The impact of input node placement in the controllability of structural brain networks.
Brain networks
Complex systems
Control energy
Structural controllability
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
22 Mar 2024
22 Mar 2024
Historique:
received:
09
08
2023
accepted:
14
03
2024
medline:
23
3
2024
pubmed:
23
3
2024
entrez:
23
3
2024
Statut:
epublish
Résumé
Network controllability refers to the ability to steer the state of a network towards a target state by driving certain nodes, known as input nodes. This concept can be applied to brain networks for studying brain function and its relation to the structure, which has numerous practical applications. Brain network controllability involves using external signals such as electrical stimulation to drive specific brain regions and navigate the neurophysiological activity level of the brain around the state space. Although controllability is mainly theoretical, the energy required for control is critical in real-world implementations. With a focus on the structural brain networks, this study explores the impact of white matter fiber architecture on the control energy in brain networks using the theory of how input node placement affects the LCC (the longest distance between inputs and other network nodes). Initially, we use a single input node as it is theoretically possible to control brain networks with just one input. We show that highly connected brain regions that lead to lower LCCs are more energy-efficient as a single input node. However, there may still be a need for a significant amount of control energy with one input, and achieving controllability with less energy could be of interest. We identify the minimum number of input nodes required to control brain networks with smaller LCCs, demonstrating that reducing the LCC can significantly decrease the control energy in brain networks. Our results show that relying solely on highly connected nodes is not effective in controlling brain networks with lower energy by using multiple inputs because of densely interconnected brain network hubs. Instead, a combination of low and high-degree nodes is necessary.
Identifiants
pubmed: 38519624
doi: 10.1038/s41598-024-57181-0
pii: 10.1038/s41598-024-57181-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
6902Informations de copyright
© 2024. The Author(s).
Références
Deco, G., Jirsa, V. K. & McIntosh, A. R. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat. Rev. Neurosci. 12, 43–56 (2011).
pubmed: 21170073
doi: 10.1038/nrn2961
Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008).
pubmed: 18597554
pmcid: 2443193
doi: 10.1371/journal.pbio.0060159
Mišić, B. et al. Cooperative and competitive spreading dynamics on the human connectome. Neuron 86, 1518–1529 (2015).
pubmed: 26087168
doi: 10.1016/j.neuron.2015.05.035
Cocchi, L., Zalesky, A., Fornito, A. & Mattingley, J. B. Dynamic cooperation and competition between brain systems during cognitive control. Trends Cogn. Sci. 17, 493–501 (2013).
pubmed: 24021711
doi: 10.1016/j.tics.2013.08.006
Deco, G. & Kringelbach, M. L. Great expectations: Using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 84, 892–905 (2014).
pubmed: 25475184
doi: 10.1016/j.neuron.2014.08.034
Cao, M. et al. Topological organization of the human brain functional connectome across the lifespan. Dev. Cogn. Neurosci. 7, 76–93 (2014).
pubmed: 24333927
doi: 10.1016/j.dcn.2013.11.004
Weiss, S. A. et al. Functional brain network characterization and adaptivity during task practice in healthy volunteers and people with schizophrenia. Front. Hum. Neurosci. 5, 81 (2011).
pubmed: 21887140
pmcid: 3157023
doi: 10.3389/fnhum.2011.00081
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F. & Bassett, D. S. Optimally controlling the human connectome: The role of network topology. Sci. Rep. 6, 1–14 (2016).
doi: 10.1038/srep30770
Becker, C. O. et al. Spectral mapping of brain functional connectivity from diffusion imaging. Sci. Rep. 8, 1–15 (2018).
doi: 10.1038/s41598-017-18769-x
Galán, R. F. On how network architecture determines the dominant patterns of spontaneous neural activity. PLoS ONE 3, e2148 (2008).
pmcid: 2374893
doi: 10.1371/journal.pone.0002148
Bullmore, E. & Sporns, O. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).
pubmed: 19190637
doi: 10.1038/nrn2575
Bassett, D. S., Zurn, P. & Gold, J. I. On the nature and use of models in network neuroscience. Nat. Rev. Neurosci. 19, 566–578 (2018).
pubmed: 30002509
pmcid: 6466618
doi: 10.1038/s41583-018-0038-8
Fornito, A., Zalesky, A. & Bullmore, E. Fundamentals of Brain Network Analysis (Academic Press, 2016).
Liu, Y.-Y., Slotine, J.-J. & Barabási, A.-L. Controllability of complex networks. Nature 473, 167–173 (2011).
pubmed: 21562557
doi: 10.1038/nature10011
Ruiz, S., Buyukturkoglu, K., Rana, M., Birbaumer, N. & Sitaram, R. Real-time fmri brain computer interfaces: Self-regulation of single brain regions to networks. Biol. Psychol. 95, 4–20 (2014).
pubmed: 23643926
doi: 10.1016/j.biopsycho.2013.04.010
Fox, M. D., Halko, M. A., Eldaief, M. C. & Pascual-Leone, A. Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcmri) and transcranial magnetic stimulation (tms). Neuroimage 62, 2232–2243 (2012).
pubmed: 22465297
doi: 10.1016/j.neuroimage.2012.03.035
Muldoon, S. F. et al. Stimulation-based control of dynamic brain networks. PLoS Comput. Biol. 12, e1005076 (2016).
pubmed: 27611328
pmcid: 5017638
doi: 10.1371/journal.pcbi.1005076
Cui, Z. et al. Optimization of energy state transition trajectory supports the development of executive function during youth. Elife 9, e53060 (2020).
pubmed: 32216874
pmcid: 7162657
doi: 10.7554/eLife.53060
Commault, C. & Dion, J.-M. Input addition and leader selection for the controllability of graph-based systems. Automatica 49, 3322–3328 (2013).
doi: 10.1016/j.automatica.2013.07.021
Ruths, J. & Ruths, D. Control profiles of complex networks. Science 343, 1373–1376 (2014).
pubmed: 24653036
doi: 10.1126/science.1242063
Ghasemi, A., Pásfai, M. & D’Souza, R. Diversity of structural controllability of complex networks with given degree sequence. IEEE Trans. Netw. Sci. Eng. 7, 2667 (2020).
doi: 10.1109/TNSE.2020.2977672
Tang, E. & Bassett, D. S. Colloquium: Control of dynamics in brain networks. Rev. Mod. Phys. 90, 031003 (2018).
doi: 10.1103/RevModPhys.90.031003
Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364 (2017).
pubmed: 28230844
pmcid: 5485642
doi: 10.1038/nn.4502
Wu, L., Li, M., Wang, J.-X. & Wu, F.-X. Controllability and its applications to biological networks. J. Comput. Sci. Technol. 34, 16–34 (2019).
doi: 10.1007/s11390-019-1896-x
Menara, T., Bassett, D. S. & Pasqualetti, F. Structural controllability of symmetric networks. IEEE Trans. Autom. Control 64, 3740–3747 (2018).
doi: 10.1109/TAC.2018.2881112
Gu, S. et al. Controllability of structural brain networks. Nat. Commun. 6, 1–10 (2015).
doi: 10.1038/ncomms9414
Yao, P., Li, C. & Li, X. The functional regions in structural controllability of human functional brain networks. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 1603–1608 (IEEE, 2017).
Yao, P. & Li, X. Toward optimizing control signal paths in functional brain networks. Chaos Interdiscipl. J. Nonlinear Sci. 29, 103144 (2019).
doi: 10.1063/1.5119974
Gu, S. et al. Optimal trajectories of brain state transitions. Neuroimage 148, 305–317 (2017).
pubmed: 28088484
doi: 10.1016/j.neuroimage.2017.01.003
Karrer, T. M. et al. A practical guide to methodological considerations in the controllability of structural brain networks. J. Neural Eng. 17, 026031 (2020).
pubmed: 31968320
pmcid: 7734595
doi: 10.1088/1741-2552/ab6e8b
Faisal, A. A., Selen, L. P. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).
pubmed: 18319728
pmcid: 2631351
doi: 10.1038/nrn2258
Kamiya, S., Kawakita, G., Sasai, S., Kitazono, J. & Oizumi, M. Optimal control costs of brain state transitions in linear stochastic systems. J. Neurosci. 43, 270–281 (2023).
pubmed: 36384681
pmcid: 9838695
doi: 10.1523/JNEUROSCI.1053-22.2022
Mitrai, I., Jones, V. O., Dewantoro, H., Stamoulis, C. & Daoutidis, P. Internal control of brain networks via sparse feedback. AIChE J. 69, e18061 (2023).
doi: 10.1002/aic.18061
Tzoumas, V., Rahimian, M. A., Pappas, G. J. & Jadbabaie, A. Minimal actuator placement with bounds on control effort. IEEE Trans. Control Netw. Syst. 3, 67–78 (2015).
doi: 10.1109/TCNS.2015.2444031
Alizadeh, S., Pósfai, M. & Ghasemi, A. Input node placement restricting the longest control chain in controllability of complex networks. Sci. Rep. 13, 3752 (2023).
pubmed: 36882620
pmcid: 9992492
doi: 10.1038/s41598-023-30810-w
Chen, Y.-Z., Wang, L.-Z., Wang, W.-X. & Lai, Y.-C. Energy scaling and reduction in controlling complex networks. R. Soc. Open Sci. 3, 160064 (2016).
pubmed: 27152220
pmcid: 4852643
doi: 10.1098/rsos.160064
Klickstein, I. S. & Sorrentino, F. Control distance and energy scaling of complex networks. IEEE Trans. Netw. Sci. Eng. 7, 726 (2018).
doi: 10.1109/TNSE.2018.2887042
Chen, Y.-Z., Wang, L., Wang, W. & Lai, Y.-C. The paradox of controlling complex networks: Control inputs versus energy requirement. Preprint at http://arXiv.org/1509.03196 (2015).
Lin, C.-T. Structural controllability. IEEE Trans. Autom. Control 19, 201–208 (1974).
doi: 10.1109/TAC.1974.1100557
Hinne, M. et al. The missing link: Predicting connectomes from noisy and partially observed tract tracing data. PLoS Comput. Biol. 13, e1005374 (2017).
pubmed: 28141820
pmcid: 5308841
doi: 10.1371/journal.pcbi.1005374
Tanner, J. et al. Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity. BioRxiv 2022, 12 (2022).
de Abril, I. M., Yoshimoto, J. & Doya, K. Connectivity inference from neural recording data: Challenges, mathematical bases and research directions. Neural Netw. 102, 120–137 (2018).
doi: 10.1016/j.neunet.2018.02.016
Rugh, W. J. & Rugh, W. J. Linear System Theory Vol. 2 (Prentice Hall, 1996).
Yan, G. et al. Spectrum of controlling and observing complex networks. Nat. Phys. 11, 779–786 (2015).
doi: 10.1038/nphys3422
Gu, S., Pasqualetti, F., Cieslak, M., Grafton, S. T. & Bassett, D. S. Controllability of brain networks. Preprint at http://arXiv.org/1406.5197 (2014).
Olshevsky, A. Minimal controllability problems. IEEE Trans. Control Netw. Syst. 1, 249–258 (2014).
doi: 10.1109/TCNS.2014.2337974
Müller, P. & Weber, H. Analysis and optimization of certain qualities of controllability and observability for linear dynamical systems. Automatica 8, 237–246 (1972).
doi: 10.1016/0005-1098(72)90044-1
Summers, T. H. & Lygeros, J. Optimal sensor and actuator placement in complex dynamical networks. IFAC Proc. Vol. 47, 3784–3789 (2014).
doi: 10.3182/20140824-6-ZA-1003.00226
Chen, H. & Yong, E. H. Optimizing target nodes selection for the control energy of directed complex networks. Sci. Rep. 10, 1–14 (2020).
Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. Neuroimage 15, 273–289 (2002).
pubmed: 11771995
doi: 10.1006/nimg.2001.0978
Reijmer, Y. D. et al. Disruption of cerebral networks and cognitive impairment in Alzheimer disease. Neurology 80, 1370–1377 (2013).
pubmed: 23486876
doi: 10.1212/WNL.0b013e31828c2ee5
Reijmer, Y., Leemans, A., Brundel, M., Kappelle, L. & Biessels, G. Utrecht vascular cognitive impairment study g: Disruption of the cerebral white matter network is related to slowing of information processing speed in patients with type 2 diabetes. Diabetes 62, 2112–2115 (2013).
pubmed: 23349494
pmcid: 3661620
doi: 10.2337/db12-1644
Reijmer, Y. D. et al. Structural network alterations and neurological dysfunction in cerebral amyloid angiopathy. Brain 138, 179–188 (2015).
pubmed: 25367025
doi: 10.1093/brain/awu316
Preuss, T. M. & Wise, S. P. Evolution of prefrontal cortex. Neuropsychopharmacology 47, 3–19 (2022).
pubmed: 34363014
doi: 10.1038/s41386-021-01076-5
Arnatkeviciute, A. et al. Genetic influences on hub connectivity of the human connectome. Nat. Commun. 12, 4237 (2021).
pubmed: 34244483
pmcid: 8271018
doi: 10.1038/s41467-021-24306-2
Mueller, S. et al. Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586–595 (2013).
pubmed: 23395382
pmcid: 3746075
doi: 10.1016/j.neuron.2012.12.028
Van Den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775–15786 (2011).
pubmed: 22049421
pmcid: 6623027
doi: 10.1523/JNEUROSCI.3539-11.2011
Zhou, S. & Mondragón, R. J. The rich-club phenomenon in the internet topology. IEEE Commun. Lett. 8, 180–182 (2004).
doi: 10.1109/LCOMM.2004.823426
Xiang, J., Hu, K., Zhang, Y., Hu, T. & Li, J.-M. Analysis and perturbation of degree correlation in complex networks. Europhys. Lett. 111, 48003 (2015).
doi: 10.1209/0295-5075/111/48003
Mayo, M., Abdelzaher, A. & Ghosh, P. Long-range degree correlations in complex networks. Comput. Soc. Netw. 2, 1–13 (2015).
doi: 10.1186/s40649-015-0011-x
Newman, M. E. Communities, modules and large-scale structure in networks. Nat. Phys. 8, 25–31 (2012).
doi: 10.1038/nphys2162
Pósfai, M., Liu, Y.-Y., Slotine, J.-J. & Barabási, A.-L. Effect of correlations on network controllability. Sci. Rep. 3, 1067 (2013).
pubmed: 23323210
pmcid: 3545232
doi: 10.1038/srep01067
Ray, J., Pinar, A. & Seshadhri, C. Are we there yet? When to stop a Markov chain while generating random graphs. In Algorithms and Models for the Web Graph: 9th International Workshop, WAW 2012, Halifax, NS, Canada, June 22–23, 2012. Proceedings 9 153–164 (Springer, 2012).
Milo, R. et al. Network motifs: Simple building blocks of complex networks. Science 298, 824–827 (2002).
pubmed: 12399590
doi: 10.1126/science.298.5594.824