# Brain Controllability: not a slam dunk yet

**Authors:** Samir Suweis, Chengyi Tu, Rodrigo P. Rocha, Sandro Zampieri, Marzo, Zorzi and, Maurizio Corbetta

arXiv: 1906.06778 · 2019-06-18

## TL;DR

This paper critically examines the methodology used in brain controllability studies, highlighting limitations and warning signs that question the validity of previous results based on the one node controllability framework.

## Contribution

The authors provide a detailed critique of existing brain controllability methods, emphasizing the need for more robust approaches beyond the current one node framework.

## Key findings

- Numerical analysis in brain controllability can lead to ill-conditioned problems.
- Current methodologies may produce results that are difficult to interpret.
- The one node controllability framework is not fully justified by existing methods.

## Abstract

In our recent article (Tu et al., Warnings and caveats in brain controllability, arXiv:1705.08261) we provided quantitative evidence to show that there are warnings and caveats in the way Gu and collaborators (Gu et al. Controllability of structural brain networks. Nature communications 6 (2015): 8414) define brain controllability. The comment by Pasqualetti et al. (Pasqualetti et al. RE: Warnings and Caveats in Brain Controllability. NeuroImage 297 (2019), 586-588) confirms the need to go beyond the methodology and approach presented in Gu et al. original work. In fact, they recognize that the source of confusion is due to the fact that assessing controllability via numerical analysis typically leads to ill-conditioned problems, and thus often generates results that are difficult to interpret. This is indeed the first warning we discussed: our work was not meant to prove that brain networks are not controllable from one node, rather we wished to highlight that the one node controllability framework and all consequent results were not properly justified based on the methodology presented in Gu et al. We used in our work the same method of Gu et al. not because we believe it is the best methodology, but because we extensively investigated it with the aim of replicating, testing and extending their results. And the warning and caveats we have proposed are the results of this investigation.

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Source: https://tomesphere.com/paper/1906.06778