Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-f7d5f74f5-4fqgl Total loading time: 0 Render date: 2023-10-05T03:59:39.334Z Has data issue: false Feature Flags: { "corePageComponentGetUserInfoFromSharedSession": true, "coreDisableEcommerce": false, "coreDisableSocialShare": false, "coreDisableEcommerceForArticlePurchase": false, "coreDisableEcommerceForBookPurchase": false, "coreDisableEcommerceForElementPurchase": false, "coreUseNewShare": true, "useRatesEcommerce": true } hasContentIssue false

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
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Cambridge Handbook of Intelligence and Cognitive Neuroscience , pp. 83 - 188
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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.CrossRefGoogle ScholarPubMed
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
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Friston, K. J. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. doi: 10.1038/nrn2787.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Hendrickson, D. E., & Hendrickson, A. E. (1980). The biological basis of individual differences in intelligence. Personality and Individual Differences, 1(1), 3–33.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed

References

Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3, e17.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Barbey, A. K., Colom, R., & Grafman, J. (2013a). Architecture of cognitive flexibility revealed by lesion mapping. Neuroimage, 82, 547–554.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. Neuroscientist, 12(6), 512–523.CrossRefGoogle ScholarPubMed
Bassett, D. S., & Bullmore, E. T. (2009). Human brain networks in health and disease. Current Opinion in Neurology, 22(4), 340–347.CrossRefGoogle ScholarPubMed
Bassett, D. S., & Bullmore, E. T. (2017). Small-world brain networks revisited. Neuroscientist, 23(5), 499–516.CrossRefGoogle ScholarPubMed
Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200–209.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Betzel, R. F., & Bassett, D. S. (2017). Multi-scale brain networks. Neuroimage, 160, 73–83.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Breakspear, M. (2017). Dynamic models of large-scale brain activity. Nature Neuroscience, 20, 340–352.CrossRefGoogle ScholarPubMed
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.Google 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.CrossRefGoogle ScholarPubMed
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186–198.CrossRefGoogle ScholarPubMed
Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13, 336–349.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Deco, G., & Corbetta, M. (2011). The dynamical balance of the brain at rest. Neuroscientist, 17(1), 107–123.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Guimera, R., & Nunes Amaral, L. A. (2005). Functional cartography of complex metabolic networks. Nature, 433, 895–900.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Haier, R. J., 2017. The neuroscience of intelligence. Cambridge University Press.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Jia, H., Hu, X., & Deshpande, G. (2014). Behavioral relevance of the dynamics of the functional brain connectome. Brain Connectivity, 4(9), 741–759.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle Scholar
Kucyi, A. (2018). Just a thought: How mind-wandering is represented in dynamic brain connectivity. Neuroimage, 180(Pt B), 505–514.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Langer, N., Pedroni, A., & Jancke, L. (2013). The problem of thresholding in small-world network analysis. PLoS One, 8, e53199.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Mattar, M. G., Betzel, R. F., & Bassett, D. S. (2016). The flexible brain. Brain, 139(8), 2110–2112.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Meunier, D., Lambiotte, R., & Bullmore, E. T. (2010). Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience, 4, 200.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Park, H. J., & Friston, K. (2013). Structural and functional brain networks: From connections to cognition. Science, 342(6158), 1238411.CrossRefGoogle Scholar
Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Posner, M. I., & Barbey, A. K. (2020). General intelligence in the age of neuroimaging. Trends in Neuroscience and Education, 18, 100126.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Power, J. D., & Petersen, S. E. (2013). Control-related systems in the human brain. Current Opinion in Neurobiology, 23(2), 223–228.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Ramón y Cajal, S., Pasik, P., & Pasik, T. (1999). Texture of the nervous system of man and the vertebrates. Wien: Springer.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews Neuroscience, 15, 683–695.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. Journal of Neuroscience, 31(44), 15775–15786.CrossRefGoogle ScholarPubMed
van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed

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.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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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 ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Evans, A. C., & Brain Development Cooperative Group. (2006). The NIH MRI study of normal brain development. Neuroimage, 30(1), 184–202.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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 ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Gross, C. (1995). Aristotle on the brain. The Neuroscientist, 1(4), 245–250. doi: 10.1177/107385849500100408.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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 ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Qi, T., Schaadt, G., & Friederici, A. D. (2019). Cortical thickness lateralization and its relation to language abilities in children. Developmental Cognitive Neuroscience, 39, 100704.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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-ZuwPHteZ4Wmwah5FWBCINOkHCi3L97VL1zuDiqoCrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. doi: 10.2307/1412107.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Wandell, B. A. (2016). Clarifying human white matter. Annual Review of Neuroscience, 39(1), 103–128.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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 ScholarPubMed

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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Bernard, J. A., & Seidler, R. D. (2012). Evidence for motor cortex dedifferentiation in older adults. Neurobiology of Aging, 33(9), 1890–1899.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Buschkuehl, M., Jaeggi, S. M., & Jonides, J. (2012). Neuronal effects following working memory training. Developmental Cognitive Neuroscience, 2 (Supp 1), S167–S179.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201–211.CrossRefGoogle ScholarPubMed
Flynn, J. R. (1984). The mean IQ of Americans: Massive gains 1932 to 1978. Psychological Bulletin, 95(1), 29–51.CrossRefGoogle Scholar
Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101(2), 171–191.CrossRefGoogle Scholar
Galton, F. (1869). Hereditary genius: An inquiry into its laws and consequences. London: Macmillan and Co.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Horn, J., & Cattell, R. B. (1967). Age differences in fluid and crystallized intelligence. Acta Psychologica, 26, 107–129.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Nyberg, L., & Pudas, S. (2019). Successful memory aging. Annual Review of Psychology, 70(1), 219–243.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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 ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60(1), 173–196.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science, 17(3), 177–182.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Salthouse, T. A. (2014a). Correlates of cognitive change. Journal of Experimental Psychology: General, 143(3), 1026–1048.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Salthouse, T. A. (2016). Continuity of cognitive change across adulthood. Psychonomic Bulletin & Review, 23(3), 932–939.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Spearman, C. (1904). “General Intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Stern, Y., Gazes, Y., Razlighi, Q., Steffener, J., & Habeck, C. (2018). A task-invariant cognitive reserve network. NeuroImage, 178, 36–45.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed