Structural brain development: A review of methodological approaches and best practices

Abstract

Continued advances in neuroimaging technologies and statistical modelling capabilities have improved our knowledge of structural brain development in children and adolescents. While this has provided an increasingly nuanced understanding of brain development, the field is still plagued by inconsistent findings. This review highlights the methodological diversity in existing longitudinal magnetic resonance imaging (MRI) studies on structural brain development during childhood and adolescence, and addresses how such variation might contribute to inconsistencies in the literature. We discuss the impact of method choices at multiple decision points across the research process, from study design and sample selection, to image processing and statistical analysis. We also highlight the extent to which different methodological considerations have been empirically examined, drawing attention to specific areas that would benefit from future investigation. Where appropriate, we recommend certain best practices that would be beneficial for the field to adopt, including greater completeness and transparency in reporting methods, in order to ultimately develop an accurate and detailed understanding of normative child and adolescent brain development.

Keywords: Brain development; Longitudinal analyses; Methodology; Structural MRI.

Fig. 1

Development of a) cortical grey matter volume and b) cortical white matter volume across four longitudinal datasets. NCD = Neurocognitive Development, CPB = (National Institute of Health) Child Psychiatry Branch. Adapted from Mills et al. (2016).

Fig. 2

Ducharme et al.’s (2015) investigation of nonlinear developmental trajectories at different levels of quality control. Greatest quadratic or cubic trajectories (areas highlighted in different shades of blue) were evident with (a) no quality control, followed by (b) standard quality control. In comparison, minimal nonlinear trajectories were identified when (c) employing stringent quality control. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Frontal lobe volume of the Neurocognitive Development sample (Tamnes et al., 2013) modeled using different polynomial and spline modelling techniques. Figures represent a) linear, b) quadratic, and c) cubic polynomial trajectories, as well as spline modelling with d) three and c) five knots.