Biology subjects

Favila, N.

Publications and source records attributed to Favila, N..

3 recordsLinked to original sources

bioRxiv2026

Basolateral amygdala dopamine signals behavioural salience across exploration and learning

Animals must balance exploration with threat avoidance, yet how neuromodulatory signals shape information-seeking states remains unclear. Using fibre photometry recordings in the basolateral amygdala in freely moving mice, we show that dopamine release is selectively elevated during self-initiated exploratory actions, adapts with repeated exposure, and tracks the behavioural relevance of predictive cues in associative learning and extinction. Our results advance current models of amygdala dopamine function by suggesting that it conveys a state-dependent salience signal that highlights behaviourally relevant moments arising from both environmental cues and self-generated exploration, thereby priming amygdala circuits for plasticity.

neuroscience↗

bioRxiv2024

Heterogeneous plasticity of amygdala interneurons in associative learning and extinction

Neural circuits undergo experience-dependent plasticity to form long-lasting memories. Excitatory projection neurons are considered to be the primary neuronal substrate for memory acquisition and storage. However, inhibitory interneurons control the activity of projection neurons in a in a spatially and temporally precise manner, yet their contribution to memory acquisition, storage and expression remains poorly understood. Here, we employ a miniature microscope imaging approach to monitor the activity of large amygdala interneuron populations in freely moving mice during fear learning and extinction at the single cell level. We find that amygdala interneurons display mixed-selectivity and show complex plastic responses at both the ensemble and single neuron level across the acquisition, expression and extinction of aversive memories. In contrast to bidirectional single cell plasticity across distinct fear states, learning-induced changes at the population level occur transiently during conditioning and do not consolidate across days. Examining molecular interneuron subpopulations revealed that disinhibitory vasoactive intestinal peptide (VIP) expressing cells are predominantly activated by high fear states. In contrast, somatostatin (SST) interneurons display a preference for safety cues and thereby suppress excitatory neuron responsiveness. However, responses of individual neurons within the SST and VIP populations are non-uniform, indicating the presence of functional subtypes within classical molecularly-defined interneuron populations. Taken together, we identify complex neuronal plasticity within amygdala interneuron ensembles that goes beyond a passive processing function, suggesting a critical role of inhibitory microcircuit elements for memory selectivity and stability.

neuroscience↗

bioRxiv2021

MicNet Toolbox: visualizing and deconstructing a microbial network

Understanding both global and local patterns in the structure and interplay of microbial communities has been a fundamental question in ecological research. In this paper, we present a python toolbox that combines two emerging techniques that have been proposed as useful when analyzing compositional microbial data. On one hand, we introduce a visualization module that incorporates the use of UMAP, a recent dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density. On the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple this with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) using simulated community networks with known topologies to show that we are able to discern between network topologies. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data. Author SummaryMicrobial communities are complex systems that cannot be wholly understood when studied by its individual components. Hence, global pattern analyses seem to be a promising complement to highly focused local approaches. Here, we introduce the MicNet toolbox, an open-source collection of several analytical methods for visualizing abundance data and creating co-occurrence networks for further analysis. We include two modules: one for visualization and one for network analysis based on graph theory. Additionally, we introduce an enhanced version of SparCC, a method to estimate correlations for co-occurrence network construction, that is faster and can support larger datasets. We performed method validations using simulated data and a simple biological network. Our toolbox is freely available in a github repository at https://github.com/Labevo/MicNetToolbox, and it is accompanied by a web dashboard that could be easily accessed and manipulated by non-specialist users. With this implementation, we attempt to provide a simple and straightforward way to explore and analyze microbial relative abundance data.

bioinformatics↗