Check out our new paper led by Christiane Rohr: https://www.sciencedirect.com/science/article/pii/S1878929319303342
Behavioral self-regulation develops rapidly during childhood. This term refers to a set of skills that allow children to succeed at school as well as socially. For instance at school, children need to follow teacher instructions despite. Socially, a child who has better self-regulation will have less frequent and less intense shifts in behavior, leading to better relationships with teachers and friends. Relatively weaker behavioral self-regulation on the other hand associates with greater daily-life challenges and an increased risk for psychiatric diagnoses. Challenges with self-regulation are common to several neurodevelopmental conditions, including Autism Spectrum Disorder (ASD). Little is known about the brain basis of behavioral regulation in children with and without neurodevelopmental conditions.
In this work, we looked at how behavioral regulation skills relate to brain networks, that is, the correlations between the neural activity in different areas. Correlations between brain areas are known to be important, because brain areas don’t operate in isolation, but rather they talk to each other all the time. This means that they are connected through the level that their activity is synchronized, and it is something we and other researchers are hoping to use for better diagnosis and treatment. In this study, we assessed the potential of a new data-driven protocol that develops predictive models of behavior using brain data from 276 children with and without ASD (8-13 years).
We identified brain networks that predicted individual differences in children’s behavioral regulation. Using these networks as models, we were able to moderately predict an unseen child’s behavioral regulation skills from brain data. We observed commonalities and differences between the specific kinds of behavioral regulation that we looked at: Inhibition relied on more posterior networks, shifting relied on more anterior networks, and both involved regions of the default mode network, which is sometimes called the brain’s autopilot.
Our findings substantially add to what is currently known on the neural expressions of behavioral regulation skills in children with and without a neurodevelopmental condition. We believe that numerous behavioral issues could be better understood using brain-based markers. Looking forward, if we can refine and improve these markers, they may indeed be useful for diagnostic purposes and tailored treatment in the future.