Autism Speaks’ Alycia Halladay, Ph.D., provides perspective on NPR’s All Things Considered. To listen to the segment, visit here.
Autism Speaks’ Alycia Halladay, Ph.D., will be live online this afternoon (4 pm EDT, 1 pm PDT) to answer your questions on the just released study showing a high risk of autism among the younger siblings of children on the spectrum. Dr. Halladay organized and continues to help lead the High-Risk Baby Siblings Research Consortium that conducted the research and which continues to study the factors that predispose some families to autism recurrence. Please join us and bring your questions. Meanwhile, please see our news item and a special commentary from Autism Speaks’ Chief Science Officer Geri Dawson, Ph.D.
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To read the entire transcript from this chat, please visit here.
Parents of a child with autism are understandably concerned about the likelihood that their subsequent children will be affected. Autism Speaks and its legacy organization, the National Alliance for Autism Research, have been funding research on younger siblings for nearly 15 years– to help us better understand their development.
In 2003, we began organizing and co-funding a very special collaboration—the High Risk Baby Siblings Research Consortium—in partnership with Eunice Kennedy Shriver National Institute for Child Health Development.
This week, we announced the results of the consortium’s largest ever siblings study. The researchers followed younger brothers and sisters from infancy through the preschool period, when autism diagnosis becomes possible. The study revealed a markedly higher risk among younger siblings than had been previously reported.
As the autism community absorbs the news, let me give you some background on the quality and importance of this research—and what it means for parents.
Our “Baby Sibs” researchers are an international network of clinical researchers who have been pooling information from studies of affected families in 21 sites in the US, Canada, Israel and the UK. Alycia Halladay, Autism Speaks director of research for environmental sciences, and Andy Shih, vice president of scientific affairs, have led the consortium from the start and continue to coordinate its activities.
In the study making headlines this week, the consortium researchers assessed 664 infants. Each had at least one older sibling diagnosed with an autism spectrum disorder (ASD). They found that 1 in 5 babies with an older sibling on the spectrum will likewise be affected—more than double previous estimates. The rate was higher among younger brothers—1 in 4, versus 1 in 9 for younger sisters. And autism affected nearly 1 in 3 infants with more than one older sibling on the spectrum. (Previous estimates came out of much smaller and sometimes less reliably conducted studies.)
So what does this mean for parents?
If you have an older child on the spectrum and you are concerned about your infant, talk to your pediatrician about your baby’s risk and your desire for close monitoring. And if you have any concerns about your child’s development, don’t wait. Speak with your doctor about screening.
Here are links to a number of helpful resources:
* Recent research funded by Autism Speaks shows that a one-page baby-toddler checklist can be used effectively as early as 12 months as an initial screen for autism and other developmental disorders. The screener is available here.
* As a parent or caregiver, one of the most important things you can do is learn the early signs of autism and understand the developmental milestones your child should be reaching. You can see the Learn the Signs guidelines on our website, here.
* Finally, families with one or more children on the spectrum can contact their nearest “Baby Sibs” consortium researcher if they would like to participate in this important research. The list is on our website, here.
By monitoring your infant closely and promptly beginning intervention if signs of autism appear, you can ensure that your child will have the best possible outcome.
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Autism risk ‘high’ for kids with older sibling with the disorder. Autism Speaks’ Alycia Halladay, Ph.D., provides perspective of NPR’s All Things Considered. To listen to the segment, visit here.
What differs in the brains of young children as the behaviors that characterize autism emerge? Researchers have sought to define that difference using all available tools, including functional imaging, blood-based markers, eye-tracking and scalp electrical recordings (EEG). Past discoveries have yielded new clues, but no single test has demonstrated the power to accurately indicate which infants might be at higher risk of developing autism. However, a new report published this week in BMC Medicine could change how autism risk is assessed in the future.
A team of Boston researchers have found a reliable marker of autism risk in a simple and non-invasive EEG test. Although many autism researchers measure EEG—some even from infants—the new study, partly funded by Autism Speaks, has a unique twist. Instead of thinking about EEG signals in the traditional way, the investigators have embraced subtle relationships in the wiggles of EEG waveforms. By capturing those relationships, the research team identified a signature that reflects the structure of neural connections in the brain.
“We’ve long assumed that there is much more signal in the EEG than we take for granted. Conventional approaches lack the power of these more advanced machine learning tools in detecting useful signals from noise,” says Charles Nelson, Ph.D. of Harvard and Children’s Hospital in Boston. Dr. Nelson is the senior author of the study and a member of Autism Speaks’ Scientific Advisory Board. In conjunction with long-time collaborator and Boston University Professor, Helen Tager-Flusberg, Ph.D., the researchers sought the skills of someone who could analyze the complex EEG signal. William Bosl, Ph.D., of the Informatics Program in the Children’s Hospital of Boston, brings the perspective of a physicist to this task, using a suite of tools designed to find subtle relationships among the EEG waveforms and creating a framework for identifying hidden patterns in those signals.
In the paper, the team draws links between the amount of complexity observed in the EEG signal and the self-similar patterns of connectivity observed in the brain. Self-similarity is a favorite pattern for mother nature. Just as the branching of a tree has a pattern that repeats from the trunk and large branches to the finer branches and extensions to leaves, one can think of the branching of neurons in the same way.
This pattern is, in fact, essential to the way the brain communicates. Each neuron is connected with relatively few others, given the billions of neurons in the brain. To make communication efficient in such a sparsely connected network, the principle of self-similarity must apply. Neurons communicate with neighboring neurons more frequently then they do with neurons in a distant brain region—this is typical. However, in the brains of individuals with autism, the bias toward local communication is even greater, and long-range communication is less than is expected. These patterns of communication between neurons create electrical signatures which can be measured using EEG.
Throughout development, many neural connections change. New synapses are formed and others are pruned as a child develops and experiences new things. Atypical connectivity may result when these normal processes fail to occur as they should. Experiments that use EEG could help us identify when development is starting to go off the normal course. Previous studies have shown that the brains of children with autism tend to have less synchronized activity between different EEG sensors than typically-developing children. This pattern would be expected from having too many neighboring neurons chatting and less communication with distant neurons.
The researchers compared EEG from infants aged 6 to 24 months from two groups: one group of children had an older sibling with autism and were therefore at greater risk themselves and a second group of children had no affected siblings. To ensure compliance of each little subject, a research assistant amused the infant by a blowing bubbles while an EEG cap—which resembles a space-age hairnet—was quickly situated on the baby’s head. The data for this analysis was measured during a baseline period, where the children were quietly observing their surroundings and not otherwise engaged in a specific task.
The period of the most dramatic EEG differences between risk groups—about 9 months—corresponds to a time of major milestones that form the foundation for later social and communication skills. Around this time babies develop the ability to perceive another person’s intention to act and they lose the ability to detect differences in speech sounds from languages that are not their own. In another study of high-risk infants, Sally Ozonoff, Ph.D. and colleagues found no differences in social and orienting behaviors before six months, however significant differences emerged in the following six months.
Nearly 80% of the 9 month old children were correctly categorized as high or low risk on the basis of a measure of disorder or randomness in the EEG signal compared across different scalp . For reasons that are as yet unclear, the signal varied more for girls. At 9 months, if only boys were considered, the percentage of correctly categorized infants rises to greater than 90%. The categorization degrades at later ages for all children, and in fact is best for baby girls at 6 months.
This study demonstrates the use of a simple, non-invasive measurement tool and an important time window for identifying children who may be at risk of developing ASD. “The next and most critical next step is to see if our brain measures can actually predict which of our children will develop autism and which will not,” says Dr. Nelson. That information will come soon as the children who participated in this study come to an age where traditional diagnosis is highly reliable. However, as Dr. Tager-Flusberg notes, “We are still a long way from understanding the clinical significance of these brain signatures. More information is need to find a marker that predicts ASD outcomes later in life.” The team’s longer term research goals include determining how this risk marker in combination with other neural, behavioral, genetic and other risk markers may eventually lead to earlier diagnoses of ASD.