When people ask what I do for a living, I tell them I study “baby brains”. People are usually startled and a bit tickled by this phrasing, and I often have to clarify that I mean the in vivo brains of healthy and wiggly infants. This phrase is mostly accurate – I am a developmental cognitive neuroscientist who uses cool machines to measure brain and behavioral responses of newborns and older infants. But for me, “baby brains” are a jumping off point for my personal and scientific curiosity about how people learn about the world. Our bodies and brains are constantly changing and evolving as we grow from infancy into toddlerhood, childhood, those rough adolescent years, and (hopefully gracefully) into adulthood. Some individuals do not grow at the same pace as their peers, and we often identify these children and adults with developmental disorders, such as autism.
One of my research goals is to gather information about baby brains as a means to identify biomarkers of autism.
The term “biomarkers” refers to a specific kind of biological indication (or marker) of a disorder. So, autism biomarkers could be related to a behavior or genetic event or brain signature that is specific to autism. By looking for biomarkers, we really hope to be able to identify signs of autism as early as possible. Autism is a developmental disorder, so by intervening during early development, we hope to give each infant or child the opportunity to develop and improve skills (e.g., attention, cognition, social interactions) for long-term positive outcomes.
Recent work has used neuroimaging to look for autism biomarkers. In one functional magnetic resonance imaging (fMRI) led by Dr. Kevin Pelphrey, we discovered that different areas of the brain are possible biomarkers of autism. In that study, when viewing videos of human motion, children with autism had reduced activation in the superior temporal sulcus and ventromedial prefrontal cortex compared to typically developing children. So these brain regions have been targeted for monitoring treatment and other interventions, such as the controlled use of oxytocin to promote social learning.
Other recent studies have used behavior, such as eye movements, to discover that early patterns of visual attention are indicative of later autism diagnosis. For instance, led by Dr. Fred Shic, six-month-old infants who are later diagnosed with autism spend more time looking at the outer areas of the face when viewing videos of adults talking. This is problematic for developing social and communicative skills, which highlight inner areas of the face – the eyes and mouth, for example.
Our team at the University of Washington is implementing eye tracking and electroencephalography (or EEG) together to learn more about how different biomarkers converge as a unique and specific indicator of autism for each child. One of the problems with research in this field is that often these studies are limited to children who have higher intellectual ability. We hope that integrating multiple technologies will allow us to answer questions about cognition, social skills, and communication abilities, regardless of level of function – for instance, with nonverbal children or infants, we can use eye tracking to measure visual attention as a proxy for a behavioral response and then see how that corresponds to the brain response.
Our team is also using genetic profiles as biomarkers. This year we are excited about new results that suggest we need to really pay attention to how having a specific genetic event can change your pattern of brain activation. Our study looks at children with a variety of different likely gene-disrupting mutations – some of which, such as DYRK1A exhibit brain responses similar to typical development. However, children with other genetic mutations, such as DSCAM and CHD8, exhibit atypical brain patterns, more similar to the autism biomarker. We have other work that suggests that individuals with 16p11.2 copy number variations have different neural habituation (i.e., reduced activation over exposure) compared to individuals without a known genetic variation. As this research continues, we are hopeful that we can truly improve early intervention and successful outcomes by identifying genetic biomarkers in conjunction with brain and behavior biomarkers.
So, back to my “baby brains.” How does studying the baby brains of infants without a suspected risk of autism help us identify biomarkers in autism?
Here is one example from my dissertation. I was interested in how EEG signals change across infancy while babies think about other people. We discovered that while babies are responding to the mental states of others at approximately six months, these responses change and strengthen by about eight months of age. This is a really neat phenomenon, telling us that early on, babies begin developing features of social cognition that will ultimately support the growth and fine-tuning of social abilities.
In fact, we can build computer models that show us how different brain systems (e.g., memory, perception, emotion encoding) team up to generate baby brain responses. From here, we can try breaking each system in the computer model to see how it changes the brain response. This way, we can predict how baby brains might respond if the emotion system is broken or if the memory system does not update. Does it produce brain responses like autism? Intellectual disabilities? If we improve the broken part of the system, can we bolster and support behavior and brain responses? What if one system is overly responsive? Ultimately, these computer models will help us test different predictions to better specify brain mechanisms in autism.
From brain to behavior to genes, all of these different techniques give us a different perspective for potential biomarkers in autism. With each discovery, we grow closer to being able to identify areas of strength and weakness for each infant and child, leading us closer to personalized, targeted treatments.