Advanced neuroimaging and its role in predicting neurodevelopmental outcomes in very preterm infants

Abstract

Up to 35% of very preterm infants survive with neurodevelopmental impairments (NDI) such as cognitive deficits, cerebral palsy, and attention deficit disorder. Advanced MRI quantitative tools such as brain morphometry, diffusion MRI, magnetic resonance spectroscopy, and functional MRI at term-equivalent age are ideally suited to improve current efforts to predict later development of disabilities. This would facilitate application of targeted early intervention therapies during the first few years of life when neuroplasticity is optimal. A systematic search and review identified 47 published studies of advanced MRI to predict NDI. Diffusion MRI and morphometry studies were the most commonly studied modalities. Despite several limitations, studies clearly showed that brain structural and metabolite biomarkers are promising independent predictors of NDI. Large representative multicenter studies are needed to validate these studies.

Keywords: Brain metabolites; Cerebral palsy; Cognitive impairment; Diffusion MRI; Functional MRI; Infant; Magnetic resonance imaging (MRI); Magnetic resonance spectroscopy; Microstructure; Morphometry; Neurodevelopmental impairment; Premature.

Fig. 1

Examples of brain advanced MRI measurements including morphometry (A), diffusion tractography (B and C), and magnetic resonance spectroscopy (D). Representative advanced MRI examples at term-equivalent age display an extremely low-birth-weight infant’s brain that was segmented into tissues classes, subcortical structures, and lobes (A), 10 white matter tracts, displayed in axial and sagittal orientations (B and C), and a proton MRS spectrum displaying the four main metabolites, including N-acetylaspartate (NAA), creatine (Cr), choline (Cho), and myoinositol (MI).

Fig. 2

Use of multivariable pattern classification to discriminate between multiple subgroups of very preterm infants with cognitive, behavioral, or motor impairments on the basis of multimodal neuroimaging data. (A) Pattern classification models are initially trained (Phase 1) on well-characterized phenotypic data obtained by structural MRI, DTI, MRS, and/or fMRI to identify patterns of potentially discriminative features. (B) These patterns can then be used to determine whether an individual patient in the validation cohort should be assigned to the impaired or control group (Phase 2). Abbreviations: sMRI, structural MRI; DTI, diffusion tensor imaging; MRS, magnetic resonance spectroscopy; fMRI, functional MRI. (Adapted with permission from Ecker and Murphy, copyright 2014.)

Fig. 3

Functional connectivity MRI from 4 somatosensory and motor networks from 5 very preterm infants with cerebral palsy (CP) and 18 without CP. The columns and green circles represent the four sensorimotor regions of interest, including supplementary motor area, post-central gyrus, pre-central gyrus, and thalamus. The red and blue circles represent regions of the brain they are connected with; red signifies a positive correlation while blue represents a negative one. Infants with CP exhibited fewer sensorimotor connections (middle panel) than those without CP (top panel). The last panel displays several networks that were present in infants without CP (red connections) but were absent in infants with CP and a few hubs (blue) where infants with CP (blue) had more connections than those without CP.

Fig. 4

The combined use of advanced MRI, genomic/epigenetic biomarkers and sophisticated pattern classification algorithms will make personalized predictions for high-risk infants a reality. A combination of biomarkers from advanced brain MRI and genomic/epigenetic biologic samples and pattern classification algorithms such as machine learning are ideally suited to classify the current heterogeneous mix of very preterm infants into different risk groups. This will facilitate the delivery of more effective personalized treatments for very preterm infants.