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Warnings and caveats in brain controllability.

Chengyi Tu1, Rodrigo P Rocha1, Maurizio Corbetta2

  • 1Dipartimento di Fisica e Astronomia, 'G. Galilei' & INFN, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy.

Neuroimage
|April 15, 2018
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Summary
This summary is machine-generated.

Brain networks are not statistically controllable from a single region, contrary to prior research. Random network models yield similar results, questioning the biological basis of brain controllability findings.

Keywords:
Brain controllabilityBrain networksComplex networksNull modelsWhole brain modelling

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Area of Science:

  • Neuroscience
  • Control Theory
  • Network Science

Background:

  • Recent work proposed characterizing brain networks via control theory, suggesting single-node controllability.
  • This framework linked brain region topology to control roles.

Purpose of the Study:

  • To critically evaluate the controllability framework applied to brain networks.
  • To investigate the robustness of findings regarding single-node controllability and network topology.

Main Methods:

  • Analysis of five distinct brain network datasets.
  • Application of control theory concepts to network topology.
  • Comparison with random null models.
  • Examination of resting-state functional magnetic resonance imaging (fMRI) networks.

Main Results:

  • Brain networks demonstrate statistical insignificance in controllability from a single region.
  • Random network models replicate observed relationships between controllability and weighted degree.
  • Resting-state networks derived from fMRI do not exhibit distinct control roles.

Conclusions:

  • The framework of brain controllability requires significant caveats and warnings.
  • The biological relevance of current controllability metrics for brain networks is questioned.
  • Further validation is needed before applying control theory to brain network analysis.