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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, 40 (14). pp. 4239-4252. doi:10.1002/hbm.24698

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Official URL: https://doi.org/10.1002/hbm.24698

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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
Subjects: R Medicine > RJ Pediatrics
Divisions: Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Mental Health and Wellbeing
Faculty of Science, Engineering and Medicine > Science > Psychology
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
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
Dates:
DateEvent
22 June 2019Published
1 October 2019Available
31 May 2019Accepted
Volume: 40
Number: 14
Page Range: pp. 4239-4252
DOI: 10.1002/hbm.24698
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): "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:
Project/Grant IDRIOXX Funder NameFunder ID
201708080036China Scholarship Councilhttp://dx.doi.org/10.13039/501100004543
SO 1336/1-1[DFG] Deutsche Forschungsgemeinschafthttp://dx.doi.org/10.13039/501100001659
01ER0801Bundesministerium für Bildung und Forschunghttp://viaf.org/viaf/132864470
01ER0803Bundesministerium für Bildung und Forschunghttp://viaf.org/viaf/132864470
8765162Kommission für Klinische ForschungUNSPECIFIED
602152Seventh Framework Programmehttp://dx.doi.org/10.13039/100011102

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