Connectivity characterization of the mouse basolateral amygdalar complex

The basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks. The basolateral amygdala is implicated in several behavior-related states including anxiety, autism, and addiction. The authors apply circuit-level pathway tracing methods combined with computational techniques to provide a comprehensive connectivity atlas of the mouse basolateral amygdala complex.

Automated summary

The authors used machine-learning based computational techniques to map the connectivity of the basolateral amygdalar complex, identifying distinct domains within the anterior BLA with target-specific projection neurons and morphological features. This study provides insights into the circuitry of BLA projection neurons and their connections to behavior networks within the brain.