Book contents
- The Cambridge Handbook of Intelligence and Cognitive Neuroscience
- Reviews
- The Cambridge Handbook of Intelligence and Cognitive Neuroscience
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Preface
- Part I Fundamental Issues
- Part II Theories, Models, and Hypotheses
- Part III Neuroimaging Methods and Findings
- Part IV Predictive Modeling Approaches
- Part V Translating Research on the Neuroscience of Intelligence into Action
- Index
- References
Part IV - Predictive Modeling Approaches
Published online by Cambridge University Press: 11 June 2021
Book contents
- The Cambridge Handbook of Intelligence and Cognitive Neuroscience
- Reviews
- The Cambridge Handbook of Intelligence and Cognitive Neuroscience
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Preface
- Part I Fundamental Issues
- Part II Theories, Models, and Hypotheses
- Part III Neuroimaging Methods and Findings
- Part IV Predictive Modeling Approaches
- Part V Translating Research on the Neuroscience of Intelligence into Action
- Index
- References
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.
- Type
- Chapter
- Information
- Publisher: Cambridge University PressPrint publication year: 2021
References
References
Allegrini, A. G., Selzam, S., Rimfeld, K., von Stumm, S., Pingault, J. B., & Plomin, R. (2019). Genomic prediction of cognitive traits in childhood and adolescence. Molecular Psychiatry, 24(6), 819–827. doi: 10.1038/s41380–019-0394-4.Google Scholar
Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507–522. doi: 10.1038/nrg.2016.86.Google Scholar
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., & Grafman, J. (2014). Distributed neural system for emotional intelligence revealed by lesion mapping. Social Cognitive and Affective Neuroscience, 9(3), 265–272. doi: 10.1093/scan/nss124.CrossRefGoogle ScholarPubMed
Barton, N. H., Etheridge, A. M., & Véber, A. (2017). The infinitesimal model: Definition, derivation, and implications. Theoretical Population Biology, 118, 50–73. doi: 10.1016/j.tpb.2017.06.001.Google Scholar
Batty, G. D., Deary, I. J., & Gottfredson, L. S. (2007). Premorbid (early life) IQ and later mortality risk: Systematic review. Annals of Epidemiology, 17(4), 278–288. doi: 10.1016/j.annepidem.2006.07.010.Google Scholar
Boomsma, D., Busjahn, A., & Peltonen, L. (2002). Classical twin studies and beyond. Nature Reviews Genetics, 3(11), 872–882. doi: 10.1038/nrg932.CrossRefGoogle ScholarPubMed
Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017). An expanded view of complex traits: From polygenic to omnigenic. Cell, 169(7), 1177–1186. doi: 10.1016/j.cell.2017.05.038.Google Scholar
Branigan, A. R., McCallum, K. J., & Freese, J. (2013). Variation in the heritability of educational attainment: An international meta-analysis. Social Forces, 92(1), 109–140. doi: 10.1093/sf/sot076.Google Scholar
Briley, D. A., & Tucker-Drob, E. M. (2013). Explaining the increasing heritability of cognitive ability across development: A meta-analysis of longitudinal twin and adoption studies. Psychological Science, 24(9), 1704–1713. doi: 10.1177/0956797613478618.Google Scholar
Bulik-Sullivan, B., Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., … Neale, B. M. (2015). An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47(11), 1236–1241. doi.org/10.1038/ng.3406.CrossRefGoogle ScholarPubMed
Bzdok, D., Altman, N., & Krzywinski, M. (2018). Statistics versus machine learning. Nature Methods, 15(4), 233–234. doi: 10.1038/nmeth.4642.Google Scholar
Bzdok, D., & Ioannidis, J. P. A. (2019). Exploration, inference, and prediction in neuroscience and biomedicine. Trends in Neurosciences, 42(4), 251–262. doi: 10.1016/j.tins.2019.02.001.Google Scholar
Bzdok, D., & Yeo, B. T. T. (2017). Inference in the age of big data: Future perspectives on neuroscience. NeuroImage, 155, 549–564. doi: 10.1016/j.neuroimage.2017.04.061.Google Scholar
Calvin, C. M., Batty, G. D., Der, G., Brett, C. E., Taylor, A., Pattie, A., … Deary, I. J. (2017). Childhood intelligence in relation to major causes of death in 68 year follow-up: Prospective population study. British Medical Journal, 357(j2708), 1–14. doi: 10.1136/bmj.j2708.Google ScholarPubMed
Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018). Next-generation machine learning for biological networks. Cell, 173(7), 1581–1592. doi: 10.1016/j.cell.2018.05.015.Google Scholar
Chabris, C. F., Hebert, B. M., Benjamin, D. J., Beauchamp, J., Cesarini, D., van der Loos, M., … Laibson, D. (2012). Most reported genetic associations with general intelligence are probably false positives. Psychological Science, 23(11), 1314–1323. doi: 10.1177/0956797611435528.Google Scholar
Chiang, M.-C., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Hickie, I., Toga, A. W., … Thompson, P. M. (2011). Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29. NeuroImage, 54(3), 2308–2317. doi: 10.1016/j.neuroimage.2010.10.015.Google Scholar
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., … Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387. doi: 10.1098/rsif.2017.0387.Google Scholar
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16(9), 1348–1355. doi: 10.1038/nn.3470.Google Scholar
Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 8988–8999. doi: 10.1523/JNEUROSCI.0536-12.2012.Google Scholar
Costello, J. C., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., … Stolovitzky, G. (2014). A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, 32(12), 1202–1212. doi: 10.1038/nbt.2877.CrossRefGoogle ScholarPubMed
Cremers, H. R., Wager, T. D., & Yarkoni, T. (2017). The relation between statistical power and inference in fMRI. PLoS One, 12(11), e0184923. doi: 10.1371/journal.pone.0184923.Google Scholar
Davies, G., Lam, M., Harris, S. E., Trampush, J. W., Luciano, M., Hill, W. D., … Deary, I. J. (2018). Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nature Communications, 9(1), 2098. doi: 10.1038/s41467–018-04362-x.Google Scholar
Deary, I. J., Johnson, W., & Houlihan, L. M. (2009). Genetic foundations of human intelligence. Human Genetics, 126(1), 215–232. doi: 10.1007/s00439–009-0655-4.Google Scholar
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201–211. doi: 10.1038/nrn2793.Google Scholar
Dor, Y., & Cedar, H. (2018). Principles of DNA methylation and their implications for biology and medicine. The Lancet, 392(10149), 777–786. doi: 10.1016/S0140–6736(18)31268-6.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.CrossRefGoogle ScholarPubMed
Elliott, M. L., Belsky, D. W., Anderson, K., Corcoran, D. L., Ge, T., Knodt, A., … Hariri, A. R. (2018). A polygenic score for higher educational attainment is associated with larger brains. Cerebral Cortex, 491(8), 56–59. doi: 10.1093/cercor/bhy219.Google Scholar
Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019). Deep learning: New computational modelling techniques for genomics. Nature Reviews Genetics, 20(7), 389–403. doi: 10.1038/s41576–019-0122-6.Google Scholar
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. doi: 10.1038/nature21056.Google Scholar
Fange, H., Knezevic, B., Burnham, K. L., Osgood, J., Sanniti, A., Lledó Lara, A., … Knight, J. C. (2019). A genetics-led approach defines the drug target landscape of 30 immune-related traits. Nature Genetics, 51(7), 1082–1091. doi: 10.1038/s41588–019-0456-1.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), 1664–1671. doi: 10.1038/nn.4135.Google Scholar
Gabrieli, J. D. E., Ghosh, S. S., & Whitfield-Gabrieli, S. (2015). Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron, 85(1), 11–26. doi: 10.1016/j.neuron.2014.10.047.Google Scholar
Gazestani, V. H., & Lewis, N. E. (2019). From genotype to phenotype: Augmenting deep learning with networks and systems biology. Current Opinion in Systems Biology, 15, 68–73. doi: 10.1016/j.coisb.2019.04.001.Google Scholar
Ge, T., Chen, C.-Y., Doyle, A. E., Vettermann, R., Tuominen, L. J., Holt, D. J., … Smoller, J. W. (2019). The shared genetic basis of educational attainment and cerebral cortical morphology. Cerebral Cortex, 29(8), 3471–3481. doi: 10.1093/cercor/bhy216.Google Scholar
Genç, E., Fraenz, C., Schlüter, C., Friedrich, P., Hossiep, R., Voelkle, M. C., … Jung, R. E. (2018). Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature Communications, 9(1), 1905. doi: 10.1038/s41467–018-04268-8.Google Scholar
Goriounova, N. A., & Mansvelder, H. D. (2019). Genes, cells and brain areas of intelligence. Frontiers in Human Neuroscience, 13, 44. doi: 10.3389/fnhum.2019.00044.Google Scholar
Gray, J. R., & Thompson, P. M. (2004). Neurobiology of intelligence: Science and ethics. Nature Reviews Neuroscience, 5(6), 471–482. doi: 10.1038/nrn1405.Google Scholar
Greene, A. S., Gao, S., Scheinost, D., & Constable, R. T. (2018). Task-induced brain state manipulation improves prediction of individual traits. Nature Communications, 9(1), 2807. doi: 10.1038/s41467–018-04920-3.Google Scholar
Hagenaars, S. P., Harris, S. E., Davies, G., Hill, W. D., Liewald, D. C. M., Ritchie, S. J., … Deary, I. J. (2016). Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia. Molecular Psychiatry, 21(11), 1624–1632. doi: 10.1038/mp.2015.225.Google Scholar
Haier, R. J. (2017). The neuroscience of intelligence. New York: Cambridge University Press.Google Scholar
Haworth, S., Mitchell, R., Corbin, L., Wade, K. H., Dudding, T., Budu-Aggrey, A. J., … Timpson, N. (2019). Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis. Nature Communications, 10(1), 333. doi: 10.1038/s41467–018-08219-1.Google Scholar
He, T., Kong, R., Holmes, A., Nguyen, M., Sabuncu, M., Eickhoff, S. B., … Yeo, B. T. T. (2020). Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. NeuroImage, 206, 116276. doi: 10.1016/j.neuroimage.2019.116276.CrossRefGoogle ScholarPubMed
Heck, A., Fastenrath, M., Ackermann, S., Auschra, B., Bickel, H., Coynel, D., … Papassotiropoulos, A. (2014). Converging genetic and functional brain imaging evidence links neuronal excitability to working memory, psychiatric disease, and brain activity. Neuron, 81(5), 1203–1213. doi: 10.1016/j.neuron.2014.01.010.Google Scholar
Hibar, D. P., Adams, H. H. H., Chauhan, G., Hofer, E., Rentería, M. E., Adams, H. H. H., … Ikram, M. A. (2017). Novel genetic loci associated with hippocampal volume. Nature Communications, 8(13624), 1–12. doi: 10.1038/ncomms13624.Google Scholar
Hill, D., Davies, G., Liewald, D. C., McIntosh, A. M., & Deary, I. J. (2016). Age-dependent pleiotropy between general cognitive function and major psychiatric disorders. Biological Psychiatry, 80(4), 266–273. doi: 10.1016/j.biopsych.2015.08.033.Google Scholar
Hill, W. D., Marioni, R. E., Maghzian, O., Ritchie, S. J., Hagenaars, S. P., McIntosh, A. M., … Deary, I. J. (2019). A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Molecular Psychiatry, 24(2), 169–181. doi: 10.1038/s41380–017-0001-5.Google Scholar
Hulshoff Pol, H. E., Schnack, H. G., Posthuma, D., Mandl, R. C. W., Baare, W. F., van Oel, C., … Kahn, R. S. (2006). Genetic contributions to human brain morphology and intelligence. Journal of Neuroscience, 26(40), 10235–10242. doi: 10.1523/JNEUROSCI.1312-06.2006.Google Scholar
Jansen, P. R., Nagel, M., Watanabe, K., Wei, Y., Savage, J. E., de Leeuw, C. A., … Posthuma, D. (2019). GWAS of brain volume on 54,407 individuals and cross-trait analysis with intelligence identifies shared genomic loci and genes. BioRxiv. 1–34. doi: 10.1101/613489.Google Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154. doi: 10.1017/S0140525X07001185.Google Scholar
Karlsgodt, K. H., Kochunov, P., Winkler, A. M., Laird, A. R., Almasy, L., Duggirala, R., … Glahn, D. C. (2010). A multimodal assessment of the genetic control over working memory. Journal of Neuroscience, 30(24), 8197–8202. doi: 10.1523/JNEUROSCI.0359-10.2010.Google Scholar
Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the environment: A systematic review. Psychological Medicine, 37(05), 615. doi: 10.1017/S0033291706009524.Google Scholar
Kendler, K. S., Turkheimer, E., Ohlsson, H., Sundquist, J., & Sundquist, K. (2015). Family environment and the malleability of cognitive ability: A Swedish national home-reared and adopted-away cosibling control study. Proceedings of the National Academy of Sciences, 112(15), 4612–4617. doi: 10.1073/pnas.1417106112.Google Scholar
Kim, M. S., Patel, K. P., Teng, A. K., Berens, A. J., & Lachance, J. (2018). Genetic disease risks can be misestimated across global populations. Genome Biology, 19(1), 179. doi: 10.1186/s13059–018-1561-7.Google Scholar
Krapohl, E., Patel, H., Newhouse, S., Curtis, C. J., von Stumm, S., Dale, P. S., … Plomin, R. (2018). Multi-polygenic score approach to trait prediction. Molecular Psychiatry, 23(5), 1368–1374. doi: 10.1038/mp.2017.163.Google Scholar
Lam, M., Trampush, J. W., Yu, J., Knowles, E., Davies, G., Liewald, D. C., … Lencz, T. (2017). Large-scale cognitive GWAS meta-analysis reveals tissue-specific neural expression and potential nootropic drug targets. Cell Reports, 21(9), 2597–2613. doi: 10.1016/j.celrep.2017.11.028.Google Scholar
Lee, J., Wedow, R., Okbay, A., Kong, E., Meghzian, O., Zacher, M., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121. doi: 10.1038/s41588–018-0147-3.Google Scholar
Lello, L., Avery, S. G., Tellier, L., Vazquez, A. I., de los Campos, G., & Hsu, S. D. H. (2018). Accurate genomic prediction of human height. Genetics, 210(2), 477–497. doi: 10.1534/genetics.118.301267.Google Scholar
Leppert, B., Havdahl, A., Riglin, L., Jones, H. J., Zheng, J., Davey Smith, G., … Stergiakouli, E. (2019). Association of maternal neurodevelopmental risk alleles with early-life exposures. JAMA Psychiatry, 76(8), 834. doi: 10.1001/jamapsychiatry.2019.0774.Google Scholar
Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321–332. doi: 10.1038/nrg3920.Google Scholar
Liégeois, R., Li, J., Kong, R., Orban, C., Van De Ville, D., Ge, T., … Yeo, B. T. T. (2019). Resting brain dynamics at different timescales capture distinct aspects of human behavior. Nature Communications, 10(1), 2317. doi: 10.1038/s41467–019-10317-7.Google Scholar
Luders, E., Narr, K. L., Thompson, P. M., & Toga, A. W. (2009). Neuroanatomical correlates of intelligence. Intelligence, 37(2), 156–163. doi: 10.1016/j.intell.2008.07.002.Google Scholar
Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., … Ideker, T. (2018). Using deep learning to model the hierarchical structure and function of a cell. Nature Methods, 15(4), 290–298. doi: 10.1038/nmeth.4627.Google Scholar
Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., … Kenny, E. E. (2017). Human demographic history impacts genetic risk prediction across diverse populations. The American Journal of Human Genetics, 100(4), 635–649. doi: 10.1016/j.ajhg.2017.03.004.Google Scholar
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584–591. doi: 10.1038/s41588–019-0379-x.Google Scholar
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., … Smith, S. M. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19(11), 1523–1536. doi: 10.1038/nn.4393.Google Scholar
Neale, M. C., & Maes, H. H. M. (1992). Methodology for genetic studies of twins and families. Dordrecht, The Netherlands: Kluwer Academic Publishers B.V.Google Scholar
Neisser, U., Boodoo, G., Bouchard, T. J. Jr., Boykin, A. W., Brody, N., Ceci, S. J., … Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51(2), 77–101. doi: 10.1037/0003-066X.51.2.77.Google Scholar
Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., & Turkheimer, E. (2012). Intelligence: New findings and theoretical developments. American Psychologist, 67(2), 130–159. doi: 10.1037/a0026699.Google Scholar
Okbay, A., Beauchamp, J. P., Fontana, M. A., Lee, J. J., Pers, T. H., Rietveld, C. A., … Benjamin, D. J. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533(7604), 539–542. doi: 10.1038/nature17671.Google Scholar
Palk, A. C., Dalvie, S., de Vries, J., Martin, A. R., & Stein, D. J. (2019). Potential use of clinical polygenic risk scores in psychiatry – Ethical implications and communicating high polygenic risk. Philosophy, Ethics, and Humanities in Medicine, 14(1), 4. doi: 10.1186/s13010–019-0073-8.Google Scholar
Pan, S., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1–15.Google Scholar
Penke, L., Maniega, S. M., Bastin, M. E., Valdés Hernández, M. C., Murray, C., Royle, N. A., … Deary, I. J. (2012). Brain white matter tract integrity as a neural foundation for general intelligence. Molecular Psychiatry, 17(10), 1026–1030. doi: 10.1038/mp.2012.66.Google Scholar
Pennington, B. F., Filipek, P. A., Lefly, D., Chhabildas, N., Kennedy, D. N., Simon, J. H., … DeFries, J. C. (2000). A twin MRI study of size variations in the human brain. Journal of Cognitive Neuroscience, 12(1), 223–232. doi: 10.1162/089892900561850.Google Scholar
Plomin, R., & von Stumm, S. (2018). The new genetics of intelligence. Nature Reviews Genetics, 19(3), 148–159. doi: 10.1038/nrg.2017.104.Google Scholar
Poldrack, R. A., & Gorgolewski, K. J. (2014). Making big data open: Data sharing in neuroimaging. Nature Neuroscience, 17(11), 1510–1517. doi: 10.1038/nn.3818.Google Scholar
Poldrack, R. A., & Yarkoni, T. (2016). From brain maps to cognitive ontologies: Informatics and the search for mental structure. Annual Review of Psychology, 67(1), 587–612. doi: 10.1146/annurev-psych-122414-033729.Google Scholar
Popejoy, A. B., & Fullerton, S. M. (2016). Genomics is failing on diversity. Nature, 538(7624), 161–164. doi: 10.1038/538161a.Google Scholar
Posthuma, D., De Geus, E. J. C., Baaré, W. F. C., Pol, H. E. H., Kahn, R. S., & Boomsma, D. I. (2002). The association between brain volume and intelligence is of genetic origin. Nature Neuroscience, 5(2), 83–84. doi: 10.1038/nn0202–83.Google Scholar
Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., … Koellinger, P. D. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340(6139), 1467–1471. doi: 10.1126/science.1235488.Google Scholar
Rosenberg, M. D., Casey, B. J., & Holmes, A. J. (2018). Prediction complements explanation in understanding the developing brain. Nature Communications, 9(1), 589. doi: 10.1038/s41467–018-02887-9.Google Scholar
Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2015). A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience, 19(1), 165–171. doi: 10.1038/nn.4179.Google Scholar
Roshchupkin, G. V., Gutman, B. A., Vernooij, M. W., Jahanshad, N., Martin, N. G., Hofman, A., … Adams, H. H. H. (2016). Heritability of the shape of subcortical brain structures in the general population. Nature Communications, 7(1), 13738. doi: 10.1038/ncomms13738.Google Scholar
Salakhutdinov, R., & Hinton, G. (2009). Deep Boltzmann machines. Proceedings of the 12th International Conference on Artificial Intelligence Statistics, 5, 448–455. www.utstat.toronto.edu/~rsalakhu/papers/dbm.pdfGoogle Scholar
Salovey, P., & Mayer, J. D. (1990). Emotional Intelligence. Imagination, Cognition and Personality, 9(3), 185–211. doi: 10.2190/DUGG-P24E-52WK-6CDG.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), 912–919. doi: 10.1038/s41588–018-0152-6.Google Scholar
Scheinost, D., Noble, S., Horien, C., Greene, A. S., Lake, E. MR., Salehi, M., … Constable, R. T. (2019). Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage, 193, 35–45. doi: 10.1016/j.neuroimage.2019.02.057.Google Scholar
Shine, J. M., Breakspear, M., Bell, P. T., Ehgoetz Martens, K. A., Shine, R., Koyejo, O., … Poldrack, R. A. (2019). Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature Neuroscience, 22(2), 289–296. doi: 10.1038/s41593–018-0312-0.Google Scholar
Siegel, J. S., Mitra, A., Laumann, T. O., Seitzman, B. A., Raichle, M., Corbetta, M., & Snyder, A. Z. (2017). Data quality influences observed links between functional connectivity and behavior. Cerebral Cortex, 27(9), 4492–4502. doi: 10.1093/cercor/bhw253.CrossRefGoogle ScholarPubMed
Smith, S. M., & Nichols, T. E. (2018). Statistical challenges in “big data” human neuroimaging. Neuron, 97(2), 263–268. doi: 10.1016/j.neuron.2017.12.018.Google Scholar
Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., Glasser, M. F., … Miller, K. L. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, 18(11), 1565–1567. doi: 10.1038/nn.4125.Google Scholar
Song, M., Zhou, Y., Li, J., Liu, Y., Tian, L., Yu, C., & Jiang, T. (2008). Brain spontaneous functional connectivity and intelligence. NeuroImage, 41(3), 1168–1176. doi: 10.1016/j.neuroimage.2008.02.036.Google Scholar
Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292.Google Scholar
Sternberg, R. J. (2004). Culture and intelligence. American Psychologist, 59(5), 325–338. doi: 10.1037/0003–066X.59.5.325.Google Scholar
Tadayon, E., Pascual-Leone, A., & Santarnecchi, E. (2019). Differential contribution of cortical thickness, surface area, and gyrification to fluid and crystallized intelligence. Cerebral Cortex, 30, 215–225. doi: 10.1093/cercor/bhz082.Google Scholar
Tam, V., Patel, N., Turcotte, M., Bossé, Y., Paré, G., & Meyre, D. (2019). Benefits and limitations of genome-wide association studies. Nature Reviews Genetics, 20(8), 467–484. doi: 10.1038/s41576–019-0127-1.Google Scholar
Thompson, P. M., Cannon, T. D., Narr, K. L., van Erp, T., Poutanen, V.-P., Huttunen, M., … Toga, A. W. (2001). Genetic influences on brain structure. Nature Neuroscience, 4(12), 1253–1258. doi: 10.1038/nn758.Google Scholar
Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018). The personal and clinical utility of polygenic risk scores. Nature Reviews Genetics, 19(9), 581–590. doi: 10.1038/s41576–018-0018-x.Google Scholar
Tucker-Drob, E. M., & Harden, K. P. (2012). Early childhood cognitive development and parental cognitive stimulation: Evidence for reciprocal gene-environment transactions: Early cognitive development and parenting. Developmental Science, 15(2), 250–259. doi: 10.1111/j.1467-7687.2011.01121.x.Google Scholar
Turnwald, B. P., Goyer, J. P., Boles, D. Z., Silder, A., Delp, S. L., & Crum, A. J. (2019). Learning one’s genetic risk changes physiology independent of actual genetic risk. Nature Human Behaviour, 3(1), 48–56. doi: 10.1038/s41562–018-0483-4.Google Scholar
Visscher, P. M., Hill, W. G., & Wray, N. R. (2008). Heritability in the genomics era – Concepts and misconceptions. Nature Reviews Genetics, 9(4), 255–266. doi: 10.1038/nrg2322.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), 2127–2137. doi: 10.1093/cercor/bhu018.Google Scholar
Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., … Gerstein, M. B. (2018). Comprehensive functional genomic resource and integrative model for the human brain. Science, 362(6420), eaat8464. doi: 10.1126/science.aat8464.Google Scholar
Watanabe, K., Umićević Mirkov, M., de Leeuw, C. A., van den Heuvel, M. P., & Posthuma, D. (2019). Genetic mapping of cell type specificity for complex traits. Nature Communications, 10(1), 3222. doi: 10.1038/s41467–019-11181-1.Google Scholar
Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008). The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience, 20(3), 470–477. doi: 10.1162/jocn.2008.20040.Google Scholar
Wojcik, G. L., Graff, M., Nishimura, K. K., Tao, R., Haessler, J., Gignoux, C. R., … Carlson, C. S. (2019). Genetic analyses of diverse populations improves discovery for complex traits. Nature, 570(7762), 514–518. doi: 10.1038/s41586–019-1310-4.Google Scholar
Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017). Building better biomarkers: Brain models in translational neuroimaging. Nature Neuroscience, 20(3), 365–377. doi: 10.1038/nn.4478.Google Scholar
Woodberry, K. A., Giuliano, A. J., & Seidman, L. J. (2008). Premorbid IQ in schizophrenia: A meta-analytic review. American Journal of Psychiatry, 165(5), 579–587. doi: 10.1176/appi.ajp.2008.07081242.Google Scholar
Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J., & Visscher, P. M. (2018). Common disease is more complex than implied by the core gene omnigenic model. Cell, 173(7), 1573–1580. doi: 10.1016/j.cell.2018.05.051.Google Scholar
Yang, J. H., Wright, S. N., Hamblin, M., McCloskey, D., Alcantar, M. A., Schrübbers, L., … Collins, J. J. (2019). A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell, 177(6), 1649-1661.e9. doi: 10.1016/j.cell.2019.04.016.Google Scholar
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122. doi: 10.1177/1745691617693393.Google Scholar
Yu, M. K., Ma, J., Fisher, J., Kreisberg, J. F., Raphael, B. J., & Ideker, T. (2018). Visible machine learning for biomedicine. Cell, 173(7), 1562–1565. doi: 10.1016/j.cell.2018.05.056.Google Scholar
Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., & Telenti, A. (2019). A primer on deep learning in genomics. Nature Genetics, 51(1), 12–18. doi: 10.1038/s41588–018-0295-5.Google Scholar
References
Allegrini, A. G., Selzam, S., Rimfeld, K., von Stumm, S., Pingault, J.-B., & Plomin, R. (2019). Genomic prediction of cognitive traits in childhood and adolescence. Molecular Psychiatry, 24(6), 819–827. doi: 10.1038/s41380-019-0394-4.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 1–20. doi: 10.1016/j.tics.2017.10.001.Google Scholar
Bates, T. C., Maher, B. S., Colodro-Conde, L., Medland, S. E., McAloney, K., Wright, M. J., … Gillespie, N. A. (2019). Social competence in parents increases children’s educational attainment: Replicable genetically-mediated effects of parenting revealed by non-transmitted DNA. Twin Research and Human Genetics, 22(1), 1–3. doi: 10.1017/thg.2018.75.Google Scholar
Beauchamp, J. P. (2016). Genetic evidence for natural selection in humans in the contemporary United States. Proceedings of the National Academy of Sciences, 113(28), 7774–7779. doi: 10.1073/pnas.1600398113.Google Scholar
Belsky, D. W., Domingue, B. W., Wedow, R., Arseneault, L., Boardman, J. D., Caspi, A., … Harris, K. M. (2018). Genetic analysis of social-class mobility in five longitudinal studies. Proceedings of the National Academy of Sciences, 115(31), E7275–E7284. doi: 10.1073/pnas.1801238115.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, 1–13. doi: 10.1016/j.intell.2019.101376.Google Scholar
de Vlaming, R., & Groenen, P. J. F. (2015). The current and future use of ridge regression for prediction in quantitative genetics. BioMed Research International, 2015, 143712. doi: 10.1155/2015/143712.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, 373(1756), 20170284. doi: 10.1098/rstb.2017.0284.Google Scholar
Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. PLoS Genetics, 9(3), e1003348. doi: 10.1371/journal.pgen.1003348.Google Scholar
Elliott, L. T., Sharp, K., Alfaro-Almagro, F., Shi, S., Miller, K. L., Douaud, G., … Smith, S. M. (2018). Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature, 562(7726), 210–216. doi: 10.1038/s41586-018-0571-7.Google Scholar
Fisher, R. A. (1918). The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh, 52(2), 399–433. doi: 10.1017/S0080456800012163.Google Scholar
Fisher, R. A. (1941). Average excess and average effect of a gene substitution. Annals of Eugenics, 11(1), 53–63. doi: 10.1111/j.1469-1809.1941.tb02272.x.Google Scholar
Fornito, A., Arnatkevičiūtė, A., & Fulcher, B. D. (2019). Bridging the gap between connectome and transcriptome. Trends in Cognitive Sciences, 23(1), 34–50. doi: 10.1016/j.tics.2018.10.005.Google Scholar
Gignac, G. E., & Bates, T. C. (2017). Brain volume and intelligence: The moderating role of intelligence measurement quality. Intelligence, 64(May), 18–29. doi: 10.1016/j.intell.2017.06.004.Google Scholar
Haier, R. J. (2011). Biological basis of intelligence. In Sternberg, R. J. & Kaufman, S. B. (eds.), The Cambridge handbook of intelligence (pp. 351–368). Cambridge University Press. doi: 10.1017/CBO9780511977244.019.Google Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154. doi: 10.1017/S0140525X07001185.Google Scholar
Karama, S., Ad-Dab’bagh, Y., Haier, R. J., Deary, I. J., Lyttelton, O. C., Lepage, C., … Brain Development Cooperative Group. (2009). Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence, 37(2), 145–155. doi: 10.1016/j.intell.2008.09.006.Google Scholar
Kong, A., Thorleifsson, G., Frigge, M. L., Vilhjálmsson, B. J., Young, A. I., Thorgeirsson, T. E., … Stefansson, K. (2018). The nature of nurture: Effects of parental genotypes. Science, 359(6374), 424–428. doi: 10.1126/science.aan6877.Google Scholar
Lande, R., & Thompson, R. (1990). Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics, 124(3), 743–756. doi: 10.1046/j.1365-2540.1998.00308.x.Google Scholar
Lee, J. J. (2012). Correlation and causation in the study of personality (with discussion). European Journal of Personality, 26(4), 372–412. doi: 10.1002/per.1863.Google Scholar
Lee, J. J., & Chow, C. C. (2013). The causal meaning of Fisher’s average effect. Genetics Research, 95(2–3), 89–109. doi: 10.1017/S0016672313000074.Google Scholar
Lee, J. J., & McGue, M. (2016). Why behavioral genetics matters: A comment on Plomin (2016). Perspectives on Psychological Science, 11(1), 29–30. doi: 10.1177/1745691615611932.Google Scholar
Lee, J. J., McGue, M., Iacono, W. G., Michael, A. M., & Chabris, C. F. (2019). The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling. Intelligence, 75, 48–58. doi: 10.1016/j.intell.2019.01.011.Google Scholar
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121. doi: 10.1038/s41588-018-0147-3.Google Scholar
Lello, L., Avery, S. G., Tellier, L., Vazquez, A. I., de los Campos, G., & Hsu, S. D. H. (2018). Accurate genomic prediction of human height. Genetics, 210(2), 477–497. doi: 10.1534/genetics.118.301267.Google Scholar
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5(5), e1000395. doi: 10.1371/journal.pcbi.1000395.Google Scholar
Liu, H. (2018). Social and genetic pathways in multigenerational transmission of educational attainment. American Sociological Review, 83(2), 278–304. doi: 10.1177/0003122418759651.Google Scholar
Mak, T. S. H., Porsch, R. M., Choi, S. W., Zhou, X., & Sham, P. C. (2017). Polygenic scores via penalized regression on summary statistics. Genetic Epidemiology, 41(6), 469–480. doi: 10.1002/gepi.22050.Google Scholar
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819–1829.Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33(7), 1004–1023. doi: 10.1016/j.neubiorev.2009.04.001.Google Scholar
Okbay, A., Beauchamp, J. P., Fontana, M. A., Lee, J. J., Pers, T. H., Rietveld, C. A., … Benjamin, D. J. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533(7604), 539–542. doi: 10.1038/nature17671.arXiv:NIHMS150003.Google Scholar
Park, G., Lubinski, D., & Benbow, C. P. (2007). Contrasting intellectual patterns predict creativity in the arts and sciences: Tracking intellectually precocious youth over 25 years. Psychological Science, 18(11), 948–952. doi: 10.1111/j.1467-9280.2007.02007.x.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, 411–432. doi: 10.1016/j.neubiorev.2015.09.017.Google Scholar
Plomin, R., DeFries, J. C., & Loehlin, J. C. (1977). Genotype-environment interaction and correlation in the analysis of human behavior. Psychological Bulletin, 84(2), 309–322. doi: 10.1037/0033-2909.84.2.309.Google Scholar
Purcell, S. M., Pato, M. T., Williams, N. M., Scolnick, E. M., Van Beck, M., O’Donovan, M. C., … Holmans, P. A. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature, 460(August), 748–752. doi: 10.1038/nature08185.Google Scholar
Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., … Koellinger, P. D. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 25(4), 57–82. doi: 10.1257/jep.25.4.57.Google Scholar
Robertson, A. (1966). A mathematical model of the culling process in dairy cattle. Animal Production, 8(1), 95–108. doi: 10.1017/S0003356100037752.Google Scholar
Schmitt, J. E., Neale, M. C., Clasen, L. S., Liu, S., Seidlitz, J., Pritikin, J. N., … Raznahan, A. (2019). A comprehensive quantitative genetic analysis of cerebral surface area in youth. Journal of Neuroscience, 13(16), 3028–3040. doi: 10.1523/JNEUROSCI.2248-18.2019.Google Scholar
Schnack, H. G., Van Haren, N. E. M., Brouwer, R. M., Evans, A., Durston, S., Boomsma, D. I., … Hulshoff Pol, H. E. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral Cortex, 25(6), 1608–1617. doi: 10.1093/cercor/bht357.Google Scholar
Shulman, C., & Bostrom, N. (2014). Embryo selection for cognitive enhancement: Curiosity or game-changer? Global Policy, 5(1), 85–92. doi: 10.1111/1758-5899.12123.Google Scholar
Spindel, J. E., & McCouch, S. R. (2016). When more is better: How data sharing would accelerate genomic selection of crop plants. New Phytologist, 212(4), 814–826. doi: 10.1111/nph.14174.Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 7619–7624. doi: 10.1523/jneurosci.1443-09.2009.Google Scholar
Vattikuti, S., Lee, J. J., Chang, C. C., Hsu, S. D. H., & Chow, C. C. (2014). Applying compressed sensing to genome-wide association studies. GigaScience, 3(1), 10. doi: 10.1186/2047-217X-3-10.Google Scholar
Vilhjálmsson, B. J., Yang, J., Finucane, H. K., Gusev, A., Lindström, S., Ripke, S., … Price, A. L. (2015). Modeling linkage disequilibrium increases accuracy of polygenic risk scores. American Journal of Human Genetics, 97(4), 576–592. doi: 10.1016/j.ajhg.2015.09.001.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), 2127–2137. doi: 10.1093/cercor/bhu018.Google Scholar
Walhovd, K. B., Krogsrud, S. K., Amlien, I. K., Bartsch, H., Bjørnerud, A., Due-Tønnessen, P., … Fjell, A. M. (2016). Neurodevelopmental origins of lifespan changes in brain and cognition. Proceedings of the National Academy of Sciences, 113(33), 9357–9362. doi: 10.1073/pnas.1524259113.Google Scholar
Willoughby, E. A., McGue, M., Iacono, W. G., Rustichini, A., & Lee, J. J. (2019). The role of parental genotype in predicting offspring years of education: Evidence for genetic nurture. Molecular Psychiatry. Online first. doi: 10.1038/s41380-019-0494-1.Google Scholar
Wray, N. R., Goddard, M. E., & Visscher, P. M. (2007). Prediction of individual genetic risk to disease from genome-wide association studies. Genome Research, 17(10), 1520–1528. doi: 10.1101/gr.6665407.Google Scholar
Wray, N. R., Kemper, K. E., Hayes, B. J., Goddard, M. E., & Visscher, P. M. (2019). Complex trait prediction from genome data: Contrasting EBV in livestock to PRS in humans. Genetics, 211(4), 1131–1141. doi: 10.1534/genetics.119.301859.Google Scholar
Yengo, L., Robinson, M. R., Keller, M. C., Kemper, K. E., Yang, Y., Trzaskowski, M., … Visscher, P. M. (2018). Imprint of assortative mating on the human genome. Nature Human Behaviour, 2(12), 948–954. doi: 10.1038/s41562-018-0476-3.Google Scholar