Biology subjects

Nunes, A.

Publications and source records attributed to Nunes, A..

3 recordsLinked to original sources

bioRxiv2018

Non-heritable variation in individual fitness adds stability to neutral theories in ecology and evolution

Neutral theories in ecology1 and evolution2 contend that high diversity of natural communities and high rates of molecular evolution conform to models where individuals have equal fitness and mutations have no effects. Demographic stochasticity makes community and population compositions inherently unstable under these models, with overall levels of diversity being maintained by random processes. This is in contrast with niche and adaptive theories which emphasize differences between species or genotypes as the key to their coexistence3. Here we show that non-heritable variation in individual fitness within species or genotypes can stabilize coexistence without evoking niche differentiation. We construct two classes of mathematical models based on experimental evidence: (1) bacterial growth with variation in cell longevity4,5; and (2) microbial transmission in a host population with variation in host susceptibility6-11. We find stable coexistence of 2 bacterial species in the first model under a single oscillating resource, and 3 or more in the second with independent distributions of host susceptibility to the various microbial species. We discuss the implications of these findings for the interpretation of common measures of relative fitness and for the maintenance of biodiversity.

ecology↗

bioRxiv2018

Characteristic impairments of goal-directed and habitual control in bulimia nervosa

The relationship between clinical eating disorder symptom severity and balance of model-based (MB) and model-free (MF) control is unclear, and these traits predictive capacity is untested in this population. In 25 healthy controls (HCs), 25 subjects with binge eating disorder (BED), and 25 subjects with bulimia nervosa (BN), we show an inverse relationship between symptom severity and MB (though not MF) control. However, trial-by-trial behavioural data discriminated BN from other groups (area under receiver operating characteristic curve of 0.78; 95% CI=0.64-0.92) based primarily on impaired MF control. Our data--including analyses of reaction time and theory-driven computational modeling--support the hypothesis that among pathological binge eating groups, BN may be characterized by impaired value function learning. Our results suggest that trial-by-trial analysis of behavioural data may provide unique insights into the BN phenotype, which may thus be computationally distinct from the related disorder of BED and the HC state.

neuroscience↗

bioRxiv2018

word2brain

Mapping brain functions to their underlying neural substrates is a central goal of cognitive neuroscience. Functional magnetic resonance imaging (fMRI) has proven indispensable in this endeavour. Recently, there has been growing interest in tackling this problem by mapping semantic concepts onto brain regions using repositories of images and text from the neuroimaging literature. However, no study has thus far approached this problem using (dense) vector representations of words. Using data from the Neurosynth database, we sought to develop a model that could (A) capture local correlations between words in text, as well as topics, (B) capture representation of distributed brain networks in relation to word embeddings, and (C) generate synthetic images given word inputs. We show that jointly embedding words and brain imaging data on a vector space can yield semantic representations that sensibly relate concepts across biological, psychological, and observational levels of analysis. Moreover, our proposed model makes no assumption about spatial orientation of fMRI voxels, which allows for embedding of distributed brain networks onto the semantic space. We demonstrate this capability by generating synthetic brain activation vectors from word inputs. Our model has the potential to advance neuroimaging meta-analysis as well as contextual word-embedding methods more broadly.

neuroscience↗