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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; <jats:italic>n<\/jats:italic> =\u200994) from those born full\u2010term (FT; <jats:italic>n<\/jats:italic> =\u200992). 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\u2010 and post\u2010central 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.<\/jats:p>","DOI":"10.1002\/hbm.24698","type":"journal-article","created":{"date-parts":[[2019,6,22]],"date-time":"2019-06-22T12:45:57Z","timestamp":1561207557000},"page":"4239-4252","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9816-6126","authenticated-orcid":false,"given":"Jing","family":"Shang","sequence":"first","affiliation":[{"name":"Department of Psychiatry and Psychotherapy Ludwig\u2010Maximilian\u2010University  Munich Germany"},{"name":"TUM\u2010NIC Neuroimaging Center, Technische Universit\u00e4t 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