Data-Driven Morphological Feature Perception of Single Neuron With Graph Neural Network

Zhu, Tianfang; Yao, Gang; Hu, Dongli; Xie, Chuangchuang; Li, Pengcheng; Yang, Xiaoquan; Gong, Hui; Luo, Qi; Li, Anan

doi:10.1109/tmi.2023.3275209

Cited by 5 publications

(1 citation statement)

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“…Similarly, MorphoGNN is a novel approach for embedding single neuron morphologies using graph neural networks (GNN) and learns spatial relationships between nodes in reconstructed neuron fibers by considering their nearest neighbors on each layer. This process generates a reduced-dimensional representation of individual neurons using an end-to-end model that incorporates densely connected Densely Connected Convolutional layers and a dual pooling operator ( Zhu et al, 2023 ).…”

Section: Image Processing: Quantifying Connectivitymentioning

confidence: 99%

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

Caznok Silveira,

Antunes,

Athié

et al. 2024

Front. Neurosci.

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The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

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Section: Image Processing: Quantifying Connectivitymentioning

confidence: 99%