Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h²= .25 vs. .13, p = 1.8x10⁻⁷). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r_g= .49, p = 2.7x10⁻²²). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.

Within the field of neuroimaging, the discovery of a constellation of brain regions silently active when we are “resting” has provided a new view into the elusive effects of meditative practice. This network, called the default mode network (DMN), has been shown by functional neuroimaging to be active when an individual is at rest. Meta-analyses of the fMRI neurocorrelates of meditation have shown that across diverse practices, the most common general effect appears to be modulation of regions within the DMN. The specific practice of mantra meditation is a form of task-positive concentration. These kinds of task-positive activities are regarded by many to be an anti-correlate of DMN activity, thereby in theory, reducing activations within the DMN as concentration on a particular task increases. Yet previous studies on mantra meditation using language tasks as controls have reported only activations, not deactivations within the DMN. A study by Berkovich-Ohana showed that word repetition as control task could mask some of the effects of mantra, which may explain why previous studies using language repetition as control task failed to find significant DMN deactivations during mantra meditation. To investigate these differences, we analyzed mantra meditation using a finger-tapping control task which required a minimal amount of focused attention, yet enough to reduce blood flow to the major hubs of the DMN and minimally reduce network activity. Using a finger-tapping control thus modifies the research question: Can a brief course of this kind be shown to modulate DMN activations during mantra practice beyond that of a minimally active/non-language finger-tapping control condition; and if so, can we see a training effect over time? Our results show that over a 2-week period of Kundalini yoga/meditation, participants successfully decreased activations within subregions of the DMN, namely the pregenual anterior cingulate cortex (PACC) and the precuneus, during mantra meditation with trends toward decreased activation in the posterior cingulate cortex (PCC) as well. These decreased activations were significant despite use of a finger-tapping control task known to deactivate these regions, indicating that mantra practice suppresses activation in these DMN regions beyond the active control task. These results emphasize the importance of the choice of control task and provide insight into the “mantra effect.” Here, we show that training in mantra meditation, like other practices such as focused attention and open monitoring, also has a suppressive effect on activity within the DMN.

We present an application, and its development process, for interactive visual analysis of brain imaging data and clinical measurements. The application targets neuroscientists interested in understanding the correlations between active brain regions and physiological or psychological factors. The application has been developed in a participatory design process and has subsequently been released as the free software ‘VisualNeuro’. From initial observations of the neuroscientists' workflow, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration requiring the use of many tools side by side. Consequently, our application has been designed to simplify the workflow combining statistical analysis with interactive visual exploration. The resulting environment comprises parallel coordinates for effective overview and selection, Welch's t‐test to filter out brain regions with statistically significant differences and multiple visualizations for comparison between brain regions and clinical parameters. These exploration concepts enable neuroscientists to interactively explore the complex bidirectional interplay between clinical and brain measurements and easily compare different patient groups. A qualitative user study has been performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights and is an effective tool for hypothesis formation.

Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder characterized by abdominal pain, altered bowel habits, and frequent comorbidity with anxiety and depression. The gut microbiota has been implicated in gut-brain axis (GBA) dysfunction, but consistent microbial signatures remain unclear. We performed whole metagenome shotgun sequencing of stool samples from 63 female patients with moderate to severe IBS and 34 female healthy controls and assessed microbial composition and functional pathways. Microbial richness and diversity were slightly reduced in IBS, though with high variability and no robust separation from controls. Differential abundance analyses revealed enrichment of Streptococcus sp. and the sulfate-reducing bacterium Desulfovibrio piger in IBS, alongside reductions in Bifidobacterium and Methanobrevibacter . Functional profiling identified 39 differentially abundant pathways: amino acid biosynthesis (e.g., L-isoleucine, L-threonine) was more prominent in IBS, while carbohydrate degradation pathways (e.g., galactose, stachyose) were enriched in healthy controls. These findings indicate modest but significant IBS-associated shifts in gut microbial composition and function that may contribute to IBS symptoms. However, high intra-group variability underscores the complexity of IBS and highlights the need for larger, multi-omics studies to define robust microbial markers. These results contribute to a growing body of evidence emphasizing the complexity of gut microbiota-host interactions and the need for high-resolution, systems-level approaches in microbiome-associated disorders.