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.

Scientific Research Shows Lavender’s Smell is Legitimately Relaxing

Using lavender candles rather than a drug with potentially terrifying side effects such as Xanax seems like one of the better ways (along with exercise) to reduce anxiety levels in this crazy world.

Lavender works its relaxing magic all around us: from garden borders to bath bombs to fabric softener. But why not in our hospitals and clinics? And what is the science behind the magic?

Research published in Frontiers in Behavioral Neuroscience shows for the first time that the vaporized lavender compound linalool must be smelt — not absorbed in the lungs- to exert its calming effects, which could be used to relieve preoperative stress and anxiety disorders.

Soothing scents

“In folk medicine, it has long been believed that odorous compounds derived from plant extracts can relieve anxiety,” says co-author Dr Hideki Kashiwadani of Kagoshima University, Japan.

Modern medicine has overlooked these scented settlers, despite a need for safer alternatives to current anxiolytic (anxiety-relieving) drugs like benzodiazepines.

Numerous studies now confirm the potent relaxing effects of linalool, a fragrant alcohol found in lavender extracts.

“However, the sites of action of linalool were usually not addressed in these studies,” Kashiwadani points out.

Many assumed that absorption into bloodstream via the airway led to direct effects on brain cell receptors such as GABAARs — also the target of benzodiazepines. But establishing the true mechanism of linalool’s relaxing effects is a key step in moving towards clinical use in humans.

A nose for success

Kashiwadani and colleagues tested mice to see whether it is the smell of linalool — i.e. stimulation of olfactory (odor-sensitive) neurons in the nose — that triggers relaxation.

“We observed the behavior of mice exposed to linalool vapor, to determine its anxiolytic effects. As in previous studies, we found that linalool odor has an anxiolytic effect in normal mice. Notably, this did not impair their movement.” This contrasts with benzodiazepines, and linalool injections, whose effects on movement are similar to those of alcohol.

However, crucially there was no anxiolytic effect in anosmic mice — whose olfactory neurons have been destroyed — indicating that the relaxation in normal mice was triggered by olfactory signals evoked by linalool odor.

What’s more, the anxiolytic effect in normal mice disappeared when they were pretreated with flumazenil, which blocks benzodiazepine-responsive GABAA receptors.

“When combined, these results suggest that linalool does not act directly on GABAA receptors like benzodiazepines do — but must activate them via olfactory neurons in the nose in order to produce its relaxing effects,” explains Kashiwadani.

Coming to theaters near you

“Our study also opens the possibility that relaxation seen in mice fed or injected with linalool could in fact be due to the smell of the compound emitted in their exhaled breath.”

Similar studies are therefore needed to establish the targets, safety and efficacy of linalool administered via different routes, before a move to human trials.

“These findings nonetheless bring us closer to clinical use of linalool to relieve anxiety — in surgery for example, where pretreatment with anxiolytics can alleviate preoperative stress and thus help to place patients under general anesthesia more smoothly. Vaporized linalool could also provide a safe alternative for patients who have difficulties with oral or suppository administration of anxiolytics, such as infants or confused elders.”

Crows Shown to Build Complex Tools from Multiple Separate Parts, Something Only Great Apes and Humans Have Been Shown Doing

Crows continue to prove that they have amazing attributes unique among animals. Crows likely have more to teach humans that study them about cognitive processes, which would aid understanding of the human mind.

Well, we didn’t think it was possible, but we should have had more faith in our feathered corvid friends: crows just got even cooler. Researchers have discovered that crows don’t just use single objects as tools; they can also make them out of multiple parts that are individually useless.

Let that sink in for a moment.

We already knew that corvids – crows and ravens – are capable of reasoning cause and effect, solving multi-step puzzles, planning for the future and even fashioning simple tools out of sticks and paper.

But making compound tools is something that has only ever been observed before in primates – specifically, humans and and great apes.

Even young humans take several years to be able to learn this skill, because cognitively speaking, it’s actually quite complex. It requires the ability to anticipate the properties of objects, and to be able to mentally map the consequences of putting them together prior to doing so.

As such, it’s considered a pretty important milestone when it comes to brain evolution. So observing it in birds is pretty spectacular.

“The finding is remarkable because the crows received no assistance or training in making these combinations, they figured it out by themselves,” said ornithologist Auguste von Bayern of the Max Planck Institute for Ornithology and the University of Oxford.

The team conducted their research on eight New Caledonian crows (Corvus moneduloides), a bird well known for its intelligence.

Research: Letting in Sunshine Helps Kill Germs Indoors

It’s flu season and so this can be especially useful to know around this time of year.

Even before Florence Nightingale advised that hospitals be designed to let daylight in, people observed that sunshine helps keep you healthy. But there was not much research to explain why that’s the case, especially inside buildings.

Researchers at the University of Oregon set up a study of dusty, dollhouse-size rooms to compare what happens in rooms exposed to daylight through regular glass, rooms exposed to only ultraviolet light and those kept dark. They used a mix of dust collected from actual homes in the Portland area and let the miniature rooms sit outdoors while keeping the insides at a normal room temperature.

After 90 days (because that’s how long dust can hang around, even if you vacuumed), they sampled the dust and analyzed the types of bacteria present.

What they found surprised them and confirmed what your grandmother already knew: Rooms exposed to daylight have fewer germs. In fact, the study showed that the lit rooms had about half the viable bacteria (those that are able to grow), compared with dark rooms. Rooms that were exposed only to UV light had just slightly less viable bacteria than ones exposed to daylight. Their research was published Wednesday in the journal Microbiome.

The study’s lead author, Ashkaan Fahimipour, a postdoctoral researcher at the University of Oregon’s Biology and the Built Environment Center, says he was surprised that the visible light and the UV light performed so similarly to keep bacteria down.

The researchers looked at both types of light because UV is known to be a good disinfectant and is used to clean drinking water. Yet typical window glass filters out most UV light.

Another surprising thing was the amount of microbes that were viable in dust. Earlier studies didn’t suggest it would be as much, says co-author Kevin Van Den Wymelenberg, co-director of the Biology and the Built Environment Center at the University of Oregon. That’s because indoor dust is like a desert — it’s too dry for most bacteria or other things to grow. This study found 12 percent of bacteria in dark rooms were viable compared to 6.8 percent in rooms with daylight and 6.1 percent in rooms exposed to UV light only.

While it may not sound like much, “6 percent of millions of cells is still a lot of microbes,” Van Den Wymelenberg says. “Until now, daylighting [illuminating a building with natural light] has been about visual comfort or broad health. But now we can say daylighting influences air quality.”

The daylit rooms in the study also had less of the types of bacteria associated with human skin, which people shed as they move around indoors, and more closely resembled outdoor bacterial communities. Some of the human-associated bacteria species that didn’t survive in the lighted rooms are from a family of bacteria known to cause respiratory disease.

In their future work, the researchers said they’d like to design studies to determine how much light is necessary to kill microbes so architects can begin to design buildings with that in mind.

Also, researchers have learned from trying to eradicate all germs in hospital and laboratory clean rooms that it’s really hard to get rid of microbes wholesale. “Sanitizing isn’t the best approach,” Fahimipour says. And some microbes are actually good for us, like the ones in yogurt. Someday, he says, “it may be better to enrich an indoor setting with microbes that are not harmful or even [with those that are] beneficial.”