Deep learning disconnectomes to accelerate and improve long-term predictions for post-stroke symptoms.

ABSTRACT: This study investigates the efficacy of deep-learning models in expediting the generation of disconnectomes for individualized prediction of neuropsychological outcomes one year after stroke. Utilising a 3D U-Net network, we trained a model on a dataset of N = 1333 synthetic lesions and corresponding disconnectomes, subsequently applying it to N = 1333 real stroke lesions. The model-generated disconnection patterns were then projected into a two-dimensional 'morphospace' via uniform manifold approximation and projection for dimension reduction dimensionality reduction. We correlated the positioning within this morphospace with one-year neuropsychological scores across 86 metrics in a novel cohort of 119 stroke patients, employing multiple regression models and validating the

SUBMITTER: Matsulevits A 

PROVIDER: S-EPMC11503950 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature