Internal control of brain networks via sparse feedback

Ilias Mitrai; Victoria O. Jones; Harman Dewantoro; Catherine Stamoulis; Prodromos Daoutidis

doi:10.1002/aic.18061

Internal control of brain networks via sparse feedback

Ilias Mitrai, Victoria O. Jones, Harman Dewantoro, Catherine Stamoulis, Prodromos Daoutidis

Chemical Engineering and Materials Science

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

The human brain is a complex system whose function depends on interactions between neurons and their ensembles across scales of organization. These interactions are restricted by anatomical and energetic constraints, and facilitate information processing and integration in response to cognitive demands. In this work, we considered the brain as a closed loop dynamic system under sparse feedback control. This controller design considered simultaneously control performance and feedback (communication) cost. As proof of principle, we applied this framework to structural and functional brain networks. Under high feedback cost only a small number of highly connected network nodes were controlled, which suggests that a small subset of brain regions may play a central role in the control of neural circuits, through a trade-off between performance and communication cost.

Original language	English (US)
Article number	e18061
Journal	AIChE Journal
Volume	69
Issue number	4
DOIs	https://doi.org/10.1002/aic.18061
State	Published - Apr 2023

Bibliographical note

Funding Information:
National Science Foundation, Grant/Award Numbers: 1938914, 1940096, 2207699, 2207733; NSF Graduate Research Fellowships Program (GRFP); University of Minnesota Graduate School Funding information

Funding Information:
This article is inspired by and dedicated to Tunde Ogunnaike. The theme of the article combines control, statistics, and a biological application—technical subjects on which Tunde made seminal contributions over his brilliant career. The authors would like to thank Calli Smith for her help with the functional connectome analyses and manuscript figures. Financial support from the National Science Foundation (Award #s 2207699, 1938914 [PD], and 1940096, 2207733 [CS]) is gratefully acknowledged. IM would like to acknowledge financial support from a Doctoral Dissertation Fellowship (DDF) from the University of Minnesota and VJ would like to acknowledge financial support from NSF‐GRFP (award number 2237827).

Publisher Copyright:
© 2023 The Authors. AIChE Journal published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.

Keywords

brain networks
centrality
feedback cost
sparsity promoting optimal control

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10.1002/aic.18061

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abstract = "The human brain is a complex system whose function depends on interactions between neurons and their ensembles across scales of organization. These interactions are restricted by anatomical and energetic constraints, and facilitate information processing and integration in response to cognitive demands. In this work, we considered the brain as a closed loop dynamic system under sparse feedback control. This controller design considered simultaneously control performance and feedback (communication) cost. As proof of principle, we applied this framework to structural and functional brain networks. Under high feedback cost only a small number of highly connected network nodes were controlled, which suggests that a small subset of brain regions may play a central role in the control of neural circuits, through a trade-off between performance and communication cost.",

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note = "Funding Information: National Science Foundation, Grant/Award Numbers: 1938914, 1940096, 2207699, 2207733; NSF Graduate Research Fellowships Program (GRFP); University of Minnesota Graduate School Funding information Funding Information: This article is inspired by and dedicated to Tunde Ogunnaike. The theme of the article combines control, statistics, and a biological application—technical subjects on which Tunde made seminal contributions over his brilliant career. The authors would like to thank Calli Smith for her help with the functional connectome analyses and manuscript figures. Financial support from the National Science Foundation (Award #s 2207699, 1938914 [PD], and 1940096, 2207733 [CS]) is gratefully acknowledged. IM would like to acknowledge financial support from a Doctoral Dissertation Fellowship (DDF) from the University of Minnesota and VJ would like to acknowledge financial support from NSF‐GRFP (award number 2237827). Publisher Copyright: {\textcopyright} 2023 The Authors. AIChE Journal published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.",

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Internal control of brain networks via sparse feedback

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