Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure.

There is considerable interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of an average group network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two new extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects of subject-level covariates on individual differences in cluster structure. The proposed Multi-Subject Stochastic Blockmodels (MS-SBMs) can flexibly account for between-subject variability in terms of homogeneous or heterogeneous covariate effects on connectivity using subject demographics such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on the Wald, likelihood ratio and permutation tests. We show that the proposed multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition (i.e. the Fast Louvain and Newman Spectral algorithms). Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting-state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; n=268 brain regions), we show that Heterogeneous Stochastic Blockmodel (Het-SBM) identifies a range of network topologies simultaneously, including modular and core structures.

Caring for the caregiver while caring for the patient: exploring the dyadic relationship between patient spirituality and caregiver quality of life.

INTRODUCTION: Caregivers of cancer patients experience much psychological stress due to the heavy responsibility of caregiving. Dyadic studies on the patient-caregiver relationship have shown that caregivers' quality of life (QOL) are affected by their care recipients' psychological variables. In this exploratory study, focus is placed on spirituality in patients--an emerging area of interest--and its impact on their caregivers' QOL. Because of spirituality's links with optimism and resilience, they were also investigated as possible mediators in the dyadic relationship. METHOD: Patients completed measures of spirituality (FACIT-Sp-12), optimism (LOT-R), and resilience (RAS); their family caregivers completed a measure of QOL (CQOLC). Both patients and family caregivers completed a sociodemographic survey. Regression analyses were used to analysis the data. RESULTS: Regression analyses following Baron and Kenny's (1986) mediation framework was carried out. Results indicated that spirituality as a whole did not predict caregiver QOL. However, further analyses showed that while the meaning-making aspect of spirituality did predict caregiver QOL, the faith aspect did not. Mediatory analyses indicated that both optimism and resilience were not mediators; hence, confirmatory Sobel's tests which had been originally planned were not conducted. Nonetheless, optimism and resilience were correlated with meaning-making. DISCUSSION: Patients who make meaning of their cancer illness exert a positive influence on their caregivers' well-being. This provides support for interventions that encourage patients to reappraise their illness situation, as such interventions not only benefit patients but also enhance the quality of life for their caregivers.

A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures.

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

A multidimensional investigation of sleep and biopsychosocialprofiles with associated neural signatures.

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.