Scalable Group Level Probabilistic Sparse Factor Analysis

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a group level scalable probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex noise models than the presently considered.Comment: 10 pages plus 5 pages appendix, Submitted to ICASSP 1

Can work ability explain the social gradient in sickness absence: a study of a general population in Sweden

<p>Abstract</p> <p>Background</p> <p>Understanding the reasons for the social gradient in sickness absence might provide an opportunity to reduce the general rates of sickness absence. The complete explanation for this social gradient still remains unclear and there is a need for studies using randomized working population samples. The main aim of the present study was to investigate if self-reported work ability could explain the association between low socioeconomic position and belonging to a sample of new cases of sick-listed employees.</p> <p>Methods</p> <p>The two study samples consisted of a randomized working population (n = 2,763) and a sample of new cases of sick-listed employees (n = 3,044), 19-64 years old. Both samples were drawn from the same randomized general population. Socioeconomic status was measured with occupational position and physical and mental work ability was measured with two items extracted from the work ability index.</p> <p>Results</p> <p>There was an association between lower socioeconomic status and belonging to the sick-listed sample among both women and men. In men the crude Odds ratios increased for each downwards step in socioeconomic status, OR 1.32 (95% CI 0.98-1.78), OR 1.53 (1.05-2.24), OR 2.80 (2.11-3.72), and OR 2.98 (2.27-3.90). Among women this gradient was not as pronounced. Physical work ability constituted the strongest explanatory factor explaining the total association between socioeconomic status and being sick-listed in women. However, among men, the association between skilled non-manual, OR 2.07 (1.54-2.78), and non-skilled manual, OR 2.03 (1.53-2.71) positions in relation to being sick-listed remained. The explanatory effect of mental work ability was small. Surprisingly, even in the sick-listed sample most respondents had high mental and physical work ability.</p> <p>Conclusions</p> <p>These results suggest that physical work ability may be an important key in explaining the social gradient in sickness absence, particularly in women. Hence, it is possible that the factors associated with the social gradient in sickness absence may differ, to some extent, between women and men.</p

Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: a randomised cross-over trial

Objective To investigate whether a whole grain diet alters the gut microbiome and insulin sensitivity, as well as biomarkers of metabolic health and gut functionality. Design 60 Danish adults at risk of developing metabolic syndrome were included in a randomised cross-over trial with two 8-week dietary intervention periods comprising whole grain diet and refined grain diet, separated by a washout period of ≥6 weeks. The response to the interventions on the gut microbiome composition and insulin sensitivity as well on measures of glucose and lipid metabolism, gut functionality, inflammatory markers, anthropometry and urine metabolomics were assessed. Results 50 participants completed both periods with a whole grain intake of 179±50 g/day and 13±10 g/day in the whole grain and refined grain period, respectively. Compliance was confirmed by a difference in plasma alkylresorcinols (p&lt;0.0001). Compared with refined grain, whole grain did not significantly alter glucose homeostasis and did not induce major changes in the faecal microbiome. Also, breath hydrogen levels, plasma short-chain fatty acids, intestinal integrity and intestinal transit time were not affected. The whole grain diet did, however, compared with the refined grain diet, decrease body weight (p&lt;0.0001), serum inflammatory markers, interleukin (IL)-6 (p=0.009) and C-reactive protein (p=0.003). The reduction in body weight was consistent with a reduction in energy intake, and IL-6 reduction was associated with the amount of whole grain consumed, in particular with intake of rye. Conclusion Compared with refined grain diet, whole grain diet did not alter insulin sensitivity and gut microbiome but reduced body weight and systemic low-grade inflammation