Biology subjects

Bruns, E.

Publications and source records attributed to Bruns, E..

3 recordsLinked to original sources

bioRxiv2023

Neural Correlates of Self-Referential Belief Processes

BackgroundBelief processing as well as self-referential processing have both been consistently associated with cortical midline structures. In addition, seminal neuroimaging papers have implicated cortical regions such as the vmPFC in general belief processing. However, the neural correlates of self-referential belief are yet to be investigated in functional magnetic resonance imaging (fMRI). MethodsIn this fMRI study, we presented 120 statements with trait adjectives as target words to N=27 young healthy participants and asked them to judge whether they believed that these trait adjectives applied to themselves, a self-chosen close person, or a public person (the German chancellor at that time). Participants subsequently rated how certain (0-100%) they were in their judgment. ResultsAs expected, self-referential processing evoked a large cluster in the vmPFC, ACC and dmPFC. For belief, we found an activated cluster in the vmPFC during statement presentation, which partly overlapped with the cluster for self-referential processing. The cluster for self-belief vs. disbelief was similar in location and size to the cluster for general belief processing and distinct from the cluster for self-referential processing. We also found dmPFC activation for uncertainty in belief evaluations. DiscussionWe successfully replicated vmPFC involvement in belief processing and found a common neural correlate for belief and self-belief in the vmPFC. The activation clusters for self-belief versus self-referential processing were distinct, implying distinct neural processes. This insight will prove relevant for investigations in clinical populations with aberrant (self-)belief processing. Furthermore, we replicated the role of the dmPFC in uncertainty, supporting a dual neural process model of belief and certainty.

neuroscience↗

bioRxiv2023

Host-pathogen coevolution promotes the evolution of general, broad-spectrum resistance and reduces foreign pathogen spillover risk

Genetic variation for disease resistance within host populations can strongly impact the spread of endemic pathogens. In plants, recent work has shown that within-population variation in resistance can also affect the transmission of foreign spillover pathogens if that resistance is general. However, most hosts also possess specific resistance mechanisms that provide strong defenses against coevolved endemic pathogens. Here we use a modeling approach to ask how antagonistic coevolution between hosts and their endemic pathogen at the specific resistance locus can affect the frequency of general resistance, and therefore a hosts vulnerability to foreign pathogens. We develop a two-locus model with variable recombination that incorporates both general (resistance to all pathogens) and specific (resistance to endemic pathogens only). We find that introducing coevolution into our model greatly expands the regions where general resistance can evolve, decreasing the risk of foreign pathogen invasion. Furthermore, coevolution greatly expands which conditions maintain polymorphisms at both resistance loci, thereby driving greater genetic diversity within host populations. This genetic diversity often leads to positive correlations between host resistance to foreign and endemic pathogens, similar to those observed in natural populations. However, if resistance loci become linked, the resistance correlations can shift to negative. If we include a third, linkage modifying locus into our model, we find that selection often favors complete linkage. Our model demonstrates how coevolutionary dynamics with an endemic pathogen can mold the resistance structure of host populations in ways that affect its susceptibility to foreign pathogen spillovers, and that the nature of these outcomes depends on resistance costs, as well as the degree of linkage between resistance genes.

evolutionary biology↗

bioRxiv2022

Multimodal disease transmission as a limiting factor for the spatial extent of a host plant

Theoretical models suggest that infectious diseases could play a substantial role in determining species ranges, but few studies have collected the empirical data required to test this hypothesis. Pathogens that sterilize their hosts or spread through frequency-dependent transmission could have especially strong effects on the limits of species distributions because sterilized hosts can serve as long-lived disease reservoirs and frequency-dependent transmission mechanisms are effective even at very low population densities. We collected spatial disease prevalence data and population abundance data for alpine carnations infected by the sterilizing pathogen M. violaceum, a disease that is spread through both frequency-dependent (vector-borne) and density-dependent (aerial spore transmission) mechanisms. Our 13-year study reveals rapid declines in population abundance without a compensatory decrease in disease prevalence. We apply a stochastic, spatial model of disease spread that accommodates spatial habitat heterogeneity to investigate how the population dynamics depend on multimodal (frequency-dependent and density-dependent) transmission. We found that the observed rate of population decline can be readily explained by multimodal transmission, but is unlikely to be explained by either frequency-dependent or density-dependent mechanisms alone. Multimodal disease transmission rates high enough to explain the observed decline predicted that eventual local extinction of the host species is highly likely. Our results add to a growing body of literature showing how multimodal transmission can constrain species distributions in nature. Open ResearchAll scripts associated with the analyses in this manuscript, as well as the data we collected, are available at https://github.com/uricchio/antherSmutDis.

ecology↗