Brain entropy and human intelligence: a resting-state fMRI study
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Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.
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Evidence on the exact nature of the relationship between intelligence and complexity measures is currently mixed.
“Evidence on the exact nature of the relationship between intelligence and complexity measures is currently mixed ( Saxe et al., 2018 ; Dreszer et al., 2020 ; Wang, 2021 ; Thiele et al., 2023 ).”
From What does it mean for consciousness to be multidimensional? A narrative review (2024) · cites this paper as Saxe et al., 2018
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A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486 · see citing works
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 49,574 · see citing works
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.015 · Cited by 11,314 · see citing works
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913 · see citing works
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · see citing works
Wechsler Abbreviated Scale of Intelligence
Dumont, R., Willis, J. O., Veizel, K., & Zibulsky, J. (2014) · Wiley eBooks · doi:10.1002/9781118660584.ese2519 · Cited by 5,547 · see citing works
Buzsáki, G. (2006) · doi:10.1093/acprof:oso/9780195301069.001.0001 · Cited by 4,873 · see citing works
Wechsler Abbreviated Scale of Intelligence
Wechsler, D. (1999) · PsycTESTS Dataset · doi:10.1037/t15170-000 · Cited by 4,681 · see citing works
Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp055 · Cited by 4,060 · see citing works
Least Median of Squares Regression
Rousseeuw, P. J. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477105 · Cited by 3,571 · see citing works
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487 · see citing works
Statistical Parametric Mapping: The Analysis of Functional Brain Images
Penny, W. D., Friston, K. J., Ashburner, J., Kiebel, S. J., & Thomas, N. (2007) · UCL Discovery (University College London) · Cited by 3,339 · see citing works
A Fast Algorithm for the Minimum Covariance Determinant Estimator
Rousseeuw, P. J., & Driessen, K. V. (1999) · Technometrics · doi:10.1080/00401706.1999.10485670 · Cited by 2,707 · see citing works
(1987) · Medical Entomology and Zoology · Cited by 2,512 · see citing works
The thermodynamics of computation—a review
Bennett, C. H. (1982) · International Journal of Theoretical Physics · doi:10.1007/bf02084158 · Cited by 2,006 · see citing works
A Fast Algorithm for the Minimum Covariance Determinant Estimator
Rousseeuw, P. J., & Driessen, K. V. (1999) · Technometrics · doi:10.2307/1270566 · Cited by 1,889 · see citing works
Two-stage model of memory trace formation: A role for “noisy” brain states
Buzsáki, G. (1989) · Neuroscience · doi:10.1016/0306-4522(89)90423-5 · Cited by 1,709 · see citing works
The anatomy of language: a review of 100 fMRI studies published in 2009
Price, C. J. (2010) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2010.05444.x · Cited by 1,387 · see citing works
Least Median of Squares Regression
Rousseeuw, P. J. (1984) · Journal of the American Statistical Association · doi:10.2307/2288718 · Cited by 1,139 · see citing works
Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography
Gottfredson, L. S. (1997) · Intelligence · doi:10.1016/s0160-2896(97)90011-8 · Cited by 1,137 · see citing works
Boltzmann, L., & Seeger, R. J. (1964) · American Journal of Physics · doi:10.1119/1.1971107 · Cited by 880 · see citing works
Neural systems behind word and concept retrieval
Damasio, H., Tranel, D., Grabowski, T. J., Adolphs, R., & Damásio, A. R. (2004) · Cognition · doi:10.1016/j.cognition.2002.07.001 · Cited by 750 · see citing works
Cortical rewiring and information storage
Chklovskii, D. B., Mel, B. W., & Svoboda, K. (2004) · Nature · doi:10.1038/nature03012 · Cited by 717 · see citing works
The Cerebellum and Cognition: Evidence from Functional Imaging Studies
Stoodley, C. J. (2012) · The Cerebellum · doi:10.1007/s12311-011-0260-7 · Cited by 686 · see citing works
Friston, K. J., & Stephan, K. E. (2007) · Synthese · doi:10.1007/s11229-007-9237-y · Cited by 673 · see citing works
Pernet, C. R., Wilcox, R., & Rousselet, G. A. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00606 · Cited by 566 · see citing works
Noonan, K. A., Jefferies, E., Visser, M., & Lambon Ralph, M. A. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00442 · Cited by 543 · see citing works
Consensus Paper: Language and the Cerebellum: an Ongoing Enigma
Mariën, P., Ackermann, H., Adamaszek, M., Barwood, C. H. S., Beaton, A. A., Desmond, J. E., Witte, E. D., Fawcett, A. J., Hertrich, I., Küper, M., et al. (2013) · The Cerebellum · doi:10.1007/s12311-013-0540-5 · Cited by 505 · see citing works
Holmes, A., Hollinshead, M. O., O’Keefe, T. M., Petrov, V. I., Fariello, G., Wald, L., Fischl, B., Rosen, B., Mair, R., Roffman, J., et al. (2015) · Scientific Data · doi:10.1038/sdata.2015.31 · Cited by 480 · see citing works
On the decrease of entropy in a thermodynamic system by the intervention of intelligent beings
Szilard, L. (1964) · Systems Research and Behavioral Science · doi:10.1002/bs.3830090402 · Cited by 435 · see citing works
LIBRA: a MATLAB library for robust analysis
Verboven, S., & Hubert, M. (2004) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2004.06.003 · Cited by 389 · see citing works
The Negentropy Principle of Information
Brillouin, L. (1953) · Journal of Applied Physics · doi:10.1063/1.1721463 · Cited by 358 · see citing works
The anatomy of choice: active inference and agency
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00598 · Cited by 340 · see citing works
Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration
Schultz, D. H., & Cole, M. W. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0358-16.2016 · Cited by 268 · see citing works
Neural correlates of establishing, maintaining, and switching brain states
Tang, Y., Rothbart, M. K., & Posner, M. I. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.05.001 · Cited by 255 · see citing works
Brain Entropy Mapping Using fMRI
Wang, Z., Yin, L., Childress, A. R., & Detre, J. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089948 · Cited by 190 · see citing works
Zagha, E., & McCormick, D. A. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.09.010 · Cited by 184 · see citing works
Yang, A. C., Huang, C., Yeh, H., Liu, M., Hong, C., Tu, P., Chen, J.-F., Huang, N. E., Peng, C., Lin, C., et al. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.05.004 · Cited by 184 · see citing works
The Functional Anatomy of Time: What and When in the Brain
Friston, K. J., & Buzsáki, G. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.05.001 · Cited by 179 · see citing works
Neural correlates of intelligence as revealed by fMRI of fluid analogies
Geake, J. G., & Hansen, P. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.01.035 · Cited by 165 · see citing works
A vision of graded hemispheric specialization
Behrmann, M., & Plaut, D. C. (2015) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12833 · Cited by 136 · see citing works
Yang, A. C., Wang, S., Lai, K., Tsai, C.-F., Yang, C., Hwang, J., Lo, M., Huang, N. E., Peng, C., & Fuh, J. (2013) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2013.07.022 · Cited by 133 · see citing works
Inferring learning rules from distributions of firing rates in cortical neurons
Lim, S., McKee, J. L., Woloszyn, L., Amit, Y., Freedman, D. J., Sheinberg, D. L., & Brunel, N. (2015) · Nature Neuroscience · doi:10.1038/nn.4158 · Cited by 112 · see citing works
How the cerebellum may monitor sensory information for spatial representation
Rondi‐Reig, L., Paradis, A., Lefort, J. M., Babayan, B. M., & Tobin, C. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00205 · Cited by 97 · see citing works
Schoenberg, M. R., Scott, J. G., Duff, K. M., & Adams, R. L. (2002) · The Clinical Neuropsychologist · doi:10.1076/clin.16.4.426.13913 · Cited by 94 · see citing works
The Network Architecture of Cortical Processing in Visuo-spatial Reasoning
Kojori, E. S., Motes, M. A., Bart Rypma, A., & Krawczyk, D. C. (2012) · Scientific Reports · doi:10.1038/srep00411 · Cited by 38 · see citing works
McNorgan, C., Chabal, S. A., O'Young, D., Lukic, S., & Booth, J. R. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.12.014 · Cited by 37 · see citing works
Charting moment-to-moment brain signal variability from early to late childhood
Miskovic, V., Owens, M., Kuntzelman, K. M., & Gibb, B. E. (2016) · Cortex · doi:10.1016/j.cortex.2016.07.006 · Cited by 31 · see citing works
Molecular Requirements for Polyamines Binding to the Antispermine Monoclonal Antibody Spm8-2
Delcros, J., Clément, S., Bouillé, N., Royou, A., Debroise, I., Thomas, V., & Moulinoux, J.-P. (1996) · Hybridoma · doi:10.1089/hyb.1996.15.177 · Cited by 11 · see citing works
Neurobiology of intelligence: Health implications?
Gray, J. R., & Thompson, P. M. (2004) · PubMed · Cited by 9 · see citing works
HZMVNTEST. Henze-Zirkler's Multivariate Normality Test.
Trujillo-Ortiz, A., Hernández-Walls, R., & Barba-Rojo, K. (2007) · doi:10.13140/rg.2.2.32317.90083 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [7]Pellionisz A. Modeling of neurons and neuronal networks. Schmitt FO, Worden FG, Neurosciences Research Program., editors. Cambridge, Mass.: MIT Press; 1979 vii, 1185 p. p.
- [30]Schoenberg MR, Scott JG, Duff K, Adams RL. Estimation of WAIS-III FSIQ from combined reading, subtest performance, and demographic variables and Combined Oklahoma Premorbid Intelligence Estimate (COPIE). Archives of Clinical Neuropsychology. 2003; 18(7): 804-.
Cited by
Sorted by citations
Computational network biology: Data, models, and applications
Liu, C., Ma, Y., Zhao, J., Nussinov, R., Zhang, Y., Cheng, F., & Zhang, Z. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.12.004 · Cited by 226 · see citing works
Carhart-Harris, R. L. (2018) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000467 · Cited by 133 · see citing works
Brain Entropy Mapping in Healthy Aging and Alzheimer’s Disease
Wang, Z., & Initiative, A. D. N. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.596122 · Cited by 77 · see citing works
Range Entropy: A Bridge between Signal Complexity and Self-Similarity
Omidvarnia, A., Mesbah, M., Pedersen, M., & Jackson, G. D. (2018) · Entropy · doi:10.3390/e20120962 · Cited by 65 · see citing works
Lin, C., Lee, S., Huang, C., Chen, G.-Y., Ho, P.-S., Liu, H., Chen, Y.-L., Lee, T. M.-C., & Wu, S. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.03.012 · Cited by 62 · see citing works
Brain Entropy is Associated with Divergent Thinking
Shi, L., Beaty, R. E., Chen, Q., Sun, J., Wei, D., Yang, W., & Qiu, J. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz120 · Cited by 58 · see citing works
Temporal irreversibility of large-scale brain dynamics in Alzheimer's disease
Cruzat, J., Herzog, R., Prado, P., Sanz-Perl, Y., Gonzalez-Gomez, R., Moguilner, S., Kringelbach, M. L., Deco, G., Tagliazucchi, E., & Ibañez, A. (2023) · The Journal of Neuroscience · doi:10.1523/jneurosci.1312-22.2022 · Cited by 57 · see citing works
Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain
Pérez‐Tomás, A. (2019) · Advanced Materials Interfaces · doi:10.1002/admi.201900471 · Cited by 50 · see citing works
Das, A., & Menon, V. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa111 · Cited by 49 · see citing works
Predicting intelligence from brain gray matter volume
Hilger, K., Winter, N. R., Leenings, R., Sassenhagen, J., Hahn, T., Basten, U., & Fiebach, C. J. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02113-7 · Cited by 41 · see citing works
Yang, S., Zhao, Z., Cui, H., Zhang, T., Zhao, L., He, Z., Liu, H., Guo, L., Liu, T., Becker, B., et al. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2019.00036 · Cited by 37 · see citing works
Anderson, E. D., & Barbey, A. K. (2022) · Human Brain Mapping · doi:10.1002/hbm.26164 · Cited by 37 · see citing works
Roy, A. V., Thai, M., Klimes‐Dougan, B., Schreiner, M. W., Mueller, B. A., Albott, C. S., Lim, K. O., Fiecas, M. J. A., Tye, S. J., & Cullen, K. R. (2020) · Journal of Psychopharmacology · doi:10.1177/0269881120928203 · Cited by 36 · see citing works
The thermodynamic brain and the evolution of intellect: the role of mental energy
Déli, É., & Kisvárday, Z. F. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09637-y · Cited by 36 · see citing works
Lett, T. A. P., Vogel, B. O., Ripke, S., Wackerhagen, C., Erk, S., Awasthi, S., Trubetskoy, V., Brandl, E. J., Mohnke, S., Veer, I. M., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz270 · Cited by 36 · see citing works
Functional connectivity inference from fMRI data using multivariate information measures
Li, Q. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.11.016 · Cited by 34 · see citing works
Chen, K., Yang, J. N., Li, F., Chen, J., Chen, M., Shao, H., He, C., Cai, D., Zhang, X., Wang, L., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26657 · Cited by 34 · see citing works
Avalanche criticality in individuals, fluid intelligence, and working memory
Xu, L., Feng, J., & Yu, L. (2022) · Human Brain Mapping · doi:10.1002/hbm.25802 · Cited by 33 · see citing works
A primer on entropy in neuroscience
Fagerholm, E. D., Dezhina, Z., Moran, R., Turkheimer, F., & Leech, R. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105070 · Cited by 32 · see citing works
The thermodynamics of cognition: A mathematical treatment
Déli, É., Peters, J. F., & Kisvárday, Z. F. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.008 · Cited by 30 · see citing works
Functional connectivity dynamics slow with descent from wakefulness to sleep
El-Baba, M., Lewis, D. J., Fang, Z., Owen, A. M., Fogel, S. M., & Morton, J. B. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224669 · Cited by 29 · see citing works
Gu, C., Liu, Z., & Woltering, S. (2022) · Brain Communications · doi:10.1093/braincomms/fcac054 · Cited by 28 · see citing works
Liu, M., Liu, X., Hildebrandt, A., & Zhou, C. (2020) · Cerebral Cortex Communications · doi:10.1093/texcom/tgaa015 · Cited by 25 · see citing works
Two Kinds of Information Processing in Cognition
Sprevak, M. D. (2019) · Review of Philosophy and Psychology · doi:10.1007/s13164-019-00438-9 · Cited by 24 · see citing works
Dreszer, J., Grochowski, M., Lewandowska, M., Nikadon, J., Gorgol, J., Bałaj, B., Finc, K., Duch, W., Kałamała, P., Chuderski, A., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25162 · Cited by 23 · see citing works
Cognitive Vigilance Enhancement Using Audio Stimulation of Pure Tone at 250 Hz
Al-Shargie, F., Tariq, U., Babiloni, F., & Al-Nashash, H. A. (2021) · IEEE Access · doi:10.1109/access.2021.3054785 · Cited by 23 · see citing works
Rankaduwa, S., & Owen, A. M. (2023) · Neuroscience of Consciousness · doi:10.1093/nc/niad001 · Cited by 22 · see citing works
Introducing Entropy into Organizational Psychology: An Entropy-Based Proactive Control Model
Jia, H., & Wang, L. (2024) · Behavioral Sciences · doi:10.3390/bs14010054 · Cited by 22 · see citing works
Altered brain entropy and functional connectivity patterns in generalized anxiety disorder patients
Fan, S., Yu, Y., Wu, Y., Kai, Y., Wang, H., Chen, Y., Zu, M., Pang, X., & Tian, Y. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.03.062 · Cited by 21 · see citing works
Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes
Omidvarnia, A., Sasse, L., Larabi, D. I., Raimondo, F., Hoffstaedter, F., Kasper, J., Dukart, J., Petersen, M., Cheng, B., Thomalla, G., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06438-5 · Cited by 19 · see citing works
Reliability of variability and complexity measures for task and task‐free BOLD fMRI
Wehrheim, M. H., Faskowitz, J., Schubert, A., & Fiebach, C. J. (2024) · Human Brain Mapping · doi:10.1002/hbm.26778 · Cited by 19 · see citing works
Xue, S., Wang, D., Tan, Z., Wang, Y., Lian, Z., Sun, Y., Hu, X., Wang, X., & Zhou, X. H. (2019) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s220743 · Cited by 19 · see citing works
Entropy, complexity, and maturity in children's neural responses to naturalistic video lessons
Amalric, M., & Cantlon, J. F. (2023) · Cortex · doi:10.1016/j.cortex.2023.02.008 · Cited by 16 · see citing works
Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns From fMRI Data
Xiao, L., Zhang, A., Cai, B., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3022335 · Cited by 14 · see citing works
Kaur, Y., Weiss, S., Zhou, C., Fischer, R., & Hildebrandt, A. (2021) · Journal of Intelligence · doi:10.3390/jintelligence9040059 · Cited by 14 · see citing works
An entropy-based estimator of the Hurst exponent in fractional Brownian motion
Zeinali, N., & Pourdarvish, A. (2021) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2021.126690 · Cited by 14 · see citing works
On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI
Omidvarnia, A., Liégeois, R., Amico, E., Preti, M. G., Zalesky, A., & Ville, D. V. D. (2022) · Entropy · doi:10.3390/e24081148 · Cited by 14 · see citing works
Information content of brain states is explained by structural constraints on state energetics
Weninger, L., Srivastava, P., Zhou, D., Kim, J. Z., Cornblath, E. J., Bertolero, M. A., Habel, U., Merhof, D., & Bassett, D. S. (2022) · Physical review. E · doi:10.1103/physreve.106.014401 · Cited by 13 · see citing works
Lin, C., Huang, C., Chang, W., Chang, Y., Liu, H., Ng, S., Lin, H., Lee, T. M. C., Lee, S., & Wu, S. (2024) · Brain and Behavior · doi:10.1002/brb3.3348 · Cited by 12 · see citing works
Bodensohn, L., Maurer, A., Daamen, M., Upadhyay, N., Werkhausen, J., Lohaus, M., Manunzio, U., Manunzio, C., Radbruch, A., Attenberger, U. I., et al. (2024) · Brain and Cognition · doi:10.1016/j.bandc.2024.106156 · Cited by 12 · see citing works
Menon, S. S., & Krishnamurthy, K. (2019) · Entropy · doi:10.3390/e21100995 · Cited by 12 · see citing works
Brain Entropy Study on Obsessive-Compulsive Disorder Using Resting-State fMRI
Jiang, X., Li, X., Xing, H., Huang, X., Xu, X. Y., & Li, J. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.764328 · Cited by 11 · see citing works
Li, H., Zhang, X., Zhang, X., Wang, Z., Feng, S., & Zhang, G. (2023) · Journal of Intelligence · doi:10.3390/jintelligence11020029 · Cited by 11 · see citing works
Einziger, T., Devor, T., Ben‐Shachar, M. S., Arazi, A., Dinstein, I., Klein, C., Auerbach, J. G., & Berger, A. (2023) · Cortex · doi:10.1016/j.cortex.2023.06.007 · Cited by 11 · see citing works
Amador‐Tejada, A., Sharma, B., Danielli, E., & Noseworthy, M. D. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.70276 · Cited by 10 · see citing works
Activity‐State Entropy: A novel brain entropy measure based on spatial patterns of activity
Hull, A., & Morton, J. B. (2023) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2023.109868 · Cited by 10 · see citing works
Li, Y., Xue, Y., Zhao, W., Li, S.-S., Li, J., & Xu, Y. J. (2020) · Brain Research · doi:10.1016/j.brainres.2020.147176 · Cited by 10 · see citing works
The Application of Complexity Analysis in Brain Blood-Oxygen Signal
Xin, X., Long, S., Sun, M., & Gao, X. (2021) · Brain Sciences · doi:10.3390/brainsci11111415 · Cited by 9 · see citing works
How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions?
Déli, É., Peters, J. F., & Kisvárday, Z. F. (2022) · Entropy · doi:10.3390/e24101498 · Cited by 9 · see citing works
EEG entropy modulation as a biomarker of emotion regulation and resilience
Creel, W. T., & Hartman, R. E. (2025) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2025.10.013 · Cited by 8 · see citing works
Allendorfer, J. B., Nenert, R. E., Goodman, A. M., Kakulamarri, P., Correia, S., Philip, N. S., LaFrance, W. C., Jr., & Szaflarski, J. P. (2024) · Epilepsia Open · doi:10.1002/epi4.12926 · Cited by 8 · see citing works
Interindividual Signatures of fMRI Temporal Fluctuations
Zhang, S., Spoletini, L. J., Gold, B. P., Morgan, V. L., Rogers, B. P., & Chang, C. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab099 · Cited by 8 · see citing works
Imaging the Intelligence of Humans
Martínez, K., & Colom, R. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.005 · Cited by 8 · see citing works
Synchronous Generative Development amidst Situated Entropy
Fox, S. (2022) · Entropy · doi:10.3390/e24010089 · Cited by 8 · see citing works
Post-error slowing is associated with intelligence
Varriale, V., Pascalis, V. D., & van der Molen, M. W. (2021) · Intelligence · doi:10.1016/j.intell.2021.101599 · Cited by 8 · see citing works
Yuluğ, B., Yalcinkaya, A., Safa, S. S., Sayman, D., Çankaya, Ş., Karakuş, A., Sayman, C., Uygur, A. B., Bircan, E. I., Ucak, A. D., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03518-w · Cited by 7 · see citing works
Breitenbach, J., Raab, D., Fezer, E., Sauter, D., Baumgartl, H., & Buettner, R. (2021) · IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob52687.2021.9606314 · Cited by 7 · see citing works
Soni, A., & Rawal, K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105476 · Cited by 6 · see citing works
Task-specific dynamical entropy variations in EEG as a biomarker for Parkinson’s disease progression
Onay, F., & Karaçalı, B. (2025) · GeroScience · doi:10.1007/s11357-025-01821-4 · Cited by 6 · see citing works
Organizational Neuroscience of Industrial Adaptive Behavior
Fox, S., & Kotelba, A. (2022) · Behavioral Sciences · doi:10.3390/bs12050131 · Cited by 6 · see citing works
McGee, M. D. (2019) · Alcoholism Treatment Quarterly · doi:10.1080/07347324.2019.1632766 · Cited by 6 · see citing works
On the Entropy of Brain Anatomic Regions for Complex Problem Solving
Değirmendereli, G. G., Newman, S. D., & Vural, F. T. Y. (2019) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2019.00115 · Cited by 6 · see citing works
Zhang, X., Yu, Y., Shi, Z.-S., Xu, K., Feng, J., Li, Z.-Y., Zhang, X., Shen, S., Yang, Y., Yan, L., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00285-8 · Cited by 6 · see citing works
Ji, S., Zhang, Y., Chen, N., Liu, X., Li, Y., Shao, X., Yang, Z., Yao, Z., & Hu, B. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00507-7 · Cited by 5 · see citing works
Feklicheva, I. V., Zakharov, I., Chipeeva, N., Павловна, М. Е., Korobova, S. Y., Adamovich, T., Ismatullina, V. I., & Malykh, S. B. (2021) · Brain Sciences · doi:10.3390/brainsci11010094 · Cited by 5 · see citing works
Wang, B., Chen, Y., Chen, K., Lu, H., & Zhang, Z. (2023) · Human Brain Mapping · doi:10.1002/hbm.26302 · Cited by 5 · see citing works
Fan, S., Zhang, J., Wu, Y., Yu, Y., Zheng, H., Guo, Y., Ji, Y., Pang, X., & Tian, Y. (2024) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2024.111788 · Cited by 5 · see citing works
Velázquez, J. L. P., Mateos, D. M., Guevara, R., & Erra, R. G. (2024) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2024.1426986 · Cited by 5 · see citing works
Velázquez, J. L. P., Mateos, D. M., & Erra, R. G. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15005 · Cited by 5 · see citing works
What Is Psychological Spin? A Thermodynamic Framework for Emotions and Social Behavior
Déli, É. (2023) · Psych · doi:10.3390/psych5040081 · Cited by 5 · see citing works
What does it mean for consciousness to be multidimensional? A narrative review
Páleník, J. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1430262 · Cited by 4 · see citing works
“Evidence on the exact nature of the relationship between intelligence and complexity measures is currently mixed (Saxe et al., 2018; Dreszer et al., 2020; Wang, 2021; Thiele et al., 2023).”
BackgroundSpontaneous brain regional dynamics contribute to generalizable brain–behaviour associations
Tian, X., Peng, Y., Liu, S., Shafiei, G., Wang, M., Sun, Y., Lou, J., Xian, J., Hu, K., He, Y., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02332-0 · Cited by 4 · see citing works
Using Algorithmic Complexity to Differentiate Cognitive States in fMRI
Ventresca, M. (2018) · Studies in computational intelligence · doi:10.1007/978-3-030-05414-4_53 · Cited by 4 · see citing works
Nan, J., Zhang, L., Zheng, Q., Zhang, M., & Lu, Z. (2019) · Europe PMC (PubMed Central) · doi:10.12122/j.issn.1673-4254.2019.09.04 · Cited by 4 · see citing works
Fox, S., & Kotelba, A. (2020) · Entropy · doi:10.3390/e22040444 · Cited by 4 · see citing works
Sevel, L. S., Stennett, B., Schneider, V. J., Bush, N. J., Nixon, S. J., Robinson, M., & Boissoneault, J. (2020) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14381 · Cited by 4 · see citing works
Start-Ups as Adaptable Stable Systems Based on Synchronous Business Models
Fox, S., & Vahala, P. (2022) · Systems · doi:10.3390/systems10030081 · Cited by 4 · see citing works
Entropy, Computing and RationalityPreprint
Pineda, L. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.10224 · Cited by 4 · see citing works
Beyond representation: rethinking intelligence in the age of LLMs
Largo, J. (2025) · Synthese · doi:10.1007/s11229-025-05373-0 · Cited by 3 · see citing works
Development of brain metastable dynamics during the equivalent of the third gestational trimester
Champaud, J. L. Y., Asite, S., & Lorenzo Fabrizi, D. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101556 · Cited by 3 · see citing works
Winters, D. E., Spitz, J., Raymond, K. M., Natvig, C. L., Waller, R. E., Mikulich‐Gilbertson, S. K., Schacht, J. P., & Sakai, J. T. (2025) · Child Psychiatry & Human Development · doi:10.1007/s10578-025-01869-5 · Cited by 3 · see citing works
Naghashzadeh, M., Yazdi, M., & Zolghadrasli, A. (2021) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2021.1972343 · Cited by 3 · see citing works
Decoding the human brain during intelligence testing
Thiele, J. A., Faskowitz, J., Sporns, O., Chuderski, A., Jung, R. E., & Hilger, K. (2025) · Communications Biology · doi:10.1038/s42003-025-09354-4 · Cited by 2 · see citing works
Villar, A. J. R. (2022) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2022.780701 · Cited by 2 · see citing works
Fox, S., Heikkila, T. A., Halbach, E., & Soutukorva, S. (2023) · Entropy · doi:10.3390/e25111541 · Cited by 2 · see citing works
Sleep Deprivation in Mice: Looking Beyond the Slow Wave Rebound
Magyar, T. Z., Szalárdy, O., & Bódizs, R. (2025) · Journal of Sleep Research · doi:10.1111/jsr.70232 · Cited by 2 · see citing works
Zhong, X. Z., Lankveld, H. V., Mathew, A., & Chen, J. J. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.1092 · Cited by 2 · see citing works
Kesserwani, H. N. (2020) · Cureus · doi:10.7759/cureus.12139 · Cited by 2 · see citing works
Energy and Entropy based Intelligence Metric for Performance Estimation in DNNs
Kartal, B., & Üstündağ, B. B. (2023) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic57133.2023.10067093 · Cited by 2 · see citing works
Age-related complexity of the resting state MEG signals: a multiscale entropy analysisPreprint
Makani, A., Akhavan, A., Shahbazi, F., Noruzi, M., & Zare, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.14.507986 · Cited by 2 · see citing works
Ji, S., Chen, F., Li, S., Zhou, C., Liu, C., & Yu, H. (2024) · Behavioural Brain Research · doi:10.1016/j.bbr.2024.115255 · Cited by 1 · see citing works
Kong, W., Sun, Z., Zhu, J., Li, L., Wang, G., Shao, X., Li, X., & Hu, B. (2025) · Clinical Neurophysiology · doi:10.1016/j.clinph.2025.01.014 · Cited by 1 · see citing works
Wijesinghe, D., Lynch, K. M., Aksman, L., Delis, D. C., Wang, D. J. J., & Jann, K. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122085 · Cited by 1 · see citing works
Cheng, K., Hu, J. M., Ni, L., Xue, Q., Zhang, D., Zhang, Y., Chunxia, H., & Wang, L. (2025) · Brain Research · doi:10.1016/j.brainres.2025.149896 · Cited by 1 · see citing works
Neuropsychologie der Bedürfnisse
Markowitsch, H. J., & Schreier, M. M. (2019) · Springer eBooks · doi:10.1007/978-3-662-58265-7_4 · Cited by 1 · see citing works
Avalanche criticality in individuals, fluid intelligence and working memoryPreprint
Xu, L., Yu, L., & Feng, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.24.260588 · Cited by 1 · see citing works
The collapse of the wave function as the mediator of free will in prime neurons
Loboguerrero, D. A. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1637217
Entropy as a Method for Identifying Treatment Resistant Autism Spectrum Disorder
Wright, N. (2018) · USF Scholarship Repository (University of San Francisco)
An alternative View on Entropic Measures Applied to the Nervous SystemPreprint
Mateos, D. M., & Velázquez, J. L. P. (2020) · Preprints.org · doi:10.20944/preprints202004.0402.v2
Mathematical modelling and brain dynamical networks
Alwiya, A. (2020) · Open Access at Essex (University of Essex)
- Authors
- Saxe, G. N., Calderone, D., & Morales, L. J.
- Affiliations
- New York University (US)
- Corresponding author
- Saxe, Glenn N.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-02-12
- Volume
- 13
- Issue
- 2
- Pages
- e0191582
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5809019
- OpenAlex
- W2785432401
- Cited by
- 133
- References
- 50
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 13 · 2023: 20 · 2022: 21 · 2021: 17 · 2020: 22 · 2019: 14 · 2018: 4
- FWCI
- 8.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Massachusetts General Hospital
- NYU Langone Medical Center
- Harvard University
- Centre d'Imagerie BioMédicale
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain entropy, intelligence, neural states, prefrontal cortex, inferior temporal lobe, cerebellum, cognitive performance, adaptive behavior
- MeSH terms
- Brain, Humans, Intelligence, Magnetic Resonance Imaging
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