Aberrant brain dynamics in individuals with clinical high risk of psychosis.

Background: Resting-state network (RSN) functional connectivity analyses have profoundly influenced our understanding of the pathophysiology of psychoses and their clinical high risk (CHR) states. However, conventional RSN analyses address the static nature of large-scale brain networks. In contrast, novel methodological approaches aim to assess the momentum state and temporal dynamics of brain network interactions. Methods: Fifty CHR individuals and 33 healthy controls (HC) completed a resting-state functional MRI scan. We performed an Energy Landscape analysis, a data-driven method using the pairwise maximum entropy model, to describe large-scale brain network dynamics such as duration and frequency of, and transition between, different brain states. We compared those measures between CHR and HC, and examined the association between neuropsychological measures and neural dynamics in CHR. Results: Our main finding is a significantly increased duration, frequency, and higher transition rates to an infrequent brain state with coactivation of the salience, limbic, default mode and somatomotor RSNs in CHR as compared to HC. Transition of brain dynamics from this brain state was significantly correlated with processing speed in CHR. Conclusion: In CHR, temporal brain dynamics are attracted to an infrequent brain state, reflecting more frequent and longer occurrence of aberrant interactions of default mode, salience, and limbic networks. Concurrently, more frequent and longer occurrence of the brain state is associated with core cognitive dysfunctions, predictors of future onset of full-blown psychosis.

Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis.

Theoretical background: Research of E-Mental Health (EMH) interventions remains a much-studied topic, as does its acceptance in different professional groups as psychotherapists-in-training (PiT). Acceptance among clinicians may vary and depend on several factors, including the characteristics of different EMH services and applications. Therefore, the aims of this study were to investigate the factors that predict acceptance of EMH among a sample of PiT using a latent class analysis. The study will 1) determine how many acceptance prediction classes can be distinguished and 2) describe classes and differences between classes based on their characteristics. Methods: A secondary analysis of a cross-sectional online survey was conducted. N = 216 PiT (88.4% female) participated. In the study, participants were asked to rate their acceptance of EMH, as operationalized by the Unified Theory of Acceptance and Use of Technology (UTAUT) model, along with its predictors, perceived barriers, perceived advantages and additional facilitators. Indicator variables for the LCA were eight items measuring the UTAUT-predictors. Results: Best model fit emerged for a two-class solution; the first class showed high levels on all UTAUT-predictors, the second class revealed moderate levels on the UTAUT-predictors. Conclusion: This study was able to show that two classes of individuals can be identified based on the UTAUT-predictors. Differences between the classes regarding Performance Expectancy and Effort Expectancy were found. Interestingly, the two classes differed in theoretical orientation but not in age or gender. Latent class analysis could help to identify subgroups and possible starting points to foster acceptance of EMH.

Reduced anterior callosal white matter in risk for psychosis associated with processing speed as a fundamental cognitive impairment.

BACKGROUND: Previous research in psychotic disorders discovered associations between reduced integrity of white matter (WM) in the corpus callosum (CC) and impaired cognitive functions, suggesting processing speed as a central construct. However, it is still largely unexplored to what extent disruption in callosal WM is related to cognitive deficits during the risk stage prior to psychosis. METHODS: To address this gap, we measured the WM integrity in CC by fractional anisotropy (FA) and assessed cognition in 60 clinical-high risk for psychosis (CHR) patients during adolescence/young adulthood and 38 healthy control (HC) subjects. We employed tract based spatial statistics to examine group differences and associations between CC-FA and processing speed, executive function, and spatial working memory. RESULTS: We revealed deficits in processing speed, executive function, and spatial working memory of CHR patients, and reductions in FA of the genu and the body of the CC (p < 0.05, corrected for multiple comparisons) compared to HC. A mediation analysis using the combined sample (CHR + HC) showed that processing speed mediates the associations between the impaired CC structure and executive function and spatial working memory, respectively. Exploratory analyses between CC-FA and the cognitive domains located associations of processing speed in the genu and the body of CC with distinct spatial distributions of executive function and spatial working memory. CONCLUSION: We suggest processing speed as a subordinate cognitive factor contributing to the associations between callosal WM, executive function and working memory. These results extend findings in psychotic disorders to the prior risk stage.