Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders

Abstract

Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders.

Keywords: Children; FreeSurfer; Neurodevelopmental disorders; Quality control; Structural MRI; Study design.

Fig. 1

Overview of structural brain metrics. Coronal slice of an individual brain indicating metrics according to the surface-based (left) and volume-based (right) processing streams as implemented in FreeSurfer. Depicted are the subcortical structures nucleus caudatus (blue-gray), thalamus (green), putamen (magenta), globus pallidus (deep sky blue), amygdala (cyan), and hippocampus (yellow). Black represents cerebrospinal fluid. Corpus callosum and ventricles are not labeled. For illustration purposes, the graphic and scaling was simplified and does not claim anatomical correctness. Figure courtesy of Anna Backhausen

Fig. 2

ROI-based versus surface vertex-wise approach as implemented in FreeSurfer. ROI-based approach depicting the lateral part of the right hemisphere with Desikan-Kiliany atlas regions (left) and surface vertex-wise approach (right) for statistical analysis of clinical developmental sMRI data. The hemisphere is inflated for a better view of gyri and sulci. The blue area on the right picture highlights a region with significant differences in cortical thickness between two groups, which falls partly into pars triangularis and rostral middle frontal cortex as indicated by Desikan-Kiliany atlas region outlines. Using the ROI-based approach this difference may or may not contribute to significant cortical thickness differences in the rostral middle frontal cortex, pars triangularis, or pars opercularis without the possibility of localizing the area more precisely. OP = pars opercularis; RMF = rostral middle frontal cortex; PT = pars triangularis