Posted on Autism

Four different autism subtypes identified in brain study

3D prism cube represents the machine learning of the three brain-behavior dimensions, etched onto the prism's glass


Machine learning of brain-behavior dimensions reveals four subtypes of autism spectrum disorder linked to distinct molecular pathways. Here, the 3D prism cube represents the machine learning of the three brain-behavior dimensions, etched onto the prism’s glass. White light or “data” passes into the prism or “machine learning algorithm,” splitting into four colored light paths that represent the spectrum of autistic people in the four autism subtypes. The painted background of a sequencing array represents the molecular associations of the autism subtypes. CREDIT Weill Cornell Medicine; Dr. Amanda Buch

People with autism can be classified into four distinct subtypes based on their brain activity and behavior, according to a study from Weill Cornell Medicine investigators.

The study, published March 9 in Nature Neuroscience, leveraged machine learning to analyze newly available neuroimaging data from 299 people with autism and 907 neurotypical people. They found patterns of brain connections linked with behavioral traits in people with autism, such as verbal ability, social affect, and repetitive or stereotypic behaviors. They confirmed that the four autism subgroups could also be replicated in a separate dataset and showed that differences in regional gene expression and protein-protein interactions explain the brain and behavioral differences.

“Like many neuropsychiatric diagnoses, individuals with autism spectrum disorder experience many different types of difficulties with social interaction, communication and repetitive behaviors. Scientists believe there are probably many different types of autism spectrum disorder that might require different treatments, but there is no consensus on how to define them,” said co-senior author Dr. Conor Liston, an associate professor of psychiatry and of neuroscience in the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine. “Our work highlights a new approach to discovering subtypes of autism that might one day lead to new approaches for diagnosis and treatment.”

A previous study published by Dr. Liston and colleagues in Nature Medicine in 2017 used similar machine-learning methods to identify four biologically distinct subtypes of depression, and subsequent work has shown that those subgroups respond differently to various depression therapies.

“If you put people with depression in the right group, you can assign them the best therapy,” said lead author Dr. Amanda Buch, a postdoctoral associate of neuroscience in psychiatry at Weill Cornell Medicine.

Building on that success, the team set out to determine if similar subgroups exist among individuals with autism, and whether different gene pathways underlie them. She explained that autism is a highly heritable condition associated with hundreds of genes that has diverse presentation and limited therapeutic options. To investigate this, Dr. Buch pioneered new analyses for integrating neuroimaging data with gene expression data and proteomics, introducing them to the lab and enabling testing and developing hypotheses about how risk variants interact in the autism subgroups.

“One of the barriers to developing therapies for autism is that the diagnostic criteria are broad, and thus apply to a large and phenotypically diverse group of people with different underlying biological mechanisms,” Dr. Buch said. “To personalize therapies for individuals with autism, it will be important to understand and target this biological diversity. It is hard to identify the optimal therapy when everyone is treated as being the same, when they are each unique.”

Until recently, there were not large enough collections of functional magnetic resonance imaging data of people with autism to conduct large-scale machine learning studies, Dr. Buch noted. But a large dataset created and shared by Dr. Adriana Di Martino, research director of the Autism Center at the Child Mind Institute, as well as other colleagues across the country, provided the large dataset needed for the study.

“New methods of machine learning that can deal with thousands of genes, brain activity differences and multiple behavioral variations made the study possible,” said co-senior author Dr. Logan Grosenick, an assistant professor of neuroscience in psychiatry at Weill Cornell Medicine, who pioneered machine-learning techniques used for biological subtyping in the autism and depression studies.

Those advances allowed the team to identify four clinically distinct groups of people with autism. Two of the groups had above-average verbal intelligence. One group also had severe deficits in social communication but less repetitive behaviors, while the other had more repetitive behaviors and less social impairment. The connections between the parts of the brain that process visual information and help the brain identify the most salient incoming information were hyperactive in the subgroup with more social impairment. These same connections were weak in the group with more repetitive behaviors.

“It was interesting on a brain circuit level that there were similar brain networks implicated in both of these subtypes, but the connections in these same networks were atypical in opposite directions,” said Dr. Buch, who completed her doctorate from Weill Cornell Graduate School of Medical Sciences in Dr. Liston’s lab and is now working in Dr. Grosenick’s lab. 

The other two groups had severe social impairments and repetitive behaviors but had verbal abilities at the opposite ends of the spectrum. Despite some behavioral similarities, the investigators discovered completely distinct brain connection patterns in these two subgroups.

The team analyzed gene expression that explained the atypical brain connections present in each subgroup to better understand what was causing the differences and found many were genes previously linked with autism. They also analyzed network interactions between proteins associated with the atypical brain connections, and looked for proteins that might serve as a hub. Oxytocin, a protein previously linked with positive social interactions, was a hub protein in the subgroup of individuals with more social impairment but relatively limited repetitive behaviors. Studies have looked at the use of intranasal oxytocin as a therapy for people with autism with mixed results, Dr. Buch said. She said it would be interesting to test whether oxytocin therapy is more effective in this subgroup.

“You could have treatment that is working in a subgroup of people with autism, but that benefit washes out in the larger trial because you are not paying attention to subgroups,” Dr. Grosenick said.

The team confirmed their results on a second human dataset, finding the same four subgroups. As a final verification of the team’s results, Dr. Buch conducted an unbiased text-mining analysis she developed of biomedical literature that showed other studies had independently connected the autism-linked genes with the same behavioral traits associated with the subgroups.

The team will next study these subgroups and potential subgroup-targeted treatments in mice. Collaborations with several other research teams that have large human datasets are also underway. The team is also working to refine their machine-learning techniques further.

“We are trying to make our machine learning more cluster-aware,” Dr. Grosenick said.

In the meantime, Dr. Buch said they’ve received encouraging feedback from individuals with autism about their work. One neuroscientist with autism spoke to Dr. Buch after a presentation and said his diagnosis was confusing because his autism was so different than others but that her data helped explain his experience.

“Being diagnosed with a subtype of autism could have been helpful for him,” Dr. Buch said.  

Posted on Fibromyalgia

Fibromyalgia: Pain out of control – find out about this new therapy

Thermode


The so-called thermode can administer heat stimuli, which the test persons could either end themselves or which the computer controlled. CREDIT © Benjamin Mosch

Fibromyalgia is a mysterious chronic pain disorder that is difficult to treat. Its causes are also still largely in the dark. A study conducted by the team at the Clinic for Psychosomatic Medicine and Psychotherapy at Ruhr University Bochum, Germany, provides evidence that certain brain areas involved in processing pain don’t function normally in fibromyalgia patients. In healthy people, they ensure that pain that we can control is easier to bear. The study found that these brain areas showed altered activity in patients with fibromyalgia.

Controlling the off switch for heat pain

The degree to which we experience pain and the restriction caused by it depend largely on how we perceive it. If we have the feeling that we can control the pain and shut it down ourselves, for example, we will tolerate it better than if we feel at its mercy. “For people with chronic pain, the inability to control repeated attacks of pain is one of the most significant causes of impaired quality of life,” explains Benjamin Mosch, lead author of the study. “And yet, the underlying neural mechanisms have so far mainly been studied in healthy controls.”

The team compared two female cohorts in the current study: 21 healthy participants and 23 fibromyalgia patients. Both groups were exposed to heat pain while their brain activities were monitored by functional magnetic resonance imaging. In one experimental run, the participants were able to stop the pain stimulus themselves. In another run, a computer controlled the start and end of the stimulus. “We kept the duration of the stimuli terminated by the computer the same on average as the stimuli terminated by the test subjects,” says Martin Diers.

Cognitive resources are impaired

When women in the healthy control group were able to terminate the pain stimulus themselves, a number of mainly frontal brain areas were activated that seem to play an important role in modulating pain. This observation is consistent with previous studies involving healthy subjects. “Interestingly, however, we didn’t detect any such activations in our patient group,” points out Martin Diers. “This can serve as evidence for impaired pain processing among patients with fibromyalgia. It indicates that the cognitive resources for dealing with acute pain are impaired in these patients.”

Posted on Diabetes

A fasting diet reduces risk markers of type 2 diabetes.

Research team provides guidelines, recommendations for intermittent fasting

A fasting diet which focuses on eating early in the day could be the key to reducing the risk of developing type 2 diabetes.

Researchers from the University of Adelaide and South Australian Health and Medical Research Institute (SAHMRI) compared two different diets:  a time restricted, intermittent fasting diet and a reduced calorie diet to see which one was more beneficial for people who were prone to developing type 2 diabetes.

“Following a time restricted, intermittent fasting diet could help lower the chances of developing type 2 diabetes,” said senior author the University of Adelaide’s Professor Leonie Heilbronn, Adelaide Medical School.

“People who fasted for three days during the week, only eating between 8am and 12pm on those days, showed a greater tolerance to glucose after 6 months than those on a daily, low-calorie diet.

“Participants who followed the intermittent fasting diet were more sensitive to insulin and also experienced a greater reduction in blood lipids than those on the low-calorie diet.”

Type 2 diabetes occurs when the body’s cells don’t respond effectively to insulin and it loses its ability to produce the hormone, which is responsible for controlling glucose in blood.

It’s estimated that nearly 60 per cent of type 2 diabetes cases could be delayed or prevented with changes to diet and lifestyle.

Almost 1.3 million Australians are currently living with the condition, for which there is no cure.

There were more than 200 participants recruited from South Australia in the 18-month study, which was published in scientific journal, Nature Medicine.

Participants on both the time restricted, intermittent fasting diet and the low-calorie diet experienced similar amounts of weight loss.

“This is the largest study in the world to date and the first powered to assess how the body processes and uses glucose after eating a meal, which is a better indicator of diabetes risk than a fasting test,” said first author Xiao Tong Teong, a PhD student at the University of Adelaide.

“The results of this study add to the growing body of evidence to indicate that meal timing and fasting advice extends the health benefits of a restricted calorie diet, independently from weight loss, and this may be influential in clinical practice.”

Further research is needed to investigate if the same benefits are experienced with a slightly longer eating window, which could make the diet more sustainable in the long term.

Posted on Sleep disorders

Decoding Insomnia: Machine learning model predicts sleep disorders from patient records

Use of machine learning to identify risk factors for insomnia


The machine learning model can effectively predict a patient’s risk for a sleep disorder using demographic and lifestyle data, physical exam results and laboratory values. CREDIT Hernan Sanchez, Unsplash, CC0 (https://creativecommons.org/publicdomain/zero/1.0/)

A machine learning model can effectively predict a patient’s risk for a sleep disorder using demographic and lifestyle data, physical exam results and laboratory values, according to a new study published this week in the open-access journal PLOS ONE

The prevalence of diagnosed sleep disorders among American patients has significantly increased over the past decade. This trend is important to understand better and reverse since sleep disorders are a significant risk factor for diabetes, heart disease, obesity, and depression.

In the new work, the researchers used the machine learning model XGBoost to analyze publicly available data on 7,929 patients in the US who completed the National Health and Nutrition Examination Survey. The data contained 684 variables for each patient, including demographic, dietary, exercise and mental health questionnaire responses and laboratory and physical exam information.

Overall, 2,302 patients in the study had a physician diagnosis of a sleep disorder. XGBoost could predict the risk of sleep disorder diagnosis with a strong accuracy (AUROC=0.87, sensitivity=0.74, specificity=0.77), using 64 of the total variables included in the full dataset. The greatest predictors for a sleep disorder, based on the machine learning model, were depression, weight, age and waist circumference.

The authors conclude that machine learning methods may be effective first steps in screening patients for sleep disorder risk without relying on physician judgement or bias. 

Samuel Y. Huang adds: “What sets this study on the risk factors for insomnia apart from others is seeing not only that depressive symptoms, age, caffeine use, history of congestive heart failure, chest pain, coronary artery disease, liver disease, and 57 other variables are associated with insomnia, but also visualizing the contribution of each in a very predictive model.”