Background The incidence of major depressive disorder (MDD) rises during adolescence, yet the neural mechanisms of MDD during this key developmental period are unclear. Altered amygdala resting-state functional connectivity (RSFC) has been associated with both adolescent and adult MDD, as well as symptom improvement in response to treatment in adults. However, no study to date has examined whether amygdala RSFC is associated with changes in depressive symptom severity in adolescents. Method We examined group differences in amygdala RSFC between medication-naïve depressed adolescents (N=48) and well-matched healthy controls (N=53) cross-sectionally. We then longitudinally examined whether baseline amygdala RSFC was associated with change in depression symptoms three months later in a subset of the MDD group (N=24). Results Compared to healthy controls, depressed adolescents showed reduced amygdala-based RSFC with the dorsolateral prefrontal cortex (DLPFC) and the ventromedial prefrontal cortex (VMPFC). Within the depressed group, more positive baseline RSFC between the amygdala and insulae was associated with greater reduction in depression symptoms three months later. Limitations Only a subset of depressed participants was assessed at follow-up and treatment type and delivery were not standardized. Conclusions Adolescent depression may be characterized by dysfunction of frontolimbic circuits (amygdala-DLPFC, amygdala-VMPFC) underpinning emotional regulation, whereas those circuits (amygdala-insula) subserving affective integration may index changes in depression symptom severity and may therefore potentially serve as a candidate biomarker for treatment response. Furthermore, these results suggest that the biomarkers of MDD presence are distinct from those associated with change in depression symptoms over time.

Improved understanding of how the human brain is “wired” on a macroscale may now be possible due to the emerging field of MRI connectomics. However, mapping the rapidly developing infant brain networks poses challenges. In this study, we applied an automated template-free “baby connectome” framework using diffusion MRI to non-invasively map the structural brain networks in subjects of different ages, including premature neonates, term-born neonates, six-month-old infants, and adults. We observed increasing brain network integration and decreasing segregation with age in term-born subjects. We also explored how the equal area nodes can be grouped into modules without any prior anatomical information – an important step toward a fully network-driven registration and analysis of brain connectivity.

Defining the structural and functional connectivity of the human brain (the human “connectome”) is a basic challenge in neuroscience. Recently, techniques for noninvasively characterizing structural connectivity networks in the adult brain have been developed using diffusion and high-resolution anatomic MRI. The purpose of this study was to establish a framework for assessing structural connectivity in the newborn brain at any stage of development and to show how network properties can be derived in a clinical cohort of six-month old infants sustaining perinatal hypoxic ischemic encephalopathy (HIE). Two different anatomically unconstrained parcellation schemes were proposed and the resulting network metrics were correlated with neurological outcome at 6 months. Elimination and correction of unreliable data, automated parcellation of the cortical surface, and assembling the large-scale baby connectome allowed an unbiased study of the network properties of the newborn brain using graph theoretic analysis. In the application to infants with HIE, a trend to declining brain network integration and segregation was observed with increasing neuromotor deficit scores.