Connected through movement: a feasibility study of online mindfulness-based dance/movement therapy for older adults with age-related cognitive decline during COVID-19.

OBJECTIVES: Social isolation and loneliness pose significant public health risks, especially among older adults experiencing age-related cognitive decline (ACD). This mixed methods feasibility study explored the potential of an online mindfulness-based dance/movement therapy (M-DMT) program to alleviate loneliness, enhance psychological well-being, and promote physical activity among older adults experiencing ACD during the COVID-19 pandemic. METHOD: Sixteen participants engaged in a 12-week online group M-DMT program. Feasibility was assessed via enrollment and retention rates, attendance, adverse events, credibility/expectancy, participant perceptions, and satisfaction. Qualitative data were collected to capture participants' perspectives on the intervention's usefulness and perceived benefits. The intervention's preliminary impact on loneliness, depression, positive affect, psychological well-being, and physical activity was also examined. RESULTS: The study met all feasibility criteria, with 65% reporting post-intervention improvement. Significant reductions in loneliness and depression, along with enhanced positive affect and psychological well-being, were observed. Though physical activity increased, statistical significance was not achieved. Qualitative feedback highlighted improved social connectedness, overall quality of life, body awareness, kinematic strategy, and satisfaction with the program. CONCLUSION: Online M-DMT holds promise in addressing well-being and loneliness challenges in older adults experiencing ACD. Further research is necessary to validate and expand upon these promising findings.

Alcohol sipping patterns, personality, and psychopathology in Children: Moderating effects of dorsal anterior cingulate cortex (dACC) activation.

BACKGROUND: Alcohol, the most consumed drug in the United States, is associated with various psychological disorders and abnormal personality traits. Despite extensive research on adolescent alcohol consumption, the impact of early alcohol sipping patterns on changes in personality and mental health over time remains unclear. There is also limited information on the latent trajectory of early alcohol sipping, beginning as young as 9-10 years old. The dorsal anterior cingulate cortex (dACC) is crucial for cognitive control and response inhibition. However, the role of the dACC remains unclear in the relationship between early alcohol sipping and mental health outcomes and personality traits over time. METHODS: Utilizing the large data from the Adolescent Brain Cognitive Development study (N = 11,686, 52% males, 52% white, mean [SD] age 119 [7.5] months, 9807 unique families, 22 sites), we aim to comprehensively examine the longitudinal impact of early alcohol sipping patterns on psychopathological measures and personality traits in adolescents, filling crucial gaps in the literature. RESULTS: We identified three latent alcohol sipping groups, each demonstrating distinct personality traits and depression score trajectories. Bilateral dACC activation during the stop-signal task moderated the effect of early alcohol sipping on personality and depression over time. Additionally, bidirectional effects were observed between alcohol sipping and personality traits. CONCLUSIONS: This study provides insights into the impact of early alcohol consumption on adolescent development. The key finding of our analysis is that poor response inhibition at baseline, along with increased alcohol sipping behaviors may accelerate the changes in personality traits and depression scores over time as individuals transition from childhood into adolescence.

Biological age and brain age in midlife: relationship to multimorbidity and mental health.

Biological age and brain age estimated using biological and neuroimaging measures have recently emerged as surrogate aging biomarkers shown to be predictive of diverse health outcomes. As aging underlies the development of many chronic conditions, surrogate aging biomarkers capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Our study investigates whether elevated biological age and brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using Gaussian processes regression and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of disease accumulation and their relationship with aging.

Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection.

Machine learning (ML) has been extensively applied in brain imaging studies to aid the diagnosis of psychiatric disorders and the selection of potential biomarkers. Due to the high dimensionality of imaging data and heterogeneous subtypes of psychiatric disorders, the reproducibility of ML results in brain imaging studies has drawn increasing attention. The reproducibility in brain imaging has been primarily examined in terms of prediction accuracy. However, achieving high prediction accuracy and discovering relevant features are two separate but related goals. An important yet under-investigated problem is the reproducibility of feature selection in brain imaging studies. We propose a new metric to quantify the reproducibility of neuroimaging feature selection via bootstrapping. We estimate the reproducibility index (R-index) for each feature as the reciprocal coefficient of variation of absolute mean difference across a larger number of bootstrap samples. We then integrate the R-index in regularized classification models as penalty weight. Reproducible features with a larger R-index are assigned smaller penalty weights and thus are more likely to be selected by our proposed models. Both simulated and multimodal neuroimaging data are used to examine the performance of our proposed models. Results show that our proposed R-index models are effective in separating informative features from noise features. Additionally, the proposed models yield similar or higher prediction accuracy than the standard regularized classification models while further reducing coefficient estimation error. Improvements achieved by the proposed models are essential to advance our understanding of the selected brain imaging features as well as their associations with psychiatric disorders.