Simon Dahan

From topography to connectome: Towards an integrated understanding of the resting brain

The paper develops a deep-learning model to translate spatial topography maps into individualized connectomes, confirming its accuracy and ability to retain brain-cognition associations.

Motifs of brain cortical folding from birth to adulthood: structural asymmetry and folding-functional links

This study identifies common cortical folding patterns across age groups using hierarchical surface registration, revealing hemispheric asymmetry and dissociation between folding and functional organisation.

Surface Generative Modelling of Neurodevelopmental Trajectories

This paper develops a generative model to simulate cortical surface development and assesses its ability to predict cognitive outcomes in preterm infants.

Geneneralising the HCP multimodal cortical parcellation to UK Biobank

This paper generalises the HCP multimodal cortical parcellation to UK Biobank data using a geometric deep learning framework, improving detection rates across cortical areas.

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

This paper benchmarks geometric and traditional deep learning models for cortical segmentation and neurodevelopmental phenotype prediction using neonatal cortical surface data.