Learning meaningful representation of single-neuron morphology via large-scale pre-training.

ABSTRACT:

Summary

Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine the effects of brain disorders and treatments. Despite the recent surge in the amount of available neuron morphology reconstructions due to advancements in microscopy imaging, existing computational and deep learning methods for modeling neuron morphology have been limited in both scale and accuracy. In this paper, we propose MorphRep, a model for learning meaningful representation of neuron morphology pre-trained with over 250 000 existing neuron morphology data. By encoding the neuron morphology into graph-structured data, using graph

SUBMITTER: Fan Y 

PROVIDER: S-EPMC11373321 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature