Lijuan Liu

This paper constructs a high-resolution mouse brain connectome by integrating multi-scale, multi-modal data and validates it against existing connectomes and gene expression data.

NeuroXiv provides an AI-powered database of neuron morphologies from mouse brains, enabling dynamic analysis and comparison across a standardized atlas.

NIEND enhances neuronal images to improve reconstruction quality and efficiency, achieving higher F1 scores and faster processing times.

The paper maps single cortical neurons across the human brain to profile morphological diversity and stereotypy using a new tomography approach.

The paper introduces a collaborative platform augmented by AI for large-scale 3D neuron morphology reconstruction in mouse and human brains, showing its capability to generate tens of thousands of reconstructions.

The paper introduces a method to detect optimal feature subspaces for clustering neurons and applies it to a large mouse neuron dataset, outperforming existing cell typing approaches.

The paper provides a detailed cellular-resolution map of the mouse primary motor cortex, defining cell types and their connectivity.

The paper introduces MorphoHub, a platform for generating petabyte-scale multi-morphometry data, enabling high-quality analysis of neuronal structures in whole brain datasets.

Complete morphologies of 1,708 neurons, sparsely labelled and imaged whole-brain with fMOST, expose region-specific long-range projection rules and finer diversity than current transcriptomic subtypes capture.

The paper introduces TeraVR, a virtual reality system for precise 3-D neuronal morphology reconstruction in whole mouse brains, enabling collaborative and efficient annotation of complex neuronal structures.