Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinized those findings and the theoretical claims that underlie them. To achieve this, we applied the framework provided by classical work on causality as well as contemporary approaches to prediction. We identified a number of conceptual, practical, and analytical challenges to this line of research and used a preregistered meta-analysis to quantify the longitudinal associations between reward processing abnormalities and depression. We also investigated the impact of measurement error on reported data. We found that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the available evidence does not preclude a possible causal role in depression.

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.

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The test-retest reliability of fMRI functional connectivity is a key factor in the identification of reproducible biomarkers for psychiatric illness. Low reliability limits the observable effect size of brain-behavior associations. Despite this important connection to clinical applications of fMRI, few studies have explored reliability in populations with psychiatric illnesses or across age groups. We investigate the test-retest reliability of functional connectivity in a longitudinal cohort of adolescents with and without major depressive disorder (MDD). Measuring reliability is complex and several metrics exist that can offer unique perspectives: for example, univariate metrics capture reliability of a single connection at a time while multivariate metrics reflect stability of the entire connectome. We compare a widely used univariate metric, intraclass correlation coefficient (ICC), and two multivariate metrics, fingerprinting and discriminability. Depressed adolescents were more reliable than healthy adolescents at the univariate level (0.34 > 0.24; Wilcoxon rank-sum:p< .001), and both groups had poor average ICCs (<0.4). Multivariate reliability was high in both groups: fingerprinting (FIHV= 0.53; FIMDD= 0.45; Poisson(1) testp< .001) and discriminability were above chance (DiscrHV= 0.75;DiscrMDD= 0.76; 500-fold permutation testp< .01). Reliability was not associated with symptoms or medication, suggesting that there is not a strong relationship between depression and reliability. These findings support the shift towards multivariate analysis for improved power and reliability.