# Understanding brain networks and brain organization

arXiv: https://arxiv.org/abs/1403.7151 · HTML: https://mlsanity.com/paper/1403.7151/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2014.

## Built on by (most cited first, 6 of 6)

- [Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention](https://mlsanity.com/paper/2205.09576/) (2022)
- [Always-Sparse Training by Growing Connections with Guided Stochastic Exploration](https://mlsanity.com/paper/2401.06898/) (2024)
- [Training Sparse Neural Network by Constraining Synaptic Weight on Unit Lp Sphere](https://mlsanity.com/paper/2103.16013/) (2021)
- [Asymptotic properties of one-layer artificial neural networks with sparse connectivity](https://mlsanity.com/paper/2112.00732/) (2021)
- [Evolving Neural Networks with Optimal Balance between Information Flow and Connections Cost](https://mlsanity.com/paper/2202.06163/) (2022)
- [On the Measure of a Model: From Intelligence to Generality](https://mlsanity.com/paper/2511.11773/) (2025)

## Abstract (arXiv)

What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. As others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal 'true' subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different 'slices' of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks.