Automated Brain Morphometric Biomarkers from MRI at Term Predict Motor Development in Very Preterm Infants

Very preterm infants are at high risk for motor impairments. Early interventions can improve outcomes in this cohort, but they would be most effective if clinicians could accurately identify the highest-risk infants early. A number of biomarkers for motor development exist, but currently none are sufficiently accurate for early risk-stratification. We prospectively enrolled very preterm (gestational age ≤31 weeks) infants from four level-III NICUs. Structural brain MRI was performed at term-equivalent age. We used a established pipeline to automatically derive brain volumetrics and cortical morphometrics - cortical surface area, sulcal depth, gyrification index, and inner cortical curvature - from structural MRI. We related these objective measures to Bayley-III motor scores (overall, gross, and fine) at two-years corrected age. Lasso regression identified the three best predictive biomarkers for each motor scale from our initial feature set. In multivariable regression, we assessed the independent value of these brain biomarkers, over-and-above known predictors of motor development, to predict motor scores. 75 very preterm infants had high-quality T2-weighted MRI and completed Bayley-III motor testing. All three motor scores were positively associated with regional cortical surface area and subcortical volumes and negatively associated with cortical curvature throughout the majority of brain regions. In multivariable regression modeling, thalamic volume, curvature of the temporal lobe, and curvature of the insula were significant predictors of overall motor development on the Bayley-III, independent of known predictors. Objective brain morphometric biomarkers at term show promise in predicting motor development in very preterm infants.