Infections During Childhood Increase Risks of Mental Disorders Developing

The connection between mind and body is further emphasized.

A new study from iPSYCH shows that the infections children contract during their childhood are linked to an increase in the risk of mental disorders during childhood and adolescence. This knowledge expands our understanding of the role of the immune system in the development of mental disorders.

High temperatures, sore throats and infections during childhood can increase the risk of also suffering from a mental disorder as a child or adolescent. This is shown by the first study of its kind to follow all children born in Denmark between 1 January 1995 and 30 June 2012. The researchers have looked at all infections that have been treated from birth and also at the subsequent risk of childhood and adolescent psychiatric disorders.

“Hospital admissions with infections are particularly associated with an increased risk of mental disorders, but so too are less severe infections that are treated with medicine from the patient’s own general practitioner,” says Ole Köhler-Forsberg from Aarhus University and Aarhus University Hospital’s Psychoses Research Unit. He is one of the researchers behind the study.

The study showed that children who had been hospitalised with an infection had an 84 per cent increased risk of suffering a mental disorder and a 42 per cent increased risk of being prescribed medicine to treat mental disorders. Furthermore, the risk for a range of specific mental disorders was also higher, including psychotic disorders, OCD, tics, personality disorders, autism and ADHD.

“This knowledge increases our understanding of the fact that there is a close connection between body and brain and that the immune system can play a role in the development of mental disorders. Once again research indicates that physical and mental health are closely connected,” says Ole Köhler-Forsberg.

Highest risk following an infection

The study has just been published in JAMA Psychiatry and is a part of the Danish iPSYCH psychiatry project.

“We also found that the risk of mental disorders is highest right after the infection, which supports the infection to some extent playing a role in the development of the mental disorder,” says Ole Köhler-Forsberg.

It therefore appears that infections and the inflammatory reaction that follows afterwards can affect the brain and be part of the process of developing severe mental disorders. This can, however, also be explained by other causes, such as some people having a genetically higher risk of suffering more infections and mental disorders.

The new knowledge could have importance for further studies of the immune system and the importance of infections for the development of a wide range of childhood and adolescent mental disorders for which the researchers have shown a correlation. This is the assessment of senior researcher on the study, Research Director Michael Eriksen Benrós from the Psychiatric Centre Copenhagen at Copenhagen University hospital.

“The temporal correlations between the infection and the mental diagnoses were particularly notable, as we observed that the risk of a newly occurring mental disorder was increased by 5.66 times in the first three months after contact with a hospital due to an infection and were also increased more than twofold within the first year,” he explains.

Michael Eriksen Benrós stresses that the study can in the long term lead to increased focus on the immune system and how infections play a role in childhood and adolescent mental disorders.

“It can have a consequence for treatment and the new knowledge can be used in making the diagnosis when new psychiatric symptoms occur in a young person. But first and foremost it corroborates our increasing understanding of how closely the body and brain are connected,” he says.

AI System Shows Somewhat Human-Like Creativity In Chess, In a Possible Landmark AI Moment

Artificial intelligence’s power brings with it the possibility of doing immense good or immense harm to humanity, and it is going to be up to society to ensure that AI functions in benevolent ways. Stockfish has also been the most dominant chess engine for quite some time, and to see it defeated consistently by a human-like, dynamic chess engine is both amazing and unsettling.

DeepMind’s artificial intelligence programme AlphaZero is now showing signs of human-like intuition and creativity, in what developers have hailed as ‘turning point’ in history.

The computer system amazed the world last year when it mastered the game of chess from scratch within just four hours, despite not being programmed how to win.

But now, after a year of testing and analysis by chess grandmasters, the machine has developed a new style of play unlike anything ever seen before, suggesting the programme is now improvising like a human.

Unlike the world’s best chess machine – Stockfish – which calculates millions of possible outcomes as it plays, AlphaZero learns from its past successes and failures, making its moves based on, a ‘nebulous sense that it is all going to work out in the long run,’ according to experts at DeepMind.

When AlphaZero was pitted against Stockfish in 1,000 games, it lost just six, winning convincingly 155 times, and drawing the remaining bouts.

Yet it was the way that it played that has amazed developers. While chess computers predominately like to hold on to their pieces, AlphaZero readily sacrificed its soldiers for a better position in the skirmish.

Speaking to The Telegraph, Prof David Silver, who leads the reinforcement learning research group at DeepMind said: “It’s got a very subtle sense of intuition which helps it balance out all the different factors.

“It’s got a neural network with millions of different tunable parameters, each learning its own rules of what is good in chess, and when you put them all together you have something that expresses, in quite a brain-like way, our human ability to glance at a position and say ‘ah ha this is the right thing to do’.

“My personal belief is that we’ve seen something of turning point where we’re starting to understand that many abilities, like intuition and creativity, that we previously thought were in the domain only of the human mind, are actually accessible to machine intelligence as well. And I think that’s a really exciting moment in history.”

AlphaZero started as a ‘tabula rasa’ or blank slate system, programmed with only the basic rules of chess and learned to win by playing millions of games against itself in a process of trial and error known as reinforcement learning.

It is the same way the human brain learns, adjusting tactics based on a previous win or loss, which allows it to search just 60 thousand positions per second, compared to the roughly 60 million of Stockfish.

[…]

The new analysis was published yesterday in the journal Science, and the DeepMind team are now hoping to use their system to help solve real world problems, such as why proteins become misfolded in diseases such as Parkinson’s and Alzheimer’s.

The new results suggest that it could come up with new solutions that humans might miss or take far longer to discover.

Past Successes of Teams in Sports Increases Their Chances of Future Victories

A study showing the power of teamwork, with implications for cooperative efforts outside of sports, such as in business or communities.

What makes a team successful? This is not only a crucial question for football coaches, it plays a role in almost all areas of life, from corporate management to politics. It goes without saying that a team can only win if the team members have the necessary skills. But there is another important element: joint successes in the past increase the chances of winning. This effect shows up in a similar way in completely different team sports.

A research team from TU Wien (Vienna), Northwestern University (Evanston, USA), and the Indian Institute of Management (Udaipur) were able to statistically prove this phenomenon by analyzing large amounts of data in physical sports (football, baseball, cricket and basketball), and also in e-sports (namely the multiplayer online game “Dota 2”). The results have now been published in the journal Nature Human Behaviour.

Skills are not everything

The research team collected extensive data on numerous teams from several sports. The strength of individual players was quantified using different parameters — for example in basketball, the number of points scored and the number of assists was taken into account. The strength of the team can then be calculated as the average strength of the players.

“This gives us a value that can predict the outcome of a game reasonably well,” says Julia Neidhardt of the E-Commerce research unit (Institute for Information Systems Engineering, TU Wien, Vienna). She conducts research in the areas of team performance, user modeling and recommender systems. She does not only consider individuals, but also models their relationships, for example with the help of social network analysis. “Teams with better individual players have of course a higher chance of winning — but that’s not the end of the story,” says Neidhardt.

The team effect

In all the sports studied, the actual results of the games can be predicted even better by not only considering the average strength of the team members, but also taking into account how often they have been victorious together in the past. It is therefore not only important to bring the best possible stars to the field, they also have to gain experience together as a team by celebrating joint victories.

Especially in elite sports, where the skills of all involved professionals are extremely high, individual differences do not necessarily play the key role. As the differences in the skill levels decrease, common experience becomes more important.

It is particularly interesting that the effect was to be seen in very different sports: In football or in the e-sport “Dota 2,” the team members permanently depend on each other. Most actions are performed by several players at the same time. In baseball, on the other hand, throwing and hitting the ball are individual actions that have nothing to do with the rest of the team. Nevertheless, the team effect can be seen in all these sports.

Robust result

There are different possible explanations for this: By training and playing together for a long time, the players become better at coordinating their actions and predicting their teammates’ reactions, but there may also be strong psychological effects, when there is a strong emotional bond between the team players. The statistical data cannot conclusively answer the question which effect is more important. “We can see clearly that in the case of similar skill levels, prior shared success is a good predictor of which team is going to win,” says Julia Neidhardt. “This effect is very robust, in a variety of sports. This leads us to suspect that similar effects also occur in other areas.”

Research Into Pain Shows That When People Expect More Pain, They Feel More Pain

A good study that’s needed to be done for a while.

Expect a shot to hurt and it probably will, even if the needle poke isn’t really so painful. Brace for a second shot and you’ll likely flinch again, even though — second time around — you should know better.

That’s the takeaway of a new brain imaging study published in the journal Nature Human Behaviour which found that expectations about pain intensity can become self-fulfilling prophecies. Surprisingly, those false expectations can persist even when reality repeatedly demonstrates otherwise, the study found.

“We discovered that there is a positive feedback loop between expectation and pain,” said senior author Tor Wager, a professor of psychology and neuroscience at the University of Colorado Boulder. “The more pain you expect, the stronger your brain responds to the pain. The stronger your brain responds to the pain, the more you expect.”

For decades, researchers have been intrigued with the idea of self-fulfilling prophecy, with studies showing expectations can influence everything from how one performs on a test to how one responds to a medication. The new study is the first to directly model the dynamics of the feedback loop between expectations and pain and the neural mechanisms underlying it.

Marieke Jepma, then a postdoctoral researcher in Wager’s lab, launched the research after noticing that even when test subjects were shown time and again that something wouldn’t hurt badly, some still expected it to.

“We wanted to get a better understanding of why pain expectations are so resistant to change,” said Jepma, lead author and now a researcher at the University of Amsterdam.

The researchers recruited 34 subjects and taught them to associate one symbol with low heat and another with high, painful heat.

Then, the subjects were placed in a functional magnetic resonance imaging (fMRI) machine, which measures blood flow in the brain as a proxy for neural activity. For 60 minutes, subjects were shown low or high pain cues (the symbols, the words Low or High, or the letters L and W), then asked to rate how much pain they expected.

Then varying degrees of painful but non-damaging heat were applied to their forearm or leg, with the hottest reaching “about what it feels like to hold a hot cup of coffee” Wager explains.

Then they were asked to rate their pain.

Unbeknownst to the subjects, heat intensity was not actually related to the preceding cue.

The study found that when subjects expected more heat, brain regions involved in threat and fear were more activated as they waited. Regions involved in the generation of pain were more active when they received the stimulus. Participants reported more pain with high-pain cues, regardless of how much heat they actually got.

“This suggests that expectations had a rather deep effect, influencing how the brain processes pain,” said Jepma.

Surprisingly, their expectations also highly influenced their ability to learn from experience. Many subjects demonstrated high “confirmation bias” — the tendency to learn from things that reinforce our beliefs and discount those that don’t. For instance, if they expected high pain and got it, they might expect even more pain the next time. But if they expected high pain and didn’t get it, nothing changed.

“You would assume that if you expected high pain and got very little you would know better the next time. But interestingly, they failed to learn,” said Wager.

This phenomenon could have tangible impacts on recovery from painful conditions, suggests Jepma.

“Our results suggest that negative expectations about pain or treatment outcomes may in some situations interfere with optimal recovery, both by enhancing perceived pain and by preventing people from noticing that they are getting better,” she said. “Positive expectations, on the other hand, could have the opposite effects.”

The research also may shed light on why, for some, chronic pain can linger long after damaged tissues have healed.

Whether in the context of pain or mental health, the authors suggest that it may do us good to be aware of our inherent eagerness to confirm our expectations.

“Just realizing that things may not be as bad as you think may help you to revise your expectation and, in doing so, alter your experience,” said Jepma.

AI System Successfully Predicts Alzheimer’s Years in Advance

Important research of Alzheimer’s disease since it’s one of those diseases where the treatment will be more effective the earlier it’s caught.

Artificial intelligence (AI) technology improves the ability of brain imaging to predict Alzheimer’s disease, according to a study published in the journal Radiology.

Timely diagnosis of Alzheimer’s disease is extremely important, as treatments and interventions are more effective early in the course of the disease. However, early diagnosis has proven to be challenging. Research has linked the disease process to changes in metabolism, as shown by glucose uptake in certain regions of the brain, but these changes can be difficult to recognize.

“Differences in the pattern of glucose uptake in the brain are very subtle and diffuse,” said study co-author Jae Ho Sohn, M.D., from the Radiology & Biomedical Imaging Department at the University of California in San Francisco (UCSF). “People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process.”

The study’s senior author, Benjamin Franc, M.D., from UCSF, approached Dr. Sohn and University of California, Berkeley, undergraduate student Yiming Ding through the Big Data in Radiology (BDRAD) research group, a multidisciplinary team of physicians and engineers focusing on radiological data science. Dr. Franc was interested in applying deep learning, a type of AI in which machines learn by example much like humans do, to find changes in brain metabolism predictive of Alzheimer’s disease.

The researchers trained the deep learning algorithm on a special imaging technology known as 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET). In an FDG-PET scan, FDG, a radioactive glucose compound, is injected into the blood. PET scans can then measure the uptake of FDG in brain cells, an indicator of metabolic activity.

The researchers had access to data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a major multi-site study focused on clinical trials to improve prevention and treatment of this disease. The ADNI dataset included more than 2,100 FDG-PET brain images from 1,002 patients. Researchers trained the deep learning algorithm on 90 percent of the dataset and then tested it on the remaining 10 percent of the dataset. Through deep learning, the algorithm was able to teach itself metabolic patterns that corresponded to Alzheimer’s disease.

Finally, the researchers tested the algorithm on an independent set of 40 imaging exams from 40 patients that it had never studied. The algorithm achieved 100 percent sensitivity at detecting the disease an average of more than six years prior to the final diagnosis.

“We were very pleased with the algorithm’s performance,” Dr. Sohn said. “It was able to predict every single case that advanced to Alzheimer’s disease.”

Although he cautioned that their independent test set was small and needs further validation with a larger multi-institutional prospective study, Dr. Sohn said that the algorithm could be a useful tool to complement the work of radiologists — especially in conjunction with other biochemical and imaging tests — in providing an opportunity for early therapeutic intervention.

“If we diagnose Alzheimer’s disease when all the symptoms have manifested, the brain volume loss is so significant that it’s too late to intervene,” he said. “If we can detect it earlier, that’s an opportunity for investigators to potentially find better ways to slow down or even halt the disease process.”

Study: Aerobic Exercise Has Antidepressant Effects for Those With Major Depression

It seems like doctors should prescribe this sort of moderate intensity aerobic exercise instead of pharmaceutical drugs much more.

An analysis of randomized controlled clinical trials indicates that supervised aerobic exercise has large antidepressant treatment effects for patients with major depression. The systematic review and meta-analysis is published in Depression and Anxiety.

Across 11 eligible trials involving 455 adult patients (18-65 years old) with major depression as a primary disorder, supervised aerobic exercise was performed on average for 45 minutes, at moderate intensity, 3 times per week, and for 9.2 weeks. It showed a significantly large overall antidepressant effect compared with antidepressant medication and/or psychological therapies.

Also, aerobic exercise revealed moderate-to-large antidepressant effects among trials with lower risk of bias, as well as large antidepressant effects among trials with short-term interventions (up to 4 weeks) and trials involving preferences for exercise.

Subgroup analyses revealed comparable effects for aerobic exercise across various settings and delivery formats, and in both outpatients and inpatients regardless of symptom severity.

“Collectively, this study has found that supervised aerobic exercise can significantly support major depression treatment in mental health services,” said lead author Dr. Ioannis D. Morres, of the University of Thessaly, in Greece.

Three Types of Depression Identified in Research for the First Time

More knowledge about the societal problem of depression should lead to more effective treatments for it.

According to the World Health Organization, nearly 300 million people worldwide suffer from depression and these rates are on the rise. Yet, doctors and scientists have a poor understanding of what causes this debilitating condition and for some who experience it, medicines don’t help.

Scientists from the Neural Computational Unit at the Okinawa Institute of Science and Technology Graduate University (OIST), in collaboration with their colleagues at Nara Institute of Science and Technology and clinicians at Hiroshima University, have for the first time identified three sub-types of depression. They found that one out of these sub-types seems to be untreatable by Selective Serotonin Reuptake Inhibitors (SSRIs), the most commonly prescribed medicines for the condition. The study was published in the journal Scientific Reports.

Serotonin is a neurotransmitter that influences our moods, interactions with other people, sleep patterns and memory. SSRIs are thought to take effect by boosting the levels of serotonin in the brain. However, these drugs do not have the same effect on everyone, and in some people, depression does not improve even after taking them. “It has always been speculated that different types of depression exist, and they influence the effectiveness of the drug. But there has been no consensus,” says Prof. Kenji Doya.

For the study, the scientists collected clinical, biological, and life history data from 134 individuals — half of whom were newly diagnosed with depression and the other half who had no depression diagnosis- using questionnaires and blood tests. Participants were asked about their sleep patterns, whether or not they had stressful issues, or other mental health conditions.

Researchers also scanned participants’ brains using magnetic resonance imaging (MRI) to map brain activity patterns in different regions. The technique they used allowed them to examine 78 regions covering the entire brain, to identify how its activities in different regions are correlated. “This is the first study to identify depression sub-types from life history and MRI data,” says Prof. Doya.

With over 3000 measurable features, including whether or not participants had experienced trauma, the scientists were faced with the dilemma of finding a way to analyze such a large data set accurately. “The major challenge in this study was to develop a statistical tool that could extract relevant information for clustering similar subjects together,” says Dr. Tomoki Tokuda, a statistician and the lead author of the study. He therefore designed a novel statistical method that would help detect multiple ways of data clustering and the features responsible for it. Using this method, the researchers identified a group of closely-placed data clusters, which consisted of measurable features essential for accessing mental health of an individual. Three out of the five data clusters were found to represent different sub-types of depression.

The three distinct sub-types of depression were characterized by two main factors: functional connectivity patterns synchronized between different regions of the brain and childhood trauma experience. They found that the brain’s functional connectivity in regions that involved the angular gyrus — a brain region associated with processing language and numbers, spatial cognition, attention, and other aspects of cognition — played a large role in determining whether SSRIs were effective in treating depression.

Patients with increased functional connectivity between the brain’s different regions who had also experienced childhood trauma had a sub-type of depression that is unresponsive to treatment by SSRIs drugs, the researchers found. On the other hand, the other two subtypes — where the participants’ brains did not show increased connectivity among its different regions or where participants had not experienced childhood trauma — tended to respond positively to treatments using SSRIs drugs.

This study not only identifies sub-types of depression for the first time, but also identifies some underlying factors and points to the need to explore new treatment techniques. “It provides scientists studying neurobiological aspects of depression a promising direction in which to pursue their research,” says Prof. Doya. In time, he and his research team hope that these results will help psychiatrists and therapists improve diagnoses and treat their patients more effectively.