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dc.contributor.authorLevman, Jacoben_US
dc.contributor.authorTakahashi, Emien_US
dc.date.accessioned2016-01-04T19:23:28Z
dc.date.issued2015en_US
dc.identifier.citationLevman, Jacob, and Emi Takahashi. 2015. “Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders.” NeuroImage : Clinical 9 (1): 532-544. doi:10.1016/j.nicl.2015.09.017. http://dx.doi.org/10.1016/j.nicl.2015.09.017.en
dc.identifier.issn2213-1582en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:23993544
dc.description.abstractMultivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.relation.isversionofdoi:10.1016/j.nicl.2015.09.017en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4625213/pdf/en
dash.licenseLAAen_US
dc.subjectMultivariate analysisen
dc.subjectMachine learningen
dc.subjectFetalen
dc.subjectNeonatalen
dc.subjectPediatricen
dc.subjectBrain MRIen
dc.titleMultivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disordersen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalNeuroImage : Clinicalen
dash.depositing.authorLevman, Jacoben_US
dc.date.available2016-01-04T19:23:28Z
dc.identifier.doi10.1016/j.nicl.2015.09.017*
dash.contributor.affiliatedLevman, Jacob


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