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Part II - Theories, Models, and Hypotheses

Published online by Cambridge University Press:  11 June 2021

Edited by
Aron K. Barbey ,
Sherif Karama  and
Richard J. Haier
Aron K. Barbey
Affiliation:
University of Illinois, Urbana-Champaign
Sherif Karama
Affiliation:
McGill University, Montréal
Richard J. Haier
Affiliation:
University of California, Irvine
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The Cambridge Handbook of Intelligence and Cognitive Neuroscience , pp. 83 - 188
Publisher: Cambridge University Press
Print publication year: 2021

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References

References

Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20. doi: 10.1016/j.tics.2017.10.001.Google Scholar
Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135(4), 1154–1164.Google Scholar
Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509. doi: 10.1016/J.TICS.2013.08.012.CrossRefGoogle ScholarPubMed
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. doi: 10.1016/j.intell.2015.04.009.Google Scholar
Basten, U., Stelzel, C., & Fiebach, C. J. (2013). Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network. Intelligence, 41(5), 517–528.CrossRefGoogle Scholar
Clark, A. (2015). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press.Google Scholar
Deary, I. J., Der, G., & Ford, G. (2001). Reaction times and intelligence differences: A population-based cohort study. Intelligence, 29(5), 389–399. doi: 10.1016/S0160–2896(01)00062-9.Google Scholar
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172–179. doi: 10.1016/j.tics.2010.01.004.Google Scholar
Dunst, B., Benedek, M., Jauk, E., Bergner, S., Koschutnig, K., Sommer, M., … Neubauer, A. C. (2014). Neural efficiency as a function of task demands. Intelligence, 42, 22–30. doi: 10.1016/j.intell.2013.09.005.CrossRefGoogle ScholarPubMed
Ertl, J. P., & Schafer, E. W. P. (1969). Brain response correlates of psychometric intelligence. Nature, 223(5204), 421–422. doi: 10.1038/223421a0.Google Scholar
Euler, M. J. (2018). Intelligence and uncertainty: Implications of hierarchical predictive processing for the neuroscience of cognitive ability. Neuroscience & Biobehavioral Reviews, 94, 93–112. doi: 10.1016/j.neubiorev.2018.08.013.CrossRefGoogle ScholarPubMed
Euler, M. J., McKinney, T. L., Schryver, H. M., & Okabe, H. (2017). ERP correlates of the decision time-IQ relationship: The role of complexity in task- and brain-IQ effects. Intelligence, 65, 1–10. doi: 10.1016/j.intell.2017.08.003.Google Scholar
Euler, M. J., Weisend, M. P., Jung, R. E., Thoma, R. J., & Yeo, R. A. (2015). Reliable activation to novel stimuli predicts higher fluid intelligence. NeuroImage, 114, 311–319. doi: 10.1016/j.neuroimage.2015.03.078.Google Scholar
Fiandaca, M. S., Mapstone, M. E., Cheema, A. K., & Federoff, H. J. (2014). The critical need for defining preclinical biomarkers in Alzheimer’s disease. Alzheimer’s & Dementia, 10(3), S196–S212. doi: 10.1016/J.JALZ.2014.04.015.Google Scholar
Friston, K. J. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. doi: 10.1038/nrn2787.Google Scholar
Genç, E., Fraenz, C., Schlüter, C., Friedrich, P., Hossiep, R., Voelkle, M. C., … Jung, R. E. (2018). Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature Communications, 9(1), 1905. doi: 10.1038/s41467–018-04268-8.CrossRefGoogle ScholarPubMed
Gläscher, J., Rudrauf, D., Colom, R., Paul, L. K., Tranel, D., Damasio, H., & Adolphs, R. (2010). Distributed neural system for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 4705–4709. doi: 10.1073/pnas.0910397107.CrossRefGoogle ScholarPubMed
Haier, R. J. (2016). The neuroscience of intelligence. Cambridge University Press.Google Scholar
Haier, R. J., Colom, R., Schroeder, D. H., Condon, C. A., Tang, C., Eaves, E., & Head, K. (2009). Gray matter and intelligence factors: Is there a neuro-g? Intelligence, 37(2), 136–144. doi: 10.1016/j.intell.2008.10.011.Google Scholar
Haier, R. J., Siegel, B. V. Jr, Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., … Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12(2), 199–217. doi: 10.1016/0160-2896(88)90016-5.Google Scholar
Hearne, L. J., Mattingley, J. B., Cocchi, L., Neisser, U., Melnick, M. D., Harrison, B. R., … He, Y. (2016). Functional brain networks related to individual differences in human intelligence at rest. Scientific Reports, 6, 32328. doi: 10.1038/srep32328.Google Scholar
Hendrickson, D. E., & Hendrickson, A. E. (1980). The biological basis of individual differences in intelligence. Personality and Individual Differences, 1(1), 3–33.Google Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017). Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 10–25. doi: 10.1016/J.INTELL.2016.11.001.CrossRefGoogle Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–187. doi: 10.1017/S0140525X07001185.CrossRefGoogle ScholarPubMed
Kapanci, T., Merks, S., Rammsayer, T. H., & Troche, S. J. (2019). On the relationship between P3 latency and mental ability as a function of increasing demands in a selective attention task. Brain Sciences, 9(2), 28. doi: 10.3390/brainsci9020028.CrossRefGoogle Scholar
Kievit, R. A., Davis, S. W., Griffiths, J., Correia, M. M., Cam-CAN, , & Henson, R. N. (2016). A watershed model of individual differences in fluid intelligence. Neuropsychologia, 91, 186–198. doi: 10.1016/J.NEUROPSYCHOLOGIA.2016.08.008.Google Scholar
Kovacs, K., & Conway, A. R. A. (2016). Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27(3), 151–177. doi: 10.1080/1047840X.2016.1153946.CrossRefGoogle Scholar
Kretzschmar, A., Spengler, M., Schubert, A.-L., Steinmayr, R., Ziegler, M., Kretzschmar, A., … Ziegler, M. (2018). The relation of personality and intelligence – What can the Brunswik symmetry principle tell us? Journal of Intelligence, 6(3), 30. doi: 10.3390/jintelligence6030030.CrossRefGoogle ScholarPubMed
Kruschwitz, J. D., Waller, L., Daedelow, L. S., Walter, H., & Veer, I. M. (2018). General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set. NeuroImage, 171, 323–331. doi: 10.1016/J.NEUROIMAGE.2018.01.018.Google Scholar
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5(5), e1000395. doi: 10.1371/journal.pcbi.1000395.Google Scholar
Mackintosh, N. J. (2011). IQ and human intelligence (2nd ed.). Oxford University Press.Google Scholar
Martínez, K., Madsen, S. K., Joshi, A. A., Joshi, S. H., Román, F. J., Villalon-Reina, J., … Colom, R. (2015). Reproducibility of brain-cognition relationships using three cortical surface-based protocols: An exhaustive analysis based on cortical thickness. Human Brain Mapping, 36(8), 3227–3245. doi: 10.1002/hbm.22843.CrossRefGoogle ScholarPubMed
McKinney, T. L., & Euler, M. J. (2019). Neural anticipatory mechanisms predict faster reaction times and higher fluid intelligence. Psychophysiology, 56(10), e13426. doi: 10.1111/psyp.13426.Google Scholar
Neubauer, A. C., & Fink, A. (2009a). Intelligence and neural efficiency: Measures of brain activation versus measures of functional connectivity in the brain. Intelligence, 37(2), 223–229. doi: 10.1016/j.intell.2008.10.008.CrossRefGoogle Scholar
Neubauer, A. C., & Fink, A. (2009b). Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews, 33(7), 1004–1023. doi: 10.1016/j.neubiorev.2009.04.001.Google Scholar
Nussbaumer, D., Grabner, R. H., & Stern, E. (2015). Neural efficiency in working memory tasks: The impact of task demand. Intelligence, 50, 196–208. doi: 10.1016/j.intell.2015.04.004.Google Scholar
Owen, A. M., & Duncan, J. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23(10), 475–483. doi: 10.1007/s11631–017-0212-0.Google Scholar
Pineda-Pardo, J. A., Martínez, K., Román, F. J., & Colom, R. (2016). Structural efficiency within a parieto-frontal network and cognitive differences. Intelligence, 54, 105–116. doi: 10.1016/J.INTELL.2015.12.002.CrossRefGoogle Scholar
Poldrack, R. A. (2015). Is “efficiency” a useful concept in cognitive neuroscience? Developmental Cognitive Neuroscience, 11, 12–17.Google Scholar
Román, F. J., Abad, F. J., Escorial, S., Burgaleta, M., Martínez, K., Álvarez-Linera, J., … Colom, R. (2014). Reversed hierarchy in the brain for general and specific cognitive abilities: A morphometric analysis. Human Brain Mapping, 35(8), 3805–3818. doi: 10.1002/hbm.22438.Google Scholar
Ryman, S. G., Yeo, R. A., Witkiewitz, K., Vakhtin, A. A., van den Heuvel, M., de Reus, M., … Jung, R. E. (2016). Fronto-Parietal gray matter and white matter efficiency differentially predict intelligence in males and females. Human Brain Mapping, 37(11), 4006–4016. doi: 10.1002/hbm.23291.Google Scholar
Santarnecchi, E., Emmendorfer, A., & Pascual-Leone, A. (2017). Dissecting the parieto-frontal correlates of fluid intelligence: A comprehensive ALE meta-analysis study. Intelligence, 63, 9–28. doi: 10.1016/J.INTELL.2017.04.008.Google Scholar
Santarnecchi, E., Emmendorfer, A., Tadayon, S., Rossi, S., Rossi, A., & Pascual-Leone, A. (2017). Network connectivity correlates of variability in fluid intelligence performance. Intelligence, 65, 35–47. doi: 10.1016/J.INTELL.2017.10.002.Google Scholar
Schubert, A.-L., Hagemann, D., & Frischkorn, G. T. (2017). Is general intelligence little more than the speed of higher-order processing? Journal of Experimental Psychology: General, 146(10), 1498–1512. doi: 10.1037/xge0000325.Google Scholar
Schultz, D. H., & Cole, M. W. (2016). Higher intelligence is associated with less task-related brain network reconfiguration. The Journal of Neuroscience, 36(33), 8551–8561. doi: 10.1523/jneurosci.0358-16.2016.Google Scholar
Sheppard, L. D., & Vernon, P. A. (2008). Intelligence and speed of information-processing: A review of 50 years of research. Personality and Individual Differences, 44(3), 535–551. doi: 10.1016/j.paid.2007.09.015.Google Scholar
Troche, S. J., Merks, S., Houlihan, M. E., & Rammsayer, T. H. (2017). On the relation between mental ability and speed of information processing in the Hick task: An analysis of behavioral and electrophysiological speed measures. Personality and Individual Differences, 118, 11–16. doi: 10.1016/J.PAID.2017.02.027.Google Scholar
Vakhtin, A. A., Ryman, S. G., Flores, R. A., & Jung, R. E. (2014). Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence. Neuroimage, 103(0), 349–354. doi: 10.1016/j.neuroimage.2014.09.055.Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 7619–7624. doi: 10.1523/JNEUROSCI.1443-09.2009.Google Scholar

References

Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3, e17.Google Scholar
Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience, 26(1), 63–72.Google Scholar
Avena-Koenigsberger, A., Yan, X., Kolchinsky, A., van den Heuvel, M. P., Hagmann, P., & Sporns, O. (2019). A spectrum of routing strategies for brain networks. PLoS Computational Biology, 15, e1006833.CrossRefGoogle ScholarPubMed
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8-20.CrossRefGoogle ScholarPubMed
Barbey, A. K., Belli, T., Logan, A., Rubin, R., Zamroziewicz, M., & Operskalski, T. (2015). Network topology and dynamics in traumatic brain injury Current Opinion in Behavioral Sciences, 4, 92–102.Google Scholar
Barbey, A. K., Colom, R., & Grafman, J. (2013a). Architecture of cognitive flexibility revealed by lesion mapping. Neuroimage, 82, 547–554.Google Scholar
Barbey, A. K., Colom, R., & Grafman, J. (2013b). Dorsolateral prefrontal contributions to human intelligence. Neuropsychologia, 51(7), 1361–1369.CrossRefGoogle ScholarPubMed
Barbey, A. K., Colom, R., Paul, E. J., & Grafman, J. (2014). Architecture of fluid intelligence and working memory revealed by lesion mapping. Brain Structure and Function, 219(2), 485–494.Google Scholar
Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135(4), 1154–1164.CrossRefGoogle ScholarPubMed
Barbey, A. K., Koenigs, M., & Grafman, J. (2013c). Dorsolateral prefrontal contributions to human working memory. Cortex, 49(5), 1195–1205.Google Scholar
Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. Neuroscientist, 12(6), 512–523.Google Scholar
Bassett, D. S., & Bullmore, E. T. (2009). Human brain networks in health and disease. Current Opinion in Neurology, 22(4), 340–347.Google Scholar
Bassett, D. S., & Bullmore, E. T. (2017). Small-world brain networks revisited. Neuroscientist, 23(5), 499–516.Google Scholar
Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200–209.Google Scholar
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences USA, 108(18), 7641–7646.Google Scholar
Beggs, J. M. (2008). The criticality hypothesis: How local cortical networks might optimize information processing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Science, 366(1864), 329–343.Google Scholar
Bertolero, M. A., Yeo, B. T., & D’Esposito, M. (2015). The modular and integrative functional architecture of the human brain. Proceedings of the National Academy of Sciences USA, 112(49), E6798–6807.Google Scholar
Betzel, R. F., & Bassett, D. S. (2017). Multi-scale brain networks. Neuroimage, 160, 73–83.Google Scholar
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016). Optimally controlling the human connectome: the role of network topology. Science Reports, 6, 30770.Google Scholar
Betzel, R. F., Satterthwaite, T. D., Gold, J. I., & Bassett, D. S. (2016). A positive mood, a flexible brain. arXiv preprint.Google Scholar
Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance Medicine, 34(4), 537–541.Google Scholar
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., … Bassett, D. S. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proceedings of the National Academy of Sciences USA, 112(37), 11678–11683.Google Scholar
Breakspear, M. (2017). Dynamic models of large-scale brain activity. Nature Neuroscience, 20, 340–352.Google Scholar
Buchel, C., Coull, J. T., & Friston, K. J. (1999). The predictive value of changes in effective connectivity for human learning. Science, 283(5407), 1538–1541.CrossRefGoogle ScholarPubMed
Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., … Johnson, K. A. (2009). Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer’s disease. Journal of Neuroscience, 29(6), 1860–1873.Google Scholar
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186–198.Google Scholar
Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13, 336–349.Google Scholar
Byrge, L., Sporns, O., & Smith, L. B. (2014). Developmental process emerges from extended brain-body-behavior networks. Trends in Cognitive Sciences, 18(8), 395–403.Google Scholar
Cabral, J., Kringelbach, M. L., & Deco, G. (2017). Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms. Neuroimage, 160, 84–96.CrossRefGoogle Scholar
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press.Google Scholar
Cattell, R. B. (1971). Abilities: Their structure, growth, and action. Boston: Houghton Mifflin.Google Scholar
Chai, L. R., Khambhati, A. N., Ciric, R., Moore, T. M., Gur, R. C., Gur, R. E., … Bassett, D. S. (2017). Evolution of brain network dynamics in neurodevelopment. Network Neuroscience, 1(1), 14–30.Google Scholar
Christoff, K., Irving, Z. C., Fox, K. C., Spreng, R. N., & Andrews-Hanna, J. R. (2016). Mind-wandering as spontaneous thought: A dynamic framework. Nature Reviews Neuroscience, 17, 718–731.CrossRefGoogle ScholarPubMed
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204.Google Scholar
Cohen, J. R., & D’Esposito, M. (2016). The segregation and integration of distinct brain networks and their relationship to cognition. Journal of Neuroscience, 36, 12083–12094.Google Scholar
Cohen, J. R., Gallen, C. L., Jacobs, E. G., Lee, T. G., & D’Esposito, M. (2014). Quantifying the reconfiguration of intrinsic networks during working memory. PLoS One, 9, e106636.Google Scholar
Cole, M. W., Ito, T., & Braver, T. S. (2015). Lateral prefrontal cortex contributes to fluid intelligence through multinetwork connectivity. Brain Connectivity, 5(8), 497–504.Google Scholar
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16(9), 1348–1355.Google Scholar
Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 8988–8999.Google Scholar
Deco, G., & Corbetta, M. (2011). The dynamical balance of the brain at rest. Neuroscientist, 17(1), 107–123.Google Scholar
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2013). Resting brains never rest: Computational insights into potential cognitive architectures. Trends in Neurosciences, 36(5), 268–274.Google Scholar
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015). Rethinking segregation and integration: Contributions of whole-brain modelling. Nature Reviews Neuroscience, 16, 430–439.Google Scholar
Dosenbach, N. U., Fair, D. A., Cohen, A. L., Schlaggar, B. L., & Petersen, S. E. (2008). A dual-networks architecture of top-down control. Trends in Cognitive Sciences, 12(3), 99–105.Google Scholar
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172–179.Google Scholar
Duncan, J., Chylinski, D., Mitchell, D. J., & Bhandari, A. (2017). Complexity and compositionality in fluid intelligence. Proceedings of the National Academy of Sciences USA, 114(20), 5295–5299.Google Scholar
Duncan, J., & Owen, A. M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23(10), 475–483.Google Scholar
Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., … Emslie, H. (2000). A neural basis for general intelligence. Science, 289(5478), 457–460.Google Scholar
Eguiluz, V. M., Chialvo, D. R., Cecchi, G. A., Baliki, M., & Apkarian, A. V. (2005). Scale-free brain functional networks. Physical Review Letters, 94, 018102.Google Scholar
Finc, K., Bonna, K., He, X., Lydon-Staley, D. M., Kuhn, S., Duch, W., & Bassett, D. S. (2020). Dynamic reconfiguration of functional brain networks during working memory training. Nature Communications, 11, 2435.Google Scholar
Friedman, N. P., & Miyake, A. (2017). Unity and diversity of executive functions: Individual differences as a window on cognitive structure. Cortex, 86, 186–204.CrossRefGoogle ScholarPubMed
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11, 127–138.Google Scholar
Gallos, L. K., Makse, H. A., & Sigman, M. (2012). A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks. Proceedings of the National Academy of Sciences USA, 109(8), 2825–2830.Google Scholar
Girn, M., Mills, C., & Christoff, K. (2019). Linking brain network reconfiguration and intelligence: Are we there yet? Trends in Neuroscience and Education, 15, 62–70.Google Scholar
Glascher, J., Rudrauf, D., Colom, R., Paul, L. K., Tranel, D., Damasio, H., & Adolphs, R. (2010). Distributed neural system for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences USA, 107(10), 4705–4709.Google Scholar
Gordon, E. M., Stollstorff, M., & Vaidya, C. J. (2012). Using spatial multiple regression to identify intrinsic connectivity networks involved in working memory performance. Human Brain Mapping, 33(7), 1536–1552.Google Scholar
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.Google Scholar
Greene, A. S., Gao, S., Scheinost, D., & Constable, R. T. (2018). Task-induced brain state manipulation improves prediction of individual traits. Nature Communications, 9, 2807.CrossRefGoogle ScholarPubMed
Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., … Bassett, D. S. (2015). Controllability of structural brain networks. Nature Communications, 6, 8414.Google Scholar
Guimera, R., & Nunes Amaral, L. A. (2005). Functional cartography of complex metabolic networks. Nature, 433, 895–900.Google Scholar
Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V. J., Meuli, R., & Thiran, J. P. (2007). Mapping human whole-brain structural networks with diffusion MRI. PLoS One, 2, e597.Google Scholar
Haier, R. J., 2017. The neuroscience of intelligence. Cambridge University Press.Google Scholar
Haier, R. J., Siegel, B. V., Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., … Buchsbaum, M. S. (1988). Cortical glucose metabolic-rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12(2), 199–217.CrossRefGoogle Scholar
Hampshire, A., Highfield, R. R., Parkin, B. L., & Owen, A. M. (2012). Fractionating human intelligence. Neuron, 76(6), 1225–1237.Google Scholar
He, Y., Chen, Z. J., & Evans, A. C. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 17(10), 2407–2419.Google Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017a). Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 10–25.Google Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017b). Intelligence is associated with the modular structure of intrinsic brain networks. Science Reports, 7(1), 16088.Google Scholar
Hilger, K., Fukushima, M., Sporns, O., & Fiebach, C. J. (2020). Temporal stability of functional brain modules associated with human intelligence. Human Brain Mapping, 41(2), 362–372.Google Scholar
Jia, H., Hu, X., & Deshpande, G. (2014). Behavioral relevance of the dynamics of the functional brain connectome. Brain Connectivity, 4(9), 741–759.Google Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154; discussion 154–187.Google Scholar
Kovacs, K., & Conway, A. R. A. (2016). Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27(3), 151–177.Google Scholar
Kruschwitz, J., Waller, L., Daedelow, L., Walter, H., & Veer, I. (2018). General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set. Neuroimage, 171, 323–331.Google Scholar
Kucyi, A. (2018). Just a thought: How mind-wandering is represented in dynamic brain connectivity. Neuroimage, 180(Pt B), 505–514.Google Scholar
Langer, N., Pedroni, A., Gianotti, L. R., Hänggi, J., Knoch, D., & Jäncke, L. (2012). Functional brain network efficiency predicts intelligence. Human Brain Mapping, 33(6), 1393–1406.Google Scholar
Langer, N., Pedroni, A., & Jancke, L. (2013). The problem of thresholding in small-world network analysis. PLoS One, 8, e53199.Google Scholar
Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.CrossRefGoogle ScholarPubMed
Liang, X., Zou, Q., He, Y., & Yang, Y. (2016). Topologically reorganized connectivity architecture of default-mode, executive-control, and salience networks across working memory task loads. Cerebral Cortex, 26(4), 1501–1511.Google Scholar
Mattar, M. G., Betzel, R. F., & Bassett, D. S. (2016). The flexible brain. Brain, 139(8), 2110–2112.Google Scholar
McGrew, K. S., & Wendling, B. J. (2010). Cattell-Horn-Carroll cognitive-achievement relations: What we have learned from the past 20 years of research. Psychology in the Schools, 47(7), 651–675.Google Scholar
Meunier, D., Lambiotte, R., & Bullmore, E. T. (2010). Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience, 4, 200.Google Scholar
Mill, R. D., Ito, T., & Cole, M. W. (2017). From connectome to cognition: The search for mechanism in human functional brain networks. Neuroimage, 160, 124–139.Google Scholar
Park, H. J., & Friston, K. (2013). Structural and functional brain networks: From connections to cognition. Science, 342(6158), 1238411.Google Scholar
Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401.Google Scholar
Petermann, T., Thiagarajan, T. C., Lebedev, M. A., Nicolelis, M. A., Chialvo, D. R., & Plenz, D. (2009). Spontaneous cortical activity in awake monkeys composed of neuronal avalanches. Proceedings of the National Academy of Sciences USA, 106(37), 15921–15926.Google Scholar
Posner, M. I., & Barbey, A. K. (2020). General intelligence in the age of neuroimaging. Trends in Neuroscience and Education, 18, 100126.Google Scholar
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … Petersen, S. E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678.Google Scholar
Power, J. D., & Petersen, S. E. (2013). Control-related systems in the human brain. Current Opinion in Neurobiology, 23(2), 223–228.Google Scholar
Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013). Evidence for hubs in human functional brain networks. Neuron, 79(4), 798–813.Google Scholar
Ramón y Cajal, S., Pasik, P., & Pasik, T. (1999). Texture of the nervous system of man and the vertebrates. Wien: Springer.Google Scholar
Robinson, P. A., Henderson, J. A., Matar, E., Riley, P., & Gray, R. T. (2009). Dynamical reconnection and stability constraints on cortical network architecture. Physical Review Letters, 103, 108104.Google Scholar
Santarnecchi, E., Galli, G., Polizzotto, N. R., Rossi, A., & Rossi, S. (2014). Efficiency of weak brain connections support general cognitive functioning. Human Brain Mapping, 35(9), 4566–4582.Google Scholar
Schneidman, E., Berry, M. J., 2nd, Segev, R., & Bialek, W. (2006). Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440, 1007–1012.CrossRefGoogle Scholar
Schultz, D. H., & Cole, M. W. (2016). Higher intelligence is associated with less task-related brain network reconfiguration. Journal of Neuroscience, 36(33), 8551–8561.Google Scholar
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., … Poldrack, R. A. (2016). The dynamics of functional brain networks: Integrated network states during cognitive task performance. Neuron, 92(2), 544–554.Google Scholar
Simon, H. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467–482.Google Scholar
Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., … WU-Minn HCP Consortium, (2013). Resting-state fMRI in the Human Connectome Project. Neuroimage, 80, 144–168.Google Scholar
Song, M., Zhou, Y., Li, J., Liu, Y., Tian, L., Yu, C., & Jiang, T. (2008). Brain spontaneous functional connectivity and intelligence. Neuroimage, 41(3), 1168–1176.Google Scholar
Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8(9), 418–425.Google Scholar
Sporns, O., Tononi, G., & Edelman, G. M. (2000a). Connectivity and complexity: The relationship between neuroanatomy and brain dynamics. Neural Networks, 13(8–9), 909–922.Google Scholar
Sporns, O., Tononi, G., & Edelman, G. M. (2000b). Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex, 10(2), 127–141.CrossRefGoogle ScholarPubMed
St Jacques, P. L., Kragel, P. A., & Rubin, D. C. (2011). Dynamic neural networks supporting memory retrieval. Neuroimage, 57(2), 608–616.Google Scholar
Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews Neuroscience, 15, 683–695.Google Scholar
Stam, C. J., Jones, B. F., Nolte, G., Breakspear, M., & Scheltens, P. (2007). Small-world networks and functional connectivity in Alzheimer’s disease. Cerebral Cortex, 17(1), 92–99.Google Scholar
van den Heuvel, M. P., Mandl, R. C., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Human Brain Mapping, 30(10), 3127–3141.Google Scholar
van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. Journal of Neuroscience, 31(44), 15775–15786.Google Scholar
van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696.Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 7619–7624.Google Scholar
van der Maas, H. L., Dolan, C. V., Grasman, R. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842–861.Google Scholar
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.Google Scholar
Wirth, M., Jann, K., Dierks, T., Federspiel, A., Wiest, R., & Horn, H. (2011). Semantic memory involvement in the default mode network: A functional neuroimaging study using independent component analysis. Neuroimage, 54(4), 3057–3066.Google Scholar
Xiao, L., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. P. (2019). Alternating diffusion map based fusion of multimodal brain connectivity networks for IQ prediction. IEEE Transactions of Biomedical Engineering, 66(8), 2140–2151.Google Scholar
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014). Time-resolved resting-state brain networks. Proceedings of the National Academy of Sciences USA, 111(28), 10341–10346.Google Scholar
Zhang, J., Cheng, W., Liu, Z., Zhang, K., Lei, X., Yao, Y., … Feng, J. (2016). Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders. Brain, 139(8), 2307–2321.Google Scholar
Zuo, X. N., He, Y., Betzel, R. F., Colcombe, S., Sporns, O., & Milham, M. P. (2017). Human connectomics across the life span. Trends in Cognitive Sciences, 21(1), 32–45.Google Scholar

References

Barbey, A. K., Colom, R., Paul, E. J., & Grafman, J. (2014). Architecture of fluid intelligence and working memory revealed by lesion mapping. Brain Structure & Function, 219(2), 485–494. doi: 10.1007/s00429–013-0512-z.Google Scholar
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. doi: 10.1016/j.intell.2015.04.009.Google Scholar
Beckwith, L., & Parmelee, A. H. (1986). EEG patterns of preterm infants, home environment, and later IQ. Child Development, 57(3), 777–789. doi: 10.2307/1130354.Google Scholar
Bender, A. R., Prindle, J. J., Brandmaier, A. M., & Raz, N. (2015). White matter and memory in healthy adults: Coupled changes over two years. NeuroImage, 131, 193–204. doi: 10.1016/j.neuroimage.2015.10.085.Google Scholar
Bengtsson, S. L., Nagy, Z., Skare, S., Forsman, L., Forssberg, H., & Ullén, F. (2005). Extensive piano practicing has regionally specific effects on white matter development. Nature Neuroscience, 8(9), 1148–1150. doi: 10.1038/nn1516.Google Scholar
Borchers, L. R., Bruckert, L., Dodson, C. K., Travis, K. E., Marchman, V. A., Ben-Shachar, M., & Feldman, H. M. (2019). Microstructural properties of white matter pathways in relation to subsequent reading abilities in children: A longitudinal analysis. Brain Structure and Function, 224(2), 891–905.Google Scholar
Brans, R. G. H., Kahn, R. S., Schnack, H. G., van Baal, G. C. M., Posthuma, D., van Haren, N. E. M., … Pol, H. E. H. (2010). Brain plasticity and intellectual ability are influenced by shared genes. Journal of Neuroscience, 30(16), 5519–5524. doi: 10.1523/JNEUROSCI.5841-09.2010.Google Scholar
Burgaleta, M., Johnson, W., Waber, D. P., Colom, R., & Karama, S. (2014). Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents. NeuroImage, 84, 810–819. doi: 10.1016/j.neuroimage.2013.09.038.Google Scholar
Dai, X., Hadjipantelis, P., Wang, J. L., Deoni, S. C., & Müller, H. G. (2019). Longitudinal associations between white matter maturation and cognitive development across early childhood. Human Brain Mapping, 40(14), 4130–4145.Google Scholar
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201–211. doi: 10.1038/nrn2793.Google Scholar
Deoni, S. C. L., O’Muircheartaigh, J., Elison, J. T., Walker, L., Doernberg, E., Waskiewicz, N., … Jumbe, N. L. (2016). White matter maturation profiles through early childhood predict general cognitive ability. Brain Structure & Function, 221, 1189–1203. doi: 10.1007/s00429–014-0947-x.Google Scholar
Dickens, W. T., & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved. Psychological Review, 108(2), 346–369. doi: 10.1037//0033-295X.Google Scholar
Estrada, E., Ferrer, E., Román, F. J., Karama, S., & Colom, R. (2019). Time-lagged associations between cognitive and cortical development from childhood to early adulthood. Developmental Psychology, 55(6), 1338–1352. doi: 10.1037/dev0000716.Google Scholar
Evans, A. C., & Brain Development Cooperative Group. (2006). The NIH MRI study of normal brain development. Neuroimage, 30(1), 184–202.Google Scholar
Evans, T. M., Kochalka, J., Ngoon, T. J., Wu, S. S., Qin, S., Battista, C., & Menon, V. (2015). Brain structural integrity and intrinsic functional connectivity forecast 6 year longitudinal growth in children’s numerical abilities. Journal of Neuroscience, 35(33), 11743–11750. doi: 10.1523/JNEUROSCI.0216-15.2015.Google Scholar
Ferrer, E. (2018). Discrete- and semi-continuous time latent change score models of fluid reasoning development from childhood to adolescence. In Boker, S. M., Grimm, K. J., & Ferrer, E. (eds.), Longitudinal multivariate psychology (pp. 38–60). New York: Routledge.Google Scholar
Ferrer, E., & McArdle, J. J. (2004). An experimental analysis of dynamic hypotheses about cognitive abilities and achievement from childhood to early adulthood. Developmental Psychology, 40(6), 935–952.Google Scholar
Ferrer, E., Shaywitz, B. A., Holahan, J. M., Marchione, K., & Shaywitz, S. E. (2010). Uncoupling of reading and IQ over time: Empirical evidence for a definition of dyslexia. Psychological Science, 21(1), 93–101. doi: 10.1177/0956797609354084.Google Scholar
Ferrer, E., Whitaker, K. J., Steele, J. S., Green, C. T., Wendelken, C., & Bunge, S. A. (2013). White matter maturation supports the development of reasoning ability through its influence on processing speed. Developmental Science, 16(6), 941–951. doi: 10.1111/desc.12088.Google Scholar
Grimm, K. J., An, Y., McArdle, J. J., Zonderman, A. B., & Resnick, S. M. (2012). Recent changes leading to subsequent changes: Extensions of multivariate latent difference score models. Structural Equation Modeling: A Multidisciplinary Journal, 19(2), 268–292. doi: 10.1080/10705511.2012.659627.Google Scholar
Gross, C. (1995). Aristotle on the brain. The Neuroscientist, 1(4), 245–250. doi: 10.1177/107385849500100408.Google Scholar
Hahn, M., Joechner, A., Roell, J., Schabus, M., Heib, D. P., Gruber, G., … Hoedlmoser, K. (2019). Developmental changes of sleep spindles and their impact on sleep‐dependent memory consolidation and general cognitive abilities: A longitudinal approach. Developmental Science, 22(1), e12706. doi: 10.1111/desc.12706.Google Scholar
Huarte, J. (1594). Examen de ingenios. [The examination of mens wits]. Trans. Camilli, M. Camillo and Esquire, R. C.. London: Adam Islip, for C. Hunt of Excester.Google Scholar
Jaekel, J., Sorg, C., Baeuml, J., Bartmann, P., & Wolke, D. (2019). Head growth and intelligence from birth to adulthood in very preterm and term born individuals. Journal of the International Neuropsychological Society, 25(1), 48–56. doi: 10.1017/S135561771800084X.Google Scholar
Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.Google Scholar
Jones, D. K., Knösche, T. R., & Turner, R. (2013). White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. NeuroImage, 73, 239–254. doi: 10.1016/j.neuroimage.2012.06.081.Google Scholar
Judd, N., Sauce, B., Wiedenhoeft, J., Tromp, J., Chaarani, B., Schliep, A., … & Becker, A. (2020). Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment. Proceedings of the National Academy of Sciences, 117(22), 12411–12418.Google Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135. doi: 10.1017/S0140525X07001185.Google Scholar
Kail, R. V. (1998). Speed of information processing in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 20(1), 98–106. doi: 10.1076/jcen.20.1.98.1483.Google Scholar
Khundrakpam, B. S., Lewis, J. D., Reid, A., Karama, S., Zhao, L., Chouinard-Decorte, F., & Evans, A. C. (2017). Imaging structural covariance in the development of intelligence. NeuroImage, 144, 227–240. doi: 10.1016/j.neuroimage.2016.08.041.Google Scholar
Kievit, R. A., Brandmaier, A. M., Ziegler, G., Van Harmelen, A. L., de Mooij, S. M., Moutoussis, M., … & Lindenberger, U. (2018). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Developmental Cognitive Neuroscience, 33, 99–117.Google Scholar
Kievit, R. A., Hofman, A. D., & Nation, K. (2019). Mutualistic coupling between vocabulary and reasoning in young children: A replication and extension of the study by Kievit et al. (2017). Psychological Science, 30(8), 1245–1252. doi: 10.1177/0956797619841265.Google Scholar
Kievit, R. A., Lindenberger, U., Goodyer, I. M., Jones, P. B., Fonagy, P., Bullmore, E. T., … Dolan, R. J. (2017). Mutualistic coupling between vocabulary and reasoning supports cognitive development during late adolescence and early adulthood. Psychological Science, 28(10), 1419–1431.Google Scholar
Koenis, M. M. G., Brouwer, R. M., Swagerman, S. C., van Soelen, I. L. C., Boomsma, D. I., & Pol, H. E. H. (2018). Association between structural brain network efficiency and intelligence increases during adolescence. Human Brain Mapping, 39(2), 822–836. doi: 10.1002/hbm.23885.Google Scholar
Koenis, M. M. G., Brouwer, R. M., van den Heuvel, M. P., Mandl, R. C. W., van Soelen, I. L. C., Kahn, R. S., … Pol, H. E. H. (2015). Development of the brain’s structural network efficiency in early adolescence: A longitudinal DTI twin study. Human Brain Mapping, 36(12), 4938–4953. doi: 10.1002/hbm.22988.Google Scholar
Madsen, K. S., Johansen, L. B., Thompson, W. K., Siebner, H. R., Jernigan, T. L., & Baare, W. F. (2020). Maturational trajectories of white matter microstructure underlying the right presupplementary motor area reflect individual improvements in motor response cancellation in children and adolescents. NeuroImage, 220, 117105.Google Scholar
McArdle, J. J., Hamgami, F., Jones, K., Jolesz, F., Kikinis, R., Spiro, A., & Albert, M. S. (2004). Structural modeling of dynamic changes in memory and brain structure using longitudinal data from the normative aging study. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 59(6), P294–304. doi: 10.1093/GERONB/59.6.P294.Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency: Measures of brain activation versus measures of functional connectivity in the brain. Intelligence, 37(2), 223–229. doi: 10.1016/j.intell.2008.10.008.Google Scholar
Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U., & Bäckman, L. (2012). Memory aging and brain maintenance. Trends in Cognitive Sciences, 16(5), 292–305. doi: 10.1016/j.tics.2012.04.005.Google Scholar
Oschwald, J., Guye, S., Liem, F., Rast, P., Willis, S., Röcke, C., … Mérillat, S. (2019). Brain structure and cognitive ability in healthy aging: A review on longitudinal correlated change. Reviews in the Neurosciences, 31(1), 1–57. doi: 10.1515/revneuro-2018-0096.Google Scholar
Peng, P., & Kievit, R. A. (2020). The development of academic achievement and cognitive abilities: A bidirectional perspective. Child Development Perspectives, 14(1), 15–20. doi: 10.31219/osf.io/9u86q.Google Scholar
Peng, P., Wang, T., Wang, C., & Lin, X. (2019). A meta-analysis on the relation between fluid intelligence and reading/mathematics: Effects of tasks, age, and social economics status. Psychological Bulletin, 145(2), 189–236. doi: 10.1037/bul0000182.Google Scholar
Pfeifer, J. H., Allen, N. B., Byrne, M. L., & Mills, K. L. (2018). Modeling developmental change: Contemporary approaches to key methodological challenges in developmental neuroimaging. Developmental Cognitive Neuroscience, 33, 1–4. doi: 10.1016/j.dcn.2018.10.001.Google Scholar
Pietschnig, J., Penke, L., Wicherts, J. M., Zeiler, M., & Voracek, M. (2015). Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? Neuroscience & Biobehavioral Reviews, 57, 411–432. doi: 10.1016/j.neubiorev.2015.09.017.Google Scholar
Qi, T., Schaadt, G., & Friederici, A. D. (2019). Cortical thickness lateralization and its relation to language abilities in children. Developmental Cognitive Neuroscience, 39, 100704.Google Scholar
Ramsden, S., Richardson, F. M., Josse, G., Thomas, M. S. C., Ellis, C., Shakeshaft, C., … Price, C. J. (2011). Verbal and non-verbal intelligence changes in the teenage brain. Nature, 479(7371), 113–116. doi: 10.1038/nature10514.Google Scholar
Raz, N., & Lindenberger, U. (2011). Only time will tell: Cross-sectional studies offer no solution to the age–brain–cognition triangle: Comment on Salthouse (2011). Psycological Bulletin, 137(5), 790–795. doi: 10.1037/a0024503.Google Scholar
Ritchie, S. J., Quinlan, E. B., Banaschewski, T., Bokde, A. L., Desrivieres, S., Flor, H., … & Ittermann, B. (under review). Neuroimaging and genetic correlates of cognitive ability and cognitive development in adolescence. Psyarxiv, https://psyarxiv.com/8pwd6/Google Scholar
Rocca, J. (2009). Galen and the ventricular system. Journal of the History of the Neurosciences, 6(3), 227–239. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/09647049709525710?casa_token=uaaDpevYWpgAAAAA:YgJ2sfv80R1vUd6M0VIqfxFd6hkCxAsKhim1_Bt-ZuwPHteZ4Wmwah5FWBCINOkHCi3L97VL1zuDiqoGoogle Scholar
Román, F. J., Morillo, D., Estrada, E., Escorial, S., Karama, S., & Colom, R. (2018). Brain-intelligence relationships across childhood and adolescence: A latent-variable approach. Intelligence, 68, 21–29. doi: 10.1016/j.intell.2018.02.006.Google Scholar
Schmitt, J. E., Raznahan, A., Clasen, L. S., Wallace, G. L., Pritikin, J. N., Lee, N. R., … Neale, M. C. (2019). The dynamic associations between cortical thickness and general intelligence are genetically mediated. Cerebral Cortex, 29(11). doi: 10.1093/cercor/bhz007.Google Scholar
Schnack, H. G., van Haren, N. E. M., Brouwer, R. M., Evans, A., Durston, S., Boomsma, D. I., … Hulshoff Pol, H. E. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral Cortex, 25(6), 1608–1617. doi: 10.1093/cercor/bht357.Google Scholar
Selmeczy, D., Fandakova, Y., Grimm, K. J., Bunge, S. A., & Ghetti, S. (2019). Longitudinal trajectories of hippocampal and prefrontal contributions to episodic retrieval: Effects of age and puberty. Developmental Cognitive Neuroscience, 36, 100599.Google Scholar
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., … Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440(7084), 676–679. doi: 10.1038/nature04513.Google Scholar
Sowell, E. R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E., & Toga, A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. Journal of Neuroscience, 24(38), 8223–8231. doi: 10.1523/JNEUROSCI.1798-04.2004.Google Scholar
Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. doi: 10.2307/1412107.Google Scholar
Tamnes, C. K., Bos, M. G. N., van de Kamp, F. C., Peters, S., & Crone, E. A. (2018). Longitudinal development of hippocampal subregions from childhood to adulthood. Developmental Cognitive Neuroscience, 30, 212–222. doi: 10.1016/j.dcn.2018.03.009.Google Scholar
Tamnes, C. K., Walhovd, K. B., Dale, A. M., Østby, Y., Grydeland, H., Richardson, G., … Fjell, A. M. (2013). Brain development and aging: Overlapping and unique patterns of change. NeuroImage, 68, 63–74. doi: 10.1016/j.neuroimage.2012.11.039.Google Scholar
Tamnes, C. K., Walhovd, K. B., Grydeland, H., Holland, D., Østby, Y., Dale, A. M., & Fjell, A. M. (2013). Longitudinal working memory development is related to structural maturation of frontal and parietal cortices. Journal of Cognitive Neuroscience, 25(10), 1611–1623. doi: 10.1162/jocn_a_00434.Google Scholar
Thompkins, A. M., Deshpande, G., Waggoner, P., & Katz, J. S. (2016). Functional magnetic resonance imaging of the domestic dog: Research, methodology, and conceptual issues. Comparative Cognition & Behavior Reviews, 11, 63–82. doi: 10.3819/ccbr.2016.110004.Google Scholar
Van Der Maas, H. L., Dolan, C. V., Grasman, R. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842.Google Scholar
Volkow, N. D., Koob, G. F., Croyle, R. T., Bianchi, D. W., Gordon, J. A., Koroshetz, W. J., … Weiss, S. R. B. (2018). The conception of the ABCD study: From substance use to a broad NIH collaboration. Developmental Cognitive Neuroscience, 32, 4–7. doi: 10.1016/j.dcn.2017.10.002.Google Scholar
Wandell, B. A. (2016). Clarifying human white matter. Annual Review of Neuroscience, 39(1), 103–128.Google Scholar
Wendelken, C., Ferrer, E., Ghetti, S., Bailey, S. K., Cutting, L., & Bunge, S. A. (2017). Frontoparietal structural connectivity in childhood predicts development of functional connectivity and reasoning ability: A large-scale longitudinal investigation. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 37(35), 8549–8558. doi: 10.1523/JNEUROSCI.3726-16.2017.Google Scholar
Wenger, E., Brozzoli, C., Lindenberger, U., & Lövdén, M. (2017). Expansion and renormalization of human brain structure during skill acquisition. Trends in Cognitive Sciences, 21(12), 930–939. doi: 10.1016/j.tics.2017.09.008.Google Scholar
Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 10–18. doi: 10.1111/j.1750-8606.2009.00110.x.Google Scholar
Young, J. M., Morgan, B. R., Whyte, H. E. A., Lee, W., Smith, M. L., Raybaud, C., … Taylor, M. J. (2017). Longitudinal study of white matter development and outcomes in children born very preterm. Cerebral Cortex, 27(8), 4094–4105. doi: 10.1093/cercor/bhw221.Google Scholar

References

Anstey, K. J., Sargent-Cox, K., Garde, E., Cherbuin, N., & Butterworth, P. (2014). Cognitive development over 8 years in midlife and its association with cardiovascular risk factors. Neuropsychology, 28(4), 653–665.Google Scholar
Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: A meta-analysis. Psychonomic Bulletin & Review, 22(2), 366–377.Google Scholar
Bäckman, L., Nyberg, L., Soveri, A., Johansson, J., Andersson, M., Dahlin, E., … Rinne, J. O. (2011). Effects of working-memory training on striatal dopamine release. Science, 333(6043), 718.Google Scholar
Barbey, A. K., Colom, R., Paul, E. J., & Grafman, J. (2014). Architecture of fluid intelligence and working memory revealed by lesion mapping. Brain Structure and Function, 219(2), 485–494.Google Scholar
Basak, C., Qin, S., & O’Connell, M. A. (2020). Differential effects of cognitive training modules in healthy aging and mild cognitive impairment: A comprehensive meta-analysis of randomized controlled trials. Psychology and Aging, 35(2), 220–249.Google Scholar
Batista, A. X., Bazán, P. R., Conforto, A. B., Martins, M. da G. M., Hoshino, M., Simon, S. S., … Miotto, E. C. (2019). Resting state functional connectivity and neural correlates of face-name encoding in patients with ischemic vascular lesions with and without the involvement of the left inferior frontal gyrus. Cortex, 113, 15–28.Google Scholar
Bernard, J. A., & Seidler, R. D. (2012). Evidence for motor cortex dedifferentiation in older adults. Neurobiology of Aging, 33(9), 1890–1899.Google Scholar
Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009). Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral Cortex, 19(12), 2767–2796.Google Scholar
Buschkuehl, M., Jaeggi, S. M., & Jonides, J. (2012). Neuronal effects following working memory training. Developmental Cognitive Neuroscience, 2 (Supp 1), S167–S179.Google Scholar
Carp, J., Park, J., Hebrank, A., Park, D. C., & Polk, T. A. (2011). Age-related neural dedifferentiation in the motor system. PLoS One, 6(12), e29411.Google Scholar
Carretta, T. R., & Ree, M. J. (1995). Near identity of cognitive structure in sex and ethnic groups. Personality and Individual Differences, 19(2), 149–155.Google Scholar
Cattell, R. B. (1941). Some theoretical issues in adult intelligence testing. Psychological Bulletin, 38(592), 10.Google Scholar
Chan, M. Y., Park, D. C., Savalia, N. K., Petersen, S. E., & Wig, G. S. (2014). Decreased segregation of brain systems across the healthy adult lifespan. Proceedings of the National Academy of Sciences, 111(46), E4997–E5006.Google Scholar
Colom, R., Burgaleta, M., Román, F. J., Karama, S., Álvarez-Linera, J., Abad, F. J., … Haier, R. J. (2013). Neuroanatomic overlap between intelligence and cognitive factors: Morphometry methods provide support for the key role of the frontal lobes. NeuroImage, 72, 143–152.Google Scholar
Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215.Google Scholar
Deary, I. J., Pattie, A., & Starr, J. M. (2013). The stability of intelligence from age 11 to age 90 years: The Lothian Birth Cohort of 1921. Psychological Science, 24(12), 2361–2368.Google Scholar
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201–211.Google Scholar
Flynn, J. R. (1984). The mean IQ of Americans: Massive gains 1932 to 1978. Psychological Bulletin, 95(1), 29–51.Google Scholar
Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101(2), 171–191.Google Scholar
Galton, F. (1869). Hereditary genius: An inquiry into its laws and consequences. London: Macmillan and Co.Google Scholar
Gläscher, J., Tranel, D., Paul, L. K., Rudrauf, D., Rorden, C., Hornaday, A., … Adolphs, R. (2009). Lesion mapping of cognitive abilities linked to intelligence. Neuron, 61(5), 681–691.Google Scholar
Harrison, T. M., Maass, A., Baker, S. L., & Jagust, W. J. (2018). Brain morphology, cognition, and β-amyloid in older adults with superior memory performance. Neurobiology of Aging, 67, 162–170.Google Scholar
Hausknecht, J. P., Halpert, J. A., Di Paolo, N. T., & Moriarty Gerrard, M. O. (2007). Retesting in selection: A meta-analysis of coaching and practice effects for tests of cognitive ability. Journal of Applied Psychology, 92(2), 373–385.Google Scholar
Hedden, T., Schultz, A. P., Rieckmann, A., Mormino, E. C., Johnson, K. A., Sperling, R. A., & Buckner, R. L. (2016). Multiple brain markers are linked to age-related variation in cognition. Cerebral Cortex, 26(4), 1388–1400.Google Scholar
Horn, J., & Cattell, R. B. (1967). Age differences in fluid and crystallized intelligence. Acta Psychologica, 26, 107–129.Google Scholar
Hu, C., & Sale, M. E. (2003). A joint model for nonlinear longitudinal data with informative dropout. Journal of Pharmacokinetics and Pharmacodynamics, 30(1), 83–103.Google Scholar
Huang, C.-M., Polk, T. A., Goh, J. O., & Park, D. C. (2012). Both left and right posterior parietal activations contribute to compensatory processes in normal aging. Neuropsychologia, 50(1), 55–66.Google Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154.Google Scholar
Kim, S.-J., & Park, E. H. (2018). Relationship of working memory, processing speed, and fluid reasoning in psychiatric patients. Psychiatry Investigation, 15(12), 1154–1161.Google Scholar
Kremen, W. S., Beck, A., Elman, J. A., Gustavson, D. E., Reynolds, C. A., Tu, X. M., … Franz, C. E. (2019). Influence of young adult cognitive ability and additional education on later-life cognition. Proceedings of the National Academy of Sciences USA, 116(6), 2021–2026.Google Scholar
MacPherson, S. E., Cox, S. R., Dickie, D. A., Karama, S., Starr, J. M., Evans, A. C., … Deary, I. J. (2017). Processing speed and the relationship between Trail Making Test-B performance, cortical thinning and white matter microstructure in older adults. Cortex, 95, 92–103.Google Scholar
Martínez, K., Burgaleta, M., Román, F. J., Escorial, S., Shih, P. C., Quiroga, M. Á., & Colom, R. (2011). Can fluid intelligence be reduced to “simple” short-term storage? Intelligence, 39(6), 473–480.Google Scholar
McDonough, I. M., Bischof, G. N., Kennedy, K. M., Rodrigue, K. M., Farrell, M. E., & Park, D. C. (2016). Discrepancies between fluid and crystallized ability in healthy adults: A behavioral marker of preclinical Alzheimer’s disease. Neurobiology of Aging, 46, 68–75.Google Scholar
McDonough, I. M., Haber, S., Bischof, G. N., & Park, D. C. (2015). The Synapse Project: Engagement in mentally challenging activities enhances neural efficiency. Restorative Neurology and Neuroscience, 33(6), 865–882.Google Scholar
Miró-Padilla, A., Bueichekú, E., Ventura-Campos, N., Flores-Compañ, M.-J., Parcet, M. A., & Ávila, C. (2019). Long-term brain effects of N-back training: An fMRI study. Brain Imaging and Behavior, 13(4), 1115–1127.Google Scholar
Nyberg, L., & Pudas, S. (2019). Successful memory aging. Annual Review of Psychology, 70(1), 219–243.Google Scholar
Østby, Y., Tamnes, C. K., Fjell, A. M., & Walhovd, K. B. (2011). Morphometry and connectivity of the fronto-parietal verbal working memory network in development. Neuropsychologia, 49(14), 3854–3862.Google Scholar
O’Sullivan, M., Jones, D. K., Summers, P. E., Morris, R. G., Williams, S. C. R., & Markus, H. S. (2001). Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology, 57(4), 632–638.Google Scholar
Pacheco, J., Goh, J. O., Kraut, M. A., Ferrucci, L., & Resnick, S. M. (2015). Greater cortical thinning in normal older adults predicts later cognitive impairment. Neurobiology of Aging, 36(2), 903–908.Google Scholar
Park, D. C. (2019). Cognitive ability in old age is predetermined by age 20. Proceedings of the National Academy of Sciences USA, 116(6):1832–1833.Google Scholar
Park, D. C., & Bischof, G. N. (2013). The aging mind: Neuroplasticity in response to cognitive training. Dialogues in Clinical Neuroscience, 15(1), 109–119.Google Scholar
Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychology and Aging, 17(2), 299–293.Google Scholar
Park, D. C., Polk, T. A., Park, P. R., Minear, M., Savage, A., & Smith, M. R. (2004). Aging reduces neural specialization in ventral visual cortex. Proceedings of the National Academy of Sciences USA, 101(35), 13091–13095.Google Scholar
Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60(1), 173–196.Google Scholar
Pietschnig, J., & Voracek, M. (2015). One century of global IQ gains: A formal meta-analysis of the Flynn Effect (1909–2013). Perspectives on Psychological Science, 10(3), 282–306.Google Scholar
Raz, N., Ghisletta, P., Rodrigue, K. M., Kennedy, K. M., & Lindenberger, U. (2010). Trajectories of brain aging in middle-aged and older adults: Regional and individual differences. NeuroImage, 51(2), 501–511.Google Scholar
Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks, K. L., Fried, D. E., Hambrick, D. Z., … Engle, R. W. (2013). No evidence of intelligence improvement after working memory training: A randomized, placebo-controlled study. Journal of Experimental Psychology: General, 142(2), 359–379.Google Scholar
Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science, 17(3), 177–182.Google Scholar
Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychology Review, 24(3), 355–370.Google Scholar
Rieck, J. R., Rodrigue, K. M., Boylan, M. A., & Kennedy, K. M. (2017). Age-related reduction of BOLD modulation to cognitive difficulty predicts poorer task accuracy and poorer fluid reasoning ability. NeuroImage, 147, 262–271.Google Scholar
Roca, M., Parr, A., Thompson, R., Woolgar, A., Torralva, T., Antoun, N., … Duncan, J. (2010). Executive function and fluid intelligence after frontal lobe lesions. Brain, 133(1), 234–247.Google Scholar
Salthouse, T. A. (2014a). Correlates of cognitive change. Journal of Experimental Psychology: General, 143(3), 1026–1048.Google Scholar
Salthouse, T. A. (2014b). Selectivity of attrition in longitudinal studies of cognitive functioning. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69(4), 567–574.Google Scholar
Salthouse, T. A. (2016). Continuity of cognitive change across adulthood. Psychonomic Bulletin & Review, 23(3), 932–939.Google Scholar
Satz, P. (1993). Brain reserve capacity on symptom onset after brain injury: A formulation and review of evidence for threshold theory. Neuropsychology, 7(3), 273.Google Scholar
Scheller, E., Schumacher, L. V., Peter, J., Lahr, J., Wehrle, J., Kaller, C. P., … Klöppel, S. (2018). Brain aging and APOE ε4 interact to reveal potential neuronal compensation in healthy older adults. Frontiers in Aging Neuroscience, 10, 1–11.Google Scholar
Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “brain-training” programs work? Psychological Science in the Public Interest, 17(3), 103–186.Google Scholar
Spearman, C. (1904). “General Intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292.Google Scholar
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(3), 448–460.Google Scholar
Stern, Y., Arenaza-Urquijo, E. M., Bartrés-Faz, D., Belleville, S., Cantilon, M., Chetelat, G., … Vuoksimaa, E. (2018). Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer’s & Dementia, 16(9), 1305–1311.Google Scholar
Stern, Y., Gazes, Y., Razlighi, Q., Steffener, J., & Habeck, C. (2018). A task-invariant cognitive reserve network. NeuroImage, 178, 36–45.Google Scholar
Storsve, A. B., Fjell, A. M., Tamnes, C. K., Westlye, L. T., Overbye, K., Aasland, H. W., & Walhovd, K. B. (2014). Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: Regions of accelerating and decelerating change. Journal of Neuroscience, 34(25), 8488–8498.Google Scholar
Turner, G. R., & Spreng, R. N. (2015). Prefrontal engagement and reduced default network suppression co-occur and are dynamically coupled in older adults: The default–executive coupling hypothesis of aging. Journal of Cognitive Neuroscience, 27(12), 2462–2476.Google Scholar
Voss, M. W., Erickson, K. I., Chaddock, L., Prakash, R. S., Colcombe, S. J., Morris, K. S., … Kramer, A. F. (2008). Dedifferentiation in the visual cortex: An fMRI investigation of individual differences in older adults. Brain Research, 1 244, 121–131.Google Scholar
Yuan, P., Voelkle, M. C., & Raz, N. (2018). Fluid intelligence and gross structural properties of the cerebral cortex in middle-aged and older adults: A multi-occasion longitudinal study. NeuroImage, 172, 21–30.Google Scholar

References

Anokhin, A. P., Muller, V., Lindenberger, U., Heath, A. C., & Myers, E. (2006). Genetic influences on dynamic complexity of brain oscillations. Neuroscience Letters, 397(1–2), 93–98.Google Scholar
Anticevic, A., Cole, M. W., Murray, J. D., Corlett, P. R., Wang, X. J., & Krystal, J. H. (2012). The role of default network deactivation in cognition and disease. Trends in Cognitive Science, 16, 584–592.Google Scholar
Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press.Google Scholar
Baars, B. J., & Franklin, S. (2007). An architectural model of conscious and unconscious brain functions: Global Workspace Theory and IDA. Neural Networks, 20(9), 955–961.Google Scholar
Baars, B. J., Franklin, S., & Ramsoy, T. Z. (2013). Global workspace dynamics: Cortical “binding and propagation” enables conscious contents. Frontiers in Psychology, 4, 200.Google Scholar
Bar, M., Kassam, K. S., Ghuman, A. S., Boshyan, J., Schmid, A. M., Dale, A. M., … Halgren, E. (2006). Top-down facilitation of visual recognition. Proceedings of the National Academy of Sciences USA, 103(2), 449–454.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Science, 22(1), 8–20.Google Scholar
Beaty, R. E., Benedek, M., Kaufman, S. B., & Silvia, P. J. (2015). Default and executive network coupling supports creative idea production. Science Reports, 5, 10964.Google Scholar
Berridge, K. C., & Kringelbach, M. L. (2008). Affective neuroscience of pleasure: Reward in humans and animals. Psychopharmacology, 199(3), 457–480.Google Scholar
Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neurosciences, 26(9), 507–513.Google Scholar
Cabral, J., Hugues, E., Sporns, O., & Deco, G. (2011). Role of local network oscillations in resting-state functional connectivity. Neuroimage, 57(1), 130–139.Google Scholar
Cabral, J., Luckhoo, H., Woolrich, M., Joensson, M., Mohseni, H., Baker, A., … Deco, G. (2014). Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations. Neuroimage, 90, 423–435.Google Scholar
Cabral, J., Vidaurre, D., Marques, P., Magalhaes, R., Silva Moreira, P., Miguel Soares, J., … Kringelbach, M. L. (2017). Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Science Reports, 7, 5135.Google Scholar
Carhart-Harris, R. L. (2018). The entropic brain – Revisited. Neuropharmacology, 142, 167–178.Google Scholar
Carhart-Harris, R. L., Leech, R., Hellyer, P., Shanahan, M., Feilding, A., Tagliazucchi, E., … Nutt, D. (2014). The entropic brain: A theory of conscious states informed by neuroimaging research with psychedelic drugs. Frontiers in Human Neuroscience, 8, 20.Google Scholar
Chanes, L., & Barrett, L. F. (2016). Redefining the role of limbic areas in cortical processing. Trends in Cognitive Sciences, 20(2), 96–106.Google Scholar
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204.Google Scholar
Colom, R., Rebollo, I., Palacios, A., Juan-Espinosa, M., & Kyllonen, P. C. (2004). Working memory is (almost) perfectly predicted by g. Intelligence, 32(3), 277–296.Google Scholar
Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences, 7(12), 547–552.Google Scholar
Dayan, P., & Balleine, B. W. (2002). Reward, motivation, and reinforcement learning. Neuron, 36(2), 285–298.Google Scholar
De Vincenzo, I., Giannoccaro, I., Carbone, G., & Grigolini, P. (2017). Criticality triggers the emergence of collective intelligence in groups. Physical Review E, 96(2–1), 022309.Google Scholar
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201–211.Google Scholar
Deco, G., & Jirsa, V. K. (2012). Ongoing cortical activity at rest: Criticality, multistability, and ghost attractors. Journal of Neuroscience, 32(10), 3366–3375.Google Scholar
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature Reviews Neuroscience, 12(1), 43–56.Google Scholar
Deco, G., Jirsa, V. K., McIntosh, A. R., Sporns, O., & Kotter, R. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. Proceedings of the National Academy of Sciences USA, 106(25), 10302–10307.Google Scholar
Deco, G., & Kringelbach, M. L. (2014). Great expectations: Using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron, 84(5), 892–905.Google Scholar
Deco, G., & Kringelbach, M. L. (2016). Metastability and coherence: Extending the communication through coherence hypothesis using a whole-brain computational perspective. Trends in Neuroscience, 39(3), 125–135.Google Scholar
Deco, G., Kringelbach, M. L., Jirsa, V. K., & Ritter, P. (2017). The dynamics of resting fluctuations in the brain: Metastability and its dynamical cortical core. Science Reports, 7(1), 3095.Google Scholar
Deco, G., Ponce-Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014). How local excitation-inhibition ratio impacts the whole brain dynamics. Journal of Neuroscience, 34(23), 7886–7898.Google Scholar
Dehaene, S., & Changeux, J.-P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227.Google Scholar
Dehaene, S., Changeux, J.-P., Naccache, L., Sackur, J., & Sergent, C. (2006). Conscious, preconscious, and subliminal processing: A testable taxonomy. Trends in Cognitive Sciences, 10(5), 204–211.Google Scholar
Dehaene, S., Kerszberg, M., & Changeux, J.-P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. Proceedings of the National Academy of Sciences, 95(24), 14529.Google Scholar
Dehaene, S., & Naccache, L. (2001). Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework. Cognition, 79(1), 1–37.Google Scholar
Dennis, M., Francis, D. J., Cirino, P. T., Schachar, R., Barnes, M. A., & Fletcher, J. M. (2009). Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. Journal of the International Neuropsychological Society, 15(3), 331–343.Google Scholar
Dimitriadis, S. I., Laskaris, N. A., Simos, P. G., Micheloyannis, S., Fletcher, J. M., Rezaie, R., & Papanicolaou, A. C. (2013). Altered temporal correlations in resting-state connectivity fluctuations in children with reading difficulties detected via MEG. Neuroimage, 83, 307–317.Google Scholar
Duncan, J. (2013). The structure of cognition: Attentional episodes in mind and brain. Neuron, 80(1), 35–50.Google Scholar
Ferguson, M. A., Anderson, J. S., & Spreng, R. N. (2017). Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture. Network Neuroscience, 1(2), 192–207.Google Scholar
Fingelkurts, A. A., Fingelkurts, A. A., Rytsala, H., Suominen, K., Isometsa, E., & Kahkonen, S. (2007). Impaired functional connectivity at EEG alpha and theta frequency bands in major depression. Human Brain Mapping, 28(3), 247–261.Google Scholar
Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology: General, 137(2), 201–225.Google Scholar
Fries, P. (2005). A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence. Trends in Cognitive Science, 9(10), 474–480.Google Scholar
Friston, K. (1997). Transients, metastability, and neuronal dynamics. Neuroimage, 5(2), 164–171.Google Scholar
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.Google Scholar
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 364(1521), 1211–1221.Google Scholar
Fuster, J. M. (2005). Cortex and mind: Unifying cognition. Oxford University Press.Google Scholar
Gardner, H. (1984). Frames of mind: The theory of multiple intelligences. London: Heinemann.Google Scholar
Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009). The mismatch negativity: A review of underlying mechanisms. Clinical Neurophysiology, 120(3), 453–463.Google Scholar
Ghosh, A., Rho, Y., McIntosh, A. R., Kotter, R., & Jirsa, V. K. (2008). Noise during rest enables the exploration of the brain’s dynamic repertoire. PLoS Computational Biology, 4(10), e1000196.Google Scholar
Glascher, J. P., & O’Doherty, J. P. (2010). Model-based approaches to neuroimaging: Combining reinforcement learning theory with fMRI data. Wiley Interdisciplinary Reviews: Cognitive Science, 1(4), 501–510.Google Scholar
Grigolini, P., Piccinini, N., Svenkeson, A., Pramukkul, P., Lambert, D., & West, B. J. (2015). From neural and social cooperation to the global emergence of cognition. Frontiers in Bioengineering and Biotechnology, 3, 78.Google Scholar
Hansenne, M., & Bianchi, J. (2009). Emotional intelligence and personality in major depression: Trait versus state effects. Psychiatry Research, 166(1), 63–68.Google Scholar
Heggli, O. A., Cabral, J., Konvalinka, I., Vuust, P., & Kringelbach, M. L. (2019). A Kuramoto model of self-other integration across interpersonal synchronization strategies. PLoS Computational Biology, 15(10), e1007422. doi: 10.1371/journal.pcbi.1007422.Google Scholar
Honey, C. J., Kotter, R., Breakspear, M., & Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences USA, 104(24), 10240–10245.Google Scholar
Houde, O. (2010). Beyond IQ comparisons: Intra-individual training differences. Nature Reviews Neuroscience, 11(5), 370.Google Scholar
Hu, G., Huang, X., Jiang, T., & Yu, S. (2019). Multi-scale expressions of one optimal state regulated by dopamine in the prefrontal cortex. Frontiers in Physiology, 10, 113.Google Scholar
Huron, D. (2006). Sweet anticipation: Music and the psychology of expectation. Cambridge, MA: MIT Press.Google Scholar
Huron, D. (2016). Voice leading: The science behind a musical art. Cambridge, MA: MIT Press.Google Scholar
Johnson-Laird, P. N. (2001). Mental models and deduction. Trends in Cognitive Science, 5(10), 434–442.Google Scholar
Jung, R. E. & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154; discussion 154–187.Google Scholar
Kanai, R., Komura, Y., Shipp, S., & Friston, K. (2015). Cerebral hierarchies: Predictive processing, precision and the pulvinar. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 370(1668), 20140169.Google Scholar
Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin & Review, 9(4), 637–671.Google Scholar
Kitzbichler, M. G., Henson, R. N. A., Smith, M. L., Nathan, P. J., & Bullmore, E. T. (2011). Cognitive effort drives workspace configuration of human brain functional networks. The Journal of Neuroscience, 31(22), 8259.Google Scholar
Koelsch, S., Vuust, P., & Friston, K. (2019). Predictive processes and the peculiar case of music. Trends in Cognitive Sciences, 23(1), 63–77.Google Scholar
Koenen, K. C., Moffitt, T. E., Roberts, A. L., Martin, L. T., Kubzansky, L., Harrington, H., … Caspi, A. (2009). Childhood IQ and adult mental disorders: A test of the cognitive reserve hypothesis. American Journal of Psychiatry, 166(1), 50–57.Google Scholar
Konvalinka, I., Vuust, P., Roepstorff, A., & Frith, C. (2009). A coupled oscillator model of interactive tapping. Proceedings of the 7th Triennial Conference of European Society for the Cognitive Sciences of Music (ESCOM 2009), University of Jyväskylä, Jyväskylä, Finland, pp. 242–245.Google Scholar
Kringelbach, M. L., & Berridge, K. C. (2017). The affective core of emotion: Linking pleasure, subjective well-being, and optimal metastability in the brain. Emotion Review, 9(3), 191–199.Google Scholar
Kringelbach, M. L., McIntosh, A. R., Ritter, P., Jirsa, V. K., & Deco, G. (2015). The rediscovery of slowness: Exploring the timing of cognition. Trends in Cognitive Science, 19(10), 616–628.Google Scholar
Kringelbach, M. L., & Rapuano, K. M. (2016). Time in the orbitofrontal cortex. Brain, 139(4), 1010–1013.Google Scholar
Kringelbach, M. L., & Rolls, E. T. (2004). The functional neuroanatomy of the human orbitofrontal cortex: Evidence from neuroimaging and neuropsychology. Progress in Neurobiology, 72(5), 341–372.Google Scholar
Kuramoto, Y. (1975) Self-entrainment of a population of coupled non-linear oscillators. In Araki, H. (ed.), International symposium on mathematical problems in theoretical physics. Lecture Notes in Physics, vol 39. (pp. 420–422). Berlin, Heidelberg: Springer. doi: 10.1007/BFb0013365.Google Scholar
Landro, N. I., Stiles, T. C., & Sletvold, H. (2001). Neuropsychological function in nonpsychotic unipolar major depression. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 14(4), 233–240.Google Scholar
Lee, W. H., Doucet, G. E., Leibu, E., & Frangou, S. (2018). Resting-state network connectivity and metastability predict clinical symptoms in schizophrenia. Schizophrenia Research, 201, 208–216.Google Scholar
Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137(Pt. 1), 12–32.Google Scholar
Li, Y., Vanni-Mercier, G., Isnard, J., Mauguière, F., & Dreher, J.-C. (2016). The neural dynamics of reward value and risk coding in the human orbitofrontal cortex. Brain, 139(4), 1295–1309. doi: 10.1093/brain/awv409.Google Scholar
Liang, S., Brown, M. R. G., Deng, W., Wang, Q., Ma, X., Li, M., … Li, T. (2018). Convergence and divergence of neurocognitive patterns in schizophrenia and depression. Schizophrenia Research, 192, 327–334.Google Scholar
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., … Smallwood, J. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences USA, 113(44), 12574–12579.Google Scholar
Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15(10), 483–506.Google Scholar
Mesulam, M. M. (1998). From sensation to cognition. Brain: A Journal of Neurology, 121(6), 1013–1052.Google Scholar
Näätänen, R., Gaillard, A. W. K., & Mäntysalo, S. (1978). Early selective-attention effect on evoked potential reinterpreted. Acta Psychologica, 42(4), 313–329.Google Scholar
Näätänen, R., Paavilainen, P., Rinne, T., & Alho, K. (2007). The mismatch negativity (MMN) in basic research of central auditory processing: a review. Clinical Neurophysiology, 118(12), 2544–2590.Google Scholar
Niv, Y., & Schoenbaum, G. (2008). Dialogues on prediction errors. Trends in Cognitive Science, 12(7), 265–272.Google Scholar
O’Doherty, J. P. (2004). Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14(6), 769–776.Google Scholar
Pamplona, G. S., Santos Neto, G. S., Rosset, S. R., Rogers, B. P., & Salmon, C. E. (2015). Analyzing the association between functional connectivity of the brain and intellectual performance. Frontiers in Human Neuroscience, 9, 61.Google Scholar
Parkinson, C., & Wheatley, T. (2015). The repurposed social brain. Trends in Cognitive Science, 19(3), 133–141.Google Scholar
Pearce, M. T., & Wiggins, G. A. (2012). Auditory expectation: The information dynamics of music perception and cognition. Topics in Cognitive Science, 4(4), 625–652.Google Scholar
Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87.Google Scholar
Ravnkilde, B., Videbech, P., Clemmensen, K., Egander, A., Rasmussen, N. A., & Rosenberg, R. (2002). Cognitive deficits in major depression. Scandinavian Journal of Psychology, 43(3), 239–251.Google Scholar
Reinhart, R. M. G., & Nguyen, J. A. (2019). Working memory revived in older adults by synchronizing rhythmic brain circuits. Nature Neuroscience, 22(5), 820–827.Google Scholar
Rohrmeier, M. A., & Koelsch, S. (2012). Predictive information processing in music cognition. A critical review. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 83(2), 164–175.Google Scholar
Rolls, E. T. (2010). Attractor networks. Wiley Interdisciplinary Reviews: Cognitive Science, 1(1), 119–134.Google Scholar
Rolls, E. T., Loh, M., Deco, G., & Winterer, G. (2008). Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nature Reviews Neuroscience, 9(9), 696.Google Scholar
Roth, G., Dicke, U. (2005). Evolution of the brain and intelligence. Trends in Cognitive Science, 9(5), 250–257.Google Scholar
Sackeim, H. A., Freeman, J., McElhiney, M., Coleman, E., Prudic, J., & Devanand, D. P. (1992). Effects of major depression on estimates of intelligence. Journal of Clinical and Experimental Neuropsychology, 14(2), 268–288.Google Scholar
Sanger, J., Muller, V., & Lindenberger, U. (2012). Intra- and interbrain synchronization and network properties when playing guitar in duets. Frontiers in Human Neuroscience, 6, 312.Google Scholar
Santarnecchi, E., Galli, G., Polizzotto, N. R., Rossi, A., & Rossi, S. (2014). Efficiency of weak brain connections support general cognitive functioning. Human Brain Mapping, 35(9), 4566–4582.Google Scholar
Schacter, D. L., Addis, D. R., & Buckner, R. L. (2007). Remembering the past to imagine the future: The prospective brain. Nature Reviews Neuroscience, 8(9), 657–661.Google Scholar
Schultz, W. (2015). Neuronal reward and decision signals: From theories to data. Physiological Review, 95(3), 853–951.CrossRefGoogle ScholarPubMed
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599.Google Scholar
Schultz, W., & Dickinson, A. (2000). Neuronal coding of prediction errors. Annual Review of Neuroscience, 23(1), 473–500.Google Scholar
Shen, X., Cox, S. R., Adams, M. J., Howard, D. M., Lawrie, S. M., Ritchie, S. J., … Whalley, H. C. (2018). Resting-state connectivity and its association with cognitive performance, educational attainment, and household income in the UK biobank. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(10), 878–886.Google Scholar
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., … Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484.Google Scholar
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., … Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359.Google Scholar
Singer, W. (2001). Consciousness and the binding problem. Annals of the New York Academy of Sciences, 929, 123–146.Google Scholar
Song, M., Zhou, Y., Li, J., Liu, Y., Tian, L., Yu, C., & Jiang, T. (2008). Brain spontaneous functional connectivity and intelligence. Neuroimage, 41(3), 1168–1176.Google Scholar
Stam, C. J., & van Straaten, E. C. (2012). The organization of physiological brain networks. Clinical Neurophysiology, 123(6), 1067–1087.Google Scholar
Steinbeck, J., & Ricketts, E. F. (1941). The log from the sea of Cortez. London: Penguin.Google Scholar
Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028.Google Scholar
Stone, A. A., Schwartz, J. E., Broderick, J. E., & Deaton, A. (2010). A snapshot of the age distribution of psychological well-being in the United States. Proceedings of the National Academy of Sciences of the United States of America, 107(22), 9985–9990.Google Scholar
Thorndike, E. L. (1920). Intelligence and its uses. Harper’s Magazine, 140, 227–235.Google Scholar
Tognoli, E., & Kelso, J. A. (2014). The metastable brain. Neuron, 81(1), 35–48.Google Scholar
Turalska, M., Geneston, E., West, B. J., Allegrini, P., & Grigolini, P. (2012). Cooperation-induced topological complexity: A promising road to fault tolerance and Hebbian learning. Frontiers in Physiology, 3, 52.Google Scholar
Turalska, M., West, B. J., & Grigolini, P. (2013). Role of committed minorities in times of crisis. Science Reports, 3, 1371.Google Scholar
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519–534.Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 7619–7624.Google Scholar
Veiel, H. O. (1997). A preliminary profile of neuropsychological deficits associated with major depression. Journal of Clinical and Experimental Neuropsychology, 19(4), 587–603.Google Scholar
Voytek, B., & Knight, R. T. (2015). Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biological Psychiatry, 77(12), 1089–1097.Google Scholar
Vuust, P., & Frith, C. D. (2008). Anticipation is the key to understanding music and the effects of music on emotion. Behavioral and Brain Sciences, 31(5), 599–600.Google Scholar
Vuust, P., & Kringelbach, M. L. (2010). The pleasure of making sense of music. Interdisciplinary Science Reviews, 35(2), 166–182.Google Scholar
Werner, G. (2007). Metastability, criticality and phase transitions in brain and its models. Biosystems, 90(2), 496–508.Google Scholar
Zakzanis, K. K., Leach, L., & Kaplan, E. (1998). On the nature and pattern of neurocognitive function in major depressive disorder. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 11(3), 111–119.Google Scholar
Zylberberg, A., Dehaene, S., Roelfsema, P. R., & Sigman, M. (2011). The human Turing machine: A neural framework for mental programs. Trends in Cognitive Sciences, 15(7), 293–300.Google Scholar

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