Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.

This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil the directionality and asymmetries of the brain hierarchy. Effective connectivity (EC), the state matrix in dynamical causal modeling (DCM), is a commonly used metric for studying dynamical properties and causal interactions within a linear state-space system description.

Frontoparietal network topology as a neural marker of musical perceptual abilities.

Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions.

The integration of omics and cultivation-dependent methods could effectively determine the biological risks associated with the utilization of soil conditioners in agriculture.

In the circular economy, reusing agricultural residues, treated biowaste, and sewage sludges-commonly referred to as soil conditioners-in agriculture is essential for converting waste into valuable resources. However, these materials can also contribute to the spread of antimicrobial-resistant pathogens in treated soils. In this study, we analyzed different soil conditioners categorized into five groups: compost from source-separated biowaste and green waste, agro-industrial digestate, digestate from anaerobic digestion of source-separated biowaste, compost from biowaste digestate, and sludges from wastewater treatment plants.