Mapping gray matter development: implications for typical development and vulnerability to psychopathology

Abstract

Recent studies with brain magnetic resonance imaging (MRI) have scanned large numbers of children and adolescents repeatedly over time, as their brains develop, tracking volumetric changes in gray and white matter in remarkable detail. Focusing on gray matter changes specifically, here we explain how earlier studies using lobar volumes of specific anatomical regions showed how different lobes of the brain matured at different rates. With the advent of more sophisticated brain mapping methods, it became possible to chart the dynamic trajectory of cortical maturation using detailed 3D and 4D (dynamic) models, showing spreading waves of changes evolving through the cortex. This led to a variety of time-lapse films revealing characteristic deviations from normal development in schizophrenia, bipolar illness, and even in siblings at genetic risk for these disorders. We describe how these methods have helped clarify how cortical development relates to cognitive performance, functional recovery or decline in illness, and ongoing myelination processes. These time-lapse maps have also been used to study effects of genotype and medication on cortical maturation, presenting a powerful framework to study factors that influence the developing brain.

Figure 1

Cortical GM development in healthy children between age 4 to 22. Right lateral and Top views of the dynamic sequences of cortical GM maturation in healthy children ages 4 - 22 (n=13; 54 scans; upper panel) rescanned every two years. Scale bar shows GM amount at each of the 65,536 cortical points across the entire cortex represented using a color scale (Red to Pink – More GM; Blue – GM Loss). Cortical GM maturation appears to progress in a ‘back-to-front’ (parieto-temporal) manner (Gogtay, Giedd et al., 2004). The graphs show total lobar volumes of frontal, parietal, temporal and occipital lobes in male (blue) and female (red) healthy children between ages 7 to 20. Arrows indicate peak GM volume for each curve and dotted lines represent confidence intervals. Adapted from Giedd et al. 1999 (J. N. Giedd et al., 1999).

Figure 2

Trajectories of cortical change in children with superior (n=91), high (n=101) and average (n=115) intelligence (total 629 scans). The brain maps (center panel) show prominent clusters where the superior and average intelligence groups differ significantly in the trajectories of cortical development (t-statistic maps show areas of significant interaction between these IQ groups and the cubic age term). a, Graph showing the trajectories at the cortical point of maximum trajectory difference in the right superior frontal gyrus (point indicated in upper brain map). b–d, Graphs showing the trajectories of the mean thickness of all cortical points in the other clusters. The graph in d relates to the area indicated in the lower brain map. The age of peak cortical thickness is arrowed and significance values of differences in shapes of trajectories are given on the graphs. Adapted from Shaw et al. (Shaw et al., 2006).

Figure 3

Comparison and Specificity of GM developmental patterns in healthy, COS and Bipolar children. Top panel shows right lateral views of dynamic sequences of cortical GM maturation in 13 healthy children between ages 4 to 22 as was displayed in Figure 1. The GM maturation (blue color) proceeds in parieto-frontal-temporal direction (Gogtay, Giedd et al., 2004). The middle panel shows dynamic sequence of significant GM loss (p-maps) in 12 prospectively scanned COS children compared to matched healthy controls between age 12 to 16. The pattern of GM loss in COS appears to be an exaggeration of the normal GM maturation(P. M. Thompson et al., 2001). The bottom panel shows dynamic sequence comparing the GM amount (using ratio maps) between 9 psychotic bipolar I children and their 18 matched controls, before and after onset of mania. There is little overlap in the GM deficit pattern between COS and bipolar GM development, establishing the diagnostic specificity of the findings(Gogtay, Ordonez et al., 2007).

Figure 4

Cortical GM thickness in healthy COS siblings (n=52; 110 scans) compared to age-, sex- and inter-scan interval-matched healthy controls (n=52; 108 scans) between ages 8 through 28 (Gogtay, Greenstein et al., 2007). For visual comparison, GM thickness in COS probands compared to matched healthy controls is shown at corresponding ages. Cortical GM is adjusted for mean cortical thickness (MCT) (Greenstein et al., 2006).

Figure 5

Tissue Growth Rates Mapped in Healthy Controls and COS Patients (Gogtay et al., 2008). These average maps (top row) show the average rates of tissue growth (red colors) and tissue loss (blue colors) throughout the brain in percent per year, for healthy controls (left panels) and COS patients (right panels). Corresponding panels in the bottom row show the significance of the tissue growth in the top row. For each group, a sagittal section through the right hemisphere is shown, at the level of the occipital horn of the ventricles, followed by an axial section, and another sagittal section though the left hemisphere. Corroborating prior findings, these are the first maps to visualize the growth profile in 3D, showing significant expansion of the white matter and ventricles (cf. Giedd et al., 1999 (J. N. Giedd, J. Blumenthal, N. O. Jeffries, J. C. Rajapakse et al., 1999). Note the unexpected hemispheric asymmetry of growth rates in COS (right slower than left; P<0.037), and the slower growth in COS patients versus healthy controls, throughout the white matter (Figure 2 directly assesses the significance of the group differences). 3D versions of these maps are obtainable as video sequences at: http://www.loni.ucla.edu/∼thompson/MOVIES/GROWTH/video.html

Figure 6

Degenerative Sequence in AD is the Reverse of the Normal Developmental Sequence. In a process termed retrogenesis (e.g., by Reisberg et al., 1999), cortical regions that mature earliest in infancy tend to degenerate last in AD. The developmental sequence echoes the phylogenetic sequence in which structures evolved. The most heavily myelinated structures, with least neuronal plasticity, may resist AD-related neurodegeneration. Arrows denote the childhood cortical maturation sequence (left panel; Gogtay et al., 2004) and the gray matter atrophy sequence in AD (right panel; Thompson et al., 2003). Images are from time-lapse films compiled from cortical models of gray matter distribution in subjects scanned longitudinally with MRI, which may be viewed at: http://www.loni.ucla.edu/∼thompson/DEVEL/dynamic.html and http://www.loni.ucla.edu/∼thompson/AD_4D/dynamic.html