Lida Kanari

This paper presents a deep learning framework that classifies neuronal morphologies using topology and graph neural networks, achieving accuracy comparable to human experts.

The paper introduces TTMD, a framework using temporal topology to analyze neuronal morphogenesis, showing it can decode developmental stages and mutations in Drosophila neurons.

The paper introduces a method to analyze organelle spatial organization in branched cells, finding distinct spatial programs in microglia under different conditions.

The paper explains neuronal network architecture through spatial continuity and validates it with a stochastic algorithm matching reference data.

This study shows that growth-repulsion feedback in branched cells leads to hyperuniform tissue patterning and efficient tiling during development.

The paper investigates how human neuronal morphology influences synaptic connectivity complexity and shows that human neuron shapes lead to more complex connectomes compared to rodents.

This study builds a detailed computational model of the human neocortex and compares it to a rat model, revealing differences in cell density, branching complexity, and synaptic connectivity.

The paper creates a detailed computational model of rat somatosensory cortex anatomy, including 4.2 million neurons and 14.2 billion synapses, and identifies limitations in connectivity prediction from morphology.

The paper generates whole-brain connectivity by predicting detailed axonal morphologies from sparse data, enabling in silico experimentation of large brain regions.

The paper develops a molecular model of the aging brain's metabolic system, identifies key disruptions, and proposes interventions to restore metabolic flexibility and neuronal function.

This study introduces an algorithm combining algebraic topology and Steiner tree methods to generate biologically accurate long-range axonal morphologies that match experimental data.

The paper creates a 3D spatial atlas of transcriptomic, morphological, and electrophysiological cell types in the mouse brain using integrated data and computational methods.

The paper presents a full-scale in silico model of rat hippocampal CA1 integrating diverse experimental data and validates it through simulations of theta rhythm and other experiments.

The paper develops a method to generate populations of detailed biophysical neuron models that reproduce experimental variability using Markov chain Monte Carlo techniques.

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 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.