Anatomical brain magnetic resonance imaging of typically developing children and adolescents.

Many psychiatric disorders, including some with adult onset such as schizophrenia, are increasingly being conceptualized as stemming from anomalies of neurodevelopment. To explore neurodevelopmental hypotheses of illness, it is useful to have well-characterized data regarding typical maturation to serve as a “yardstick” from which to assess possible deviations. Studies of typical development, and the influences on that development, may also unveil the timing and mechanisms of brain maturation guiding the way for novel interventions. In this overview, we will touch on methodological issues relevant to magnetic resonance imaging (MRI) studies of brain anatomy, summarize MRI findings of neuroanatomic changes during childhood and adolescence, and discuss possible influences on brain development trajectories. As indicated in the previous articles of this series, one of the first steps in measuring brain morphometric characteristics in a conventional anatomic MRI is to classify (or “segment”) individual voxels (the smallest elements of different MRI signals—usually approximately 1 mL) as corresponding to CSF, white matter (WM), or gray matter (GM). Once categorized by tissue type, various parcellations can be performed to derive volumes at the level of lobes (e.g., frontal, temporal, parietal, occipital); regions defined by gyral, sulcal, or GM, WM, and CSF boundaries (e.g., caudate nucleus); or individual voxels. Segmentation and parcellation of MRIs was originally exclusively done by trained individuals outlining particular regions of interest (frequently abbreviated as ROIs) by hand. Although having a highly trained individual manually identify brain regions is considered the closest thing to a “gold standard” available, the time and anatomic expertise necessary for training raters and performing this type of analysis can be prohibitive. This has motivated many laboratories to develop computer algorithms capable of automatically classifying regions of MRI images as belonging to different tissue types and anatomic regions. The rapid progress in this area has made it feasible to perform the type of large scale studies necessary to capture many of the changes associated with typical and atypical brain development. Automated methods have also opened the door to innovative ways of looking at brain structure, such as analyzing the shape and thickness of the cortical sheet. However, the fidelity of automated methods depends on the clarity of the borders between structures, which in turn is determined by a combination of the anatomy of a particular structure and the quality of the MRI image. For example, the amygdala and hippocampus are difficult for automated methods to separate properly because they represent adjacent GM structures. In cases such as these, hand measurements may still be the best approach, although even human raters may need considerable experience before they can consistently identify the borders of such structures on conventional MRI. The data for this overview are largely derived from 387 typically developing subjects (829 scans) participating in an ongoing longitudinal study at the Child Psychiatry Branch of the National Institute of Mental Health. Begun in 1989 by Markus Kruesi, M.D., and Judith Rapoport, M.D., the study design is for participants aged 3 to 30 years to come to the National Institutes of Health at approximately 2-year intervals for brain imaging, psychological and behavioral assessment, and collection of DNA. The emphasis on this single source is not to devalue the many excellent contributions of other investigators but to provide an integrated account from the world’s largest collection of child and adolescent brain MRI scans with data acquired using uniform screening/assessment batteries, the same scanner, and the same methods of image analyses. We have supplemented with references to studies by other laboratories, although a complete review of the field is beyond the scope of this article.