Kathryn P. Hess

The paper introduces a topological framework to detect and disentangle multiple cyclic processes from single-cell transcriptomic data, validated on synthetic datasets and biological samples.

The paper shows that heterogeneous and higher-order cortical connectivity allows neural networks to balance efficiency and robustness by supporting subpopulations with different roles.

The paper compares dendritic architecture in human and mice neuronal networks, finding that human pyramidal cells have greater dendritic complexity, contributing to increased network complexity despite lower neuron density.

The paper introduces MorphOMICs, a topological data analysis method to identify sex- and region-dependent microglial morphological phenotypes across development and neurodegeneration.

The paper quantifies neurons and glia in the Drosophila larval CNS, revealing sexual dimorphism with more neurons in females and topological differences.

The paper synthesizes cortical dendrites using a topological descriptor to create statistically similar digital reconstructions, enabling the study of neuronal morphology and brain function.

The paper digitally reconstructs the neuro-glia-vascular system in rat somatosensory cortex to understand astrocyte-vascular-neuron interactions and their anatomical constraints.

The paper objectively classifies neocortical pyramidal cells using algebraic topology, identifying seventeen types in the rat somatosensory cortex without expert input.

The paper uses algebraic topology to classify artificial neuronal network dynamics by analyzing topological features from spike-train distances, showing improved classification accuracy with multiple distance measures.