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A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm
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Shang, Jing, Fisher, Paul, Bäuml, Josef G., Daamen, Marcel, Baumann, Nicole, Zimmer, Claus, Bartmann, Peter, Boecker, Henning, Wolke, Dieter, Sorg, Christian, Koutsouleris, Nikolaos and Dwyer, Dominic B. (2019) A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm. Human Brain Mapping . doi:10.1002/hbm.24698 (In Press)
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Official URL: https://doi.org/10.1002/hbm.24698
Abstract
Imaging studies have characterized functional and structural brain abnormalities in adults after premature birth, but these investigations have mostly used univariate methods that do not account for hypothesized interdependencies between brain regions or quantify accuracy in identifying individuals. To overcome these limitations, we used multivariate machine learning to identify gray matter volume (GMV) and amplitude of low frequency fluctuations (ALFF) brain patterns that best classify young adults born very preterm/very low birth weight (VP/VLBW; n = 94) from those born full-term (FT; n = 92). We then compared the spatial maps of the structural and functional brain signatures and validated them by assessing associations with clinical birth history and basic cognitive variables. Premature birth could be predicted with a balanced accuracy of 80.7% using GMV and 77.4% using ALFF. GMV predictions were mediated by a pattern of subcortical and middle temporal reductions and volumetric increases of the lateral prefrontal, medial prefrontal, and superior temporal gyrus regions. ALFF predictions were characterized by a pattern including increases in the thalamus, pre- and post-central gyri, and parietal lobes, in addition to decreases in the superior temporal gyri bilaterally. Decision scores from each classification, assessing the degree to which an individual was classified as a VP/VLBW case, were predicted by the number of days in neonatal hospitalization and birth weight. ALFF decision scores also contributed to the prediction of general IQ, which highlighted their potential clinical significance. Combined, the results clarified previous research and suggested that primary subcortical and temporal damage may be accompanied by disrupted neurodevelopment of the cortex. [Abstract copyright: © 2019 Wiley Periodicals, Inc.]
| Item Type: | Journal Article | |||||||||||||||||||||
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| Subjects: | R Medicine > RJ Pediatrics | |||||||||||||||||||||
| Divisions: | Faculty of Medicine > Warwick Medical School > Health Sciences > Mental Health and Wellbeing Faculty of Science > Psychology Faculty of Medicine > Warwick Medical School |
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| SWORD Depositor: | Library Publications Router | |||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Developmental disabilities -- Research, Cognition in children, Premature infants, Machine learning, Diagnostic imaging -- Data processing | |||||||||||||||||||||
| Journal or Publication Title: | Human Brain Mapping | |||||||||||||||||||||
| Publisher: | John Wiley and Sons | |||||||||||||||||||||
| ISSN: | 1065-9471 | |||||||||||||||||||||
| Official Date: | 22 June 2019 | |||||||||||||||||||||
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| Date of first compliant deposit: | 23 July 2019 | |||||||||||||||||||||
| DOI: | 10.1002/hbm.24698 | |||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||
| Publication Status: | In Press | |||||||||||||||||||||
| Publisher Statement: | "This is the peer reviewed version of the following article: Shang, J, Fisher, P, Bäuml, JG, et al. A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm. Hum Brain Mapp. 2019; 1– 14, which has been published in final form at https://doi.org/10.1002/hbm.24698. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions." | |||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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