MedicalResearch.com Interview with: Henrik Ullman, MD, PhD Candidate Department of Neuroscience Karolinska Institutet Stockholm, Sweden Megan Spencer-Smith, PhD School of Psychological Sciences Monash University Melbourne, Australia     Medical Research: What is the background for this study? What are the main findings? Response: Infants born preterm are at risk for school-age cognitive and academic impairments. While some will suffer severe impairments, many more will experience mild impairments, and it is these children who might not raise sufficient concern for referral and intervention. Identifying early markers and methods for classifying preterm infants at risk for school-age impairments, many years before difficulties emerge, would provide important information for clinicians in advising families regarding intervention and ongoing monitoring. Brain alterations are common in preterm populations. Any brain alterations associated with school-age impairments are likely already present in the neonatal period but are not detected with the current standard clinical and radiological evaluations. In this study we wanted to see how well we could use advanced analysis of volumetric and diffusion MRI collected in the neonatal period from 224 very preterm children to predict cognitive functions at five and seven years of age. We used statistical models to look for localised regions as well as machine learning methods to correlate patterns in the neonatal MRI data that could predict school-age outcomes. We found that localised volumes in the insula and basal ganglia as well as a distributed patterns of diffusion MRI could predict working memory and early mathematical skills even after co-varying for important perinatal clinical factors. It has previously been shown that quantitative and pattern analysis can catch subtle patterns in MRI data not easily detected by eye and may predict cognitive development. The current study builds further on these results showing clinically relevant predictions in preterm children. (more…)