Brain Aging in Major Depressive Disorder Results from the ENIGMA Major Depressive Disorder working group, bioRxiv, 2019-02-26

Background Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset. Methods We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted brain age and chronological age was calculated to indicate brain predicted age difference (brain-PAD). Findings On average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohen's d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004). Interpretation This highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates.

biorxiv neuroscience 100-200-users 2019

Interdependent Phenotypic and Biogeographic Evolution Driven by Biotic Interactions, bioRxiv, 2019-02-26

Biotic interactions are hypothesized to be one of the main processes shaping trait and biogeographic evolution during lineage diversification. Theoretical and empirical evidence suggests that species with similar ecological requirements either spatially exclude each other, by preventing the colonization of competitors or by driving coexisting populations to extinction, or show niche divergence when in sympatry. However, the extent and generality of the effect of interspecific competition in trait and biogeographic evolution has been limited by a dearth of appropriate process-generating models to directly test the effect of biotic interactions. Here, we formulate a phylogenetic parametric model that allows interdependence between trait and biogeographic evolution, thus enabling a direct test of central hypotheses on how biotic interactions shape these evolutionary processes. We adopt a Bayesian data augmentation approach to estimate the joint posterior distribution of trait histories, range histories, and co-evolutionary process parameters under this analytically intractable model. Through simulations, we show that our model is capable of distinguishing alternative scenarios of biotic interactions. We apply our model to the radiation of Darwin's finches---a classic example of adaptive divergence---and find support for in situ trait divergence in beak size, convergence in traits such as beak shape and tarsus length, and strong competitive exclusion throughout their evolutionary history. Our modeling framework opens new possibilities for testing more complex hypotheses about the processes underlying lineage diversification. More generally, it provides a robust probabilistic methodology to model correlated evolution of continuous and discrete characters.

biorxiv evolutionary-biology 0-100-users 2019

Task-evoked activity quenches neural correlations and variability in large-scale brain systems, bioRxiv, 2019-02-26

Many studies of large-scale neural systems have emphasized the importance of communication through increased inter-region correlations (functional connectivity) during task states relative to resting state. In contrast, local circuit studies have demonstrated that task states reduce correlations among local neural populations, likely enhancing their information coding. Here we sought to adjudicate between these conflicting perspectives, assessing whether large-scale system correlations tend to increase or decrease during task states. To establish a mechanistic framework for interpreting changes in neural correlations, we conceptualized neural populations as having a sigmoidal neural transfer function. In a computational model we found that this straightforward assumption predicts reductions in neural populations' dynamic output range as task-evoked activity levels increase, reducing responsiveness to inputs from other regions (i.e., reduced correlations). We demonstrated this empirically in large-scale neural populations across two highly distinct data sets human functional magnetic resonance imaging data and non-human primate spiking data. We found that task states increased global neural activity, while globally quenching neural variability and correlations. Further, this global reduction of neural correlations led to an overall increase in dimensionality (reflecting less information redundancy) during task states, providing an information-theoretic explanation for task-induced correlation reductions. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.

biorxiv neuroscience 0-100-users 2019

Accurate inference of tree topologies from multiple sequence alignments using deep learning, bioRxiv, 2019-02-25

AbstractReconstructing the phylogenetic relationships between species is one of the most formidable tasks in evolutionary biology. Multiple methods exist to reconstruct phylogenetic trees, each with their own strengths and weaknesses. Both simulation and empirical studies have identified several “zones” of parameter space where accuracy of some methods can plummet, even for four-taxon trees. Further, some methods can have undesirable statistical properties such as statistical inconsistency andor the tendency to be positively misleading (i.e. assert strong support for the incorrect tree topology). Recently, deep learning techniques have made inroads on a number of both new and longstanding problems in biological research. Here we designed a deep convolutional neural network (CNN) to infer quartet topologies from multiple sequence alignments. This CNN can readily be trained to make inferences using both gapped and ungapped data. We show that our approach is highly accurate on simulated data, often outperforming traditional methods, and is remarkably robust to bias-inducing regions of parameter space such as the Felsenstein zone and the Farris zone. We also demonstrate that the confidence scores produced by our CNN can more accurately assess support for the chosen topology than bootstrap and posterior probability scores from traditional methods. While numerous practical challenges remain, these findings suggest that deep learning approaches such as ours have the potential to produce more accurate phylogenetic inferences.

biorxiv evolutionary-biology 100-200-users 2019

 

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