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Associations between early deprivation/neglect in the form of institutional care with the cortisol awakening response (CAR) were examined as a function of pubertal status among 12- and 13-year-old postinstitutionalized youth. CARs indexed hypothalamic-pituitary-adrenocortical reactivity. Postinstitutionalized youth were compared to youth adopted internationally from foster care (adoption control) and to nonadopted youth reared in families comparable in parental education and income to the adoptive families. Postinstitutionalized youth exhibited a blunted CAR if they were at earlier, but not if they were at later, stages of puberty. Similarly, for both groups of internationally adopted youth combined, earlier but not later stages of puberty were associated with more blunted CARs at higher but not lower levels of parent-reported preadoption physical and social neglect. (Contains 3 tables and 2 figures.)

Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides insight into certain neurodegenerative diseases and psychiatric disorders, and thus is of clinical importance. To help inform physicians regarding patient diagnoses, unsupervised clustering of subjects based on FC is desired, allowing the data to inform us of groupings of patients based on shared features of connectivity. Since heterogeneity in FC is present even between patients within the same group, it is important to allow subject-level differences in connectivity, while still pooling information across patients within each group to describe group-level FC. To this end, we propose a random covariance clustering model (RCCM) to concurrently cluster subjects based on their FC networks, estimate the unique FC networks of each subject, and to infer shared network features. Although current methods exist for estimating FC or clustering subjects using fMRI data, our novel contribution is to cluster or group subjects based on similar FC of the brain while simultaneously providing group- and subject-level FC network estimates. The competitive performance of RCCM relative to other methods is demonstrated through simulations in various settings, achieving both improved clustering of subjects and estimation of FC networks. Utility of the proposed method is demonstrated with application to a resting-state fMRI data set collected on 43 healthy controls and 61 participants diagnosed with schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Given the prevalence of adolescent depression and the modest effects of current treatments, research ought to inform development of effective intervention strategies. Self-compassion is inversely associated with depression, and self-compassion interventions have demonstrated promising effects on reducing depression. However, little is known about the neural mechanisms underlying that relationship. Maladaptive self-processing is a characteristic of depression that contributes to the onset and chronicity of depression. Because our own face is an automatic and direct cue for self-processing, this study investigated whether self-compassion was associated with neural responses during sad v. neutral self-face recognition and explore their relationship with depression severity in depressed adolescents and healthy controls (HCs). Methods: During functional magnetic resonance imaging, 81 depressed youth and 37 HCs were instructed to identify whether morphed self or other faces with sad, happy, or neutral expressions resembled their own. Results: Self-compassion correlated negatively with activity during sad v. neutral self-face recognition in the dorsal anterior cingulate cortex in the total sample, and in the right posterior cingulate cortex/precuneus in HCs, respectively. In depressed adolescents, higher self-compassion correlated with lower activity during sad v. neutral self-face recognition in the right dorsolateral prefrontal cortex (DLPFC), implying that less cognitive effort might be needed to avoid dwelling on sad self-faces and/or regulate negative affect induced by them. Moreover, higher self-compassion mediated the relationship between lower DLPFC activity and reduced depression severity. Conclusions: Our findings imply that DLPFC activity might be a biological marker of a successful self-compassion intervention as potential treatment for adolescent depression. [ABSTRACT FROM AUTHOR]

We consider a novel problem, bi‐level graphical modeling, in which multiple individual graphical models can be considered as variants of a common group‐level graphical model and inference of both the group‐ and individual‐level graphical models is of interest. Such a problem arises from many applications, including multi‐subject neuro‐imaging and genomics data analysis. We propose a novel and efficient statistical method, the random covariance model, to learn the group‐ and individual‐level graphical models simultaneously. The proposed method can be nicely interpreted as a random covariance model that mimics the random effects model for mean structures in linear regression. It accounts for similarity between individual graphical models, identifies group‐level connections that are shared by individuals, and simultaneously infers multiple individual‐level networks. Compared to existing multiple graphical modeling methods that only focus on individual‐level graphical modeling, our model learns the group‐level structure underlying the multiple individual graphical models and enjoys computational efficiency that is particularly attractive for practical use. We further define a measure of degrees‐of‐freedom for the complexity of the model useful for model selection. We demonstrate the asymptotic properties of our method and show its finite‐sample performance through simulation studies. Finally, we apply the method to our motivating clinical data, a multi‐subject resting‐state functional magnetic resonance imaging dataset collected from participants diagnosed with schizophrenia, identifying both individual‐ and group‐level graphical models of functional connectivity. [ABSTRACT FROM AUTHOR]