Altered white-matter architecture in treatment-naive adolescents with clinical depression.

BACKGROUND: Depressive disorders are highly prevalent in adolescence and confer a heightened risk of recurrence in adulthood. Insight into the developmental neurocircuitry of depression could advance our understanding of depression and aid the development of effective treatment strategies. Whereas white-matter (WM) abnormalities are strongly implicated in adult depression, we still lack a firm understanding of WM architecture in adolescent depression. Using diffusion tensor imaging (DTI), we set out to investigate WM microstructure in a sample of clinically depressed adolescents relative to matched controls. METHOD: We employed tract-based spatial statistics (TBSS) to examine WM microstructure in 25 treatment-naive adolescents with clinical depression relative to 21 matched controls. Using TBSS, we examined fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD). Threshold-free cluster enhancement (TFCE) with family-wise error (FWE) correction was used to control for multiple comparisons. RESULTS: Our analysis revealed abnormal WM microstructure in clinically depressed adolescents. More specifically, whole-brain analysis revealed that patients had lower FA values in the body of the corpus callosum (CC), coupled with elevated RD and MD, and preserved AD. Conversely, region-of-interest analysis revealed that patients had higher FA values in the uncinate fasciculus (UF), coupled with elevated AD, reduced RD and preserved MD. CONCLUSIONS: In line with neurocircuitry models of depression, our findings suggest that WM abnormalities within pathways facilitating cognitive and emotional functioning are involved in the pathophysiology of depression. Importantly, our findings show that these WM abnormalities are already present early in the course of the disorder.

A new symptom model for autism cross-validated in an independent sample.

BACKGROUND: Results from several studies indicated that a symptom model other than the DSM triad might better describe symptom domains of autism. The present study focused on a) investigating the stability of a new symptom model for autism by cross-validating it in an independent sample and b) examining the invariance of the model regarding three covariates: symptom severity, intelligence, and age. METHOD: The validity of the symptom model was examined in an independent sample of N = 263 children and adolescents with autism spectrum disorders, and model invariance was studied in a larger sample of N = 356 children and adolescents with autism spectrum disorders. The fit of the symptom model to the sample data was compared to that of alternative models (including the DSM triad), and the invariance of the new model was investigated for each covariate by multiple-group comparisons. RESULTS: The fit of the new symptom model was better than that of two alternative models. It could not be compared to that of the DSM triad, because the latter encountered empirical identification problems. There were no significant or substantive differences between the estimated model in each of the dichotomised groups for any of the three covariates, which indicated factorial invariance of both structural form and factor loadings. CONCLUSIONS: The symptom model appeared to be relatively stable: It could be cross-validated in the independent sample and factorial invariance was shown between the dichotomised groups for each covariate. Further model validation with instruments other than the Autism Diagnostic Interview-Revised (ADI-R) is recommended.