ABSTRACT
Cocaine use disorder (CUD) is a major public health concern with devastating social, economic, and mental health implications. A better understanding of the underlying neurobiology and phenotypic variations in individuals with CUD is necessary for the development of effective and targeted treatments. In this study, 39 women and 54 men with CUD completed a 6-min resting-state functional magnetic resonance imaging scan after intranasal oxytocin (OXY) or placebo administration. Graph-theory network analysis was used to quantify functional connectivity changes caused by OXY in striatum, anterior cingulate cortex (ACC), insula, and amygdala nodes of interest. OXY increased connectivity in the right ACC and left amygdala in males, whereas OXY increased connectivity in the right ACC and right accumbens in females. Machine learning was then used to associate treatment response (placebo minus OXY) in nodes of interest with years of cocaine use and severity of childhood trauma separately for males and females. Childhood trauma and years of cocaine use were associated with OXY-induced changes in ACC connectivity for both men and women, but connectivity changes in the amygdala were associated with years of cocaine use in men and connectivity changes in the right insula were associated with years of cocaine use in women. These findings suggest that salience network nodes (ACC and insula) are potential OXY treatment targets in CUD, with the amygdala as a treatment target for men and the accumbens as a treatment target for women.
ABSTRACT
Face processing capacities become more specialized and advanced during development, but neural underpinnings of these processes are not fully understood. The present study applied graph theory-based network analysis to task-negative (resting blocks) and task-positive (viewing faces) functional magnetic resonance imaging data in children (5-17 years) and adults (18-42 years) to test the hypothesis that the development of a specialized network for face processing is driven by task-positive processing (face viewing) more than by task-negative processing (visual fixation) and by both progressive and regressive changes in network properties. Predictive modeling was used to predict age from node-based network properties derived from task-positive and task-negative states in a whole-brain network (WBN) and a canonical face network (FN). The best-fitting model indicated that FN maturation was marked by both progressive and regressive changes in information diffusion (eigenvector centrality) in the task-positive state, with regressive changes outweighing progressive changes. Hence, FN maturation was characterized by reductions in information diffusion potentially reflecting the development of more specialized modules. In contrast, WBN maturation was marked by a balance of progressive and regressive changes in hub-connectivity (betweenness centrality) in the task-negative state. These findings suggest that the development of specialized networks like the FN depends on dynamic developmental changes associated with domain-specific information (e.g., face processing), but maturation of the brain as a whole can be predicted from task-free states.
