Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI

Abstract

Multivariate analysis (MVA) is a class of statistical and pattern recognition techniques that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neurological medical imaging related challenges including the evaluation of healthy brain development, the automated analysis of brain tissues and structures through image segmentation, evaluating the effects of genetic and environmental factors on brain development, evaluating sensory stimulation's relationship with functional brain activity and much more. Compared to adult imaging, pediatric, neonatal and fetal imaging have attracted less attention from MVA researchers, however, recent years have seen remarkable MVA research growth in pre-adult populations. This paper presents the results of a systematic review of the literature focusing on MVA applied to healthy subjects in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in brain MRI, the field is still young and significant research growth will continue into the future.

Cerebral White Matter Maturation Patterns in Preterm Infants: An MRI T2 Relaxation Anisotropy and Diffusion Tensor Imaging Study

“…This data set therefore contained median values of FA, RD, AxD, and T2 for all 15 WM tracts in the TBSS-positive ROI (60 variables) for all 11 preterm singletons and 20 twin infants. Principal component analysis (PCA) 30 was first used to reduce the dimensionality of this 60-dimensional space to a 2-dimensional space. Linear discriminant analysis (LDA) was then applied to seek a hyperplane best distinguishing the preterm singletons from twins in the 2dimensional reduced principal component basis.…”

Biotyping in psychosis: using multiple computational approaches with one data set

“…Recently, there has been a strong move towards employing multivariate and data-driven approaches that more closely portray complex brain biology [6,7]. In simplistic terms, big data describes a situation where massive amounts of both structured and unstructured data are collected to solve a problem.…”

Fully automated measurement of intracranial CSF and brain parenchyma volumes in pediatric hydrocephalus by segmentation of clinical MRI studies

“…14 Furthermore, these techniques cannot be successfully applied when severe anatomical alterations are present. 19 As a consequence, pediatric applications of automated MRI segmentation methods have been essentially limited to studying tissue volume changes occurring with normal brain development, 20,21 or in pathologies that do not display major alterations of the general shape of the brain, such as psychiatric and/or neurodevelopmental brain disorders. 22 In pediatric hydrocephalus, despite the presence of extensive changes in iCSF volume, which render it in principle amenable to successfully perform singlesubject analysis for both diagnosis and therapy monitoring, at best semi-automated methods have been validated and applied to dedicated sequences (see Table 1), a setting that does not allow for widespread clinical use.…”

“…As a consequence, pediatric applications of automated MRI segmentation methods have been essentially limited to studying tissue volume changes occurring with normal brain development, 20,21 or in pathologies that do not display major alterations of the general shape of the brain, such as psychiatric and/or neurodevelopmental brain disorders 22 …”