fWBMs unify brain structure and cognitive function modeling

In a new paper submitted to arXiv, Mario Senden and colleagues introduce functional whole-brain models (fWBMs) as a unified framework that merges two traditionally separate approaches in computational neuroscience. Bottom-up whole-brain modeling (WBM) creates biophysically detailed simulations of brain structure and dynamics but lacks functional performance. Top-down neuroconnectionism optimizes deep neural networks for specific tasks but has limited biological grounding. fWBMs bridge this gap by requiring four minimal criteria: structural grounding in empirical connectomes and regional biology, continuous-time dynamical realism, functional competence across cognitive domains, and mappable observables to neuroimaging, electrophysiological, and behavioral data.

The authors establish a three-pillar roadmap for developing fWBMs across short-, mid-, and long-term horizons, and outline the scientific and clinical opportunities this paradigm enables. They argue that the disciplined pursuit of this integrative vision will generate the tools, common language, and cross-scale hypotheses needed to advance our understanding of the brain. The paper, available on arXiv with ID 2605.18118, is classified under Neurons and Cognition (q-bio.NC) and aims to set a new standard for unifying brain structure and cognitive function.