Tracking and predicting cognitive development using magnetic resonance imaging
Author: Ullman, Henrik
Date: 2016-06-03
Location: Hillarpsalen, Karolinska Institutet, Solna
Time: 14.00
Department: Inst för neurovetenskap / Dept of Neuroscience
Abstract
Neuroimaging of the developing brain has helped describing and quantifying many of the biological processes underlying cognitive development. The protracted development of higher
order cognitive functions has allowed detailed description of their neural correlates. While
primary sensory and motor functions have been found to be relatively localized, higher order cognitive functions including Working Memory (WM) have been found to be distributed
over many brain regions. A growing amount of literature is describing a complex interaction
of anatomically separate nodes making up networks sub-serving WM. The development of
these networks is dependent on both predetermined maturation and environmental stimulation. The current thesis aims to expand the current knowledge by exploring if WM development can be predicted by using Magnetic Resonance Imaging (MRI) data explaining
future development rather than correlating to current capacity. We further apply this principle on a sample of premature born children to predict future cognitive outcome using MRI
at birth. Finally we address the question if individual variability in developmental timing
affects cognitive abilities in childhood and adolescence.
Study I: In this study we show that WM development to some degree can be predicted using structural and functional MRI. The prediction was based on a multivariate model of MRI data and could significantly predict WM two years after the scans. This significance was retained after controlling for three concurrent WM tests. Analysis to localize the predictive effect of MRI suggests basal ganglia and thalamic structures as important for future development while classical cortical WM areas correlate to concurrent WM capacity.
Study II: We apply a similar analysis strategy as in Study I on a longitudinal sample of preterm born children. T2 and Diffusion Tensor Imaging (DTI) sequences were collected in the perinatal period and used to predict WM and Numerical Ability (NA) at five and seven years of age. We show that multivariate models can predict NA and WM capacity at five years of age. This was the strongest predictor when compared with previously known important clinical features. T2 based volumetric analysis points towards reductions in insula and basal ganglia volume in the perinatal period among children with low cognitive function at five years of age.
Study III: The study explores weather the individual time course of development affects WM abilities when children start school. We trained a multivariate model of brain development using DTI from a sample of normally developing children. We then apply the model on a sample of seven year old children to show that brain maturation correlates strongly with WM abilities while age does not.
In summary the articles add to the developmental neuroscience literature by showing the ability of MRI to predict cognitive development. Prediction of development is an area discussed as a promising target for clinical implementation of cognitive neuroimaging. We show the feasibility and clinically relevant effects of prediction in a clinical sample. Finally the measuring of variability in developmental timing in Study III highlight the view of WM development as result of multiple processes.
Study I: In this study we show that WM development to some degree can be predicted using structural and functional MRI. The prediction was based on a multivariate model of MRI data and could significantly predict WM two years after the scans. This significance was retained after controlling for three concurrent WM tests. Analysis to localize the predictive effect of MRI suggests basal ganglia and thalamic structures as important for future development while classical cortical WM areas correlate to concurrent WM capacity.
Study II: We apply a similar analysis strategy as in Study I on a longitudinal sample of preterm born children. T2 and Diffusion Tensor Imaging (DTI) sequences were collected in the perinatal period and used to predict WM and Numerical Ability (NA) at five and seven years of age. We show that multivariate models can predict NA and WM capacity at five years of age. This was the strongest predictor when compared with previously known important clinical features. T2 based volumetric analysis points towards reductions in insula and basal ganglia volume in the perinatal period among children with low cognitive function at five years of age.
Study III: The study explores weather the individual time course of development affects WM abilities when children start school. We trained a multivariate model of brain development using DTI from a sample of normally developing children. We then apply the model on a sample of seven year old children to show that brain maturation correlates strongly with WM abilities while age does not.
In summary the articles add to the developmental neuroscience literature by showing the ability of MRI to predict cognitive development. Prediction of development is an area discussed as a promising target for clinical implementation of cognitive neuroimaging. We show the feasibility and clinically relevant effects of prediction in a clinical sample. Finally the measuring of variability in developmental timing in Study III highlight the view of WM development as result of multiple processes.
List of papers:
I. Ullman H., Almeida R., Klingberg T. (2014). “Structural maturation and brain activity predict future working memory capacity during childhood development.” In: J. Neurosci. 34.5, pp. 1592–8.
Fulltext (DOI)
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II. Ullman H., Spencer-Smith M., Thompson D., Doyle L., Inder T., Anderson P., Klingberg T. (2015). “Neonatal MRI is associated with future cognition and academic achievement in preterm children.” In: Brain 138.Pt 11, pp. 3251–62.
Fulltext (DOI)
Pubmed
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III. Ullman H., Klingberg T. “Timing of white matter development determines cognitive abilities at school entry but not in late adolescence”. [Submitted]
I. Ullman H., Almeida R., Klingberg T. (2014). “Structural maturation and brain activity predict future working memory capacity during childhood development.” In: J. Neurosci. 34.5, pp. 1592–8.
Fulltext (DOI)
Pubmed
View record in Web of Science®
II. Ullman H., Spencer-Smith M., Thompson D., Doyle L., Inder T., Anderson P., Klingberg T. (2015). “Neonatal MRI is associated with future cognition and academic achievement in preterm children.” In: Brain 138.Pt 11, pp. 3251–62.
Fulltext (DOI)
Pubmed
View record in Web of Science®
III. Ullman H., Klingberg T. “Timing of white matter development determines cognitive abilities at school entry but not in late adolescence”. [Submitted]
Institution: Karolinska Institutet
Supervisor: Klingberg, Torkel
Issue date: 2016-05-11
Rights:
Publication year: 2016
ISBN: 978-91-7676-316-2
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