Wednesday, July 1, 2015

It’s not what you say, but how you say it

Parents and therapists have long known that even when individuals with autism spectrum disorders (ASD) meet normal language milestones, there is still something odd or different about the way they talk. It is sometimes hard “to put a finger on” what exactly differs in the language of a person with ASD, even though people can hear it. Researchers have studied language differences using language tests that examine the grammar, syntax, gestures, the ability to communicate a story in a clear and logical order, and the prosody of speech. Prosody includes the emotional emphasis a person places on a word in order to highlight how important it is to what he or she is saying—it’s the tone, pitch, or emphasis on specific syllables.

In 2010, Dr. Van Santen and colleagues at the Oregon Health & Science University began to create new tools that could measure differences in language with computer algorithms. In this first study, children ages 4-8 years of age completed different language tasks. In some of them children had to imitate words or sentences spoken by a computer, and in one task children had to correct a computer when it made a mistake in describing a picture of an animal. The computer algorithms were better than human raters at picking up very subtle prosodic differences in the tone, pitch or emphasis of how children with ASD spoke. This was particularly true when children were coming up with their own words and not directly imitating the computer.

Here at the Center for Autism Research our scientists are starting to apply this research in more natural settings. Dr. Julia Parish-Morris recently received a grant from the Autism Science Foundation to record children with ASD having a conversation with our staff. She will then use cutting-edge computer algorithms to understand what aspects of language not only identify children with ASD from typically developing children, but can also serve as a measuring stick for treatment. This study will focus on how skilled children are at taking turns in conversation, and the use of contractions.

Thursday, April 23, 2015

Factors Associated with Driving in Teens with Autism Spectrum Disorder

Getting a permit and learning to drive is a ‘rite of passage’ that many teens anticipate for years. A driver’s license comes with independence and responsibility, which can be simultaneously exciting and intimidating. For teens with Autism Spectrum Disorder (ASD), the decision to learn to drive is met with additional challenges and considerations.

Driving is a complex task because drivers must integrate information from multiple inputs simultaneously, and navigate unpredictable and potentially challenging situations on the road. Knowing when your teen is ready to take this step can be difficult.

Patty Huang, M.D. and her research team at The Children’s Hospital of Philadelphia and University of Pennsylvania recently investigated factors associated with driving in teens with ASD. Parents of teens ages 15-18 with a diagnosis of ASD completed an online survey.

Teens with ASD were more likely to have a permit or license if any (or most) of the following were true:

1. Age: Teens at least 17 years old were more likely to be driving.
2. Classroom placement: Teens in a full-time regular classroom placement or college setting.
3. Job: Having a paid job outside of the home.
4. Life Skills: Independent use of public transportation.
5. Socio-Economic Status: Families with a reported annual income greater than $100,000.
6. IEP: Specific driving goals outlined in the IEP.
7. Parent Experience: Parent previously taught at least one teen to drive.

This is not an exhaustive list to determine driving readiness, but these factors may indicate if a teen would be successful in learning to drive. Many items on this list are skills that could be learned or accommodations that could be implemented in the school, home, or community setting. The independence, responsibility, and multitasking that are learned from having a job or navigating a public transportation system, for example, could also translate to success in acquiring a license.

Interestingly, Dr. Huang and colleagues also noted that many of the teens in this study did not have driving goals in their IEPs. Incorporating driving goals into transition planning could be another useful step towards driving readiness.

The results of this study provide useful information that may be helpful for parents of driving-age teenagers with ASD. Dr. Huang and colleagues reported that many of the teens in their study expressed interest in driving. Furthermore, driving teens were less likely to have received a citation or been in an accident in comparison to the general teen population. Overall, researchers suggest that teens with ASD who are more “rule-bound” and who have limits placed on their driving may have a more positive driving experience.

These scientists who are in the Center for Injury Research Prevention and the Center for Autism Research now have a pilot grant from The Children’s Hospital of Philadelphia to look at the licensing rates, citations, and crash history of our patients with ASD living in NJ. This line of research is laying the foundation to create a tailored driving education program for teens with ASD.

Huang, P., Kao, T., Curry, A. E., & Durbin, D. R. (2012). Factors associated with driving in teens with autism spectrum disorders. Journal of Developmental & Behavioral Pediatrics, 33, 70-74.

Tuesday, April 7, 2015

Genetics and language processing in the brain

Difficulty communicating with others is one of the defining features of autism spectrum disorder (ASD). For a large subset of individuals with ASD, language disorders or delays contribute to these communication problems. The causes of these communication impairments are not yet known.

A recent study at the Children’s Hospital of Philadelphia (CHOP), led by Dr. Timothy L. Roberts, investigated the possible link between genetics, language disorder, and ASD. Previous research had found that DNA mutations on chromosome 16 – specifically, deletions or duplications of DNA information at the genetic site 16p11.2 – were associated both with ASD and with language impairment. In order to explore how these genetic changes might be related to problems with language and social communication, Dr. Roberts looked at how people with mutations at 16p11.2 process sounds in the brain. This study enrolled children with 16p11.2 deletions, 16p11.2 duplications, and typical development. While all of the participants with genetic differences at 16p11.2 had some neurological or learning disabilities, only a portion had diagnoses of ASD.

The study looked at how children’s brains responded to a series of simple tones. A magnetoencephalography machine was used to measure brain activity. Researchers for this study analyzed a very early brain response to processing a sound. This early response reflects how the brain takes in and processes auditory information from the world around it.

Results showed that the brains of children with a 16p11.2 deletion were significantly delayed in responding to sounds compared to typically developing children. This delay was not found among children with 16p11.2 duplication. Though the delay seems small (23 ms), it is very large when it comes to fast-changing sounds like speech. A 23 ms delay means that a child hearing the word ‘elephant’ would still be processing the ‘el’ sound while other children moved on to processing ‘phant’. Over the course of a conversation, these delays can lead to children falling behind by full words and phrases.

This study shows a clear connection between the 16p11.2 gene and how our brain processes auditory information. Because this gene is also related to ASD, it could be an underlying cause of language problems for some people with ASD.    
Dr. Roberts and his team are continuing their research into the relationship between genetics and the brain’s delay in processing sound. This includes a new study on minimally verbal children with ASD. His team’s goal is to shed light on the possible underlying genetic and biologic causes of language impairment in ASD.

Jenkins, J., Chow, V., Blaskey, L., Kuschner, E., Qasmieh, S., Gaetz, L., Edgar, J.C., Mukherjee, P., Buckner, R., Nagarajan, S.S., Chung, W.K., Spiro, J.E., Sherr, E.H., Berman, J.I., & Roberts, T. P. (2015). Auditory Evoked M100 Response Latency is Delayed in Children with 16p11.2 Deletion but not 16p11.2 Duplication. Cerebral Cortex,

Tuesday, March 24, 2015

Comparing Early Intervention Outcomes

We know very little about which preschool intervention placements produce the best outcomes for which children with Autism Spectrum Disorder (ASD). A team from the University of Pennsylvania examined the effectiveness of three early intervention preschool placements: inclusive, mixed disability, and autism-only.

Children in this study had completed early intervention and were now enrolled in elementary school autism support classes. The researchers assessed each child’s current cognitive skills and reviewed their preschool early intervention educational records.

The three early intervention placements were categorized as follows:

  • Inclusive placements took place in a variety of settings, including center-based programs run by a special education teacher for typically developing children that included children with ASD, Head Start preschools, community-based typical preschools, and day-care settings.
  • Mixed disability placements included children with developmental or other disabilities as well as children with ASD.  
  • Autism-only placements took place in center-based autism support preschool programs.

Children who attended inclusive early intervention placements scored higher on cognitive measures when they started elementary school than the children who were in mixed disability and autism-only placements. This was particularly true for children with at least minimal communication skills, lower adaptive behavior skills, and lower social skills. 

These findings suggest that having more opportunities to interact with, and learn from, typically developing peers may be particularly important for the cognitive outcomes of some children with ASD. However children with ASD with other strengths and weaknesses may benefit from other types of intervention settings.

Based on the findings from this project, the University of Pennsylvania has begun a new study that is following children currently enrolled in the preschool early intervention system in Philadelphia. The researchers plan to follow the children over the course of nine months and observe whether children with different strengths and weaknesses benefit more or less in certain types of settings (inclusive, mixed-disability, or autism-only).

Source: Nahmias, A.S., Kase, C., Mandell, D.S. (2014). “Comparing cognitive outcomes among children with autism spectrum disorders receiving community-based early intervention in one of three placements.” Autism, 18(3), 311-320. PMID: 23188885

Tuesday, March 10, 2015

Sleep Behaviors and Sleep Quality

Getting a good night of sleep is crucial to a child’s development in the areas of attention, learning, memory, mood regulation, and behavior. Poor sleep quality in children also affects parents’ sleep quality. If a child isn’t sleeping well, chances are his/her parents aren’t sleeping well either.

Many children with Autism Spectrum Disorder (ASD) have difficulty falling asleep or staying asleep, but research into this has been limited. A study conducted by a team at the University of Pennsylvania (UPenn) School of Nursing and CHOP aimed to answer two critical questions: how many children with ASD have sleep problems, and what types of sleep problems are most common in ASD.

For the study, families were asked to answer sleep questionnaires and keep a sleep diary over a 17-day period. Parents noted the time the child went to bed, night wakings, morning wake time, naps, and health status during this period.

The children also wore a device that measures motion, called an actigraph, for 10 nights. An actigraph is a miniaturized wristwatch-like computer, and it is now a part of many fitness wristbands and cell phones. The actigraph measures how much children move at night and provides a measure of how much time children spend in different sleep phases. There were concerns about the children’s ability to fall asleep with something on their wrist, so the researchers created a special pocket for the actigraph that could be sewn into the upper sleeve of a pajama top.

When the researchers analyzed the sleep diaries, actigraphy, and sleep questionnaires, they found that 66.7% of children with ASD had some form of insomnia. The children with ASD took longer to fall asleep and had longer waking episodes during the night.

The most prevalent sleep disorders in this group were behavioral insomnia and required intensive strategies to be implemented by the parents to help their children fall asleep. Strategies included repeated reassurance about fears, rocking, patting, and frequently returning them back to bed. Of note, about a third of the children with ASD that had insomnia had strong bedtime routines, fell asleep by themselves, and did not have any medical conditions that might disrupt sleep. Insomnia in these children with ASD may be due to intrinsic causes. Research suggests that a fairly high state of hyperarousal or anxiety may be causing insomnia in these individuals. Ongoing treatment studies are testing whether alleviating the anxiety will also alleviate sleep difficulties.

Greater understanding of insomnia in the ASD population is critical because good sleep is strongly tied to the ability to attend, learn, and self-regulate. The UPenn School of Nursing and CHOP are presently conducting a Pilot Randomized Control study for sleep impairments in ASD. This study is funded by the Department of Defense to develop and refine a tailored behavioral intervention for children with ASD and insomnia that includes a calming module that addresses arousal dysregulation and anxiety.

Source: Souders, M.C., Mason, T.B., Valladares, O., Bucan, M., Levy, S.E., Mandell, D.S., Weaver, T.E., Pinto-Martin, J. (2009). “Sleep Behaviors and Sleep Quality in Children with Autism Spectrum Disorders.” Sleep, 32(12), 1566-1578. PMID: 20041592