Jennifer D. Whitesell

The paper uses whole-brain imaging to show that tau pathology in mice propagates retrogradely through brain networks.

This paper fills gaps in cell-class specific connectivity data using a statistical model and kernel learning method, producing a mesoscale mouse brain connectivity matrix.

The paper investigates circuit-specific vulnerability in the default mode network (DMN) in Alzheimer's disease, finding that dysfunction is restricted to in-DMN projecting circuits despite widespread amyloid burden.

The paper presents a dataset and analysis of spiking activity across multiple mouse visual areas, revealing that functional connectivity mirrors anatomical hierarchy.

This paper characterizes microglial metabolic changes in an APP knock-in mouse model of AD, finding lipid dysregulation and immunometabolic perturbations linked to fibrillar Aβ.

This paper provides a voxel-level description of the directed mouse connectome, revealing hierarchical structure and functional organization.

This study identifies the anatomical composition and connectivity patterns of the mouse default mode network at the regional, layer, and cell-class levels.

This paper uses a deep generative model to analyze brain structure variability across mouse brains and validates the model through structured perturbations.

The paper expands the Allen Mouse Brain Connectivity Atlas by analyzing intracortical connections at the layer and cell class level, revealing modular organization and distinct projection patterns.

The paper uses whole brain imaging to compare amyloid beta deposition patterns in three Alzheimer's disease mouse models, revealing distinct spatial distributions across brain regions.

The paper constructs a high-resolution voxel-scale model of the mouse connectome using anterograde tracing data, outperforming previous regional models.