune system set out to chart how the mouse and human compare in this area. Tal Shay, a postdoctoral associate in Aviv Regev’s lab at the Broad Institute of Harvard and MIT, led a team from Harvard Medical School, the Broad and Stanford University who compared two large compendia containing transcriptional profiles—how genes are expressed—in human and mouse immune cell types.

The researchers found remarkable consistency between gene expression profiles in the mouse and human immune systems but also some instances of divergence. The majority of gene expression patterns—conservatively estimated at 80 percent—were the same in mouse and human. In addition, they suggest a role for transcriptional regulators that may guide some of the similarities.

Shay and her colleagues reported their findings in PNAS and also deposited their data and analysis in a web portal, which they hope will serve as a reference map for other investigators. Their work is part of the ImmGen Consortium, a collaboration of immunologists and computational biologists generating a complete compendium of gene expression and its regulation in the mouse immune system.

“We wanted to pinpoint where immune system genes and gene expression are different and where you should be very suspicious if something is found in mouse and likely to be translated to human,” said Shay, who is a lead author of the paper. “We thought we might be able to map those places where the comparison is less robust, but we had a very hard time pinpointing convincing differences.”

The researchers had to take extraordinary pains to make sure they were comparing only what was comparable—apples to apples. Not all mouse genes had a corresponding gene in the human data set, or they had more than one: There might be one gene in humans versus five in mice for smell receptors, for example. Sometimes differences were a matter of timing: Genes were activated earlier or later, depending on the species, said David Puyraimond-Zemmour, an HMS graduate student in immunology in the lab of Christophe Benoist and Diane Mathis and a co-author of the PNAS paper.

In all, they found several dozen genes in seven immune cell types that have different expression in 80 human and 137 mouse samples. Their conclusions are based on comparing data from the Differentiation Map—which measures gene expression in about 40 human cell types—and data from ImmGen, which does the same for about 200 mouse cell types. They did further analyses of gene expression when cells were activated in different states, such as responding to infection, based on a data set produced by Ei Wakamatsu and Ting Feng, postdoctoral fellows in the Benoist-Mathis lab. Shay also worked with the Differentiation Map data from the lab of Benjamin Ebert, HMS associate professor of medicine at Brigham and Women’s Hospital and Dana-Farber Cancer Institute and an associate member of the Broad Institute, as well as from the ImmGen Project.

“What we assume most people will be interested in knowing is, if they are working on gene X, whether gene X has the same expression pattern in human and mouse immune systems,” Shay said. “Most lineages have the same expression signature but some genes behave differently and we think it’s important for why some things work in mice but not humans and the other way around.”

Benoist, Morton Grove-Rasmussen Professor of Immunohematology at Harvard Medical School, said the continuing debate about the usefulness of mouse models in understanding humans “is often at the level of the emotional and not necessarily very informed.” Wildly different experimental conditions—hugely varying doses or duration in clinical trials—make comparisons suspect, he said.

Having clear data that scientists can freely access will be useful, said Benoist, who is also a co-author of the PNAS paper.

“The value here is putting up signposts, signaling when the function of a gene in mice may not be relevant to humans,” he said, referring to data and analysis from the work published in PNAS. “Because the differentiation and function of human and mouse lineages are highly related, there is the expectation of conservation, so it is important to know when inter-species inferences may be an issue. Mouse models are far too valuable to be jettisoned for pre-clinical exploration, but it is important to know when caution is needed.”

Filed under cell differentiation immune system immune cells gene expression mouse model medicine science

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Hearing What’s Important: Penn Researchers Pinpoint Brain Mechanisms That Make the Auditory System Sensitive to Behaviorally Relevant Sounds
How do we hear?  More specifically, how does the auditory center of the brain discern important sounds – such as communication from members of the same species – from relatively irrelevant background noise? The answer depends on the regulation of sound by specific neurons in the auditory cortex of the brain, but the precise mechanisms of those neurons have remained unclear. Now, a new study from the Perelman School of Medicine at the University of Pennsylvania has isolated how neurons in the rat’s primary auditory cortex (A1) preferentially respond to natural vocalizations from other rats over intentionally modified vocalizations (background sounds). A computational model developed by the study authors, which successfully predicted neuronal responses to other new sounds, explained the basis for this preference. The research is published in the Journal of Neurophysiology.
Rats communicate with each other mostly through ultrasonic vocalizations (USVs) beyond the range of human hearing. Although the existence of these USV conversations has been known for decades, “the acoustic richness of them has only been discovered in the last few years,” said senior study author Maria N. Geffen, PhD, assistant professor of Otorhinolaryngology: Head and Neck Surgery at Penn. That acoustical complexity raises questions as to how the animal brain recognizes and responds to the USVs. “We set out to characterize the responses of neurons to USVs and to come up with a model that would explain the mechanism that makes these neurons preferentially responsive to these relevant sounds.”
Geffen and her colleagues obtained recordings of USVs from two rats kept together in a cage, then played the recordings to a separate group of male rats, while their neuronal responses were acquired and recorded. The researchers also used USV recordings that were modified in several ways, such as having background sounds filtered out and being played backwards and at different speeds to mimic unimportant background noise. “We found that neurons in the auditory cortex respond strongly and selectively to the original ultrasonic vocalizations and not the transformed versions we created,” says Geffen.
Using the data collected on the responses of A1 neurons to various USVs, the researchers developed a computational model that could predict the activity of an individual neuron based on the pitch and duration of the USV. Geffen observes that “the details of their responses could be predicted with high accuracy.” It was possible to determine which aspects of the acoustic input best drove individual neurons. Remarkably, it turned out that the acoustic parameters that worked best in driving the neuronal responses corresponded to the statistics of the natural vocalizations rats produce.
The work makes clear for the first time, says Geffen, “the mechanisms of how the auditory system picks out behaviorally relevant sounds, such as same species communication signals, and processes them more effectively than less relevant sounds. This information is fundamental in understanding how sound perception helps animals survive. We conclude that neurons in the auditory cortex are specialized for processing and efficiently responding to natural and behaviorally relevant sounds.”
(Image: National Institute on Deafness and Other Communication)

Hearing What’s Important: Penn Researchers Pinpoint Brain Mechanisms That Make the Auditory System Sensitive to Behaviorally Relevant Sounds

How do we hear?  More specifically, how does the auditory center of the brain discern important sounds – such as communication from members of the same species – from relatively irrelevant background noise? The answer depends on the regulation of sound by specific neurons in the auditory cortex of the brain, but the precise mechanisms of those neurons have remained unclear. Now, a new study from the Perelman School of Medicine at the University of Pennsylvania has isolated how neurons in the rat’s primary auditory cortex (A1) preferentially respond to natural vocalizations from other rats over intentionally modified vocalizations (background sounds). A computational model developed by the study authors, which successfully predicted neuronal responses to other new sounds, explained the basis for this preference. The research is published in the Journal of Neurophysiology.

Rats communicate with each other mostly through ultrasonic vocalizations (USVs) beyond the range of human hearing. Although the existence of these USV conversations has been known for decades, “the acoustic richness of them has only been discovered in the last few years,” said senior study author Maria N. Geffen, PhD, assistant professor of Otorhinolaryngology: Head and Neck Surgery at Penn. That acoustical complexity raises questions as to how the animal brain recognizes and responds to the USVs. “We set out to characterize the responses of neurons to USVs and to come up with a model that would explain the mechanism that makes these neurons preferentially responsive to these relevant sounds.”

Geffen and her colleagues obtained recordings of USVs from two rats kept together in a cage, then played the recordings to a separate group of male rats, while their neuronal responses were acquired and recorded. The researchers also used USV recordings that were modified in several ways, such as having background sounds filtered out and being played backwards and at different speeds to mimic unimportant background noise. “We found that neurons in the auditory cortex respond strongly and selectively to the original ultrasonic vocalizations and not the transformed versions we created,” says Geffen.

Using the data collected on the responses of A1 neurons to various USVs, the researchers developed a computational model that could predict the activity of an individual neuron based on the pitch and duration of the USV. Geffen observes that “the details of their responses could be predicted with high accuracy.” It was possible to determine which aspects of the acoustic input best drove individual neurons. Remarkably, it turned out that the acoustic parameters that worked best in driving the neuronal responses corresponded to the statistics of the natural vocalizations rats produce.

The work makes clear for the first time, says Geffen, “the mechanisms of how the auditory system picks out behaviorally relevant sounds, such as same species communication signals, and processes them more effectively than less relevant sounds. This information is fundamental in understanding how sound perception helps animals survive. We conclude that neurons in the auditory cortex are specialized for processing and efficiently responding to natural and behaviorally relevant sounds.”

(Image: National Institute on Deafness and Other Communication)

Filed under auditory cortex auditory system neurons vocalizations ultrasonic vocalizations neuroscience science

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Information-Theoretic Analysis of the Dynamics of an Executable Biological Model
To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.

Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.

Filed under biological systems dynamical systems network models boolean network neuroscience science

446 notes

Easing Brain Fatigue With a Walk in the Park

Scientists have known for some time that the human brain’s ability to stay calm and focused is limited and can be overwhelmed by the constant noise and hectic, jangling demands of city living, sometimes resulting in a condition informally known as brain fatigue.

With brain fatigue, you are easily distracted, forgetful and mentally flighty — or, in other words, me.

But an innovative new study from Scotland suggests that you can ease brain fatigue simply by strolling through a leafy park.

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The idea that visiting green spaces like parks or tree-filled plazas lessens stress and improves concentration is not new. Researchers have long theorized that green spaces are calming, requiring less of our so-called directed mental attention than busy, urban streets do. Instead, natural settings invoke “soft fascination,” a beguiling term for quiet contemplation, during which directed attention is barely called upon and the brain can reset those overstretched resources and reduce mental fatigue.

But this theory, while agreeable, has been difficult to put to the test. Previous studies have found that people who live near trees and parks have lower levels of cortisol, a stress hormone, in their saliva than those who live primarily amid concrete, and that children with attention deficits tend to concentrate and perform better on cognitive tests after walking through parks or arboretums. More directly, scientists have brought volunteers into a lab, attached electrodes to their heads and shown them photographs of natural or urban scenes, and found that the brain wave readouts show that the volunteers are more calm and meditative when they view the natural scenes.

But it had not been possible to study the brains of people while they were actually outside, moving through the city and the parks. Or it wasn’t, until the recent development of a lightweight, portable version of the electroencephalogram, a technology that studies brain wave patterns.

For the new study, published this month in The British Journal of Sports Medicine, researchers at Heriot-Watt University in Edinburgh and the University of Edinburgh attached these new, portable EEGs to the scalps of 12 healthy young adults. The electrodes, hidden unobtrusively beneath an ordinary looking fabric cap, sent brain wave readings wirelessly to a laptop carried in a backpack by each volunteer.

The researchers, who had been studying the cognitive impacts of green spaces for some time, then sent each volunteer out on a short walk of about a mile and half that wound through three different sections of Edinburgh.

The first half mile or so took walkers through an older, historic shopping district, with fine, old buildings and plenty of pedestrians on the sidewalk, but only light vehicle traffic.

The walkers then moved onto a path that led through a park-like setting for another half mile.

Finally, they ended their walk strolling through a busy, commercial district, with heavy automobile traffic and concrete buildings.

The walkers had been told to move at their own speed, not to rush or dawdle. Most finished the walk in about 25 minutes.

Throughout that time, the portable EEGs on their heads continued to feed information about brain wave patterns to the laptops they carried.

Afterward, the researchers compared the read-outs, looking for wave patterns that they felt were related to measures of frustration, directed attention (which they called “engagement”), mental arousal and meditativeness or calm.

What they found confirmed the idea that green spaces lessen brain fatigue.

When the volunteers made their way through the urbanized, busy areas, particularly the heavily trafficked commercial district at the end of their walk, their brain wave patterns consistently showed that they were more aroused, attentive and frustrated than when they walked through the parkland, where brain-wave readings became more meditative.

While traveling through the park, the walkers were mentally quieter.

Which is not to say that they weren’t paying attention, said Jenny Roe, a professor in the School of the Built Environment at Heriot-Watt University, who oversaw the study. “Natural environments still engage” the brain, she said, but the attention demanded “is effortless. It’s called involuntary attention in psychology. It holds our attention while at the same time allowing scope for reflection,” and providing a palliative to the nonstop attentional demands of typical, city streets.

Of course, her study was small, more of a pilot study of the nifty new, portable EEG technology than a definitive examination of the cognitive effects of seeing green.

But even so, she said, the findings were consistent and strong and, from the viewpoint of those of us over-engaged in attention-hogging urban lives, valuable. The study suggests that, right about now, you should consider “taking a break from work,” Dr. Roe said, and “going for a walk in a green space or just sitting, or even viewing green spaces from your office window.” This is not unproductive lollygagging, Dr. Roe helpfully assured us. “It is likely to have a restorative effect and help with attention fatigue and stress recovery.”

-by Gretchen Reynolds, The New York Times

Filed under brain brain fatigue stress anxiety cortisol mental fatigue EEG psychology neuroscience science

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Mindfulness from meditation associated with lower stress hormone

Focusing on the present rather than letting the mind drift may help to lower levels of the stress hormone cortisol, suggests new research from the Shamatha Project at the University of California, Davis.

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The ability to focus mental resources on immediate experience is an aspect of mindfulness, which can be improved by meditation training.

"This is the first study to show a direct relation between resting cortisol and scores on any type of mindfulness scale," said Tonya Jacobs, a postdoctoral researcher at the UC Davis Center for Mind and Brain and first author of a paper describing the work, published this week in the journal Health Psychology.

High levels of cortisol, a hormone produced by the adrenal gland, are associated with physical or emotional stress. Prolonged release of the hormone contributes to wide-ranging, adverse effects on a number of physiological systems.

The new findings are the latest to come from the Shamatha Project, a comprehensive long-term, control-group study of the effects of meditation training on mind and body.

Led by Clifford Saron, associate research scientist at the UC Davis Center for Mind and Brain, the Shamatha Project has drawn the attention of both scientists and Buddhist scholars including the Dalai Lama, who has endorsed the project.

In the new study, Jacobs, Saron and their colleagues used a questionnaire to measure aspects of mindfulness among a group of volunteers before and after an intensive, three-month meditation retreat. They also measured cortisol levels in the volunteers’ saliva.

During the retreat, Buddhist scholar and teacher B. Alan Wallace of the Santa Barbara Institute for Consciousness Studies trained participants in such attentional skills as mindfulness of breathing, observing mental events, and observing the nature of consciousness. Participants also practiced cultivating benevolent mental states, including loving kindness, compassion, empathic joy and equanimity.

At an individual level, there was a correlation between a high score for mindfulness and a low score in cortisol both before and after the retreat. Individuals whose mindfulness score increased after the retreat showed a decrease in cortisol.

"The more a person reported directing their cognitive resources to immediate sensory experience and the task at hand, the lower their resting cortisol," Jacobs said.

The research did not show a direct cause and effect, Jacobs emphasized. Indeed, she noted that the effect could run either way — reduced levels of cortisol could lead to improved mindfulness, rather than the other way around. Scores on the mindfulness questionnaire increased from pre- to post-retreat, while levels of cortisol did not change overall.

According to Jacobs, training the mind to focus on immediate experience may reduce the propensity to ruminate about the past or worry about the future, thought processes that have been linked to cortisol release.

"The idea that we can train our minds in a way that fosters healthy mental habits and that these habits may be reflected in mind-body relations is not new; it’s been around for thousands of years across various cultures and ideologies," Jacobs said. "However, this idea is just beginning to be integrated into Western medicine as objective evidence accumulates. Hopefully, studies like this one will contribute to that effort."

Saron noted that in this study, the authors used the term “mindfulness” to refer to behaviors that are reflected in a particular mindfulness scale, which was the measure used in the study.

"The scale measured the participants’ propensity to let go of distressing thoughts and attend to different sensory domains, daily tasks, and the current contents of their minds. However, this scale may only reflect a subset of qualities that comprise the greater quality of mindfulness, as it is conceived across various contemplative traditions," he said.

Previous studies from the Shamatha Project have shown that the meditation retreat had positive effects on visual perception, sustained attention, socio-emotional well-being, resting brain activity and on the activity of telomerase, an enzyme important for the long-term health of body cells.

(Source: news.ucdavis.edu)

Filed under mindfulness meditation cortisol stress anxiety psychology neuroscience science

70 notes

Researchers Discover New Clues About How Amyotrophic Lateral Sclerosis (ALS) Develops

Johns Hopkins scientists say they have evidence from animal studies that a type of central nervous system cell other than motor neurons plays a fundamental role in the development of amyotrophic lateral sclerosis (ALS), a fatal degenerative disease. The discovery holds promise, they say, for identifying new targets for interrupting the disease’s progress.

In a study described online in Nature Neuroscience, the researchers found that, in mice bred with a gene mutation that causes human ALS, dramatic changes occurred in oligodendrocytes — cells that create insulation for the nerves of the central nervous system — long before the first physical symptoms of the disease appeared. Oligodendrocytes located near motor neurons — cells that govern movement — died off at very high rates, and new ones regenerated in their place were inferior and unhealthy.

The researchers also found, to their surprise, that suppressing an ALS-causing gene in oligodendrocytes of mice bred with the disease — while still allowing the gene to remain in the motor neurons — profoundly delayed the onset of ALS. It also prolonged survival of these mice by more than three months, a long time in the life span of a mouse. These observations suggest that oligodendrocytes play a very significant role in the early stage of the disease.

“The abnormalities in oligodendrocytes appear to be having a negative impact on the survival of motor neurons,” says Dwight E. Bergles, Ph.D., a co-author and a professor of neuroscience at the Johns Hopkins University School of Medicine. “The motor neurons seem to be dependent on healthy oligodendrocytes for survival, something we didn’t appreciate before.”

“These findings teach us that cells we never thought had a role in ALS not only are involved but also clearly contribute to the onset of the disease,” says co-author Jeffrey D. Rothstein, M.D., Ph.D., a professor of neurology at Johns Hopkins and director of the Johns Hopkins Medicine Brain Science Institute.

Scientists have long believed that oligodendrocytes functioned only as structural elements of the central nervous system. They wrap around nerves, making up the myelin sheath that provides the “insulation” that allows nerve signals to be transmitted rapidly and efficiently. However, Rothstein and others recently discovered that oligodendrocytes also deliver essential nutrients to neurons, and that most neurons need this support to survive.

The Johns Hopkins team of Bergles and Rothstein published a paper in 2010 that described in mice with ALS an unexpected massive proliferation of oligodendrocyte progenitor cells in the spinal cord’s motor neurons, and that these progenitors were being mobilized to make new oligodendrocytes. The researchers believed that these cells were multiplying because of an injury to oligodendrocytes, but they weren’t sure what was happening. Using a genetic method of tracking the fate of oligodendrocytes, in the new study, the researchers found that cells present in young mice with ALS were dying off at an increasing rate in concert with advancing disease. Moreover, the development of the newly formed oligodendrocytes was stalled and they were not able to provide motor neurons with a needed source of cell nutrients.

To determine whether the changes to the oligodendrocytes were just a side effect of the death of motor neurons, the scientists used a poison to kill motor neurons in the ALS mice and found no response from the progenitors, suggesting, says Rothstein, that it is the mutant ALS gene that is damaging oligodendrocytes directly.

Meanwhile, in separate experiments, the researchers found similar changes in samples of tissues from the brains of 35 people who died of ALS. Rothstein says it may be possible to see those changes early on in the disease and use MRI technology to follow progression.

“If our research is confirmed, perhaps we can start looking at ALS patients in a different way, looking for damage to oligodendrocytes as a marker for disease progression,” Rothstein says. “This could not only lead to new treatment targets but also help us to monitor whether the treatments we offer are actually working.”

ALS, also known as Lou Gehrig’s disease, named for the Yankee baseball great who died from it, affects nerve cells in the brain and spinal cord that control voluntary muscle movement. The nerve cells waste away or die, and can no longer send messages to muscles, eventually leading to muscle weakening, twitching and an inability to move the arms, legs and body. Onset is typically around age 50 and death often occurs within three to five years of diagnosis. Some 10 percent of cases are hereditary.

There is no cure for ALS and there is only one FDA-approved drug treatment, which has just a small effect in slowing disease progression and increasing survival.

Even though myelin loss has not previously been thought to occur in the gray matter, a region in the brain where neurons process information, the researchers in the new study found in ALS patients a significant loss of myelin in one of every three samples of human tissue taken from the brain’s gray matter, suggesting that the oligodendrocytes were abnormal. It isn’t clear if the oligodendrocytes that form this myelin in the gray matter play a different role than in white matter — the region in the brain where signals are relayed.

The findings further suggest that clues to the treatment of other diseases long believed to be focused in the brain’s gray matter — such as Alzheimer’s disease, Huntington’s disease and Parkinson’s disease — may be informed by studies of diseases of the white matter, such as multiple sclerosis (MS). Bergles says ALS and MS researchers never really thought their diseases had much in common before.

Oligodendrocytes have been under intense scrutiny in MS, Bergles says. In MS, the disease over time can transform from a remitting-relapsing form — in which myelin is attacked but then is regenerated when existing progenitors create new oligodendrocytes to re-form myelin — to a more chronic stage in which oligodendrocytes are no longer regenerated. MS researchers are working to identify new ways to induce the creation of new oligodendrocytes and improve their survival. “It’s possible that we may be able to dovetail with some of the same therapeutics to slow the progression of ALS,” Bergles says.

(Source: newswise.com)

Filed under ALS Lou Gehrig's disease motor neurons oligodendrocytes CNS gene mutation neuroscience science

154 notes

Pablo Garcia Lopez: The Cortical Garden

"Like the entomologist in pursuit of brightly coloured butterflies, my attention hunted, in the flower garden of the gray matter, cells with delicate and elegant forms, the mysterious butterflies of the soul, the beating of whose wings may someday -who knows?- clarify the secret of mental life" - Santiago Ramon y Cajal, Recollections of My Life.

My work as an artist is directly inspired by my experience as a neuroscientist. I completed my PhD in conjunction with the Museum Cajal, working with the original slides and scientific drawings of Santiago Ramon y Cajal (1852–1934). Besides being completely astonished by the historical and current neuroscientific concepts, and esthetics of his histological slides, drawings, articles, and books, I was impressed by the great abundance of metaphors that he employed in his scientific writings. Possibly, even more impressive concerning Cajal’s metaphors are their naturalistic and organic essence. Many of these metaphors could be considered rhetorical ornaments, although they also function as explanatory and even as heuristic tools for proposing his models and theories about brain functioning. - Pablo Garcia Lopez, Sculpting the brain

Filed under Pablo Garcia Lopez cortical garden art neuroscience Santiago Ramon y Cajal science

667 notes

Exploring Temple Grandin’s Brain

The world’s most famous person with autism uses her unusual cognitive abilities to reduce animal suffering.

Animal scientist Temple Grandin has an extraordinary mind. Probably the world’s most famous person with autism, she designed widely used livestock handling systems to reduce animal suffering. She is not just autistic but an autistic savant, meaning that she has unusual cognitive abilities, such as a photographic memory and excellent spatial skills. She “thinks in pictures,” she says, helping her understand what animals perceive.

Her brain is equally remarkable, according to a team of neuroimaging experts who study brain changes in autism at the University of Utah. Neuroscientist Jason Cooperrider and colleagues scanned Grandin’s brain using three different methods: high-resolution magnetic resonance imaging (MRI), which captures the structure of the brain; diffusion tensor imaging (DTI), a method to trace the connections between brain regions; and functional MRI, which indicates brain activity. The images reveal an unusual neural landscape that reflects Grandin’s deficits and talents. 

Overall, the right side of her brain dominates. One theory of autistic savantism suggests that during fetal development or early in life, some developmental abnormality affects the brain’s left side, resulting in the difficulties that many autistic people have with words and social interaction, functions typically processed by the left hemisphere.

To make up for this, the right hemisphere sometimes overcompensates, which can lead to special abilities in music, art, and visual memory. Savantism is not well-understood, but between a tenth and a third of people with autism may have some of these abilities. 

Cooperrider’s team also discovered that Grandin’s amygdala, the almond-shaped organ said to play an important role in emotional processing, is larger than normal. This was not a surprising finding because among other functions, this region processes fear and anxiety, affective states often affected by autism. Her fusiform gyrus is smaller than normal—also not a surprise, since this region is involved in recognizing faces, a social skill that autism may disrupt.

Every brain is different, especially where autism is concerned, and Cooperrider’s study compares Grandin’s brain with only three controls, not enough to draw broad conclusions. But some of the patterns Cooperrider and his colleagues discovered back up other studies, and suggest new regions to explore.

Filed under brain brain development Temple Grandin autism savants neuroimaging neuroscience psychology science

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Predicting the future of artificial intelligence has always been a fool’s game
From the Dartmouth Conferences to Turing’s test, prophecies about AI have rarely hit the mark. But there are ways to tell the good from the bad when it comes to futurology.
In 1956, a bunch of the top brains in their field thought they could crack the challenge of artificial intelligence over a single hot New England summer. Almost 60 years later, the world is still waiting.
The “spectacularly wrong prediction” of the Dartmouth Summer Research Project on Artificial Intelligence made Stuart Armstrong, research fellow at the Future of Humanity Institute at University of Oxford, start to think about why our predictions about AI are so inaccurate.
The Dartmouth Conference had predicted that over two summer months ten of the brightest people of their generation would solve some of the key problems faced by AI developers, such as getting machines to use language, form abstract concepts and even improve themselves.
If they had been right, we would have had AI back in 1957; today, the conference is mostly credited merely with having coined the term ”  artificial intelligence”.
Their failure is “depressing” and “rather worrying”, says Armstrong. “If you saw the prediction the rational thing would have been to believe it too. They had some of the smartest people of their time, a solid research programme, and sketches as to how to approach it and even ideas as to where the problems were.”
Now, to help answer the question why “AI predictions are very hard to get right”, Armstrong has recently analysed the Future of Humanity Institute’s library of 250 AI predictions. The library stretches back to 1950, when Alan Turing, the father of computer science, predicted that a computer would be able to pass the “Turing test” by 2000. (In the  Turing test, a machine has to demonstrate behaviour indistinguishable from that of a human being.)
Later experts have suggested 2013, 2020 and 2029 as dates when a machine would pass the Turing test, which gives us a clue as to why Armstrong feels that such timeline predictions — all 95 of them in the library — are particularly worthless. “There is nothing to connect a timeline prediction with previous knowledge as AIs have never appeared in the world before — no one has ever built one — and our only model is the human brain, which took hundreds of millions of years to evolve.”
His research also suggests that predictions by philosophers are more accurate than those of sociologists or even computer scientists. “We know very little about the final form an AI would take, so if they [the experts] are grounded in a specific approach they are likely to go wrong, while those on a meta level are very likely to be right”.

Predicting the future of artificial intelligence has always been a fool’s game

From the Dartmouth Conferences to Turing’s test, prophecies about AI have rarely hit the mark. But there are ways to tell the good from the bad when it comes to futurology.

In 1956, a bunch of the top brains in their field thought they could crack the challenge of artificial intelligence over a single hot New England summer. Almost 60 years later, the world is still waiting.

The “spectacularly wrong prediction” of the Dartmouth Summer Research Project on Artificial Intelligence made Stuart Armstrong, research fellow at the Future of Humanity Institute at University of Oxford, start to think about why our predictions about AI are so inaccurate.

The Dartmouth Conference had predicted that over two summer months ten of the brightest people of their generation would solve some of the key problems faced by AI developers, such as getting machines to use language, form abstract concepts and even improve themselves.

If they had been right, we would have had AI back in 1957; today, the conference is mostly credited merely with having coined the term ” artificial intelligence”.

Their failure is “depressing” and “rather worrying”, says Armstrong. “If you saw the prediction the rational thing would have been to believe it too. They had some of the smartest people of their time, a solid research programme, and sketches as to how to approach it and even ideas as to where the problems were.”

Now, to help answer the question why “AI predictions are very hard to get right”, Armstrong has recently analysed the Future of Humanity Institute’s library of 250 AI predictions. The library stretches back to 1950, when Alan Turing, the father of computer science, predicted that a computer would be able to pass the “Turing test” by 2000. (In the Turing test, a machine has to demonstrate behaviour indistinguishable from that of a human being.)

Later experts have suggested 2013, 2020 and 2029 as dates when a machine would pass the Turing test, which gives us a clue as to why Armstrong feels that such timeline predictions — all 95 of them in the library — are particularly worthless. “There is nothing to connect a timeline prediction with previous knowledge as AIs have never appeared in the world before — no one has ever built one — and our only model is the human brain, which took hundreds of millions of years to evolve.”

His research also suggests that predictions by philosophers are more accurate than those of sociologists or even computer scientists. “We know very little about the final form an AI would take, so if they [the experts] are grounded in a specific approach they are likely to go wrong, while those on a meta level are very likely to be right”.

Filed under AI AI predictions Turing test Dartmouth Conference computer science science

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