Tracking Brain Development and Dimensional Psychiatric Symptoms in Children: A Longitudinal Population-Based Neuroimaging Study

Abstract

Objective: Psychiatric symptomatology during childhood predicts persistent mental illness later in life. While neuroimaging methodologies are routinely applied cross-sectionally to the study of child and adolescent psychopathology, the nature of the relationship between childhood symptoms and the underlying neurodevelopmental processes remains unclear. The authors used a prospective population-based cohort to delineate the longitudinal relationship between childhood psychiatric problems and brain development.

Method: A total of 845 children participated in the study. Psychiatric symptoms were measured with the parent-rated Child Behavior Checklist at ages 6 and 10. MRI data were collected at ages 8 and 10. Cross-lagged panel models and linear mixed-effects models were used to determine the associations between psychiatric symptom ratings and quantitative anatomic and white matter microstructural measures over time.

Results: Higher ratings for externalizing and internalizing symptoms at baseline predicted smaller increases in both subcortical gray matter volume and global fractional anisotropy over time. The reverse relationship did not hold; thus, baseline measures of gray matter and white matter were not significantly related to changes in symptom ratings over time.

Conclusions: Children presenting with behavioral problems at an early age show differential subcortical and white matter development. Most neuroimaging models tend to explain brain differences observed in psychopathology as an underlying (causal) neurobiological substrate. However, the present work suggests that future neuroimaging studies showing effects that are pathogenic in nature should additionally explore the possibility of the downstream effects of psychopathology on the brain.

Keywords: Brain Morphology; Child Psychiatry; Cross-Lagged Panel Model; Diffusion Tensor Imaging; Fiber Tractography; Linear Mixed-Effects Model.