Decoding negative affect personality trait from patterns of brain activation to threat stimuli

INTRODUCTION: Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample. METHODS: fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli. RESULTS: The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions). CONCLUSION: These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts

BCG vaccination following latent TB treatment: Possible implications for different settings

Despite much progress globally, TB is still one of the top 10 causes of death worldwide. Several studies have shown the importance of implementing different preventive strategies alongside treatment of TB disease, including BCG vaccination and treatment of latent tuberculosis infection (LTBI). Large-scale population level LTBI treatment is not currently part of WHO guidelines which recommend LTBI treatment only to high risk populations. Moreover, BCG has been widely used in the past decades to both prevent infection with M. tuberculosis and reduce rates of reactivation. In this viewpoint we discuss the hypothesis of BCG vaccination following latent TB treatment and its potential impact across different settings

Mammalian cell entry genes in Streptomyces may provide clues to the evolution of bacterial virulence

Understanding the evolution of virulence is key to appreciating the role specific loci play in pathogenicity. Streptomyces species are generally non-pathogenic soil saprophytes, yet within their genome we can find homologues of virulence loci. One example of this is the mammalian cell entry (mce) locus, which has been characterised in Mycobacterium tuberculosis. To investigate the role in Streptomyces we deleted the mce locus and studied its impact on cell survival, morphology and interaction with other soil organisms. Disruption of the mce cluster resulted in virulence towards amoebae (Acanthamoeba polyphaga) and reduced colonization of plant (Arabidopsis) models, indicating these genes may play an important role in Streptomyces survival in the environment. Our data suggest that loss of mce in Streptomyces spp. may have profound effects on survival in a competitive soil environment, and provides insight in to the evolution and selection of these genes as virulence factors in related pathogenic organisms