Do you obsess over your appearance? Your brain might be wired abnormally

Body dysmorphic disorder is a disabling but often misunderstood psychiatric condition in which people perceive themselves to be disfigured and ugly, even though they look normal to others. New research at UCLA shows that these individuals have abnormalities in the underlying connections in their brains.

Dr. Jamie Feusner, the study’s senior author and a UCLA associate professor of psychiatry, and his colleagues report that individuals with BDD have, in essence, global “bad wiring” in their brains — that is, there are abnormal network-wiring patterns across the brain as a whole.

And in line with earlier UCLA research showing that people with BDD process visual information abnormally, the study discovered abnormal connections between regions of the brain involved in visual and emotional processing.

The findings, published in the May edition of the journal Neuropsychopharmacology, suggest that these patterns in the brain may relate to impaired information processing.

"We found a strong correlation between low efficiency of connections across the whole brain and the severity of BDD," Feusner said. "The less efficient patients’ brain connections, the worse the symptoms, particularly for compulsive behaviors, such as checking mirrors."

People suffering from BDD tend to fixate on minute details, such as a single blemish on their face or body, rather than viewing themselves in their entirety. They become so distressed with their appearance that they often can’t lead normal lives, are fearful of leaving their homes and occasionally even commit suicide. Patients frequently have to be hospitalized. BDD affects approximately 2 percent of the population and is more prevalent than schizophrenia or bipolar disorder. Despite its prevalence and severity, scientists know relatively little about the neurobiology of BDD.

In the current study, Feusner and his colleagues performed brain scans of 14 adults diagnosed with BDD and 16 healthy controls. The goal of the study was to map the brain’s connections to examine how the white-matter networks are organized. White matter is made up of nerve cells that carry impulses from one part of the brain to another.

To do this, they used a sensitive form of brain imaging called diffusion tensor imaging, or DTI. DTI is a variant of magnetic resonance imaging that can measure the structural integrity of the brain’s white matter. From these scans, they were able to create whole brain “maps” of reconstructed white-matter tracks. Next, they used a form of advanced analysis called graph theory to characterize the patterns of connections throughout the brains of people with BDD and then compared them with those of healthy controls.

The researchers found people with BDD had a pattern of abnormally high network “clustering” across the entire brain. This suggests that these individuals may have imbalances in how they process “local” or detailed information. The researchers also discovered specific abnormal connections between areas involved in processing visual input and those involved in recognizing emotions.

"How their brain regions are connected in order to communicate about what they see and how they feel is disturbed," said Feusner, who also directs the Adult Obsessive-Compulsive Disorder Program and the Body Dysmorphic Disorder Research Program at UCLA.

"Their brains seem to be fine-tuned to be very sensitive to process minute details, but this pattern may not allow their brains to be well-synchronized across regions with different functions," he said. "This could affect how they perceive their physical appearance and may also result in them getting caught up in the details of other thoughts and cognitive processes."

The study, Feusner noted, advances the understanding of BDD by providing evidence that the “hard wiring” of patients’ brain networks is abnormal.

"These abnormal brain networks could relate to how they perceive, feel and behave," he said. "This is significant because it could possibly lead to us being able to identify early on if someone is predisposed to developing this problem."

Australian scientists map mouse brains in greatest detail yet

Hopes for a cure for many brain diseases may rest on the humble mouse, now that scientists can map the rodents’ brains more thoroughly than ever before.

Researchers at The University of Queensland’s Centre for Advanced Imaging (CAI) and Curtin University have created the most detailed atlas of the mouse brain, a development that is helping in the fight against brain disease.

This new tool will allow researchers to map what parts of the brain are affected in mouse models of brain disease – such as brain cancer, Parkinson’s disease and Alzheimers disease, which affect nearly 1 in 6 of the world’s population.

Lead author, Dr Jeremy Ullmann said that the new brain atlas provided a fundamental tool for the neuroscience community.

“The mouse is now the most widely used animal model for neuroscience research and magnetic resonance imaging (MRI) is fundamental to investigating changes in the brain,” Dr Ullman said.

“Our atlas is already much in demand internationally because it allows researchers to use MRI to automatically map brain structures.”

The atlas was created in the laboratory of Professor David Reutens, CAI Director.

“In making these world-first maps, we had the advantage of using the most powerful MRI scanners in the Southern Hemisphere, backed up by leaders in digital image analysis, resulting in remarkably clear images of the brain,” Professor Reutens said.

The project’s lead neuroanatomist, Professor Charles Watson from Curtin University, believes that the study will open the door to accurate analysis of gene targeting in the mouse brain.

“The invention of gene targeting in the mouse has made this species the centrepiece of studies on models of human brain disease. MRI allows researchers to follow changes in the brain over time in the same animals,” Professor Watson said.

The atlas was recently described in an article published in the journal NeuroImage.

New methods to explore astrocyte effects on brain function

A study in The Journal of General Physiology [1, 2] presents new methods to evaluate how astrocytes contribute to brain function, paving the way for future exploration of these important brain cells at unprecedented levels of detail.

Astrocytes—the most abundant cell type in the human brain—play crucial roles in brain physiology, which may include modulating synaptic activity and regulating local blood flow. Existing research tools can be used to monitor calcium signals associated with interactions between astrocytes and neurons or blood vessels. Until now, however, astrocytic calcium signals have been investigated mainly in their somata (cell bodies) and large processes, rather than in distal fine processes close to neuronal synapses or the endfeet that surround blood vessels. Previous studies have also mainly investigated immature specimens rather than mature brain cells.

Now, a team of California researchers provides detailed methods to visualize calcium signals throughout entire astrocytes in hippocampal slices from adult mice. The team observed numerous spontaneous localized calcium signals throughout the entire astrocyte, including the branchlets and endfeet. Their results indicated that calcium signals in endfeet were independent of those in somata and occurred more frequently. In addition to the specific findings, their methods can be used in future studies to advance our understanding of the physiology of astrocytes and their interactions with neurons and the microvasculature of the brain.

Νeuroscientists use statistical model to draft fantasy teams of neurons

This past weekend teams from the National Football League used statistics like height, weight and speed to draft the best college players, and in a few weeks, armchair enthusiasts will use similar measures to select players for their own fantasy football teams. Neuroscientists at Carnegie Mellon University are taking a similar approach to compile “dream teams” of neurons using a statistics-based method that can evaluate the fitness of individual neurons.

After assembling the teams, a computer simulation pitted the groups of neurons against one another in a playoff-style format to find out which population was the best. Researchers analyzed the winning teams to see what types of neurons made the most successful squads.

The results were published in the early online edition of the Proceedings of the National Academy of Sciences the week of April 29.

"We wanted to know what team of neurons would be most likely to perform best in response to a variety of stimuli," said Nathan Urban, the Dr. Frederick A. Schwertz Distinguished Professor of Life Sciences and head of the Department of Biological Sciences at Carnegie Mellon.

The human brain contains more than 100 billion neurons that work together in smaller groups to complete certain tasks like processing an odor, or seeing a color. Previous work by Urban’s lab found that no two neurons are exactly alike and that diverse teams of neurons were better able to determine a stimulus than teams of similar neurons.

"The next step in our work was to figure out how to assemble the best possible population of neurons in order to complete a task," said Urban, who is also a member of the joint Carnegie Mellon/University of Pittsburgh Center for the Neural Basis of Cognition (CNBC).

However, using existing methods, scouting for the best team of neurons was a seemingly daunting task. It would be impossible for scientists to determine how each of the billions of neurons in the brain would individually respond to a multitude of stimuli. Urban and Shreejoy Tripathy, the article’s lead author and graduate student in the CNBC’s Program in Neural Computation, solved this problem using a statistical modeling approach, known as generalized linear models (GLMs), to analyze the cell-to-cell variability. Urban and Tripathy found that by applying this approach they were able to accurately reproduce the behavior of individual neurons in a computer, allowing them to gather statistics on each single cell.

Then, much like in fantasy football, the computer model used the statistics to put together thousands of teams of neurons. The teams competed against one another in a computer simulation to see which were able to most accurately recreate a stimulus delivered to the team of neurons. In the end researchers identified a small set of teams that they could study to see what characteristics made those populations successful.

They found that the winning teams of neurons were diverse but not as diverse as they would be if they were selected at random from the general population of neurons. The most successful sets contained a heterogeneous group of neurons that were flexible and able to respond well to a variety of stimuli.

"You can’t have a football team made up of only linebackers. You need linebackers and tight ends, a quarterback and a kicker. But, the players can’t just be random people off of the street; they all need to be good athletes. And you need to draft for positions, not just the best player available. If your best player is a quarterback — you don’t take another quarterback with your first pick," Urban said. "It’s the same with neurons. To make the most effective grouping of neurons, you need a diverse bunch that also happens to be more robust and flexible than your average neuron."

Urban believes that GLMs can be used to further understand the importance of neuronal diversity. He plans to use the models to predict how alterations in the variability of neurons’ responses, which can be caused by learning or disease, impact function.

(Image courtesy: University of Iowa)

How We Know It Hurts: Item Analysis of Written Narratives Reveals Distinct Neural Responses to Others’ Physical Pain and Emotional Suffering

People are often called upon to witness, and to empathize with, the pain and suffering of others. In the current study, we directly compared neural responses to others’ physical pain and emotional suffering by presenting participants (n = 41) with 96 verbal stories, each describing a protagonist’s physical and/or emotional experience, ranging from neutral to extremely negative. A separate group of participants rated “how much physical pain”, and “how much emotional suffering” the protagonist experienced in each story, as well as how “vivid and movie-like” the story was. Although ratings of Pain, Suffering and Vividness were positively correlated with each other across stories, item-analyses revealed that each scale was correlated with activity in distinct brain regions. Even within regions of the “Shared Pain network” identified using a separate data set, responses to others’ physical pain and emotional suffering were distinct. More broadly, item analyses with continuous predictors provided a high-powered method for identifying brain regions associated with specific aspects of complex stimuli – like verbal descriptions of physical and emotional events.

Neurobiology of Attention Deficit/Hyperactivity Disorder

Attention deficit/hyperactivity disorder (ADHD), a prevalent neurodevelopmental disorder, has been associated with various structural and functional CNS abnormalities but findings about neurobiological mechanisms linking genes to brain phenotypes are just beginning to emerge. Despite the high heritability of the disorder and its main symptom dimensions, common individual genetic variants are likely to account for a small proportion of the phenotype’s variance. Recent findings have drawn attention to the involvement of rare genetic variants in the pathophysiology of ADHD, some being shared with other neurodevelopmental disorders. Traditionally, neurobiological research on ADHD has focused on catecholaminergic pathways, the main target of pharmacological treatments. However, more distal and basic neuronal processes in relation with cell architecture and function might also play a role, possibly accounting for the coexistence of both diffuse and specific alterations of brain structure and activation patterns. This article aims to provide an overview of recent findings in the rapidly evolving field of ADHD neurobiology with a focus on novel strategies regarding pathophysiological analyses.

"I feel like I have been dropped into my body. I know this is my voice and these are my memories, but they don’t feel like they belong to me."

It happened out of the blue. Louise Airey was 8 years old, off sick from school, when suddenly she felt like she had been dropped into her own body. “It’s just so difficult to verbalise what this feels like,” she says. “All of a sudden you’re hyper aware, and everything else in the world seems unreal, like a movie.”

She panicked, but told no one. The feeling soon passed but returned several times until, at the age of 19, a migraine triggered a sensation of being disconnected from the world that was to last 18 months. When she was in her 30s she was diagnosed with depersonalisation disorder – an altered sense of self with all-encompassing feelings of not occupying your own body, and detachment from your thoughts and actions. It has come and gone throughout her life, but since a traumatic pregnancy 20 months ago, these feelings have remained constant.

"Other people seem like robots," Airey says. "It’s like I’m watching a film, like I’m on my own in the centre of everything and nothing else is real. I’ll be speaking to my children and I’ll catch my voice talking and it seems really alien and foreign. It makes you feel very separated and lonely from everything, like you’re the only person that is real."

Not so rare

Depersonalisation disorder is not as rare as you might think, says Anthony David at King’s College London and the Maudsley Hospital: it may affect almost 1 per cent of the British population (Social Psychiatry and Psychiatric Epidemiology). We’ve all probably experienced mild versions of it at some point, in the unreal, spaced-out feeling you might get while severely jet-lagged or hung-over, for example. Now neuroscientists are beginning to uncover what goes wrong in those who persistently feel unreal. Their findings could tell us something about how we all form a sense of self, and potentially, bring a treatment for those who have the disorder.

The sense of self has much to do with our awareness of our physicality and how we interact with the outside world. The brain integrates all the information coming in from the external world and from internal sensations and forms a default setting of “this is me here and now”, says Nick Medford, who studies depersonalisation at the Brighton and Sussex Medical School, UK. “If that setting changes somehow, then you feel ‘not right’, in a way that might be very hard to put into words.”

There are probably several ways that change can occur, but Medford’s work is looking at the emotional detachment characteristic of depersonalisation. In people who have the disorder, areas of the brain that are key to emotion are much less active than normal. These people also show unusual autonomic physical responses to external stimuli, such as evocative images (Emotion Review).

David and his colleagues are also looking at why people with depersonalisation disorder report emotional “numbing” – the feeling that the world is somehow alien. They have found that some areas in the brain’s frontal lobes, which help keep emotions in check, are overactive, or too controlling.

Living the scream

One symptom related to this skewed brain activity is the sensation of all sounds competing against each other to be heard. It’s like living inside Edvard Munch’s painting The Scream, Airey says, which some critics have suggested is about depersonalisation. “The person and the landscape are screaming, you can’t get any peace.”

Another area of the brain that appears to be less responsive in depersonalisation is the anterior insula, responsible for integrating physical and emotional sensations. This might explain why sufferers don’t feel in touch with the world, Medford says.

It’s not only the outside world that seems strange, says Airey. The disorder makes it almost impossible for her to relate to herself. “Everything that you’re familiar with yourself – your thoughts, your memories – become alien,” she says. “Memories of things you’ve done don’t feel like they belong to you; it robs you of your past. I know rationally that they’re my thoughts, my voice, my memories, but they’re all wrong – that why it’s so frightening. It takes away the core of who you are.”

Airey says she would investigate any potential treatment. There is an epilepsy drug, Lamotrigine, that has shown some promise when combined with an antidepressant in trials. Transcranial magnetic stimulation – in which an electromagnet stimulates or suppresses neuronal activity – is also being explored by David’s team to retrain the depersonalised brain.

"Rationally knowing that I’m real, that these memories are real, that my voice is my own, but not feeling like they all belong to me is somehow worse than being away with fairies," Airey says. "It’s like I’m a sane person gone mad."

Mindscapes: The woman who was dropped into her body by Helen Thomson

Energy Efficient Brain Simulator Outperforms Supercomputers

In November 2012, IBM announced that it had used the Blue Gene/Q Sequoia supercomputer to achieve an unprecedented simulation of more than 530 billion neurons. The Blue Gene/Q Sequoia accomplished this feat thanks to its blazing fast speed; it clocks in at over 16 quadrillion calculations per second. In fact, it currently ranks as the second-fastest supercomputer in the world.

But, according to Kwabena Boahen, Ph.D., the Blue Gene still doesn’t compare to the computational power of the brain itself.

"The brain is actually able to do more calculations per second than even the fastest supercomputer," says Boahen, a professor at Stanford University, director of the Brains in Silicon research laboratory and an NSF Faculty Early Career grant recipient.

That’s not to say the brain is faster than a supercomputer. In fact, it’s actually much slower. The brain can do more calculations per second because it’s “massively parallel,” meaning networks of neurons are working simultaneously to solve a great number of problems at once. Traditional computing platforms, no matter how fast, operate sequentially, meaning each step must be complete before the next step is begun.

Boahen works at the forefront of a field called neuromorphic engineering, which seeks to replicate the brain’s extraordinary computational abilities using innovative hardware and software applications. His laboratory’s most recent accomplishment is a new computing platform called Neurogrid, which simulates the activity of 1 million neurons.

Neurogrid is not a supercomputer. It can’t be used to simulate the big bang, or forecast hurricanes, or predict epidemics. But what it can do sets it apart from any computational platform on earth.

Neurogrid is the first simulation platform that can model a million neurons in real time. As such, it represents a powerful tool for investigating the human brain. In addition to providing insight into the normal workings of the brain, it has the potential to shed light on complex brain diseases like autism and schizophrenia, which have so far been difficult to model.

The proven ability to simulate brain function in real time has, so far, been underwhelming. For example, the Blue Gene/Q Sequoia supercomputer’s simulation took over 1,500 times longer than it would take the brain to do the same activity.

Cheaper brain simulation platforms that combine the computing power of traditional central processing units (CPUs) with graphical processing units (GPUs) and field programmable gate arrays (FPGAs) to achieve results comparable to the Blue Gene are emerging on the market. However, while these systems are more affordable, they are still frustratingly slower than the brain.

As Boahen puts it, “The good news is now you too can have your own supercomputer. The bad news is now you too can wait an hour to simulate a second of brain activity.”

When you consider that the simulations sometimes need to be checked, tweaked, re-checked and run again hundreds of times, the value of a system that can replicate brain activity in real time becomes obvious.

"Neurogrid doesn’t take an hour to simulate a second of brain activity," says Boahen. "It takes a second to simulate a second of brain activity."

Each of Neurogrid’s 16 chips contains more than 65,000 silicon “neurons” whose activity can be programmed according to nearly 80 parameters, allowing the researchers to replicate the unique characteristics of different types of neurons. Soft-wired “synapses” crisscross the board, shuttling signals between every simulated neuron and the thousands of neurons it is networked with, effectively replicating the electrical chatter that constitutes communication in the brain.

But the fundamental difference between the way traditional computing systems model the brain and the way Neurogrid works lies in the way the computations are performed and communicated throughout the system.

Most computers, including supercomputers, rely on digital signaling, meaning the computer carries out instructions by essentially answering “true” or “false” to a series of questions. This is similar to how neurons communicate: they either fire an action potential, or they don’t.

The difference is that the computations that underlie whether or not a neuron fires are driven by continuous, non-linear processes, more akin to an analog signal. Neurogrid uses an analog signal for computations, and a digital signal for communication. In doing so, it follows the same hybrid analog-digital approach as the brain.

In addition to its superior simulations, it also uses a fraction of the energy of a supercomputer. For example, the Blue Gene/Q Sequoia consumes nearly 8 megawatts of electricity, enough to power over 160,000 homes. Eight megawatts at $0.10/kWh is $800 an hour, or a little over $7 million a year.

Neurogrid, on the other hand, operates on a paltry 5 watts, the amount of power used by a single cell phone charger.

Ultimately, Neurogrid represents a cost-effective, energy-efficient computing platform that Boahen hopes will revolutionize our understanding of the brain.

For more information about this project, check out Dr. Boahen’s website.