Background Grip strength is a widely used and well-validated measure of overall health that is increasingly understood to index risk for psychiatric illness and neurodegeneration in older adults. However, existing work has not examined how grip strength relates to a comprehensive set of mental health outcomes, which can detect early signs of cognitive decline. Furthermore, whether brain structure mediates associations between grip strength and cognition remains unknown. Methods Based on cross-sectional and longitudinal data from over 40,000 participants in the UK Biobank, this study investigated the behavioral and neural correlates of handgrip strength using a linear mixed effect model and mediation analysis. Results In cross-sectional analysis, we found that greater grip strength was associated with better cognitive functioning, higher life satisfaction, greater subjective well-being, and reduced depression and anxiety symptoms while controlling for numerous demographic, anthropometric, and socioeconomic confounders. Further, grip strength of females showed stronger associations with most behavioral outcomes than males. In longitudinal analysis, baseline grip strength was related to cognitive performance at ~9 years follow-up, while the reverse effect was much weaker. Further, baseline neuroticism, health, and financial satisfaction were longitudinally associated with subsequent grip strength. The results revealed widespread associations between stronger grip strength and increased grey matter volume, especially in subcortical regions and temporal cortices. Moreover, grey matter volume of these regions also correlated with better mental health and considerably mediated their relationship with grip strength. Conclusions Overall, using the largest population-scale neuroimaging dataset currently available, our findings provide the most well-powered characterization of interplay between grip strength, mental health, and brain structure, which may facilitate the discovery of possible interventions to mitigate cognitive decline during aging.

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Functional connectome-based predictive models continue to grow in popularity and predictive performance. As these models become more widely used, researchers have begun to question the idea of bias in the models, which is a crucial component of ethics in artificial intelligence. However, we show that model trustworthiness is a more important but vastly overlooked component of the ethics of functional connectome-based predictive models. In this work, we define “trust” as robustness to adversarial attacks, or data alterations designed to trick a model. We show that typical implementations of connectome-based models are untrustworthy and can easily be manipulated through adversarial attacks. We use classification of self-reported biological sex in three datasets (Adolescent Brain Cognitive Development Study, Human Connectome Project, and Philadelphia Neurodevelopmental Cohort) and for three types of predictive models (support vector machine (SVM), logistic regression, kernel SVM) as a benchmark to show that many forms of adversarial attacks are effective against connectome-based models. The attacks include changing the prediction by altering the data at test time, real-world changes at the time of scanning, and improving performance by injecting a pattern into the data. Despite drastic changes in prediction performance after adversarial attacks, the corrupted connectomes appear nearly identical to the original ones and perform similarly in downstream analyses. These findings demonstrate a need to evaluate the trustworthiness and ethics of connectome-based models before we can apply them broadly, as well as a need to develop methods that are robust to a wide range of adversarial attacks.

Aberrant brain dynamics putatively characterize bipolar disorder (BD) and schizophrenia (SCZ). Previous studies often adopted a state discretization approach when investigating how individuals recruited recurring brain states. Since multiple brain states are likely engaged simultaneously at any given moment, focusing on the dominant state can obscure changes in less prominent but critical brain states in clinical populations. To address this limitation, we introduced a novel framework to simultaneously assess brain state engagement for multiple rain states, and we examined how brain state engagement differs in patients with BD or SCZ compared to healthy controls (HC). Using task-based data from the Human Connectome Project, we applied nonlinear manifold learning and K-means clustering to identify four recurring brain states. We then examined how the engagement and transition variability of these four states differed between patients with BD, SCZ, and HC across two other international, open-source datasets. Comparing these measures across groups revealed significantly altered state transition variability, but not engagement, across all four states in individuals with BD and SCZ during both resting-state and task-based fMRI. In our post hoc and exploratory analysis, we also observed associations between state transition variability and age as well as avolition. Our results suggest that disrupted state transition variability affects multiple brain states in BD and SCZ. By studying several brain states simultaneously, our framework more comprehensively reveals how brain dynamics differ across individuals and in psychiatric disorders.