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Optimal trajectories of brain state transitions
Open accessAbstract
The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering-based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury.
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“(in line with the overarching finding that regions with a large number of long distance connections tend to be optimal controllers79.”
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Cites
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
The attention system of the human brain
Posner, M. I., & Petersen, S. E. (1990) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.13.030190.000325 · Cited by 8,070 · cited 2×
“We take a hypothesis-driven approach and define the “control set” to be composed of dorsal and ventral attention (Posner and Petersen, 1989), fronto-parietal, and cingulo-opercular cognitive control regions (Gu et al., 2015).”
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
(2005) · Choice Reviews Online · doi:10.5860/choice.43-0626 · Cited by 4,726
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124
The brain's default mode network
Raichle, M. E. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-014030 · Cited by 3,904
Flagged first, then by citations
Research works printed but not linked: 1
- Kandel, E.R., Schwartz, J.H., Jessell, T.M. et al., 2000. Principles of Neural Science, vol. 4. McGraw-Hill, New York.
Cited by
Sorted by citations
Items 101–183 of 183
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Yu, L., Gong, L., Chen, X., He, Y., Li, R., Hong, X., Zhang, Q., & Yu, S. (2025) · Communications Biology · doi:10.1038/s42003-025-08439-4 · Cited by 12
Age-associated network controllability changes in first episode drug-naïve schizophrenia
Tang, B., Zhang, W., Deng, S., Liu, J., Hu, N., Gong, Q., Gu, S., & Lui, S. (2022) · BMC Psychiatry · doi:10.1186/s12888-021-03674-5 · Cited by 11
Self-citationTang, B., Zhang, W., Liu, J., Deng, S., Hu, N., Li, S., Zhao, Y., Liu, N., Zeng, J., Cao, H., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac421 · Cited by 10
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A pattern of cognitive resource disruptions in childhood psychopathology
Stier, A. J., Cardenas‐Iniguez, C., Kardan, O., Moore, T. M., Meyer, F. A. C., Rosenberg, M. D., Kaczkurkin, A. N., Lahey, B. B., & Berman, M. G. (2023) · Network Neuroscience · doi:10.1162/netn_a_00322 · Cited by 12
Luppi, A. I., Singleton, S. P., Hansen, J. Y., Bzdok, D., Kuceyeski, A. F., Betzel, R. F., & Mišić, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.16.532981 · Cited by 10
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Li, A., & Liu, Y. (2019) · Advances in Complex Systems · doi:10.1142/s0219525919500218 · Cited by 9
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Blair, D. S., Miller, R. L., & Calhoun, V. D. (2024) · Entropy · doi:10.3390/e26070545 · Cited by 8
Structure-informed functional connectivity driven by identifiable and state-specific control regions
Chiêm, B., Crevecoeur, F., & Delvenne, J. (2021) · Network Neuroscience · doi:10.1162/netn_a_00192 · Cited by 9
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Chen, Q., Chen, F., Zhu, Y., Long, C., Lu, J., Zhang, X., Nedelská, Z., Hort, J., Chen, J., Ma, G., et al. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.03.006 · Cited by 8
Tottori, T., & Kobayashi, T. J. (2025) · Physical Review Research · doi:10.1103/gvl6-cvby · Cited by 6
Papallo, S., Nardo, F. D., Siciliano, M., Esposito, S., Canale, F., Cirillo, G., Cirillo, M., Trojsi, F., & Esposito, F. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13185367 · Cited by 6
Zhang, A., Zhang, Q., Zhao, Z., Li, Q., Li, F. Y., Hu, Y., Huang, X., Kuang, W., Kemp, G. J., Zhao, Y., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70198 · Cited by 7
Khambhati, A. N., Kahn, A. E., Costantini, J. G., Ezzyat, Y., Solomon, E. A., Gross, R. E., Jobst, B. C., Sheth, S. A., Zaghloul, K. A., Worrell, G. A., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/292748 · Cited by 6
Self-citationJu, H., Kim, J. Z., & Bassett, D. S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/504761 · Cited by 6
Self-citationCornblath, E. J., Ashourvan, A., Kim, J. Z., Betzel, R. F., Ćirić, R., Baum, G. L., He, X., Ruparel, K., Moore, T. M., Gur, R. C., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/412429 · Cited by 10
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Dong, Y., Wei, J., Peng, S., Wu, X., Xu, Y., Feng, J., Zhang, J., Jirsa, V., & Xiang, J. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013486 · Cited by 4
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Park, H., & Kang, J. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.590019 · Cited by 4
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Liang, Z., Zhao, G., Zhang, Y., Sun, W., Lin, J., Wang, J., & Liu, Q. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4001-0_10 · Cited by 3
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Zarghami, T. S. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02697-w · Cited by 2
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Wang, W., Wang, W., Zhao, H., Wang, Z., Luo, X., & Ouyang, J. (2023) · Brain‐X · doi:10.1002/brx2.41 · Cited by 2
Kang, J., Eo, J., Lee, D. M., & Park, H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117805 · Cited by 2
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Lim, J., Mitrai, I., Daoutidis, P., & Stamoulis, C. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121562 · Cited by 4
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Self-citationNeumann, D., Razlighi, Q. R., Stern, Y., Devanand, D. P., Jamison, K. W., Kuceyeski, A. F., & Tozlu, C. (2025) · Human Brain Mapping · doi:10.1002/hbm.70427 · Cited by 1
Thalamic organization differentially contributes to clinical conditions in epilepsy
Zhang, Q., Javidi, S. S., Ankeeta, A., Sperling, M. R., Zhang, Z., & Tracy, J. I. (2026) · Communications Medicine · doi:10.1038/s43856-026-01530-9 · Cited by 1
Niu, J., Xia, J., Liu, Q., He, Y., Li, W. W., Chen, K., Zhang, X., Qiu, J., Chen, H., Li, J., et al. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04025-2 · Cited by 1
Gender effects on the controllability of hemispheric white matter networks
Li, D., Mao, M., Zhang, X., Hou, D., Zhang, S., Hao, J., Cui, X., Niu, Y., Xiang, J., & Wang, B. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac162 · Cited by 1
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Liang, Z., Zhang, Y., Wu, J., & Liu, Q. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782716 · Cited by 1
Functional control of network dynamical systems: An information theoretic approach
Singh, M. S., Pasumarthy, R., Vaidya, U., & Leonhardt, S. K. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121781 · Cited by 1
Binary mesoscale architecture does not explain controllability of structural brain networksPreprint
Patankar, S. P., Kim, J. Z., Pasqualetti, F., & Bassett, D. S. (2020) · arXiv (Cornell University) · Cited by 1
Self-citationLow-dimensional controllability of brain networksPreprint
Messaoud, R. B., Du, V. L., Kaufmann, B. C., Couvy‐Duchesne, B., Migliaccio, R., Bartolomeo, P., Chávez, M., & Fallani, F. D. V. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.11132 · Cited by 1
Medaglia, J. D., Harvey, D. Y., White, N. C., Bassett, D. S., & Hamilton, R. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.05022
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Hedges, D., Farrer, T., Bigler, E., & Hopkins, R. (2019) · The Brain at Risk · doi:10.1007/978-3-030-14260-5_13
Bassignana, G., Fransson, J., Henry, V., Colliot, O., Zujovic, V., & Fallani, F. D. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08913
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Un medio imprescindible para el desarrollo
Cuevas, J. M. (2003) · Dirección y progreso
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Ernsting, J., Holstein, V., Winter, N. R., Sarink, K., Leenings, R., Gruber, M., Repple, J., Risse, B., Dannlowski, U., & Hahn, T. (2023) · medRxiv · doi:10.1101/2023.06.22.23291748
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Orieux, M., Guillamon, A., & Huguet, G. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4636547
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Zhang, Y., Chen, H., Chen, Q., Ding, J., & Li, X. (2024) · New Journal of Physics · doi:10.1088/1367-2630/ad6ead
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Kim, J. Z., Betzel, R. F., Beyh, A., Howell, A. M., Kuceyeski, A. F., Larsen, B., Seguin, C., Zhang, X.-H., Holmes, A. J., & Parkes, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.24.650287
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Zhang, J., Han, S., Shen, Y., Wu, X., Zhao, Y., Wu, Z., Luo, N., Yang, Z., Li, D., Song, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.27.650883
Motallebi, S., Yazdanpanah, M. J., & Vahabie, A. (2025) · IET Control Theory and Applications · doi:10.1049/cth2.70048
Target Controllability of Complex Networks Based on Greedy Optimization
Ding, J., Zhuo, Y., Hu, X., Zhao, Y. M., Li, C., & Li, X. (2025) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2025.3585397
Topology optimization for optimal target control of complex networks
Song, Z., Ding, J., & Li, X. (2025) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/adf508
Shi, L., Li, H., Song, M., Yan, P., Yan, Y., & Yao, R. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120856
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Dong, K., Chen, S., Dan, Y., Zhang, L., Li, X., Liang, W. H., Zhao, Y., & Sun, Y. (2025) · Communications Biology · doi:10.1038/s42003-025-09184-4
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Dagnino, P. C., Acero-Pousa, I., Carhart-Harris, R. L., Erritzøe, D., Nutt, D., Kringelbach, M. L., Perl, Y. S., & Deco, G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.22.733731
Zeng, N., Ding, M., Wang, M., & Hu, Q. (2026) · Addictive Behaviors · doi:10.1016/j.addbeh.2026.108802
Metadata
- Authors
- Gu, S., Betzel, R. F., Mattar, M. G., Cieslak, M., Delio, P. R., Grafton, S. T., Pasqualetti, F., & Bassett, D. S.
- Affiliations
- University of Pennsylvania (US)
- Princeton University (US)
- University of California, Santa Barbara (US)
- University of California, Riverside (US)
- Corresponding author
- Bassett, Danielle S.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 148
- Pages
- 305-317
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC5489344
- OpenAlex
- W2963186114
- Cited by
- 183
- References
- 105
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 20 · 2023: 19 · 2022: 22 · 2021: 19 · 2020: 23 · 2019: 16 · 2018: 21 · 2017: 13
- FWCI
- 11.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (W911NF-10-2-0022, 1430280, 1R01HD086888-01, 1554488, 1430087, CAREER, 2-R01-DC-009209-11, 1441502, W911NF-14-1-0679)
- John D. and Catherine T. MacArthur Foundation (W911NF-10-2-0022, W911NF-14-1-0679, 2-R01-DC-009209-11, 1R01HD086888-01, BCS-1441502, PHY-1554488)
- Alfred P. Sloan Foundation (W911NF-10-2-0022, W911NF-14-1-0679, 2-R01-DC-009209-11, BCS-1441502, PHY-1554488, 1R01HD086888-01)
- National Institutes of Health (5r01ns099348-04, 2r01dc009209-11, 1r01hd086888-01)
- Office of Naval Research (BCS-1441502, PHY-1554488, W911NF-10-2-0022, W911NF-14-1-0679, CAREER PHY-1554488, 1R01HD086888-01, BCS-1430087, 2-R01-DC-009209-11)
- National Institute of Mental Health (2R01‐DC‐009209‐11, R01-DC-009209-11, R21MH106799)
- National Institute on Deafness and Other Communication Disorders (R01-DC009209)
- National Institute of Neurological Disorders and Stroke ((R01 NS099348)
- National Institute of Child Health and Human Development (1R01HD086888-01)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD086888)
- Army Research Office (1R01HD086888-01, PHY-1554488, W911NF-14–1-0679, BCS-1441502, W911NF-14-1, R01-DC-009209-11, W911NF-10-2-0022, W911NF, 2-R01-DC-009209-11)
- Army Research Laboratory (W911-NF-10-2-0022, W911NF-14-1-0679, 1R01HD086888-01, PHY-1554488, W911NF, 2-R01-DC-009209-11, BCS-1441502, W911NF-10)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- brain state transitions, network control theory, white matter structure, attention, executive function, mild traumatic brain injury, cognitive states, neural dynamics
- MeSH terms
- Brain Injuries, Traumatic, Adult, Algorithms, Attention, Brain, Cerebral Cortex, Cognition, Cognition Disorders, Energy Metabolism, Female, Humans, Male, Psychomotor Performance, Diffusion Magnetic Resonance Imaging, Young Adult, Executive Function, Healthy Volunteers, White Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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