Discovering the effective connectome of the brain with dynamic Bayesian DAG learning.

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering DEC. The presented dynamic DAG enables us to discover direct feedback loop edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process.

Torpor energetics are related to the interaction between body mass and climate in bats of the family Vespertilionidae.

Torpor is an adaptive strategy allowing heterothermic animals to cope with energy limitations. In birds and mammals, intrinsic and extrinsic factors, such as body mass and ambient temperature, are the main variables influencing torpor use. A theoretical model of the relationship between metabolic rate during torpor and ambient temperature has been proposed. Nevertheless, no empirical attempts have been made to assess the model predictions under different climates. Using open-flow respirometry, we evaluated the ambient temperature at which bats entered torpor and when torpid metabolic rate reached its minimum, the reduction in metabolic rate below basal values, and minimum torpid metabolic rate in 11 bat species of the family Vespertilionidae with different body mass from warm and cold climates. We included data on the minimum torpid metabolic rate of five species we retrieved from the literature. We tested the effects using mixed-effect phylogenetic models. All models showed a significant interaction between body mass and climate. Smaller bats went into torpor and reached minimum torpid metabolic rates at warmer temperatures, showed a higher reduction in the metabolic rate below basal values, and presented lower torpid metabolic rates than larger ones. The slopes of the models were different for bats from different climates. These results are likely explained by differences in body mass and the metabolic rate of bats, which may favor larger bats expressing torpor in colder sites and smaller bats in the warmer ones. Further studies to assess torpor use in bats from different climates are proposed.