Filip Milisav

This study reconstructs brainstem connectomes using MRI and finds structural and functional connectivity correlations, revealing heterogeneous structure-function coupling across brainstem nuclei.

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.

This paper introduces a generative model of brain maps based on homophily and shows it can accurately reconstruct maps using biological and functional similarity.

This paper uses AI to analyze neuroscientific literature and discovers conserved cognitive circuits across species.

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

This study maps neuropeptide receptor distribution across the human brain, revealing cortical-subcortical gradients and their functional and evolutionary significance.

This paper introduces a simulated annealing algorithm for randomizing weighted networks while preserving strength sequences, outperforming existing methods and showing broad applicability in neuroscience and beyond.

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.

The paper develops a method to analyze inter-regional communication in the brain connectome and finds that communication pathways align with functional systems and higher-order cognitive functions.

The paper uses natural language processing to analyze how brain states respond to movie narratives, finding the default network is most involved in semantic integration.