Structural MRI and Brain Development

• Published in: International Review of Neurobiology Vol. 67; no. 4; pp. 285 - 323
• Main Authors: Thompson, Paul M., Sowell, Elizabeth R., Gogtay, Nitin, Giedd, Jay N., Vidal, Christine N., Hayashi, Kiralee M., Leow, Alex, Nicolson, Rob, Rapoport, Judith L., Toga, Arthur W.
• Format: Book Chapter Journal Article
• Language: English
• Published: United States Elsevier Science & Technology 01.01.2005
• Subjects: Animals; Brain - anatomy & histology; Brain - growth & development; Brain Mapping; Humans; Magnetic Resonance Imaging - methods; Medical imaging; Neurology & clinical neurophysiology; Neurosciences; Primates; Prunus
• ISBN: 9780123668684; 0123668689
• ISSN: 0074-7742; 0360-3016; 2162-5514
• DOI: 10.1016/S0074-7742(05)67009-2

Magnetic resonance imaging (MRI) scans provide exceptionally detailed information on how the human brain changes throughout childhood, adolescence, and into old age. We describe several approaches for understanding developmental changes in brain structures based on MRI. Atlas‐based “parcellation” methods, for example, measure volumes of brain substructures, revealing how they change with age. Growth curves for different brain structures can be compiled, describing the expected trajectories of normal development. Additional computational anatomy techniques can be used to map spatial patterns of brain growth and tissue loss in individual children. Changes in cortical features, such as gray matter thickness, asymmetry, and complexity, can also be mapped. Individual maps can then be combined across subjects to make statistical maps or dynamic “time‐lapse movies” that reveal systematic features of brain development in population subgroups while retaining information on their variance. We review several large‐scale studies of brain development, including longitudinal studies in which children were scanned repeatedly with structural MRI at 2‐year intervals for periods of up to 10 years. Image processing algorithms were then applied to recover detailed information from the resulting image databases. We describe the approaches necessary to compare brain MRI data across groups differing in age, covaried with gender, developmental disorders, or genotype. These methods reveal unsuspected links between development and cognition and can help discover genetic and environmental factors that affect development. These brain maps also chart the anatomical sequence of healthy brain maturation and visualize how it is derailed in neuropsychiatric disorders such as schizophrenia, autism, fetal alcohol syndrome, and Williams syndrome.