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Extra Stable Brain Activity Patterns Found in ASD

By Shana R. Spindler, PhD and Chelsea E. Toledo, M.A. on August 14, 2017
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Background: Even at rest, the brain never really rests. Brain cells, or neurons, use chemicals to shoot information at lightening speed throughout the brain all the time. This brain activity occurs in special patterns that are related to the different functions of the brain. Scientists think that measuring patterns of brain activity during a resting state can offer clues about why some people have autism.

 

What’s new: On July 5, 2017, the journal Nature Communications released a study comparing resting brain activity in typically developing individuals versus their peers with high-functioning autism spectrum disorder (ASD). Researchers looked at brain imaging data taken in a resting state from a total of 50 male individuals between the ages of 18 and 39.

 

The researchers used a special type of mathematical formula to make brain activity look like peaks and valleys on a topographical map. They found that individuals with ASD had differently sized peaks and valleys as compared to their typically developing peers. This particular method of analyzing brain activity allowed autism identification—based solely on brain scans—with about 85% accuracy. The researchers also found a correlation between the peak and valley sizes and the severity of autism symptoms.

 

Why it’s important: The study is an important step forward in establishing ways to extract information from brain scans, which in this case was a functional magnetic resonance imaging (fMRI) scan. The findings of the study suggest a stability of brain activity pattern that differs between typically developing individuals and those with autism spectrum disorder. These differences may one day aid autism diagnosis or indicate severity along the spectrum, but additional investigation is required.


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Autism Linked to Disproportionate Gene Inheritance

By Chelsea E. Toledo, M.A. and Sharmila Banerjee-Basu, Ph.D. on June 30, 2017
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Background: Autism spectrum disorder, or ASD, is highly heritable. For instance, children who have a sibling with an ASD diagnosis are far more likely to be diagnosed with the disorder themselves than children whose siblings don’t have ASD. While that trend and other findings have pointed to genetic risk factors contributing to ASD, it is not yet clear how the inheritance and expression of genes leads to the disorder.

 

What’s New: On May 15, 2017, the journal Nature Genetics published a study exploring genetic architecture underlying ASD. In this study, the authors analyzed data from 6,454 families with at least one child with a diagnosis of ASD. The team of scientists calculated common polygenic risk for ASD, educational attainment, and schizophrenia for all genotyped family members. They found that polygenic risk—variations in multiple genes associated with the condition—was significantly over-transmitted to affected children but not to unaffected siblings. Moreover, the common polygenic variants contributed to ASD risk even in children with damaging autism-associated genetic change that is not present in either parent.

 

Why it’s important: This study suggests that autism risk is additive. Both common and rare variants comprise the genetic architecture in ASD. Children with ASD also over-inherited genetic variants related to schizophrenia and educational attainment indicating their positive association.


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Future Autism Diagnosis Linked to Early Medical Conditions

By Shana R. Spindler, Ph.D. on May 30, 2017
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Background: Early intervention for autism leads to fewer autism symptoms later in childhood. Unfortunately, autism diagnosis usually doesn’t occur until after three years of age. To improve time to therapy, researchers are looking for clues to diagnose autism as early as possible.

 

What’s new: In a large medical record study of 3,911 children with autism, researchers found that 38 medical conditions were associated with a future autism diagnosis. Medical conditions that showed the strongest link to autism included:

 

  • Language delays
  • Learning and cognitive disorders
  • Global delays (significant delay in two or more areas of development)
  • Motor delays
  • Attention Deficit/Hyperactivity Disorder (ADHD)
  • Anxiety disorders
  • Cerebral palsy
  • Epilepsy and recurrent seizures
  • Disorders of the central nervous system

A combination of language delay with global delay most strongly correlated with an autism diagnosis. In total, the researchers identified 14 combinations of medical conditions that were associated with a future autism diagnosis.

 

Why it’s important: This study offers evidence that early life medical conditions could help doctors identify children who need close follow-up for autism assessment. Many of these medical conditions appear a year or more before autism symptoms become apparent.


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Researchers Probe Environmental Risk Factors for Autism

By Chelsea E. Toledo, M.A. on April 19, 2017

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Background: Environmental risk factors comprise exposures and other biological features unrelated to DNA that are associated with an increased likelihood of developing a certain condition. In the case of Autism Spectrum Disorder (ASD), chemical exposures, birth complications, parental age and other factors encountered before or after birth have been identified as environmental risk factors.

 

What’s New: On March 17, 2017, the journal Molecular Autism published an evidence-based review of more than 100 potential environmental risk factors for ASD, ranking the strength of their association with ASD. The researchers looked at the evidence from 32 previously published meta-analyses and systemic reviews, each of those analyzing up to 25 studies on a single risk factor. From that extensive pool of data, they were able to conclude that:

 

  • Advanced parental age and birth complications that cause trauma or oxygen deprivation are strongly related to a person’s risk of developing ASD. In addition, vitamin D deficiency appears to be common in children with ASD.
  • Cesarean births, as well as obesity and diabetes during pregnancy, have a slight association with ASD in the resulting offspring.
  • Vaccination, smoking during pregnancy, thimerosal exposure, and assisted reproductive technology most likely do not influence a person’s risk of developing ASD.
  • Some heavy metals, such as mercury and lead, may have an association with ASD, and merit further investigation.

 

Why it’s important: This study helps to define which environmental factors for ASD merit a closer look from the research community. Future studies could further investigate those relationships to determine whether they do indeed influence the likelihood of ASD.


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New Brain-Imaging Study Identifies Autism in Infants

By Chelsea E. Toledo, M.A. on March 7, 2017
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Background: Early diagnosis of Autism Spectrum Disorder (ASD) is a topic of great interest to parents and researchers alike. Studies have repeatedly shown that the earlier a child received intervention for ASD, the more benefits that child will gain related to the behavioral, communicative, and social symptoms of the disorder. However, in the United States – where one in 68 children receives an ASD diagnosis – the average age of diagnosis is 4 years old. Furthermore, standard practices aren’t yet in place for clinicians to diagnose ASD before 2 years old, when a great deal of the development of language and social skills has already taken place.

 

What’s new: On February 16, 2017, the journal Nature published a study outlining a potential measure for predicting ASD diagnosis before symptoms are apparent in young children. The researchers performed brain imaging at three different points in time on a total of 148 infants – 42 of whom defined as low-risk and 106 of whom were defined as high-risk, based on whether or not the infants had older siblings with ASD. Measuring the growth of the brain’s surface area at 6, 12, and 24 months, the researchers noted a trend in accelerated growth (especially in the brain’s cortex, which processes information from the environment) in the infants who were ultimately diagnosed with ASD. The researchers used this information to develop an algorithm that accurately predicted 80 percent of ASD cases in a separate group of infants.

 

Why it’s important: This is the first study to follow the same children from infancy to toddlerhood, tracing risk factors for diagnosis to actual clinical outcomes. Future studies could combine the researchers’ algorithm with other useful predictors (such as genetics and behavior) to improve its accuracy, with the aim of developing an early diagnostic technique.


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