Connectivity characterization of the mouse basolateral amygdalar complex
Houri Hintiryan, Ian Bowman, David L. Johnson, Laura Korobkova, Muye Zhu, Neda Khanjani, Lin Gou, Lei Gao, Seita Yamashita, Michael S. Bienkowski, Luis Garcia, Nicholas N. Foster, Nora L. Benavidez, Monica Y. Song, D.C.W. Lo, Kaelan Cotter, Marlene Becerra, Sarvia Aquino, Chunru Cao, Ryan P. Cabeen, Jim Stanis, Marina Fayzullina, Sarah Ustrell, Tyler Boesen, Amanda J. Tugangui, Zhenggang Zhang, Bo Peng, Michael S. Fanselow, Peyman Golshani, Joel D. Hahn, Ian R. Wickersham, Giorgio A. Ascoli, Li I. Zhang, Hong‐Wei Dong
Abstract
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.