Laura E. Suárez

The paper introduces netneurotools, a Python toolkit developed by trainees to streamline brain imaging workflows by integrating diverse data types and addressing interoperability challenges.

This paper introduces hierarchical modular networks as reservoirs to study computational advantages of brain-like modularity, showing enhanced memory, multitasking, and temporal dynamics.

The paper introduces a computational engine to systematically discover cause-effect relationships between brain structure and function using in-silico brain models.

This study uses computational whole-brain modeling to show that competitive interactions in mammalian connectomes enhance computational capacity and fit brain dynamics better than cooperative-only models.

conn2res is a Python toolbox that translates connectomes into artificial neural networks to study brain network computation.

This paper compares neural connectomes across 124 mammalian species using a unified protocol, finding that connectome organization aligns with traditional taxonomies.

Neuromaps provides a software package to standardize and compare brain maps across different coordinate systems, enabling comprehensive annotation analysis of the human brain.

This paper uses human brain connectomes to train neuromorphic networks, showing that critical dynamics and modular organization enhance cognitive task performance.

The paper examines structure-function relationships in the neocortex, finding that structural and functional networks diverge in higher-order areas.