Individuals who experience early adversity, such as child maltreatment, are at heightened risk for a broad array of social and health difficulties. However, little is known about how this behavioral risk is instantiated in the brain. Here we examine a neurobiological contribution to individual differences in human behavior using methodology appropriate for use with pediatric populations paired with an in-depth measure of social behavior. We show that alterations in the orbitofrontal cortex among individuals who experienced physical abuse are related to social difficulties. These data suggest a biological mechanism linking early social learning to later behavioral outcomes.

We present a unified statistical framework for analyzing temporally varying brain morphology using the 3D displacement vector field from a nonlinear deformation required to register a subject's brain to an atlas brain. The unification comes from a single model for structural change, rather than two separate models, one for displacement and one for volume changes. The displacement velocity field rather than the displacement itself is used to set up a linear model to account for temporal variations. By introducing the rate of the Jacobian change of the deformation, the local volume change at each voxel can be computed and used to measure possible brain tissue growth or loss. We have applied this method to detecting regions of a morphological change in a group of children and adolescents. Using structural magnetic resonance images for 28 children and adolescents taken at different time intervals, we demonstrate how this method works.

We present a novel data smoothing and analysis framework for cortical thickness data defined on the brain cortical manifold. Gaussian kernel smoothing, which weights neighboring observations according to their 3D Euclidean distance, has been widely used in 3D brain images to increase the signal-to-noise ratio. When the observations lie on a convoluted brain surface, however, it is more natural to assign the weights based on the geodesic distance along the surface. We therefore develop a framework for geodesic distance-based kernel smoothing and statistical analysis on the cortical manifolds. As an illustration, we apply our methods in detecting the regions of abnormal cortical thickness in 16 high functioning autistic children via random field based multiple comparison correction that utilizes the new smoothing technique.

Cognitive deficits have been reported in children who experienced early neglect, especially children raised in institutionalized settings. Previous research suggests early neglect may differentially affect the directional organization of white matter in the prefrontal cortex (PFC). This may be one mechanism to explain cognitive deficits associated with neglect. To test this idea, properties of white matter and neurocognitive performance was assessed in children who suffered early neglect and those raised in typical environments (n=63, Mean Age=11.75 years). As predicted, prefrontal white matter microstructure was affected, consistent with more diffuse organization, in children that suffered early neglect and this was related to neurocognitive deficits. Such findings underscore how early adversity may affect the PFC and explain cognitive deficits associated with neglect.

As an illustration, the WFS is applied in quantifying the amount of gray matter in a group of high functioning autistic subjects. Within the WFS framework, cortical thickness and gray matter density are computed and compared.