BACKGROUND: Research using latent variable models demonstrates that pre-attentive measures of early auditory processing (EAP) and cognition may initiate a cascading effect on daily functioning in schizophrenia. However, such models fail to account for relationships among individual measures of cognition and EAP, thereby limiting their utility. Hence, EAP and cognition may function as complementary and interacting measures of brain function rather than independent stages of information processing. Here, we apply a data-driven approach to identifying directional relationships among neurophysiologic and cognitive variables. METHODS: Using data from the Consortium on the Genetics of Schizophrenia 2, we estimated Gaussian Graphical Models and Bayesian networks to examine undirected and directed connections between measures of EAP, including mismatch negativity and P3a, and cognition in 663 outpatients with schizophrenia and 630 control participants. RESULTS: Chain structures emerged among EAP and attention/vigilance measures in schizophrenia and control groups. Concerning differences between the groups, object memory was an influential variable in schizophrenia upon which other cognitive domains depended, and working memory was an influential variable in controls. CONCLUSIONS: Measures of EAP and attention/vigilance are conditionally independent of other cognitive domains that were used in this study. Findings also revealed additional causal assumptions among measures of cognition that could help guide statistical control and ultimately help identify early-stage targets or surrogate endpoints in schizophrenia.

BACKGROUND: Exposure to natural vegetation (ie, greenspace) is related to beneficial outcomes, including higher positive and lower negative affect, in individuals with and those without mental health concerns. Researchers have yet to examine dynamic associations between greenspace exposure and affect within individuals over time. Smartphone-based ecological momentary assessment (EMA) and passive sensors (eg, GPS, microphone) allow for frequent sampling of data that may reveal potential moment-to-moment mechanisms through which greenspace exposure impacts mental health. OBJECTIVE: In this study, we examined associations between greenspace exposure and affect (both self-reported and inferred through speech) in people with and those without schizophrenia spectrum disorder (SSD) at the daily level using smartphones. METHODS: Twenty people with SSD and 14 healthy controls reported on their current affect 3 times per day over 7 days using smartphone-based EMA. Affect expressed through speech was labeled from ambient audio data collected via the phones microphone using Linguistic Inquiry and Word Count (LIWC). Greenspace exposure, defined as the normalized difference vegetation index (NDVI), was quantified based on continuous geo-location data collected from the phones GPS. RESULTS: Overall, people with SSD used significantly more positive affect words (P=.04) and fewer anger words (P=.04) than controls. Groups did not significantly differ in mean EMA-reported positive or negative affect, LIWC total word count, or NDVI exposure. Greater greenspace exposure showed small to moderate associations with lower EMA-reported negative affect across groups. In controls, greenspace exposure on a given day was associated with significantly lower EMA-reported anxiety on that day (b=-0.40, P=.03, 95% CI -0.76 to -0.04) but significantly higher use of negative affect words (b=0.66, P<.001, 95% CI 0.29-1.04). There were no significant associations between greenspace exposure and affect at the daily level among participants with SSD. CONCLUSIONS: Our findings speak to the utility of passive and active smartphone assessments for identifying potential mechanisms through which greenspace exposure influences mental health. We identified preliminary evidence that greenspace exposure could be associated with improved mental health by reducing experiences of negative affect. Future directions will focus on furthering our understanding of the relationship between greenspace exposure and affect on individuals with and those without SSD.

Motivation in general, and social motivation in particular are important for interpersonal functioning in individuals with schizophrenia. Still, their roles after accounting for social cognition, are not well understood. The sample consisted of 147 patients with schizophrenia. General motivation was measured using the Behavioral inhibition/activation scale (BIS/BAS). Social motivation was measured by Passive social withdrawal and Active social avoidance items from PANSS. Interpersonal functioning was evaluated with Birchwoods Social Functioning Scale (SFS). We used Exploratory Graph Analysis for network estimation and community detection. Active social avoidance, passive social withdrawal, and social withdrawal/engagement (from SFS) were the most important nodes. In addition, three distinct communities were identified: Social cognition, Social motivation, and Interpersonal functioning. Notably, the BIS and BAS measures of general motivation were not part of any community. BAS showed stronger links to functioning than BIS. Passive social withdrawal was more strongly linked to interpersonal functioning than social cognitive abilities. Results suggest that social motivation, especially social approach, is more closely related to interpersonal functioning in schizophrenia than general motivation. In contrast, we found that general motivation was largely unrelated to social motivation. This pattern highlights the importance of type of motivation for understanding variability in interpersonal difficulties in schizophrenia.

Social disconnection, including objective social isolation and subjective loneliness, is linked to substantial health risks. Yet, little is known about the predictors of social disconnection in individuals with mental illness. Here, we used machine learning to identify predictors of social isolation and loneliness in schizophrenia (N = 72), a psychiatric condition associated with social disconnection. For comparison, we also included two other groups: a psychiatric comparison sample of bipolar disorder (N = 48) and a community sample enriched for social isolation (N = 151). We fitted statistical models of social isolation and loneliness within and across groups. Each model included five candidate predictors: social avoidance motivation, depression, nonsocial cognition, social anhedonia, and social cognition. The results showed that social anhedonia explained unique variance in social isolation and loneliness in all samples, suggesting that it contributes to social isolation and loneliness broadly. However, nonsocial cognition explained unique variance in social isolation only within schizophrenia. Thus, social anhedonia could be a potential intervention target across populations, whereas nonsocial cognition may play a unique role in determining social disconnection in schizophrenia.