Alzheimer’s Disease: Possibly Caused From Haywire Immune System Eating Brain Connections?

By: Alexandria Addesso

Memory loss and absent-mindedness has long been seen as an inevitable flaw that comes with old age. Although there is a slew of medications on the market that are prescribed for those suffering from Alzheimer’s Disease, none seem to change it by too large of a margin. This has led scientists to rethink what in particular is the root cause of Alzheimer’s.

New studies done on laboratory test rodents have found that there is a marked loss of synapses, which are a junction between two nerve cells, consisting of a minute gap across which impulses pass by diffusion of a neurotransmitter. Specifically synapses that are located in brain regions that are highly significant and key to memory.



These junctions between nerve cells are where neurotransmitters are released to spark the brain’s electrical activity. Currently, all pharmaceutical drugs on the market for the treatment of Alzheimer’s, focus on eliminating β amyloid, a protein that forms telltale sticky plaques around neurons in people with the disease. But, more β amyloid does not always mean more severe symptoms such as memory loss or poor attention.

Researchers at the University of Virginia, School of Medicine, in Charlottesville found that a protein called ‘C1q’ sets off a series of chemical reactions that ultimately mark a synapse for destruction. After this occurs immune cells called microglia-glial cells derived from mesoderm that function as macrophages (scavengers) in the central nervous system and form part of the reticuloendothelial system, destroy or “eat” the synapse.



“It is beautiful new work brings into light what’s happening in the early stage of the disease,” said one of the researchers at the University of Virginia School of Medicine neuroscientist Jonathan Kipnis.

These findings could mean that treatment that blocks C1q could be pivotal and highly successful in fighting Alzheimer’s Disease. When researchers gave the laboratory rodent test subjects an antibody to stop the destruction of cells by microglia, synapse loss did not appear. This could also mean a slowing in cognitive decline, but according to Edward Ruthazer, a neuroscientist at the Montreal Neurological Institute and Hospital in Canada, using microglia as such a central role to fight the disease is “still on the controversial side.”

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Parallel Computation Provides Deeper Insight into Brain Function

Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. While many computational neuroscientists use simplified mathematical models of neurons, researchers in the Computational Neuroscience Unit at the Okinawa Institute of Science and Technology Graduate University (OIST) develop software that models neurons to the detail of molecular interactions with the goal of eliciting new insights into neuronal function. Applications of the software were limited in scope up until now because of the intense computational power required for such detailed neuronal models, but recently Dr. Weiliang Chen, Dr. Iain Hepburn, and Professor Erik De Schutter published two related papers in which they outline the accuracy and scalability of their new high-speed computational software, "Parallel STEPS". The combined findings suggest that Parallel STEPS could be used to reveal new insights into how individual neurons function and communicate with each other.

The first paper, published in The Journal of Chemical Physics in August 2016, focusses on ensuring that the accuracy of Parallel STEPS is comparable with conventional methods. In conventional approaches, computations associate with neuronal chemical reactions and molecule diffusion are all calculated on one computational processing unit or 'core' sequentially. However, Dr. Iain Hepburn and colleagues introduced a new approach to perform computations of reaction and diffusion in parallel which can then be distributed over multiple computer cores, whilst maintaining simulation accuracy to a high degree. The key was to develop an original algorithm separated into two parts - one that computed chemical reaction events and the other diffusion events.

"We tested a range of model simulations from simple diffusion models to realistic biological models and found that we could achieve improved performance using a parallel approach with minimal loss of accuracy. This demonstrated the potential suitability of the method on a larger scale," says Dr. Hepburn.



In a related paper published in Frontiers in Neuroinformatics this February, Dr. Weiliang Chen presented the implementation details of Parallel STEPS and investigated its performance and potential applications. By breaking a partial model of a Purkinje cell - one of the largest neurons in the brain - into 50 to 1000 sections and simulating reaction and diffusion events for each section in parallel on the Sango supercomputer at OIST, Dr. Chen and colleagues saw dramatically increased computation speeds. They tested this approach on both simple models and more complicated models of calcium bursts in Purkinje cells and demonstrated that parallel simulation could speed up computations by more than several hundred times that of conventional methods.

"Together, our findings show that Parallel STEPS implementation achieves significant improvements in performance, and good scalability," says Dr. Chen. "Similar models that previously required months of simulation can now be completed within hours or minutes, meaning that we can develop and simulate more complex models, and learn more about the brain in a shorter amount of time."

Dr. Hepburn and Dr. Chen from OIST's Computational Neuroscience Unit, led by Professor Erik De Schutter, are actively collaborating with the Human Brain Project, a world-wide initiative based at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, to develop a more robust version of Parallel STEPS that incorporates electric field simulation of cell membranes.

So far STEPS is only realistically capable of modeling parts of neurons but with the support of Parallel STEPS, the Computational Neuroscience Unit hopes to develop a full-scale model of a whole neuron and subsequently the interactions between neurons in a network. By collaborating with the EPFL team and by making use of the IBM 'Blue Gene/Q' supercomputer located there, they aim to achieve these goals in the near future.



"Thanks to modern supercomputers we can study molecular events within neurons in a much more transparent way than before," says Prof. De Schutter. "Our research opens up interesting avenues in computational neuroscience that links biochemistry with electrophysiology for the first time."
Source: Journal of Chemical Physics. Provided by: Okinawa Institute of Science and Technology

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Autism diagnosis by brain scan? It’s time for a reality check

Recent reports that it might be possible to use MRI to identify at-risk children are exciting, but we are still a long way from autism diagnosis by brain scan

A brain scan for autism would be a major step forward. But is the hype justified?

What if I told you that we can now identify babies who are going to develop autism based on a simple brain scan? This, in essence, is the seductive pitch for a study published last week in the journal Nature, and making headlines around the world.

Early identification and diagnosis is one of the major goals of autism research. By definition, people with autism have difficulties with social interaction and communication. But these skills take many years to develop, even in typically developing (i.e., non-autistic) children. Potential early signs of autism are extremely difficult to pick out amidst the natural variation in behavior and temperament that exists between all babies.
A brain scan for autism would be a major step forward. But is the hype justified? Are we really on the brink of a new era in autism diagnostics? Without wishing to detract from the efforts of everyone involved in the study, it’s important to look at the results critically, both in terms of the scientific findings and their potential implications for clinical practice.



The study, led by Heather Cody Hazlett at the University of North Carolina, was part of a larger research program investigating the development of babies who have an older sibling with autism. Because autism runs in families, these babies are much more likely to develop autism than babies from the general population.

The babies were given MRI brain scans at 6, 12, and 24 months of age and were then assessed for autism. As expected for this “high risk” sample, around 1 in 5 met the diagnostic criteria. The researchers were then able to look back at the brain scans to see if there were any differences between the autistic and the non-autistic babies.

Hazlett and colleagues first looked at three measures of overall brain size: the total volume of the brain; its total surface area; and the average thickness of the cortex (the brain’s outer layer). Consistent with previous studies of older children, the autistic babies had slightly larger brain volume and greater surface area. However, these effects were only statistically significant for the last scan at 24 months.

Most autistic infants had brains that wouldn’t set them apart from non-autistic infants. In other words, overall brain size isn’t in itself a very good predictor of whether or not an individual baby will go on to an autism diagnosis.

So Hazlett and colleagues tried a different approach, calculating the volume and surface area for 78 different regions within each infant’s brain. They did this twice: once for the 6 month scan and again for the 12 month scan, giving them 312 data points, or “features”, for each baby.



Next, they fed that information (plus the sex and skull volume of each baby) into a computer that they trained to differentiate between the autistic and non-autistic babies.

Importantly, they only trained it on 90% of the babies at a time. They then fed in the brain features from the remaining 10% and asked the computer to predict the diagnosis of each baby. They did this 10 times, leaving out a different subgroup of babies each time.

The computer correctly diagnosed 30 of 34 autistic babies in the sample and incorrectly flagged just 7 of 145 non-autistic babies. So the excitement is understandable.

Of 34 babies with autism, 30 were correctly identified. False positives occurred for 7 out of 145 non-autistic babies.

However, as the researchers themselves note, the study really needs to be replicated. Because it was a first-of its-kind, the researchers would necessarily have been feeling their way, making decisions as they went along. This tweaking inevitably biases the outcome towards a more compelling result. But having learnt the lessons from this first study, researchers are now in a position to preregister any replication attempt, nailing down all the details before they begin. If the current results are robust, they should replicate even without the tweaking.



Assuming the results do hold up, the next big question is whether this approach actually translates to real life clinical applications. Will we really see the everyday use of MRI scans to predict whether or not babies have or will develop autism?

An important practical consideration is the requirement for brain scans to be acquired at both 6 and 12 months. MRI scanners are noisy and claustrophobic. Any movement and the scan is ruined. The researchers scanned the babies while they were asleep but, despite their best efforts, only around half of the babies had two useable scans. Once we add the babies with incomplete data to the picture, the results start to look less useful. In particular, only 30 of the 70 autistic babies in the study could be identified based on their brain scans.

Including babies with incomplete data, only 30 out of 70 babies with autism were correctly identified.

As a final point, the use of MRI scans for autism detection is unlikely to be of much practical benefit beyond high-risk populations. This is simply an issue of numbers. In the general population, it’s estimated that one person in 68 has autism. In the figure below, I’ve assumed that the computer maintains the same ability to differentiate between autistic and non-autistic brains but is now faced with 67 non-autistic babies for every one autistic baby.

Assuming an estimate of 1 in 68 people having autism, in order to identify the 30 babies with autism in the original sample, we would need to scan a total of 4760 babies.

We’d still miss the other 40 autistic babies. And because of the scaling up, 132 non-autistic babies would incorrectly test positive. In other words, 81% of babies who tested positive would not actually be autistic.

These are, of course, inexact back-of-the-envelope calculations. The computer algorithm may perform much better when it is trained to differentiate between autistic and low risk babies. And there are, no doubt, ways of improving the success rate of scanning. But it illustrates the profound challenges in translating the research finding into widespread clinical practice. For now at least, it’s time to dial back the hype. We are still a very long way from autism diagnosis by brain scan.

But from a scientific point of view, I remain excited by these findings. They’re part of a growing body of evidence for subtle differences in the brains of young infants who go on to be diagnosed with autism. Some of these findings are perhaps more robust than others, but each represents an important step towards a greater understanding of the developmental origins of autism in the
Source: Jon Brock, The Guardian

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Understanding the Brain with the Help of Artificial Intelligence

Neurobiologists aim to decode the brain’s circuitry with the help of artificial neural networks. NeuroscienceNews.com image is credited to Julia Kuhl.

Researchers have trained neural networks to accelerate the reconstruction of neural circuits.

How does consciousness arise? Researchers suspect that the answer to this question lies in the connections between neurons. Unfortunately, however, little is known about the wiring of the brain. This is due also to a problem of time: tracking down connections in collected data would require man-hours amounting to many lifetimes, as no computer has been able to identify the neural cell contacts reliably enough up to now. Scientists from the Max Planck Institute of Neurobiology in Martinsried plan to change this with the help of artificial intelligence. They have trained several artificial neural networks and thereby enabled the vastly accelerated reconstruction of neural circuits.

Neurons need company. Individually, these cells can achieve little, however when they join forces neurons form a powerful network which controls our behaviour, among other things. As part of this process, the cells exchange information via their contact points, the synapses. Information about which neurons are connected to each other when and where is crucial to our understanding of basic brain functions and superordinate processes like learning, memory, consciousness and disorders of the nervous system. Researchers suspect that the key to all of this lies in the wiring of the approximately 100 billion cells in the human brain.



To be able to use this key, the connectome, that is every single neuron in the brain with its thousands of contacts and partner cells, must be mapped. Only a few years ago, the prospect of achieving this seemed unattainable. However, the scientists in the Electrons – Photons – Neurons Department of the Max Planck Institute of Neurobiology refuse to be deterred by the notion that something seems “unattainable”. Hence, over the past few years, they have developed and improved staining and microscopy methods which can be used to transform brain tissue samples into high-resolution, three-dimensional electron microscope images. Their latest microscope, which is being used by the Department as a prototype, scans the surface of a sample with 91 electron beams in parallel before exposing the next sample level. Compared to the previous model, this increases the data acquisition rate by a factor of over 50. As a result an entire mouse brain could be mapped in just a few years rather than decades.

Although it is now possible to decompose a piece of brain tissue into billions of pixels, the analysis of these electron microscope images takes many years. This is due to the fact that the standard computer algorithms are often too inaccurate to reliably trace the neurons’ wafer-thin projections over long distances and to identify the synapses. For this reason, people still have to spend hours in front of computer screens identifying the synapses in the piles of images generated by the electron microscope.



Training for neural networks
However the Max Planck scientists led by Jörgen Kornfeld have now overcome this obstacle with the help of artificial neural networks. These algorithms can learn from examples and experience and make generalizations based on this knowledge. They are already applied very successfully in image process and pattern recognition today. “So it was not a big stretch to conceive of using an artificial network for the analysis of a real neural network,” says study leader Jörgen Kornfeld. Nonetheless, it was not quite as simple as it sounds. For months the scientists worked on training and testing so-called Convolutional Neural Networks to recognize cell extensions, cell components and synapses and to distinguish them from each other.

Following a brief training phase, the resulting SyConn network can now identify these structures autonomously and extremely reliably. Its use on data from the songbird brain showed that SyConn is so reliable that there is no need for humans to check for errors. “This is absolutely fantastic as we did not expect to achieve such a low error rate,” says Kornfeld with obvious delight at the success of SyConn, which forms part of his doctoral study. And he has every reason to be delighted as the newly developed neural networks will relieve neurobiologists of many thousands of hours of monotonous work in the future. As a result, they will also reduce the time needed to decode the connectome and, perhaps also, the consciousness, by many years.
Source: Max Planck Institute / Neuroscience.news

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The Incredible Benefits of Exercise

Many people hit the gym or pound the pavement to improve cardiovascular health, build muscle, and of course, get a rockin’ bod, but working out has above-the-neck benefits too. For the past decade or so, scientists have pondered how exercising can boost brain function. Regardless of age or fitness level (yup, this includes everyone from mall-walkers to marathoners), studies show that making time for exercise provides some serious mental benefits. Get inspired to exercise by reading up on these unexpected ways that working out can benefit mental health, relationships, and lead to a healthier and happier life overall.

1. Reduce stress. Rough day at the office? Take a walk or head to the gym for a quick workout. One of the most common mental benefits of exercise is stress relief. Working up a sweat can help manage physical and mental stress. Exercise also increases concentrations of norepinephrine, a chemical that can moderate the brain’s response to stress. So go ahead and get sweaty—working out can reduce stress and boost the body’s ability to deal with existing mental tension. Win-win!



2. Boost happy chemicals. Slogging through a few miles on the ‘mill can be tough, but it’s worth the effort! Exercise releases endorphins, which create feelings of happiness and euphoria. Studies have shown that exercise can even alleviate symptoms among the clinically depressed. For this reason, docs recommend that people suffering from depression or anxiety (or those who are just feeling blue) pencil in plenty of gym time. In some cases, exercise can be just as effective as antidepressant pills in treating depression. Don’t worry if you’re not exactly the gym rat type—getting a happy buzz from working out for just 30 minutes a few times a week can instantly boost overall mood.

3. Improve self-confidence. Hop on the treadmill to look (and more importantly, feel) like a million bucks. On a very basic level, physical fitness can boost self-esteem and improve positive self-image. Regardless of weight, size, gender, or age, exercise can quickly elevate a person’s perception of his or her attractiveness, that is, self-worth. How’s that for feeling the (self) love?



4. Enjoy the great outdoors. For an extra boost of self-love, take that workout outside. Exercising in the great outdoors can increase self-esteem even more. Find an outdoor workout that fits your style, whether it’s rock-climbing, hiking, renting a canoe, or just taking a jog in the park. Plus, all that Vitamin D acquired from soaking up the sun (while wearing sunscreen, of course!) can lessen the likelihood of experiencing depressive symptoms. Why book a spa day when a little fresh air and sunshine (and exercise) can work wonders for self-confidence and happiness?

5. Prevent cognitive decline. It’s unpleasant, but it’s true—as we get older, our brains get a little…hazy. As aging and degenerative diseases like Alzheimer’s kill off brain cells, the noggin actually shrinks, losing many important brain functions in the process. While exercise and a healthy diet can’t “cure” Alzheimer’s, they can help shore up the brain against cognitive decline that begins after age 45. Working out, especially between age 25 and 45, boosts the chemicals in the brain that support and prevent degeneration of the hippocampus, an important part of the brain for memory and learning.

6. Alleviate anxiety. Quick Q&A: Which is better at relieving anxiety—a warm bubble bath or a 20-minute jog? You might be surprised at the answer. The warm and fuzzy chemicals that are released during and after exercise can help people with anxiety disorders calm down. Hopping on the track or treadmill for some moderate-to-high intensity aerobic exercise (intervals, anyone?) can reduce anxiety sensitivity. And we thought intervals were just a good way to burn calories!

7. Boost brainpower. Those buff lab rats might be smarter than we think. Various studies on mice and men have shown that cardiovascular exercise can create new brain cells (aka neurogenesis) and improve overall brain performance. Ready to apply for a Nobel Prize? Studies suggest that a tough workout increases levels of a brain-derived protein (known as BDNF) in the body, believed to help with decision making, higher thinking, and learning. Smarty (spandex) pants, indeed.



8. Sharpen memory. Get ready to win big at Go Fish. Regular physical activity boosts memory and ability to learn new things. Getting sweaty increases production of cells in hippocampus responsible for memory and learning. For this reason, research has linked children’s brain development with level of physical fitness (take that, recess haters!). But exercise-based brainpower isn’t just for kids. Even if it’s not as fun as a game of Red Rover, working out can boost memory among grown-ups, too. A study showed that running sprints improved vocabulary retention among healthy adults.

9. Help control addiction. The brain releases dopamine, the “reward chemical” in response to any form of pleasure, be that exercise, sex, drugs, alcohol, or food. Unfortunately, some people become addicted to dopamine and dependent on the substances that produce it, like drugs or alcohol (and more rarely, food and sex). On the bright side, exercise can help in addiction recovery. Short exercise sessions can also effectively distract drug or alcohol addicts, making them de-prioritize cravings (at least in the short term). Working out when on the wagon has other benefits, too. Alcohol abuse disrupts many body processes, including circadian rhythms. As a result, alcoholics find they can’t fall asleep (or stay asleep) without drinking. Exercise can help reboot the body clock, helping people hit the hay at the right time.



10. Increase relaxation. Ever hit the hay after a long run or weight session at the gym? For some, a moderate workout can be the equivalent of a sleeping pill, even for people with insomnia. Moving around five to six hours before bedtime raises the body’s core temperature. When the body temp drops back to normal a few hours later, it signals the body that it’s time to sleep.

11. Get more done. Feeling uninspired in the cubicle? The solution might be just a short walk or jog away. Research shows that workers who take time for exercise on a regular basis are more productive and have more energy than their more sedentary peers. While busy schedules can make it tough to squeeze in a gym session in the middle of the day, some experts believe that midday is the ideal time for a workout due to the body’s circadian rhythms.

12. Tap into creativity. Most people end a tough workout with a hot shower, but maybe we should be breaking out the colored pencils instead. A heart-pumping gym session can boost creativity for up to two hours afterwards. Supercharge post-workout inspiration by exercising outdoors and interacting with nature (see benefit No. 4). Next time you need a burst of creative thinking, hit the trails for a long walk or run to refresh the body and the brain at the same time.

13. Inspire others. Whether it’s a pick-up game of soccer, a group class at the gym, or just a run with a friend, exercise rarely happens in a bubble. And that’s good news for all of us. Studies show that most people perform better on aerobic tests when paired up with a workout buddy. Pin it to inspiration or good old-fashioned competition, nobody wants to let the other person down. In fact, being part of a team is so powerful that it can actually raise athletes’ tolerances for pain. Even fitness beginners can inspire each other to push harder during a sweat session, so find a workout buddy and get moving!

Working out can have positive effects far beyond the gym (and beach season). Gaining self-confidence, getting out of a funk, and even thinking smarter are some of the motivations to take time for exercise on a regular basis.
Source: Sophia Breene for Greatist.com

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Understanding the Obsessive Compulsive Disorder: Conceptual Background and Brain Etiology

Patients suffering from obsessive–compulsive disorder (OCD) experience a combination of anxiety-producing obsessive thought patterns and related compulsive behaviors designed to reduce the distress associated with the obsessions.

Obsessions are recurrent thoughts, impulses, or images that are threatening because they are perceived as either unacceptable or leading to a dreaded outcome, and thus cause marked anxiety. Common obsessional ‘themes’ include contamination (thinking one has contacted dangerous germs or toxins), aggression (image or urge to drive into oncoming traffic or stab one’s spouse), accidental harm (fear that one has hit a pedestrian or doubting whether one turned off the stove), blasphemy (thinking one has offended God by doing a religious ritual incorrectly), and sexuality (intrusive images of having sex with a child or parent). Sometimes an obsession is vague, yet still evokes a looming sense of danger: a ‘bad feeling’ that occurs during an action, or the inexplicable sense that a behavior has not been done correctly. Multiple types of obsessions are found in most affected individuals and can change over time.



Compulsions include behaviors (e.g., hand washing, checking, ordering, or arranging things) and mental actions (e.g., praying, counting, repeating words silently) that are aimed at preventing or neutralizing the threat associated with the obsession, and thus temporarily reduce anxiety. This relief from the distress is highly reinforcing, resulting in the persistent use of compulsions. Compulsive behaviors are often repeated (checking the stove 15 times), or have to be performed according to rules that must be applied rigidly (a sterilization ritual for plates and silverware before meals).

Sometimes compulsions are ‘logically’ linked to the obsessions, as in the case of washing one’s hands in response to a contamination obsession, or driving back to a spot where one fears they may have hit someone. Done once, such behavior might seem reasonable; it is the repetitive, time-consuming, and rigid quality that distinguishes compulsions. Sometimes there is no ‘logical’ action to prevent the obsessional threat so, compulsions develop that are more akin to superstitious rituals. For example, going through doorways can often trigger an obsession (‘bad feeling’). Given no clear antidote to the vague threat, individuals may develop a ritualized compulsion aimed at neutralizing the obsession in some magical way. This might involve having to go through the door on the left side, touching both sides of the threshold 3 times, or passing through the doorway repeatedly until it is accomplished without any ‘bad thoughts.’



Individuals with OCD generally have some degree of insight that their symptoms are excessive or unreasonable. Nonetheless, the disorder is time-consuming, distressing, and severely impairing within the realms of both social and occupational functioning. It is also associated with increased risk of suicide. OCD has an estimated, lifetime prevalence in the general US population of 2–3%, and is equally common in both males and females. The age of onset follows a bimodal distribution: early onset (prepubescent, the majority of cases) and late onset (early 20s). Early-onset cases are more likely to be male, have a family history of OCD, greater symptom severity, and co-occurring tics, OCD spectrum (discussed in section Differential Diagnosis), and disruptive behavioral disorders (e.g., attention deficit hyperactivity disorder).

Differential Diagnosis
It is important to distinguish OCD from worry, intrusive thoughts, and compulsions seen in everyday life. OCD obsessions are experienced as unwanted and anxiety-producing, whereas worry functions more as a mental coping strategy that provides a sense of control and preparation for a perceived future threat. Intrusive thoughts (i.e., suddenly envisioning a family member falling off a cliff while hiking together) are common, but in OCD they occur at a higher frequency, and are experienced as having unusual importance, so are more distressing to the affected individual. Compulsive behaviors are also frequently seen in normal populations in the form of superstitious behavior and repetitive checking. The diagnosis of OCD is made only if they are time consuming or if they result in significant psychosocial impairment or distress.

There are a number of disorders that share the features of OCD, and are sometimes considered as ‘OCD spectrum disorders.’ Disorders such as body dysmorphic disorder, hypochondriasis, and hoarding and eating disorders include obsessive-like fears (that one has a serious illness or is fat), but the thoughts are not experienced themselves as highly intrusive and inappropriate. Derma-tillomania (skin picking) and trichotillomania (hair pulling) have repetitive behaviors that may bring some anxiety relief, but they are neither triggered by obsessions nor have the magical or ritualistic quality of OCD compulsions. Although impulse-control disorders such as kleptomania, pyromania, and pathological gambling also have recurrent thoughts and behaviors that are difficult to resist, the drive tends to be more pleasure-seeking than distress reducing.



Schizophrenia is often characterized by strongly held beliefs that are clearly false (delusions) as well as by stereotyped behaviors. Individuals with OCD, however, generally show considerable insight into their symptoms. In major depression, the depressed individual may have distressing, repetitive thoughts, but these are rarely resisted, and are often focused on a past incident rather than on a current or future threat. Although it has a similar name, obsessive–compulsive personality disorder is actually quite different from OCD. Obsessive–compulsive personality disorder does not involve obsessions or compulsions; rather, it is characterized by a pervasive pattern of maladaptive orderliness, perfectionism, and control.

Other disorders may mimic OCD. Tics and stereotyped movements are similar to compulsions in their appearance but not in their function. Generally, the cognitive elements involved in OCD compulsions are much more complex, whereas in tics and stereotypic movements, the individual does not report any specific reason for the behavior, but only a nonspecific tension that builds until the behavior is performed. Of note, Tourette’s syndrome and OCD are frequently co-occurring disorders, and individuals with Tourette’s should be routinely asked about the presence of obsessions and compulsions.



Etiology
There is converging evidence that OCD involves dysfunction of the corticostriatal-thalamic circuits, which help integrate cognitive and sensorimotor functions, and in particular initiate automatic, procedural behaviors. The high co-occurrence of OCD with Tourette’s – a disorder involving cortical and striatal pathways – is suggestive of a similar etiology. There are also data supporting an association between an autoimmune response to Group A β-hemolytic Streptococcus, affecting the striatal regions, and the acute emergence of OCD, often with tic symptoms (including Tourette’s). The term pediatric autoimmune neuropsychiatric disorder associated with Streptococcus refers to a group of children with this presumed immunological etiology. The role of serotonin in the corticostriatal-thalamic circuits is thought to be important, and several studies suggest that serotonin reuptake inhibitors may normalize activity in these pathways. Medications that boost serotonin activity reliably reduce OCD symptoms. Research also suggests that abnormalities in the glutamate and dopamine systems are involved in OCD as well.

The evidence for a genetic contribution is supported by the monozygotic twin studies showing a concordance rate from 63% to 87%, and first-degree relatives showing rates of OCD in the range of 10–22.5%. No candidate gene has been identified that can reliably account for the broad phenotype of OCD. Animal models of OCD, such as those found naturally in dogs or induced in laboratory mice identify the potential genes for further study.



From the standpoint of neuroimaging, OCD is one of the most investigated illnesses in the anxiety cluster. As of yet, it remains impossible to attribute causality to particular brain structures in the cognitions and clinical features of OCD. In animal models, abnormalities in the orbitofronto-striatal circuits are associated with an impaired ability to modify behavior in response to new information, for example: impaired inhibition of previously important, but now inappropriate response to stimuli. Humans with injuries to the striatum, or areas to which it projects, often develop obsessive –compulsive behaviors. Nevertheless, no consistent structural abnormality has been identified in patients meeting the criteria for OCD. This may suggest that the causative abnormalities are present at the level of a system or network, not at the level of isolated neuroanatomical structures, or because of a marked heterogeneity within the diagnosis. Illnesses with components of compulsive and impulsive behaviors, such as Tourette’s syndrome and trichotillomania, tend to occur in comorbidity with OCD, or cluster with OCD within families. Further research into these disorders of overlapping end phenotype may serve to illuminate the rest of the OCD picture as it relates to the brain structure.
Source: Vimen L. Beckner, University of California San Francisco, and San Francisco Group for Evidence-Based Psychotherapy, San Francisco, CA, USA.

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How humans bond: The Brain Chemistry Revealed

In a new study researches found for the first time that dopamine is involved in human bonding bringing the brain’s reward into understanding of how we form human attachments.

In new research published Monday in the journal Proceedings of the National Academy of Sciences, Northeastern University psychology professor Lisa Feldman Barrett found, for the first time, that the neurotransmitter dopamine is involved in human bonding, bringing the brain's reward system into our understanding of how we form human attachments. The results, based on a study with 19 mother-infant pairs, have important implications for therapies addressing postpartum depression as well as disorders of the dopamine system such as Parkinson's disease, addiction, and social dysfunction.



"The infant brain is very different from the mature adult brain -- it is not fully formed," says Barrett, University Distinguished Professor of Psychology and author of the forthcoming book How Emotions Are Made: The Secret Life of the Brain. "Infants are completely dependent on their caregivers. Whether they get enough to eat, the right kind of nutrients, whether they're kept warm or cool enough, whether they're hugged enough and get enough social attention, all these things are important to normal brain development. Our study shows clearly that a biological process in one person's brain, the mother's, is linked to behavior that gives the child the social input that will help wire his or her brain normally. That means parents' ability to keep their infants cared for leads to optimal brain development, which over the years results in better adult health and greater productivity."

To conduct the study, the researchers turned to a novel technology: a machine capable of performing two types of brain scans simultaneously -- functional magnetic resonance imaging, or fMRI, and positron emission tomography, or PET.

fMRI looks at the brain in slices, front to back, like a loaf of bread, and tracks blood flow to its various parts. It is especially useful in revealing which neurons are firing frequently as well as how different brain regions connect in networks. PET uses a small amount of radioactive chemical plus dye (called a tracer) injected into the bloodstream along with a camera and a computer to produce multidimensional images to show the distribution of a specific neurotransmitter, such as dopamine or opioids.



Barrett's team focused on the neurotransmitter dopamine, a chemical that acts in various brain systems to spark the motivation necessary to work for a reward. They tied the mothers' level of dopamine to her degree of synchrony with her infant as well as to the strength of the connection within a brain network called the medial amygdala network that, within the social realm, supports social affiliation.

"We found that social affiliation is a potent stimulator of dopamine," says Barrett. "This link implies that strong social relationships have the potential to improve your outcome if you have a disease, such as depression, where dopamine is compromised. We already know that people deal with illness better when they have a strong social network. What our study suggests is that caring for others, not just receiving caring, may have the ability to increase your dopamine levels."



Before performing the scans, the researchers videotaped the mothers at home interacting with their babies and applied measurements to the behaviors of both to ascertain their degree of synchrony. They also videotaped the infants playing on their own.

Once in the brain scanner, each mother viewed footage of her own baby at solitary play as well as an unfamiliar baby at play while the researchers measured dopamine levels, with PET, and tracked the strength of the medial amygdala network, with fMRI.

The mothers who were more synchronous with their own infants showed both an increased dopamine response when viewing their child at play and stronger connectivity within the medial amygdala network. "Animal studies have shown the role of dopamine in bonding but this was the first scientific evidence that it is involved in human bonding," says Barrett. "That suggests that other animal research in this area could be directly applied to humans as well."

The findings, says Barrett, are "cautionary." "They have the potential to reveal how the social environment impacts the developing brain," she says. "People's future health, mental and physical, is affected by the kind of care they receive when they are babies. If we want to invest wisely in the health of our country, we should concentrate on infants and children, eradicating the adverse conditions that interfere with brain development."
Source: Materials provided by Northeastern University, original written by Thea Singer.

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Soccer Success in the Young Can Be Measured in the Brain

Executive functions are special control functions in the brain that allow us to adapt to an environment in a perpetual state of change. They include creative thinking in order to quickly switch strategy, find new, effective solutions and repress erroneous impulses. The functions are dependent on the brain’s frontal lobes, which continue to develop until the age of 25. NeuroscienceNews.com image is adapted from Karolinska Institute press release.

Cognitive function can be quantified and linked to how well a child performs in a game of soccer, researchers report.

The working memory and other cognitive functions in children and young people can be associated with how successful they are on the football pitch, a new study from Karolinska Institutet, Sweden, shows. Football clubs that focus too much on physical attributes therefore risk overlooking future stars.



Physical attributes such as size, fitness and strength in combination with ball control have long been considered critical factors in the hunt for new football talent. The third, slightly elusive factor of “game intelligence” — to always be at the rights place at the right time — has been difficult to measure. In 2012, researchers at Karolinska Institutet provided a possible scientific explanation for the phenomenon, and showed that the so-termed “executive cognitive functions” in adult players could be associated with their success on the pitch. In a new study, which is published in the scientific journal PLOS ONE, they show that cognitive faculties can be similarly quantified and linked to how well children and young people do in the game.

“This is interesting since football clubs focus heavily on the size and strength of young players,” says study leader Predrag Petrovic, at Karolinska Institutet’s Department of Clinical Neuroscience. “Young players who have still to reach full physical development rarely get a chance to be picked as potential elite players, which means that teams risk missing out on a new Pele, Maradona or Messi.”



Executive functions are special control functions in the brain that allow us to adapt to an environment in a perpetual state of change. They include creative thinking in order to quickly switch strategy, find new, effective solutions and repress erroneous impulses. The functions are dependent on the brain’s frontal lobes, which continue to develop until the age of 25.

For this present study, the researchers measured certain executive functions in 30 elite footballers aged between 12 and 19, and then cross-referenced the results with the number of goals they scored during two years. The metrics were taken in part using the same standardized tests used in healthcare. Strong results for several executive functions were found to be associated with success on the pitch, even after controlling for other factors that could conceivably affect performance. The clearest link was seen for simpler forms of executive function, such as working memory, which develops relatively early in life.

“This was expected since cognitive function is less developed in young people than it is in adults, which is probably reflected in how young people play, with fewer passes that lead to goals,” says Predrag Petrovic.
The young elite players also performed significantly better than the average population in the same age group on several tests of executive function. Whether these faculties are inherited or can be trained remains the object of future research, as does the importance of the different executive functions for the various positions on the field.



“We think that the players’ positions on the pitch are linked to different cognitive profiles,” continues Dr Petrovic. “I can imagine that trainers will start to use cognitive tests more and more, both to find talented newcomers and to judge the position they should play in.”
Source: Karolinska Institute
Image Source: NeuroscienceNews.com image is adapted from Karolinska Institute press release.
Original Research: Full open access research for “Core executive functions are associated with success in young elite soccer players” by Torbjörn Vestberg, Gustaf Reinebo, Liselotte Maurex, Martin Ingvar, Predrag Petrovic in PLOS ONE. Neuroscience.news

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