Functional Whole-Brain Models: A New Framework for Unifying Brain Structure and Cognitive Function

TL;DR

This paper introduces functional whole-brain models (fWBMs) as a unified framework that combines structural realism, dynamical fidelity, and task performance to better understand brain function.

Contribution

It proposes a new paradigm for brain modeling that unifies structural, dynamical, and functional aspects, with a formal criteria set and a roadmap for development.

Findings

fWBMs integrate structural and functional brain data.

The framework enables cross-scale hypotheses and clinical applications.

A roadmap guides future research in this unified modeling approach.

Abstract

Contemporary computational neuroscience features two prominent modeling traditions. Bottom-up whole-brain modeling (WBM) builds biophysically detailed simulations of brain structure and dynamics, whereas top-down neuroconnectionism optimizes deep neural networks for functional performance. Each has achieved remarkable success yet remains incomplete with WBMs lacking functional competence and neuroconnectionist models showing limited biological grounding. Here we propose functional whole-brain models (fWBMs) as a unified modeling paradigm that integrates structural and dynamical realism with task-performing capacity. fWBMs are defined by 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, electrophysiologcal and behavioral data. To formalize this integration, we establish a three-pillar roadmap across short-, mid-, and long-term horizons, and outline the scientific and clinical opportunities this paradigm enables. We 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.