Humans survive and thrive through social exchange. Yet, social dependency also comes at a cost. Perceived social isolation, or loneliness, affects physical and mental health, cognitive performance, overall life expectancy, and increases vulnerability to Alzheimer’s disease-related dementias. Despite severe consequences on behavior and health, the neural basis of loneliness remains elusive. Using the UK Biobank population imaging-genetics cohort (n = ~40,000, aged 40–69 years when recruited, mean age = 54.9), we test for signatures of loneliness in grey matter morphology, intrinsic functional coupling, and fiber tract microstructure. The loneliness-linked neurobiological profiles converge on a collection of brain regions known as the ‘default network’. This higher associative network shows more consistent loneliness associations in grey matter volume than other cortical brain networks. Lonely individuals display stronger functional communication in the default network, and greater microstructural integrity of its fornix pathway. The findings fit with the possibility that the up-regulation of these neural circuits supports mentalizing, reminiscence and imagination to fill the social void.

SignificanceThe default mode network (DMN) encompasses supramodal association areas involved in higher-order cognition. One speculation is that this neural system is important for brain-wide information flow. We tested this account by exploring whether DMN patterns are informative about functional coupling or structural associations in the rest of the brain. Our multimodal pattern analysis findings highlight how the DMN nodes are fractionated: In specific subnodes, gray-matter morphology was linked to fiber tracts from the hippocampus in the medial temporal limbic system. In adjacent subnodes, fluctuations in neural activity were linked to between-network connectivity shifts. Such a mosaic architecture may be a prerequisite for many of the roles the DMN may play in advanced cognitive processes.

Despite recent advances in cancer immunotherapy, certain tumor types, such as Glioblastomas, are highly resistant due to their tumor microenvironment disabling the anti-tumor immune response. Here we show, by applying an in-silico multidimensional model integrating spatially resolved and single-cell gene expression data of 45,615 immune cells from 12 tumor samples, that a subset of Interleukin-10-releasing HMOX1+ myeloid cells, spatially localizing to mesenchymal-like tumor regions, drive T-cell exhaustion and thus contribute to the immunosuppressive tumor microenvironment. These findings are validated using a human ex-vivo neocortical glioblastoma model inoculated with patient derived peripheral T-cells to simulate the immune compartment. This model recapitulates the dysfunctional transformation of tumor infiltrating T-cells. Inhibition of the JAK/STAT pathway rescues T-cell functionality both in our model and in-vivo, providing further evidence of IL-10 release being an important driving force of tumor immune escape. Our results thus show that integrative modelling of single cell and spatial transcriptomics data is a valuable tool to interrogate the tumor immune microenvironment and might contribute to the development of successful immunotherapies.

Music-making is a widespread leisure and professional activity that has garnered interest over the years due to its effect on brain and cognitive development and its potential as a rehabilitative and restorative therapy of brain dysfunctions. We investigated whether music-making has a potential age-protecting effect on the brain. For this, we studied anatomical magnetic resonance images obtained from three matched groups of subjects who differed in their lifetime dose of music-making activities (i.e., professional musicians, amateur musicians, and non-musicians). For each subject, we calculated a so-called BrainAGE score which corresponds to the discrepancy (in years) between chronological age and the "age of the brain", with negative values reflecting an age-decelerating brain and positive values an age-accelerating brain, respectively. The index of "brain age" was estimated using a machine-learning algorithm that was trained in a large independent sample to identify anatomical correlates of brain-aging. Compared to non-musicians, musicians overall had lower BrainAGE scores, with amateur musicians having the lowest scores suggesting that music-making has an age-decelerating effect on the brain. Unlike the amateur musicians, the professional musicians showed a positive correlation between their BrainAGE scores and years of music-making, possibly indicating that engaging more intensely in just one otherwise enriching activity might not be as beneficial than if the activity is one of several that an amateur musician engages in. Intense music-making activities at a professional level could also lead to stress-related interferences and a less enriched environment than that of amateur musicians, possibly somewhat diminishing the otherwise positive effect of music-making.