# The explanatory power of activity flow models of brain function

arXiv: https://arxiv.org/abs/2402.02191 · HTML: https://mlsanity.com/paper/2402.02191/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Self-orthogonalizing attractor neural networks emerging from the free energy principle](https://mlsanity.com/paper/2505.22749/) (2025)

## Abstract (arXiv)

Tremendous neuroscientific progress has recently been made by mapping brain connectivity, complementing extensive knowledge of task-evoked brain activation patterns. However, despite evidence that they are related, these connectivity and activity lines of research have mostly progressed separately. Here I review the notable productivity and future promise of combining connectivity and task-evoked activity estimates into activity flow models. These data-driven computational models simulate the generation of task-evoked activations (including those linked to behavior), producing empirically-supported explanations of the origin of neurocognitive functions based on the flow of task-evoked activity over empirical brain connections. Critically, by incorporating causal principles and extensive empirical constraints from brain data, this approach can provide more mechanistic accounts of neurocognitive phenomena than purely predictive (as opposed to explanatory) models or models optimized primarily for task performance (e.g., standard artificial neural networks). The variety of activity-flow-based explanations reported so far are covered here along with important methodological and theoretical considerations when discovering new activity-flow-based explanations. Together, these considerations illustrate the promise of activity flow modeling for the future of neuroscience and ultimately for the development of novel clinical treatments (e.g., using brain stimulation) for brain disorders.