Objective: Extremely preterm children are at high risk for adverse neurodevelopmental outcomes. Identifying predictors of discrete developmental outcomes early in life would allow for targeted neuroprotective therapies when neuroplasticity is… Click to show full abstract
Objective: Extremely preterm children are at high risk for adverse neurodevelopmental outcomes. Identifying predictors of discrete developmental outcomes early in life would allow for targeted neuroprotective therapies when neuroplasticity is at its peak. Our goal was to examine whether diffusion magnetic resonance imaging (MRI) metrics of the inferior longitudinal and uncinate fasciculi early in life could predict later cognitive and language outcomes. Study Design: In this pilot study, 43 extremely low-birth-weight preterm infants were scanned using diffusion MRI at term-equivalent age. White matter tracts were assessed via diffusion tensor imaging metrics of fractional anisotropy and mean diffusivity. The Language and Cognitive subscale scores of the Bayley Scales of Infant & Toddler Development-III at 18-22 months corrected age were our outcomes of interest. Multiple linear regression models were created to assess diffusion metrics of the inferior longitudinal and uncinate fasciculi as predictors of Bayley scores. We controlled for brain injury score on structural MRI, maternal education, birth weight, and age at MRI scan. Results: Of the 43 infants, 36 infants had high-quality diffusion tensor imaging and returned for developmental testing. The fractional anisotropy of the inferior longitudinal fasciculus was associated with Bayley-III scores in univariate analyses and was an independent predictor of Bayley-III cognitive and language development over and above known predictors in multivariable analyses. Conclusions: Incorporating new biomarkers such as the fractional anisotropy of the inferior longitudinal fasciculus with structural MRI findings could enhance accuracy of neurodevelopment predictive models. Additional research is needed to validate our findings in a larger cohort.
               
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