Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-05T05:07:28.792Z Has data issue: false hasContentIssue false

Part I - Fundamental Issues

Published online by Cambridge University Press:  11 June 2021

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

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
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

Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2005). Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131(1), 3060.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 820.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, 1027.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), 517528.CrossRefGoogle Scholar
Benedek, M., Jauk, E., Sommer, M., Arendasy, M., & Neubauer, A. C. (2014). Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in intelligence and creativity, Intelligence, 46, 7383.Google Scholar
Bezzola, L., Mérillat, S., Gaser, C., & Jäncke, L. (2011). Training-induced neural plasticity in golf novices. Journal of Neuroscience, 31(35), 1244412448.Google Scholar
Binet, A., & Simon, T. (1916). The development of intelligence in children. Baltimore, MD: Williams & Wilkins (reprinted 1973, New York: Arno Press).Google Scholar
Blum, D., & Holling, H. (2017). Spearman’s law of diminishing returns. A meta-analysis. Intelligence, 65, 6066.CrossRefGoogle Scholar
Bouchard, T. J. (1997). Experience producing drive theory: How genes drive experience and shape personality. Acta Paediatrica, 86(Suppl. 422), 6064.Google Scholar
Brown, K. G., Le, H., & Schmidt, F. L. (2006). Specific aptitude theory revisited: Is there incremental validity for training performance? International Journal of Selection and Assessment, 14(2), 87100.Google Scholar
Brown, R. E. (2016) Hebb and Cattell: The genesis of the theory of fluid and crystallized intelligence. Frontiers in Human Neuroscience, 10, 111.Google Scholar
Canivez, G. L., & Watkins, M. W. (2010). Exploratory and higher-order factor analyses of the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) adolescent subsample. School Psychology Quarterly, 25(4), 223235.CrossRefGoogle Scholar
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 122.CrossRefGoogle Scholar
Cattell, R. B. (1987). Intelligence: Its structure, growth and action. New York: North-Holland.Google Scholar
Colom, R., Haier, R. J., Head, K., Álvarez-Linera, J., Ángeles Quiroga, M., Chun Shih, P., & Jung, R. E. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence, 37(2), 124135.CrossRefGoogle Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006a). Finding the g-factor in brain structure using the method of correlated vectors. Intelligence, 34(6), 561570.Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006b). Distributed brain sites for the g-factor of intelligence. Neuroimage, 31(3), 13591365.CrossRefGoogle ScholarPubMed
Colom, R., Rebollo, I., Palacios, A., Juan-Espinosa, M., & Kyllonen, P. C. (2004). Working memory is (almost) perfectly predicted by g. Intelligence, 32(3), 277296.Google Scholar
Coyle, T. R. (2013). Effects of processing speed on intelligence may be underestimated: Comment on Demetriou et al. (2013). Intelligence, 41(5), 732734.CrossRefGoogle Scholar
Coyle, T. R. (2015). Relations among general intelligence (g), aptitude tests, and GPA: Linear effects dominate. Intelligence, 53, 1622.Google Scholar
Coyle, T. R. (2018a). Non-g factors predict educational and occupational criteria: More than g. Journal of Intelligence, 6(3), 115.Google Scholar
Coyle, T. R. (2018b). Non-g residuals of group factors predict ability tilt, college majors, and jobs: A non-g nexus. Intelligence, 67, 1925.Google Scholar
Coyle, T. R. (2019). Tech tilt predicts jobs, college majors, and specific abilities: Support for investment theories. Intelligence, 75, 3340.Google Scholar
Coyle, T. R., Elpers, K. E., Gonazalez, M. C., Freeman, J., & Baggio, J. A. (2018). General intelligence (g), ACT scores, and theory of mind: ACT(g) predicts limited variance among theory of mind tests. Intelligence, 71, 8591.Google Scholar
Coyle, T. R., & Pillow, D. R. (2008). SAT and ACT predict college GPA after removing g. Intelligence, 36(6), 719729.Google Scholar
Coyle, T. R., Purcell, J. M., Snyder, A. C., & Kochunov, P. (2013). Non-g residuals of the SAT and ACT predict specific abilities. Intelligence, 41(2), 114120.CrossRefGoogle Scholar
Coyle, T. R., Snyder, A. C., Richmond, M. C., & Little, M. (2015). SAT non-g residuals predict course specific GPAs: Support for investment theory. Intelligence, 51, 5766.Google Scholar
Deary, I. J., Egan, V., Gibson, G. J., Brand, C. R., Austin, E., & Kellaghan, T. (1996). Intelligence and the differentiation hypothesis. Intelligence, 23(2), 105132.CrossRefGoogle Scholar
Deary, I. J., Ferguson, K. J., Bastin, M. E., Barrow, G. W. S., Reid, L. M., Seckl, J. R., … MacLullich, A. M. J. (2007). Skull size and intelligence, and King Robert Bruce’s IQ. Intelligence, 35(6), 519525.Google Scholar
Frey, M. C., & Detterman, D. K. (2004). Scholastic assessment or g? The relationship between the scholastic assessment test and general cognitive ability. Psychological Science, 15(6), 373378.Google Scholar
Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., DeFries, J. C., & Hewitt, J. K. (2006). Not all executive functions are related to intelligence. Psychological Science, 17(2), 172179.Google Scholar
Gardner, H. (1983/2003). Frames of mind. The theory of multiple intelligences. New York: Basic Books.Google Scholar
Gerber, P., Schlaffke, L., Heba, S., Greenlee, M. W., Schultz, T., & Schmidt-Wilcke, T. (2014). Juggling revisited – A voxel-based morphometry study with expert jugglers. Neuroimage, 95, 320325.Google Scholar
Gignac, G. E. (2015). Raven’s is not a pure measure of general intelligence: Implications for g factor theory and the brief measurement of g. Intelligence, 52, 7179.CrossRefGoogle Scholar
Gignac, G. E., & Bates, T. C. (2017). Brain volume and intelligence: The moderating role of intelligence measurement quality. Intelligence, 64, 1829.CrossRefGoogle Scholar
Gignac, G., Vernon, P. A., & Wickett, J. C. (2003). Factors influencing the relationship between brain size and intelligence. In Nyborg, H. (ed.), The scientific study of general intelligence: Tribute to Arthur R. Jensen (pp. 93106). New York: Pergamon.CrossRefGoogle Scholar
Gignac, G. E., & Watkins, M. W. (2015). There may be nothing special about the association between working memory capacity and fluid intelligence. Intelligence, 52, 1823.Google Scholar
Gladwell, M. (2008). Outliers: The story of success. New York: Little, Brown & Co.Google Scholar
Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography. Intelligence, 24(1), 1323.Google Scholar
Haier, R. J. (2017). The neuroscience of intelligence. New York: Cambridge University Press.Google Scholar
Haier, R. J. & Jung, R. E. (2007). Beautiful minds (i.e., brains) and the neural basis of intelligence. Behavioral and Brain Sciences, 30(2), 174178.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), 199217.CrossRefGoogle Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavior & Brain Sciences, 33(2–3), 6183.CrossRefGoogle ScholarPubMed
Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallized general intelligences. Journal of Educational Psychology, 57(5), 253270.Google Scholar
Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.Google Scholar
Jensen, A. R. (2006). Clocking the mind: Mental chronometry and individual differences. Amsterdam, The Netherlands: Elsevier.Google Scholar
Johnson, W., Bouchard, T. J. Jr, Krueger, R. F., McGue, M., & Gottesman, I. I. (2004). Just one g: Consistent results from three test batteries. Intelligence, 32(1), 95107.Google Scholar
Johnson, W., te Nijenhuis, J., & Bouchard, T. J. Jr. (2008). Still just 1 g: Consistent results from five test batteries. Intelligence, 36(1), 8195.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), 135154.CrossRefGoogle ScholarPubMed
Kuncel, N. R., & Hezlett, S. A. (2007). Standardized tests predict graduate students’ success. Science, 315(5815), 10801081.Google Scholar
Lee, J. J., Wedow, R., Okbay, A., Kong, O., Maghzian, M., Zacher, M., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment. Nature Genetics, 50(8), 11121121.CrossRefGoogle ScholarPubMed
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), 117.CrossRefGoogle ScholarPubMed
Lubinski, D. (2016). From Terman to today: A century of findings on intellectual precocity. Review of Educational Research, 86(4), 900944.Google Scholar
Major, J. T., Johnson, W., & Bouchard, T. J. (2011). The dependability of the general factor of intelligence: Why small, single-factor models do not adequately represent g. Intelligence, 39(5), 418433.CrossRefGoogle Scholar
McDaniel, M. A. (2005). Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence, 33(4), 337346.Google Scholar
McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 110.Google Scholar
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49100.Google Scholar
Nave, G., Jung, W. H., Linnér, R. K., Kable, J. W., & Koellinger, P. D. (2019). Are bigger brains smarter? Evidence from a large-scale preregistered study. Psychological Science, 30(1), 4354.Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33(7), 10041023.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 and Biobehavioral Reviews, 57, 411432.Google Scholar
Ree, M. J., Earles, J. A., & Teachout, M. S. (1994). Predicting job performance: Not much more than g. Journal of Applied Psychology, 79(4), 518524.Google Scholar
Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades: A meta-analysis. Intelligence, 53, 118137.Google Scholar
Sackett, P. R., Kuncel, N. R., Arneson, J. J., Cooper, S. R., & Waters, S. D. (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance? Psychological Bulletin, 135(1), 122.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), 45664582.Google Scholar
Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype ➔ environment effects. Child Development, 54(2), 424435.Google Scholar
Scarr-Salapatek, S. (1971). Race, social class, and IQ. Science, 174(4016), 12851295.CrossRefGoogle ScholarPubMed
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262274.Google Scholar
Schmidt, F. L., & Hunter, J. E. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86(1), 162173.CrossRefGoogle ScholarPubMed
Spearman, C. (1927). The abilities of man: Their nature and measurement. New York: The Macmillan Company.Google Scholar
Taubert, M., Draganski, B., Anwander, A., Müller, K., Horstmann, A., Villringer, A., & Ragert, P. (2010). Dynamic properties of human brain structure: Learning-related changes in cortical areas and associated fiber connections. Journal of Neuroscience, 30(35), 1167011677.Google Scholar
Thomas, C., & Baker, C. I. (2013). Teaching an adult brain new tricks: A critical review of evidence for training-dependent structural plasticity in humans. Neuroimage, 73, 225236.Google Scholar
Thorndike, R. L. (1984). Intelligence as information processing: The mind and the computer. Bloomington, IN: Center on Evaluation, Development, and Research.Google Scholar
Tucker-Drob, E. M. (2009). Differentiation of cognitive abilities across the life span. Developmental Psychology, 45(4), 10971118.Google Scholar
Tucker-Drob, E. M., & Bates, T. C. (2015). Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychological Science, 27(2), 138149.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), 76197624.CrossRefGoogle ScholarPubMed
Warne, R. T., & Burningham, C. (2019). Spearman’s g found in 31 non-Western nations: Strong evidence that g is a universal phenomenon. Psychological Bulletin, 145(3), 237272.Google Scholar
Wechsler, D. (1944). The measurement of adult intelligence (3rd ed.). Baltimore, MD: Williams & Wilkins.Google Scholar
Woodley of Menie, M. A., Pallesen, J., & Sarraf, M. A. (2018). Evidence for the Scarr-Rowe effect on genetic expressivity in a large U.S. sample. Twin Research and Human Genetics, 21(6), 495501.Google Scholar

References

Amico, E., Arenas, A., & Goñi, J. (2019) Centralized and distributed cognitive task processing in the human connectome. Network Neuroscience, 3(2), 455474.Google Scholar
Anokhin, A. P., Lutzenberger, W., & Birbaumer, N. (1999). Spatiotemporal organization of brain dynamics and intelligence: An EEG study in adolescents. International Journal of Psychophysiology, 33(3), 259273.Google Scholar
Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018). Communication dynamics in complex brain networks. Nature Reviews Neuroscience, 19(1), 1733.Google Scholar
Barabási, A. L. (2016). Network science. Cambridge University Press.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 113.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, 1027.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), 517528.Google Scholar
Betzel, R. F., & Bassett, D. S. (2017a). Generative models for network neuroscience: Prospects and promise. Journal of the Royal Society Interface, 14(136), 20170623.Google Scholar
Betzel, R. F., & Bassett, D. S. (2017b). Multi-scale brain networks. Neuroimage, 160, 7383.Google Scholar
Bielczyk, N. Z., Uithol, S., van Mourik, T., Anderson, P., Glennon, J. C., & Buitelaar, J. K. (2019). Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches. Network Neuroscience, 3(2), 237273.Google Scholar
Birn, R. M., Molloy, E. K., Patriat, R., Parker, T., Meier, T. B., Kirk, G. R., … Prabhakaran, V. (2013). The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage, 83, 550558.Google Scholar
Buckner, R. L., Krienen, F. M., & Yeo, B. T. (2013). Opportunities and limitations of intrinsic functional connectivity MRI. Nature Neuroscience, 16(7), 832837.Google Scholar
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186198.Google Scholar
Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13(5), 336349.Google Scholar
Cheung, M., Chan, A. S., Han, Y. M., & Sze, S. L. (2014). Brain activity during resting state in relation to academic performance. Journal of Psychophysiology, 28(2), 4753.Google Scholar
Chiang, M.-C., Barysheva, M., Shattuck, D. W., Lee, A. D., Madsen, S. K., Avedissian, C., … Thompson, P. M. (2009). Genetics of brain fiber architecture and intellectual performance. Journal of Neuroscience, 29(7), 22122224.Google Scholar
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014). Intrinsic and task-evoked network architectures of the human brain. Neuron, 83(1), 238251.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), 89888999.Google Scholar
Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting system of the human brain: From environment to theory of mind. Neuron, 58(3), 306324.Google Scholar
Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201215.Google Scholar
Craddock, R. C., Jbabdi, S., Yan, C. G., Vogelstein, J. T., Castellanos, F. X., Di Martino, A., … Milham, M. P. (2013). Imaging human connectomes at the macroscale. Nature Methods, 10(6), 524539.Google Scholar
Crossley, N. A., Mechelli, A., Vértes, P. E., Winton-Brown, T. T., Patel, A. X., Ginestet, C. E., … Bullmore, E. T. (2013). Cognitive relevance of the community structure of the human brain functional coactivation network. Proceedings of the National Academy of Sciences USA, 110(28), 1158311588.Google Scholar
Damiani, D., Pereira, L. K., Damiani, D., & Nascimento, A. M. (2017). Intelligence neurocircuitry: Cortical and subcortical structures. Journal of Morphological Sciences, 34(3), 123129.Google Scholar
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences USA, 103(37), 1384813853.Google Scholar
Dosenbach, N. U. F., Fair, D. A, Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A. T., … Petersen, S. E. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences USA, 104(26), 1107311078.Google Scholar
Duan, F., Watanabe, K., Yoshimura, Y., Kikuchi, M., Minabe, Y., & Aihara, K. (2014). Relationship between brain network pattern and cognitive performance of children revealed by MEG signals during free viewing of video. Brain and Cognition, 86, 1016.Google Scholar
Dubois, J., Galdi, P., Paul, L. K., Adolphs, R., Engineering, B., Angeles, L., … Dubois, J. (2018). A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philosophical Transactions of the Royal Society of London B Biological Sciences, 373(1756), 20170284.Google Scholar
Dunst, B., Benedek, M., Koschutnig, K., Jauk, E., & Neubauer, A. C. (2014). Sex differences in the IQ-white matter microstructure relationship: A DTI study. Brain and Cognition, 91, 7178.Google Scholar
Ewald, A., Avarvand, F. S., & Nolte, G. (2013). Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: A simulation study. Biomedizinische Technik, 58(2), 165178.CrossRefGoogle ScholarPubMed
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), 192207.Google Scholar
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., … Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 16641671.Google Scholar
Fornito, A., Zalesky, A., & Bullmore, E. (2016). Fundamentals of brain network analysis. Cambridge, MA: Academic Press.Google Scholar
Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 144.Google Scholar
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences USA, 102(27), 96739678.Google Scholar
Fukushima, M., Betzel, R. F., He, Y., de Reus, M. A., van den Heuvel, M. P., Zuo, X. N., & Sporns, O. (2018). Fluctuations between high- and low-modularity topology in time-resolved functional connectivity. NeuroImage, 180(Pt. B), 406416.Google Scholar
Girn, M., Mills, C., & Christo, K. (2019). Linking brain network reconfiguration and intelligence: Are we there yet? Trends in Neuroscience and Education, 15, 6270.Google Scholar
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., … Smith, S. M. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171178.Google Scholar
Gollo, L. L., Roberts, J. A., Cropley, V. L., Di Biase, M. A., Pantelis, C., Zalesky, A., & Breakspear, M. (2018). Fragility and volatility of structural hubs in the human connectome. Nature Neuroscience, 21(8), 11071116.CrossRefGoogle ScholarPubMed
Gonzalez-Castillo, J., Hoy, C. W., Handwerker, D. A., Robinson, M. E., Buchanan, L. C., Saad, Z. S., & Bandettini, P. A. (2015). Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns. Proceedings of the National Academy of Sciences USA, 112(28), 87628767.Google Scholar
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016). Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebral Cortex, 26(1), 288303.Google Scholar
Greene, A. S., Gao, S., Scheinost, D., & Costable, T. (2018). Task-induced brain states manipulation improves prediction of individual traits. Nature Communications, 9(1), 2807.Google Scholar
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences USA, 100(1), 253258.CrossRefGoogle Scholar
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009). Resting-state functional connectivity reflects structural connectivity in the default mode network. Cerebral Cortex, 19(1), 7278.CrossRefGoogle ScholarPubMed
Haász, J., Westlye, E. T., Fjær, S., Espeseth, T., Lundervold, A., & Lundervold, A. J. (2013). General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults. NeuroImage, 83, 372383.Google Scholar
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), e159.Google Scholar
Hearne, L. J., Mattingley, J. B., & Cocchi, L. (2016). Functional brain networks related to individual differences in human intelligence at rest. Scientific Reports, 6, 32328.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, 1025.Google Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017b). Intelligence is associated with the modular structure of intrinsic brain networks. Scientific 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), 362372.Google Scholar
Honey, C. J., Kötter, 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), 1024010245.Google Scholar
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences USA, 106(6), 20352040.Google Scholar
Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S., & Menon, R. S. (2013). Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Human Brain Mapping, 34(9), 21542177.Google Scholar
Jahidin, A. H., Taib, M. N., Tahir, N. M., Megat Ali, M. S. A., & Lias, S. (2013). Asymmetry pattern of resting EEG for different IQ levels. Procedia – Social and Behavioral Sciences, 97, 246251.Google Scholar
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C., & Behrens, T. E. (2015). Measuring macroscopic brain connections in vivo. Nature Neuroscience, 18(11), 1546.Google Scholar
Jeub, L. G., Sporns, O., & Fortunato, S. (2018). Multiresolution consensus clustering in networks. Scientific Reports, 8(1), 3259.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), 135154.Google Scholar
Kievit, R. A., Davis, S. W., Griffiths, J. D., Correia, M. M., & Henson, R. N. A. (2016). A watershed model of individual differences in fluid intelligence. Neuropsychologia, 91, 186198.Google Scholar
Kievit, R. A., van Rooijen, H., Wicherts, J. M., Waldorp, L. J., Kan, K. J., Scholte, H. S., & Borsboom, D. (2012). Intelligence and the brain: A model-based approach. Cognitive Neuroscience, 3(2), 8997.Google Scholar
Kim, D.-J., Davis, E. P., Sandman, C. A., Sporns, O., O’Donnell, B. F., Buss, C., & Hetrick, W. P. (2015). Children’s intellectual ability is associated with structural network integrity. NeuroImage, 124(Pt. A), 550556.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., … Hulshoff Pol, H. E. (2015). Development of the brain’s structural network efficiency in early adolescence: A longitudinal DTI twin study. Human Brain Mapping, 36(12), 49384953.Google Scholar
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, 323331.Google Scholar
Langer, N., Pedroni, A., Gianotti, L. R. R., Hänggi, J., Knoch, D., & Jäncke, L. (2012). Functional brain network efficiency predicts intelligence. Human Brain Mapping, 33(6), 13931406.Google Scholar
Langer, N., Pedroni, A., & Jäncke, L. (2013). The problem of thresholding in small-world network analysis. PLoS One, 8(1), e53199.CrossRefGoogle ScholarPubMed
Langeslag, S. J. E., Schmidt, M., Ghassabian, A., Jaddoe, V. W., Hofman, A., van der Lugt, A., … White, T. J. H. (2013). Functional connectivity between parietal and frontal brain regions and intelligence in young children: The generation R study. Human Brain Mapping, 34(12), 32993307.Google Scholar
Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.Google Scholar
Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M. Y., … Schlaggar, B. L. (2015). Functional system and areal organization of a highly sampled individual human brain. Neuron, 87(3), 657670.Google Scholar
Lee, T. W., Wu, Y. Te, Yu, Y. W. Y., Wu, H. C., & Chen, T. J. (2012). A smarter brain is associated with stronger neural interaction in healthy young females: A resting EEG coherence study. Intelligence, 40(1), 3848.Google Scholar
Lerch, J. P., Worsley, K., Shaw, W. P., Greenstein, D. K., Lenroot, R.K., Giedd, J., & Evans, A. C. (2006). Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage, 31(3), 9931003.Google Scholar
Li, Y. H., Liu, Y., Li, J., Qin, W., Li, K. C., Yu, C. S., & Jiang, T. Z. (2009). Brain anatomical network and intelligence. Plos Computational Biology, 5(5), e1000395.Google Scholar
Ma, J., Kang, H. J., Kim, J. Y., Jeong, H. S., Im, J. J., Namgung, E., … Yoon, S. (2017). Network attributes underlying intellectual giftedness in the developing brain. Scientific Reports, 7(1), 11321.Google Scholar
Maier-Hein, K. H., Neher, P. F., Houde, J. C., Côté, M. A., Garyfallidis, E., Zhong, J., … Reddick, W. E. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications, 8(1), 1349.CrossRefGoogle ScholarPubMed
Malpas, C. B., Genc, S., Saling, M. M., Velakoulis, D., Desmond, P. M., & Brien, T. J. O. (2016). MRI correlates of general intelligence in neurotypical adults. Journal of Clinical Neuroscience, 24, 128134.Google Scholar
Navas-Sánchez, F. J., Alemán-Gómez, Y., Sánchez-Gonzalez, J., Guzmán-De-Villoria, J. A, Franco, C., Robles, O., … Desco, M. (2013). White matter microstructure correlates of mathematical giftedness and intelligence quotient. Human Brain Mapping, 35(6), 26192631.Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33(7), 10041023.Google Scholar
Neubauer, A. C., Wammerl, M., Benedek, M., Jauk, E., & Jausovec, N. (2017). The influence of transcranial alternating current on fluid intelligence. A fMRI study. Personality and Individual Differences, 118, 5055.Google Scholar
Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.Google Scholar
Pahor, A., & Jaušovec, N. (2014). Theta–gamma cross-frequency coupling relates to the level of human intelligence. Intelligence, 46, 283290.Google Scholar
Pamplona, G. S. P., Santos Neto, G. S., Rosset, S. R. E., Rogers, B. P., & Salmon, C. E. G. (2015). Analyzing the association between functional connectivity of the brain and intellectual performance. Frontiers in Human Neuroscience, 9, 61.Google Scholar
Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014). Evaluation and statistical inference for human connectomes. Nature Methods, 11(10), 10581063.CrossRefGoogle ScholarPubMed
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), 665678.Google Scholar
Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage, 105, 536551.Google Scholar
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, 98(2), 676682.Google Scholar
Rubinov, M. (2016). Constraints and spandrels of interareal connectomes. Nature Communications, 7(1), 13812.Google Scholar
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 10591069.Google Scholar
Rubinov, M., & Sporns, O. (2011). Weight-conserving characterization of complex functional brain networks. Neuroimage, 56(4), 20682079.Google Scholar
Ryyppö, E., Glerean, E., Brattico, E., Saramäki, J., & Korhonen, O. (2018). Regions of interest as nodes of dynamic functional brain networks. Network Neuroscience, 2(4), 513535.Google Scholar
Santarnecchi, E., Muller, T., Rossi, S., Sarkar, A., Polizzotto, N. R., Rossi, A., & Cohen Kadosh, R. (2016). Individual differences and specificity of prefrontal gamma frequency-tACS on fluid intelligence capabilities. Cortex, 75, 3343.Google Scholar
Santarnecchi, E., Rossi, S., & Rossi, A. (2015). The smarter, the stronger: Intelligence level correlates with brain resilience to systematic insults. Cortex, 64, 293309.CrossRefGoogle ScholarPubMed
Schmithorst, V. J. (2009). Developmental sex differences in the relation of neuroanatomical connectivity to intelligence. Intelligence, 37(2), 164173. Schultz, X. D. H., & Cole, X. W. (2016). Higher intelligence is associated with less ask-related brain network reconfiguration. Journal of Neuroscience, 36(33), 8551–8561.Google Scholar
Sherman, L. E., Rudie, J. D., Pfeifer, J. H., Masten, C. L., McNealy, K., & Dapretto, M. (2014). Development of the default mode and central executive networks across early adolescence: A longitudinal study. Developmental Cognitive Neuroscience, 10, 148159.Google Scholar
Shinn, M., Romero-Garcia, R., Seidlitz, J., Váša, F., Vértes, P. E., & Bullmore, E. (2017). Versatility of nodal affiliation to communities. Scientific Reports, 7(1), 4273.Google Scholar
Smit, D. J. A, Stam, C. J., Posthuma, D., Boomsma, D. I., & De Geus, E. J. C. (2008). Heritability of “small-world” networks in the brain: A graph theoretical analysis of resting-state EEG functional connectivity. Human Brain Mapping, 29(12), 13681378.Google Scholar
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., … Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences USA, 106(31), 1304013045.Google Scholar
Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E., Glasser, M. F., … Miller, K. L. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, 18(11), 15651567.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), 11681176.Google Scholar
Sporns, O. (2014). Contributions and challenges for network models in cognitive neuroscience. Nature Neuroscience, 17(5), 652660.Google Scholar
Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annual Review of Psychology, 67, 613640.Google Scholar
Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 28(11), 11781193.Google Scholar
Tang, C. Y., Eaves, E. L., Ng, J. C., Carpenter, D. M., Mai, X., Schroeder, D. H., … Haier, R. J. (2010). Brain networks for working memory and factors of intelligence assessed in males and females with fMRI and DTI. Intelligence, 38(3), 293303.Google Scholar
Tavor, I., Jones, O. P., Mars, R. B., Smith, S. M., Behrens, T. E., & Jbabdi, S. (2016). Task-free MRI predicts individual differences in brain activity during task performance. Science, 352(6282), 216220.Google Scholar
Telesford, Q. K., Lynall, M. E., Vettel, J., Miller, M. B., Grafton, S. T., & Bassett, D. S. (2016). Detection of functional brain network reconfiguration during task-driven cognitive states. NeuroImage, 142, 198210.Google Scholar
Thomas, C., Frank, Q. Y., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014). Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proceedings of the National Academy of Sciences USA, 111(46), 1657416579.Google Scholar
Vaiana, M., & Muldoon, S. F. (2018). Multilayer brain networks. Journal of Nonlinear Science, 1–23.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), 76197624.Google Scholar
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393(6684), 440442.Google Scholar
Wolf, D., Fischer, F. U., Fesenbeckh, J., Yakushev, I., Lelieveld, I. M., Scheurich, A., … Fellgiebel, A. (2014). Structural integrity of the corpus callosum predicts long-term transfer of fluid intelligence-related training gains in normal aging. Human Brain Mapping, 35(1), 309318.Google Scholar
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 11001122.Google Scholar
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., … Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 11251165.Google Scholar
Yeo, R. A., Ryman, S. G., van den Heuvel, M. P., de Reus, M. A., Jung, R. E., Pommy, J., … Calhoun, V. D. (2016). Graph metrics of structural brain networks in individuals with schizophrenia and healthy controls: Group differences, relationships with intelligence, and genetics. Journal of the International Neuropsychological Society, 22(2), 240249.Google Scholar
Yu, C. S., Li, J., Liu, Y., Qin, W., Li, Y. H., Shu, N., … Li, K. C. (2008). White matter tract integrity and intelligence in patients with mental retardation and healthy adults. Neuroimage, 40(4), 15331541.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, 111(28), 1034110346.Google Scholar
Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010). Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage, 50(3), 970983.Google Scholar
Zalesky, A., Fornito, A., Seal, M. L., Cocchi, L., Westin, C., Bullmore, E. T., … Pantelis, C. (2011). Disrupted axonal fiber connectivity in schizophrenia. Biological Psychiatry, 69(1), 8089.Google Scholar
Zuo, X. N., & Xing, X. X. (2014). Test–retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective. Neuroscience & Biobehavioral Reviews, 45, 100118.Google Scholar

References

Ad-Dab’bagh, Y., Lyttelton, O., Muehlboeck, J. S., Lepage, C., Einarson, D., Mok, K., … Evans, A. C. (2006). The CIVET image-processing environment: A fully automated comprehensive pipeline for anatomical neuroimaging research. Proceedings of the 12th annual meeting of the organization for human brain mapping (Vol. 2266). Florence, Italy.Google Scholar
Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry – The methods. Neuroimage, 11(6), 805821.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 820.Google Scholar
Basten, U., Hilger, K., & Fieback, C. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 1027.Google Scholar
Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., … Schonberg, T. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582, 8488.Google Scholar
Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365376.Google Scholar
Caspi, A., & Moffitt, T. E. (2018). All for one and one for all: Mental disorders in one dimension. American Journal of Psychiatry, 175(9), 831844.Google Scholar
Chekroud, A. M., Ward, E. J., Rosenberg, M. D., & Holmes, A. J. (2016). Patterns in the human brain mosaic discriminate males from females. Proceedings of the National Academy of Sciences, 113(14), E1968E1968.Google Scholar
Chen, C. H., Fiecas, M., Gutierrez, E. D., Panizzon, M. S., Eyler, L. T., Vuoksimaa, E., … & Kremen, W. S. (2013). Genetic topography of brain morphology. Proceedings of the National Academy of Sciences, 110(42), 1708917094.Google Scholar
Chuderski, A. (2019). Even a single trivial binding of information is critical for fluid intelligence. Intelligence, 77, 101396.Google Scholar
Cole, M. W., Yarkoni, T., Repovš, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 89888999.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, 143152. doi: 10.1016/j.neuroimage.2013.01.032.Google Scholar
Colom, R., Chuderski, A., & Santarnecchi, E. (2016). Bridge over troubled water: Commenting on Kovacs and Conway’s process overlap theory. Psychological Inquiry, 27(3), 181189.Google Scholar
Colom, R., Haier, R. J., Head, K., Álvarez-Linera, J., Quiroga, M. Á., Shih, P. C., & Jung, R. E. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence, 37(2), 124135.Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006). Distributed brain sites for the g-factor of intelligence. NeuroImage, 31(3), 13591365.Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2007). General intelligence and memory span: Evidence for a common neuroanatomic framework. Cognitive Neuropsychology, 24(8), 867878.Google Scholar
Colom, R., Karama, S., Jung, R. E., & Haier, R. J. (2010). Human intelligence and brain networks. Dialogues in Clinical Neuroscience, 12(4), 489501.Google Scholar
Colom, R., & Román, F. (2018). Enhancing intelligence: From the group to the individual. Journal of Intelligence, 6(1), 11.Google Scholar
Colom, R., & Thompson, P. M. (2011). Understanding human intelligence by imaging the brain. In Chamorro-Premuzic, T., von Stumm, S., & Furnham, A. (eds.), The Wiley-Blackwell handbook of individual differences (p. 330352). Hoboken, NJ: Wiley-Blackwell.Google Scholar
Daugherty, A. M., Sutton, B. P., Hillman, C., Kramer, A. F., Cohen, N. J., & Barbey, A. K. (2020). Individual differences in the neurobiology of fluid intelligence predict responsiveness to training: Evidence from a comprehensive cognitive, mindfulness meditation, and aerobic exercise intervention. Trends in Neuroscience and Education, 18, 100123.Google Scholar
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201211.Google Scholar
Dubois, J., & Adolphs, R. (2016). Building a science of individual differences from fMRI. Trends in Cognitive Sciences, 20(6), 425443.Google Scholar
Dubois, J., Galdi, P., Paul, L. K., & Adolphs, R. (2018). A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1756), 20170284.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), 13381352.Google Scholar
Euler, M. J. (2018). Intelligence and uncertainty: Implications of hierarchical predictive processing for the neuroscience of cognitive ability. Neuroscience & Biobehavioral Reviews, 94, 93112.Google Scholar
Evans, A. C., & Brain Development Cooperative Group (2006). The NIH MRI study of normal brain development. NeuroImage, 30(1), 184202.Google Scholar
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., … Constable, R. T. (2015). Functional connectome fingerprint: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 16641671.Google Scholar
Fjell, A. M., Westlye, L. T., Amlien, I., Tamnes, C. K., Grydeland, H., Engvig, A., … Walhovd, K. B. (2015). High-expanding cortical regions in human development and evolution are related to higher intellectual abilities. Cerebral Cortex, 25(1), 2634.Google Scholar
Frost, M. A., & Goebel, R. (2012). Measuring structural–functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment. Neuroimage, 59(2), 13691381.Google Scholar
Gignac, G. E., & Bates, T. C. (2017). Brain volume and intelligence: The moderating role of intelligence measurement quality. Intelligence, 64, 1829. doi: 10.1016/j.intell.2017.06.004.Google Scholar
Gratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., … Petersen, S. E. (2018). Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron, 98(2), 439452.Google Scholar
Grotzinger, A. D., Cheung, A. K., Patterson, M. W., Harden, K. P., & Tucker-Drob, E. M. (2019). Genetic and environmental links between general factors of psychopathology and cognitive ability in early childhood. Clinical Psychological Science, 7(3), 430444.Google Scholar
Haier, R. J. (2017). The neuroscience of intelligence. Cambridge University Press.Google Scholar
Haier, R. J., Colom, R., Schroeder, D., Condon, C., Tang, C., Eaves, E., & Head, K. (2009). Gray matter and intelligence factors: Is there a neuro-g? Intelligence, 37(2), 136144.Google Scholar
Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2005). The neuroanatomy of general intelligence: Sex matters. NeuroImage, 25(1), 320327.Google Scholar
Hearne, L. J., Mattingley, J. B., & Cocchi, L. (2016). Functional brain networks related to individual differences in human intelligence at rest. Scientific Reports, 6, 32328. doi: 10.1038/srep32328.Google Scholar
Hill, W. D., Harris, S. E., & Deary, I. J. (2019). What genome-wide association studies reveal about the association between intelligence and mental health. Current Opinion in Psychology, 27, 2530. doi: 10.1016/j.copsyc.2018.07.007.Google Scholar
Horien, C., Shen, X., Scheinost, D., & Constable, R. T. (2019). The individual functional connectome is unique and stable over months to years. NeuroImage, 189, 676687. doi: 10.1016/j.neuroimage.2019.02.002.Google Scholar
Hunt, E. B. (2011). Human intelligence. Cambridge University Press.Google Scholar
Im, K., Lee, J. M., Lyttelton, O., Kim, S. H., Evans, A. C., & Kim, S. I. (2008). Brain size and cortical structure in the adult human brain. Cerebral Cortex, 18(9), 21812191.Google Scholar
Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M. A., Ruparel, K., … Verma, R. (2014). Sex differences in the structural connectome of the human brain. Proceedings of the National Academy of Sciences, 111(2), 823828.Google Scholar
Jensen, A. R. (1998). The g factor. The science of mental ability. Westport, CT: Praeger. doi: 10.1093/cercor/bhm244.Google Scholar
Johnson, W., & Bouchard, T. (2005). The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized. Intelligence, 33, 393416.Google Scholar
Johnson, W., Bouchard, T. J. Jr, Krueger, R. F., McGue, M., & Gottesman, I. I. (2004). Just one g: Consistent results from three batteries. Intelligence, 32(1), 95107.Google Scholar
Johnson, W., te Nijenhuis, J., & Bouchard, T. (2008). Still just 1 g: Consistent results from five test batteries. Intelligence, 36(1), 8195.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), 135187.Google Scholar
Karama, S., Colom, R., Johnson, W., Deary, I. J., Haier, R., Waber, D. P., … Brain Development Cooperative Group (2011). Cortical thickness correlates of specific cognitive performance accounted for by the general factor of intelligence in healthy children aged 6 to 18. NeuroImage, 55(4), 14431453.Google Scholar
Kim, J. S., Singh, V., Lee, J. K., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., … Evans, A. C. (2005). Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage, 27(1), 210221.Google Scholar
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, 323331. doi: 10.1016/j.neuroimage.2018.01.018.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), 13931406.Google Scholar
MacDonald, D., Kabani, N., Avis, D., & Evans, A. C. (2000). Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage, 12(3), 340356.Google Scholar
Martínez, K., Madsen, S. K., Joshi, A. A., Joshi, S. H., Roman, 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), 32273245.Google Scholar
Mechelli, A., Price, C. J., Friston, K. J., & Ashburner, J. (2005). Voxel-based morphometry of the human brain: Methods and applications. Current Medical Imaging Reviews, 1(2), 105113.Google Scholar
Pakkenberg, B., & Gundersen, H. J. G. (1997). Neocortical neuron number in humans: Effect of sex and age. Journal of Comparative Neurology, 384(2), 312320.Google Scholar
Panizzon, M. S., Fennema-Notestine, C., Eyler, L. T., Jernigan, T. L., Prom-Wormley, E., Neale, M., … Kremen, W. S. (2009). Distinct genetic influences on cortical surface area and cortical thickness. Cerebral Cortex, 19(11), 27282735.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, 411432.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, 105116. doi: 10.1016/j.intell.2015.12.002.Google Scholar
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2016). Top 10 replicated findings from behavioral genetics. Perspectives on Psychological Science, 11(1), 323.Google Scholar
Ponsoda, V., Martínez, K., Pineda‐Pardo, J. A., Abad, F. J., Olea, J., Román, F. J., … Colom, R. (2017). Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis. Human Brain Mapping, 38(2), 803816.Google Scholar
Price, C. J. (2018). The evolution of cognitive models: From neuropsychology to neuroimaging and back. Cortex, 107, 3749.Google Scholar
Rakic, P. (1988). Specification of cerebral cortical areas. Science, 241(4862), 170176.Google Scholar
Ritchie, S. J., Cox, S. R., Shen, X., Lombardo, M. V., Reus, L. M., Alloza, C., … Deary, I. J. (2018). Sex differences in the adult human brain: Evidence from 5216 UK Biobank participants. Cerebral Cortex, 28(8), 29592975.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), 38053818.Google 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, 2129. doi: 10.1016/j.intell.2018.02.006.Google Scholar
Roth, G., & Dicke, U. (2005). Evolution of the brain and intelligence. Trends in Cognitive Sciences, 9(5), 250257.Google Scholar
Santarnecchi, E., Rossi, S., & Rossi, A. (2015). The smarter, the stronger: Intelligence level correlates with brain resilience to systematic insults. Cortex, 64, 293309. doi: 10.1016/j.cortex.2014.11.005.Google Scholar
Saxe, G. N., Calderone, D., & Morales, L. J. (2018). Brain entropy and human intelligence: A resting-state fMRI study. PloS One, 13(2), e0191582.Google Scholar
Schneider, W. J., & McGrew, K. S. (2018). The Cattell–Horn–Carroll theory of cognitive abilities. In Flanagan, D. P., & McDonough, E. M. (eds.), Contemporary intellectual assessment: Theories, tests, and issues (pp. 73163). New York: The Guilford Press.Google Scholar
Sella, G., & Barton, N. H. (2019). Thinking about the evolution of complex traits in the era of genome-wide association studies. Annual Review of Genomics and Human Genetics, 20, 461493.Google Scholar
Thompson, P. M., Hayashi, K. M., Sowell, E. R., Gogtay, N., Giedd, J. N., Rapoport, J. L., … Toga, A. W. (2004). Mapping cortical change in Alzheimer’s disease, brain development, and schizophrenia. NeuroImage, 23, S2S18. doi: 10.1016/j.neuroimage.2004.07.071.Google Scholar
Thompson, P. M., Jahanshad, N., Ching, C. R., Salminen, L. E., Thomopoulos, S. I., Bright, J., … for the ENIGMA Consortium (2020). ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Translational Psychiatry, 10(1), 128.Google Scholar
Valizadeh, S. A., Liem, F., Mérillat, S., Hänggi, J., & Jäncke, L. (2018). Identification of individual subjects on the basis of their brain anatomical features. Scientific Reports, 8(1), 19.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), 76197624.Google Scholar
Viviano, R. P., Raz, N., Yuan, P., & Damoiseaux, J. S. (2017). Associations between dynamic functional connectivity and age, metabolic risk, and cognitive performance. Neurobiology of Aging, 59, 135143. doi: 10.1016/j.neurobiolaging.2017.08.003.Google Scholar
Vuoksimaa, E., Panizzon, M. S., Chen, C. H., Fiecas, M., Eyler, L. T., Fennema-Notestine, C., … Kremen, W. S. (2015). The genetic association between neocortical volume and general cognitive ability is driven by global surface area rather than thickness. Cerebral Cortex, 25(8), 21272137.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. Journal of Neuroscience, 37(35), 85498558.Google Scholar
Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., … Glahn, D. C. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage, 53(3), 11351146.Google Scholar

References

Alexander, L. M., Escalera, J., Ai, L., Andreotti, C., Febre, K., Mangone, A., … Milham, M. P. (2017). Data descriptor: An open resource for transdiagnostic research in pediatric mental health and learning disorders. Scientific Data, 4, 126.Google Scholar
Andreasen, N. C., Flaum, M., Swayze, V., O’Leary, D. S., Alliger, R., Cohen, G., …, Yuh, W. T. (1993). Intelligence and brain structure in normal individuals. American Journal of Psychiatry, 150(1), 130134.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), 485494. doi: 10.1007/s00429-013-0512-z.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(Pt 4), 11541164. doi: 10.1093/brain/aws021.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, 1027.Google Scholar
Bedford, S. A., Park, M. T. M., Devenyi, G. A., Tullo, S., Germann, J., Patel, R., … Chakravarty, M. M. (2020). Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder. Molecular Psychiatry, 25(3), 614628.Google Scholar
Biswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., … Milham, M. P. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences USA, 107(10), 47344739.Google Scholar
Book, G. A., Stevens, M. C., Assaf, M., Glahn, D. C., & Pearlson, G. D. (2016). Neuroimaging data sharing on the neuroinformatics database platform. Neuroimage, 124(Pt. B), 10891092.Google Scholar
Brown, M. R. G. G., Sidhu, G. S., Greiner, R., Asgarian, N., Bastani, M., Silverstone, P. H., … Dursun, S. M. (2012). ADHD-200 global competition: Diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements. Frontiers in Systems Neuroscience, 6, 122.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, 810819.Google Scholar
Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., … Dale, A. M. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 4354.Google Scholar
Colom, R., Haier, R. J., Head, K., Álvarez-Linera, J., Quiroga, M. Á., Shih, P. C., & Jung, R. E. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence, 37(2), 124135.Google Scholar
Colom, R., Karama, S., Jung, R. E., & Haier, R. J. 2010. Human intelligence and brain networks. Dialogues in Clinical Neuroscience, 12(4), 489501.Google Scholar
Cox, S. R., Ritchie, S. J., Fawns-Ritchie, C., Tucker-Drob, E. M., & Deary, I. J. (2019). Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence, 76, 101376.Google Scholar
Craddock, C., Benhajali, Y., Chu, C., Chouinard, F., Evans, A., Jakab, A., … Bellec, P. (2013). The Neuro Bureau Preprocessing Initiative: Open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics, 7. doi: 10.3389/conf.fninf.2013.09.00041.Google Scholar
Daugherty, A., Sutton, B., Hillman, C. H., Kramer, A., Cohen, N., & Barbey, A. K. (2020). Individual differences in the neurobiology of fluid intelligence predict responsiveness to training: Evidence from a comprehensive cognitive, mindfulness meditation, and aerobic exercise intervention. Trends in Neuroscience and Education, 18, 100123.Google Scholar
Deary, I. J., Gow, A. J., Taylor, M. D., Corley, J., Brett, C., Wilson, V., … Starr, J. M. (2007). The Lothian Birth Cohort 1936: A study to examine influences on cognitive ageing from age 11 to age 70 and beyond. BMC Geriatrics, 7, 28.Google Scholar
Di Martino, A., O’Connor, D., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., … Milham, M. P. (2017). Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific Data, 4, 170010.Google Scholar
Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., … Milham, M. P. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659667.Google Scholar
Dubois, J., Galdi, P., Paul, L. K., & Adolphs, R. (2018). A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philosophical Transactions of the Royal Society B Biological Science, 373(1756), 20170284.Google Scholar
Eickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K., & Fox, P. T. (2009). Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: A random-effects approach based on empirical estimates of spatial uncertainty. Human Brain Mapping, 30(9), 29072926.Google Scholar
Evans, A. C., & Brain Development Cooperative Group. (2006). The NIH MRI study of normal brain development. Neuroimage, 30(1), 184202.Google Scholar
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., … Shinohara, R. T. (2018). Harmonization of cortical thickness measurements across scanners and sites. Neuroimage, 167, 104120.Google Scholar
Ghiassian, S., Greiner, R., Jin, P., & Brown, M. R. G. 2016. Using functional or structural magnetic resonance images and personal characteristic data to identify ADHD and autism. PLoS One, 11(12), e0166934.Google Scholar
Gray, J. R., & Thompson, P. M. (2004). Neurobiology of intelligence: Science and ethics. Nature Reviews Neuroscience, 5(6), 471482.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), 199217.CrossRefGoogle Scholar
HD-200 Consortium TA-200, Milham, P. M., Damien, F., Maarten, M., & Stewart, H. M. (2012). The ADHD-200 Consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience, 6, 15.Google Scholar
Hammer, R., Paul, E. J., Hillman, C. H., Kramer, A. F., Cohen, N. J., & Barbey, A. K. (2019). Individual differences in analogical reasoning revealed by multivariate task-based functional brain imaging. Neuroimage, 184, 9931004. doi: 10.1016/j.neuroimage.2018.09.011.Google Scholar
Huguet, G., Schramm, C., Douard, E., Jiang, L., Labbe, A., Tihy, F., … Jacquemont, S. (2018). Measuring and estimating the effect sizes of copy number variants on general intelligence in community-based samples. JAMA Psychiatry, 75(5), 447457.Google Scholar
Jernigan, T. L., Brown, T. T., Hagler, D. J., Akshoomoff, N., Bartsch, H., Newman, E., … Pediatric Imaging, Neurocognition and Genetics Study. (2016). The Pediatric Imaging, Neurocognition, and Genetics (PING) data repository. Neuroimage. 124(Pt. B), 11491154.Google Scholar
Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1), 118127.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), 135154.Google Scholar
Karama, S., Ad-Dab’bagh, Y., Haier, R. J., Deary, I. J., Lyttelton, O. C., Lepage, C., & Evans, A. C. (2009). Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence. 37(2), 145155.Google Scholar
Karama, S., Bastin, M. E., Murray, C., Royle, N. A., Penke, L., Muñoz Maniega, S., … Deary, I. J. (2014). Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age. Molecular Psychiatry, 19(3), 555559.Google Scholar
Karama, S., Colom, R., Johnson, W., Deary, I. J., Haier, R., Waber, D. P., … Evans, A. C. (2011). Cortical thickness correlates of specific cognitive performance accounted for by the general factor of intelligence in healthy children aged 6 to 18. Neuroimage, 55(4), 14431453.Google Scholar
Khundrakpam, B. S., Lewis, J. D., Reid, A., Karama, S., Zhao, L., Chouinard-Decorte, F., … Brain Development Cooperative Group. (2017). Imaging structural covariance in the development of intelligence. Neuroimage, 144(Pt. A), 227240.Google Scholar
Kievit, R. A., Fuhrmann, D., Borgeest, G. S., Simpson-Kent, I. L., & Henson, R. N. A. (2018). The neural determinants of age-related changes in fluid intelligence: A pre-registered, longitudinal analysis in UK Biobank. Wellcome Open Research, 3, 38.Google Scholar
King, J. B., Prigge, M. B. D., King, C. K., Morgan, J., Weathersby, F., Fox, J. C., … Anderson, J. S. (2019). Generalizability and reproducibility of functional connectivity in autism. Molecular Autism, 10, 27.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.Google Scholar
Loughnan, R. J., Palmer, C. E., Thompson, W. K., Dale, A. M., Jernigan, T. L., & Fan, C. C. (2019). Polygenic score of intelligence is more predictive of crystallized than fluid performance among children. bioRxiv. 637512. doi: 10.1101/637512.Google Scholar
Luders, E., Narr, K. L., Thompson, P. M., & Toga, A. W. (2009). Neuroanatomical correlates of intelligence. Intelligence, 37(2), 156163.Google Scholar
McDaniel, M. A. (2005). Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence, 33(4), 337346.Google Scholar
Mennes, M., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2013). Making data sharing work: The FCP/INDI experience. Neuroimage, 82, 683691.Google Scholar
Mihalik, A., Brudfors, M., Robu, M., Ferreira, F. S., Lin, H., Rau, A., … Oxtoby, N. P. (2019). ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression. In Pohl, K., Thompson, W., Adeli, E., & Linguraru, M. (eds.), Adolescent brain cognitive development neurocognitive prediction. ABCD-NP 2019. Lecture Notes in Computer Science, vol. 11791. Cham: Springer. doi: 10.1007/978-3-030-31901-4_16.Google Scholar
Narr, K. L., Woods, R. P., Thompson, P. M., Szeszko, P., Robinson, D., Dimtcheva, T., … Bilder, R. M. (2007). Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cerebral Cortex, 17(9), 21632171.Google Scholar
Nielson, D. M., Pereira, F., Zheng, C. Y., Migineishvili, N., Lee, J. A., Thomas, A. G., & Bandettini, P. A. (2018). Detecting and harmonizing scanner differences in the ABCD study – Annual release 1.0. bioRxiv. 309260. doi: 10.1101/309260.Google Scholar
Nielsen, J. A., Zielinski, B. A., Fletcher, P. T., Alexander, A. L., Lange, N., Bigler, E. D., … Anderson, J. S. (2013). Multisite functional connectivity MRI classification of autism: ABIDE results. Frontiers in Human Neuroscience, 7, 599.Google Scholar
Parikh, M. N., Li, H., & He, L. (2019). Enhancing diagnosis of autism with optimized machine learning models and personal characteristic data. Frontiers in Computational Neuroscience, 13, 15.Google Scholar
Plomin, R., & Von Stumm, S. (2018). The new genetics of intelligence. Nature Reviews Genetics, 19(3), 148159.Google Scholar
Poldrack, R. A., & Gorgolewski, K. J. (2014). Making big data open: Data sharing in neuroimaging. Nature Neuroscience, 17(11), 15101517.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, 928.Google Scholar
Satterthwaite, T. D., Connolly, J. J., Ruparel, K., Calkins, M. E., Jackson, C., Elliott, M. A., … Gur, R. E. (2016). The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth. Neuroimage, 124(Pt. B), 11151119.Google Scholar
Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., … Posthuma, D. (2018). Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 50(7), 912919.Google Scholar
Schumann, G., Loth, E., Banaschewski, T., Barbot, A., Barker, G., Büchel, C., … Struve, M. (2010). The IMAGEN study: Reinforcement-related behaviour in normal brain function and psychopathology. Molecular Psychiatry, 15(12), 11281139.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), 676679.Google Scholar
Sniekers, S., Stringer, S., Watanabe, K., Jansen, P. R., Coleman, J. R. I., Krapohl, E., … Posthuma, D. (2017). Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics, 49(7), 11071112.Google Scholar
Sripada, C., Angstadt, M., Rutherford, S., & Taxali, A. (2019). Brain network mechanisms of general intelligence. bioRxiv. 657205. doi: 10.1101/657205.Google Scholar
Stein, J. L., Medland, S. E., Vasquez, A. A., Hibar, D. P., Senstad, R. E., Winkler, A. M., … Thompson, P. M. (2012). Identification of common variants associated with human hippocampal and intracranial volumes. Nature Genetics, 44(5), 552561.Google Scholar
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., … Collins, R. (2015). UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Medicine, 12(3), e1001779.Google Scholar
Talukdar, T., Roman, F. J., Operskalski, J. T., Zwilling, C. E., & Barbey, A. K. (2018). Individual differences in decision making competence revealed by multivariate fMRI. Human Brain Mapping, 39(6), 26642672. doi: 10.1002/hbm.24032.Google Scholar
Thompson, P. M., Dennis, E. L., Gutman, B. A., Hibar, D. P., Jahanshad, N., Kelly, S., … Ye, J. (2017). ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide. Neuroimage, 145(Pt. B), 389408.Google Scholar
Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., … Drevets, W. (2014). The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behavior, 8(2), 153182.Google Scholar
Turner, B. O., Paul, E. J., Miller, M. B., & Barbey, A. K. (2018). Small sample sizes reduce the replicability of task-based fMRI studies. Communications Biology, 1, 62. doi: 10.1038/s42003-018-0073-z.Google Scholar
Turner, J. A. (2014). The rise of large-scale imaging studies in psychiatry. Gigascience, 3, 18.Google Scholar
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., Ugurbil, K., & WU-Minn HCP Consortium. (2013). The WU-Minn human connectome project: An overview. Neuroimage, 80, 6279.Google Scholar
Wachinger, C., Becker, B. G., & Rieckmann, A. (2018). Detect, quantify, and incorporate dataset bias: A neuroimaging analysis on 12,207 individuals. arXiv:1804.10764.Google Scholar
Xiao, L., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y.-P. (2019). A manifold regularized multi-task learning model for IQ prediction from two fMRI paradigms. IEEE Transaactions in Biomedical Engineering, 67(3), 796806.Google Scholar
Zhao, Y., Klein, A., Castellanos, F. X., & Milham, M. P. (2019). Brain age prediction: Cortical and subcortical shape covariation in the developing human brain. Neuroimage, 202, 116149.Google Scholar
Zwilling, C. E., Daugherty, A. M., Hillman, C. H., Kramer, A. F., Cohen, N. J., & Barbey, A. K. (2019). Enhanced decision-making through multimodal training. NPJ Science of Learning, 4, 11. doi: 10.1038/s41539-019-0049-x.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Fundamental Issues
  • Edited by Aron K. Barbey, University of Illinois, Urbana-Champaign, Sherif Karama, McGill University, Montréal, Richard J. Haier, University of California, Irvine
  • Book: The Cambridge Handbook of Intelligence and Cognitive Neuroscience
  • Online publication: 11 June 2021
  • Chapter DOI: https://doi.org/10.1017/9781108635462.002
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Fundamental Issues
  • Edited by Aron K. Barbey, University of Illinois, Urbana-Champaign, Sherif Karama, McGill University, Montréal, Richard J. Haier, University of California, Irvine
  • Book: The Cambridge Handbook of Intelligence and Cognitive Neuroscience
  • Online publication: 11 June 2021
  • Chapter DOI: https://doi.org/10.1017/9781108635462.002
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Fundamental Issues
  • Edited by Aron K. Barbey, University of Illinois, Urbana-Champaign, Sherif Karama, McGill University, Montréal, Richard J. Haier, University of California, Irvine
  • Book: The Cambridge Handbook of Intelligence and Cognitive Neuroscience
  • Online publication: 11 June 2021
  • Chapter DOI: https://doi.org/10.1017/9781108635462.002
Available formats
×