The central amygdala (CeA) orchestrates defensive behaviors, pain processing, and stress responses, yet how its molecularly defined neuronal subtypes are embedded in brain-wide circuits remains unclear. Here, we reconstructed the brain-wide input and output architecture of three neuronal subtypes, CeASst, CeAPkc-δ, and CeACrh neurons, at single-cell resolution in male mice. Single-cell reconstruction revealed various projection-defined classes within each molecular-defined subtype, ranging from locally restricted neurons to broadly broadcasting neurons that coinnervate hypothalamic, midbrain, and brainstem. CeA outputs exhibit subtype-specific hemispheric asymmetry, providing an anatomical substrate for functional lateralization of CeA circuits. Coprojection analysis showed segregated and convergent CeA output pathways across midbrain and brainstem structures, supporting functional heterogeneity in the selection of appropriate defensive behaviors. Mapping of the input of these neurons further uncovered segregated and spatially organized upstream networks. Cortical projection neurons, particularly from the insular cortex, sent lateralized projections to distinct CeA subregions. Joint analysis of cortical and CeA projection architectures revealed structural signatures of both serial and parallel coordination. Taken together, these findings demonstrate a multilevel, subtype-specific, and lateralized architecture linking molecular identity to brain-wide CeA connectivity.
Lung-Chuang Wang, Wei Song, Xu Chen et al.· Journal of Neuroscience· 0 citations
Fine analysis of the spatial distribution and morphology of Aβ plaques is crucial for understanding the pathological progression of Alzheimer's disease (AD). However, at the whole-brain scale, the enormous number of plaques, wide size range, diffuse morphologies, and complex imaging background pose challenges that existing digitization methods often fail to address comprehensively. To this end, this study developed a Generalized Plaque Digitization Framework (GPDigit). The framework adopts a two-step strategy of detection followed by segmentation: first, a customized object detection network achieves precise spatial localization of plaques; second, adaptive foreground signal segmentation is performed within local regions. This design circumvents the difficulty of global threshold selection and the high annotation cost of segmentation networks. GPDigit supports both 2D and 3D data scenarios, ensures detection accuracy through targeted feature extraction mechanisms, and significantly improves training data preparation efficiency with a self-developed annotation tool and multiple data augmentation strategies. Experimental results demonstrate that GPDigit achieves satisfactory digitization performance under challenging conditions such as dense plaque distribution, severe background interference, and weak signals. Application to whole-brain plaque analysis in 5xFAD mice across multiple key ages revealed, at the fine brain-region scale, the spatiotemporal heterogeneity of plaque density and load development. This study provides a systematic solution for plaque digitization in neuropathological mesoscopic high-resolution images and offers a practical tool to advance AD pathology research.
