Valid functional measures are essential for clinical and research efforts that address recovery and community functioning in people with serious mental illness. Although there is a great deal of interest in functional assessment, there is limited research supporting how well current evaluation methods provide a true assessment of real world functioning or naturalistic behavior. To address this gap in the literature, the present study examined the performance of individuals with serious mental illness (i.e., diagnosis of schizophrenia-spectrum, bipolar disorder, or other depression/anxiety diagnoses and accompanying functional disability) on the Test of Grocery Shopping Skills (TOGSS), a performance-based naturalistic task. We compared TOGSS performance to two dimensions of real world functioning: directly observed real world grocery shopping and ratings of community functioning. Results indicated that the TOGSS was significantly associated with real life grocery shopping, in terms of both shopping accuracy (r = 0.424) and time (r = 0.491). Further, self-report and observer-rated methods of assessing real world shopping behaviors were significantly correlated (r = 0.455). To our knowledge, this is one of the first studies to directly compare a performance-based naturalistic skill assessment with carefully observed real world performance of that skill in people with serious mental illness. These findings support the feasibility and ecological validity of performance-based naturalistic assessment with the TOGSS.

Resting state functional brain networks have been widely studied in brain disease research. Conventional network analysis methods are hampered by differences in network size, density and normalization. Minimum spanning tree (MST) analysis has been recently suggested to ameliorate these limitations. Moreover, common MST analysis methods involve calculating quantifiable attributes and selecting these attributes as features in the classification. However, a disadvantage of these methods is that information about the topology of the network is not fully considered, limiting further improvement of classification performance. To address this issue, we propose a novel method combining brain region and subgraph features for classification, utilizing two feature types to quantify two properties of the network. We experimentally validated our proposed method using a major depressive disorder (MDD) patient dataset. The results indicated that MSTs of MDD patients were more similar to random networks and exhibited significant differences in certain regions involved in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit, which is considered to be a major pathological circuit of depression. Moreover, we demonstrated that this novel classification method could effectively improve classification accuracy and provide better interpretability. Overall, the current study demonstrated that different forms of feature representation provide complementary information.

Depression is associated with alterations in hypothalamic-pituitary-adrenal (HPA) axis activity. A proposed mechanism to explain these alterations are changes in DNA methylation levels, secondary to early life adversity (ELA), at stress-related genes. Two gene regions that have been implicated in the literature, the glucocorticoid receptor gene (NR3C1) exon 1F and the FKBP5 gene intron 7 were examined in 67 individuals (33 depressed patients and 34 controls). We investigated whether cortisol concentrations, evaluated in 25 depressed patients and 20 controls, and measures of ELA were associated with the degree of methylation at these candidate gene regions. Mean NR3C1 exon 1F DNA methylation levels were significantly increased in the depressed cohort and the degree of methylation was found to be positively associated with morning cortisol concentrations. DNA methylation levels at specific CG sites within the NR3C1 exon 1F were related to childhood emotional abuse severity. DNA methylation at CG38 was related to both HPA axis and childhood emotional abuse measures in the depressed group. No FKBP5 differences were revealed. Our findings suggest that hypermethylation at the NR3C1 exon 1F may occur in depression. This locus-specific epigenetic change is associated with higher basal HPA axis activity, possibly reflecting acquired glucocorticoid receptor resistance.

Facial expressions signaling threat and mood-congruent loss have been used to probe abnormal neural reactivity in major depressive disorder (MDD) and may be implicated in genetic vulnerability to MDD. This study investigated electro-cortical reactivity to facial expressions 101 unaffected, adult first degree relatives of probands with MDD and non-relative controls (n = 101). We investigated event-related potentials (ERPs) to five facial expressions of basic emotion: fear, anger, disgust, sadness and happiness under both subliminal (masked) and conscious (unmasked) presentation conditions, and the source localization of group differences. In the conscious condition, controls showed a distinctly positive-going shift in responsive to negative versus happy faces, reflected in a greater positivity for the VPP frontally and the P300 parietally, and less negativity for the N200. By contrast, relatives showed less differentiation of emotions, reflected in less VPP and P300 positivity, particularly for anger and disgust, and which produced an enhanced N200 for sadness. These group differences were consistently source localized to the anterior cingulate cortex. The findings contribute new evidence for neural disruptions underlying the differentiation of salient emotions in familial risk for depression. These disruptions occur in the appraisal (∼200 ms post-stimulus) through to the context evaluation (∼300 ms+ post-stimulus) phases of of emotion processing, consistent with theories that risk for depression involves biased or attenuated processing of emotion.