Applying MRI to Map Typical and Potentially Atypical Brain Development at Fetal, Neonatal and Infant Time-Points

Background: Until now, we have had few tools to explore early human brain development in vivo. This is changing with the advent of sophisticated but safe, Magnetic Resonance (MR) techniques that can be applied at fetal, neonatal and infant stages of development.

Objectives: We aim to acquire brain structural, functional and biochemical data from infants with and without a family history (parent or sibling) of Autism Spectrum Disorder (ASD) from before birth through to 6 months of age. We will first test the hypothesis that a familial risk of ASD causes differences in brain maturational indices. When outcome data becomes available, we will test the hypothesis that these brain maturational differences predict the later appearance of autistic behaviours.

Methods: Advanced MRI protocols at 3Tesla have been optimized for very young children and fetuses. Structural, resting-state functional and magnetic resonance spectroscopy datasets are in progress. At the time of writing data is available for approximately n = 50 fetuses (at approximately 34 weeks gestation); n = 60 neonates; n = 50 6 month old infants; and increasing. A number of these children have been scanned on at least 2 occasions. Approximately half have a family history of ASD. We are currently characterizing brain maturation profiles across the whole group and also testing for potential between group differences. In addition, the infants are invited to return for follow-up developmental assessments (including for traits of ASD), until the age of 3- years, at which time we will examine whether brain indices in very early life predict later childhood outcomes.

Results: I will present preliminary evidence for group differences in brain structure and function and maturation of the glutamate system in very young children at risk of ASD. There appears to be a concentration of differences in the subcortical and interconnected brain regions.

Conclusions: This data will contribute to the longer-term objective to identify early imaging biomarkers which predate and help predict neurodevelopmental outcomes (adverse or not) in childhood.