Advanced MRI Methods Can Predict Academic Difficulties in Preterm Children

Henrik Ullman, MD, PhD Candidate Department of Neuroscience Karolinska Institutet Stockholm, Sweden 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, AustraliaMegan 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.

Medical Research: What should clinicians and patients take away from your report?

Response: Our cohort of preterm children experienced high rates of school-age cognitive and academic impairments, highlighting the clinical importance of classifying early those who will later suffer impairments.

With new and advanced MRI methods clinicians may be able to better predict at a very early age the risk of future cognitive and academic difficulties in preterm children. The benefit of early predictions for these children is opening of a time window for early interventions. Both working memory and early mathematics may be improved through early cognitive interventions, possibly mitigating manifest school problems. Currently, however, these MRI methods are not yet ready for clinical use.

Medical Research: What recommendations do you have for future research as a result of this study?

Response: In order for statistical methods such as those used in our study to come into clinical use, the statistical models need to be made more robust and this could be achieved by combining data from different clinical centres. While neonatal MRI is routinely available, automated analyses such as these become more sensitive to rather small changes in data quality and technical parameters. Our results should be replicated in other contemporary preterm cohorts.

There is a need to examine whether neuroimaging guided cognitive therapies can provide preventive effects or mitigating manifest cognitive difficulties. Cognitive predictions that do not precede an effective intervention will be of little patient benefit.

We hope that the current study can raise enthusiasm for MRI data standardisation and allow methods developed in preclinical neuroimaging to enter the clinic.


Henrik Ullman, Megan Spencer-Smith, Deanne K. Thompson, Lex W. Doyle, Terrie E. Inder, Peter J. Anderson, Torkel Klingberg. Neonatal MRI is associated with future cognition and academic achievement in preterm children. Brain, 2015; awv244 DOI: 10.1093/brain/awv244

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Henrik Ullman, MD, PhD candidate and Megan Spencer-Smith, PhD (2015). Advanced MRI Methods Can Predict Academic Difficulties in Preterm Children om