The study covered in this summary was published on MedRxiv.org as a preprint and has not yet been peer reviewed.
Key Takeaways
MRI provides an important source of information for the study of autism spectrum disorder (ASD).
Predictive MRI biomarkers enable longitudinal follow-ups and prospective epidemiology.
Infants at risk for ASD could be scanned longitudinally, which could allow us to develop early biomarkers useful when behavior is not a sufficient basis for diagnosis.
Why This Matters
Autism spectrum disorder is a lifelong neurodevelopmental disorder that affects more than 1% of the population.
MRI is an important tool to explore the brain of individuals with ASD, as it is a widely available, fast, and non-invasive method to measure brain anatomy and function.
MRI has been extensively used to identify anatomical and functional differences in ASD. However, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices.
Study Design
Researchers launched a data-science prediction challenge, inviting data scientists to submit algorithms to predict ASD diagnostic from provided MRI data. The 10 best submissions were analyzed.
The algorithms were first applied to different imaging modalities: only functional MRI, or only anatomical MRI.
