Skip to main content Accessibility help

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

Close cookie message
×
Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-27T08:49:11.843Z Has data issue: false hasContentIssue false

19 - The Biological Basis of Intelligence

from Part IV - Biology of Intelligence

Published online by Cambridge University Press:  13 December 2019

By
Richard J. Haier
Edited by
Robert J. Sternberg
Robert J. Sternberg
Affiliation:
Cornell University, New York
Get access

Summary

Genetic studies provide a compelling story of gene influences on intelligence, and neuroimaging studies provide insights about relevant brain structure and function. Polygenetic scores based on DNA and brain connectivity patterns based on neuroimaging are beginning to show correlations with individual differences in intelligence. Imaging studies also provide insights on specific brain networks related to intelligence, especially the PFIT model. The concept of brain efficiency is now being explored at the network and the dendrite levels. As we push inexorably deeper into the brain from cortex to neurons to synapses, we are at the threshold of developing a molecular biology of intelligence based both on gene expression related to brain development and function, and on the cascades of neurobiological events at the neuron and synapse levels. As prediction advances and the biological mechanisms underlying intelligence are identified, a major step will be manipulation of those mechanisms to enhance intelligence. That is why the study of intelligence has never been more exciting or important.

Keywords

intelligenceneuroscienceneuroimaginggeneticsDNAbrain connectivitypolygenetic scoresPFIT model
Type
Chapter
Information
The Cambridge Handbook of Intelligence , pp. 451 - 468
Publisher: Cambridge University Press
Print publication year: 2020

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

Alavash, M., Lim, S. J., Thiel, C., Sehm, B., Deserno, L., & Obleser, J. (2018). Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance. Neuroimage, 172, 341–356. https://doi.org/10.1016/j.neuroimage.2018.01.048Google Scholar
Barbey, A. K., Colom, R., Paul, E., Forbes, C., Krueger, F., Goldman, D., & Grafman, J. (2014a). Preservation of general intelligence following traumatic brain injury: Contributions of the Met66 brain-derived neurotrophic factor. PLoS One, 9(2). https://doi.org/10.1371/journal.pone.0088733CrossRefGoogle ScholarPubMed
Barbey, A. K., Colom, R., Paul, E. J., & Grafman, J. (2014b). Architecture of fluid intelligence and working memory revealed by lesion mapping. Brain Structure and Function, 219(2), 485–494.CrossRefGoogle ScholarPubMed
Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135(Pt 4), 1154–1164. https://doi.org/10.1093/brain/aws021Google Scholar
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. http://dx.doi.org/10.1016/j.intell.2015.04.009CrossRefGoogle Scholar
Basten, U., Stelzel, C., & Fiebach, C. J. (2013). Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network. Intelligence, 41(5), 517–528.Google Scholar
Biazoli, C. E. Jr., Salum, G. A., Pan, P. M., Zugman, A., Amaro, E. Jr., Rohde, L. A., et al. (2017). Commentary: Functional connectome fingerprint: Identifying individuals using patterns of brain connectivity. Frontiers in Human Neuroscience, 11, 47. https://doi.org/10.3389/fnhum.2017.00047Google Scholar
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge, UK: Cambridge University Press.Google Scholar
Chalke, F. C., & Ertl, J. (1965). Evoked potentials and intelligence. Life Sciences, 4(13), 1319–1322.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. https://doi.org/10.1523/JNEUROSCI.0536-12.2012Google Scholar
Colom, R., Karama, S., Jung, R. E., & Haier, R. J. (2010). Human intelligence and brain networks. Dialogues in Clinical Neuroscience, 12(4), 489–501.CrossRefGoogle ScholarPubMed
Colom, R., & Roman, F. J. (2018). Enhancing intelligence: From the group to the individual. Journal of Intelligence, 6(11). https://doi.org/10.3390/jintelligence6010011Google Scholar
Davis, J. M., Searles, V. B., Anderson, N., Keeney, J., Raznahan, A., Horwood, L. J., et al. (2015). DUF1220 copy number is linearly associated with increased cognitive function as measured by total IQ and mathematical aptitude scores. Human Genetics, 134(1), 67–75. https://doi.org/10.1007/s00439-014-1489-2Google Scholar
Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35(1), 13–21.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 Transactios of the Royal Society B. https://doi.org/10.1098/rstb.2017.0284Google Scholar
Duncan, J., Emslie, H., Williams, P., Johnson, R., & Freer, C. (1996). Intelligence and the frontal lobe: The organization of goal-directed behavior. Cognitive Psychology, 30(3), 257–303.Google Scholar
Nature (2017). Intelligence test (editorial). 545, 385–386.Google Scholar
Ertl, J. P., & Schafer, E. W. (1969). Brain response correlates of psychometric intelligence. Nature, 223(204), 421–422.Google Scholar
Euler, M. J., Weisend, M. P., Jung, R. E., Thoma, R. J., & Yeo, R. A. (2015). Reliable activation to novel stimuli predicts higher fluid intelligence. Neuroimage, 114, 311–319. https://doi.org/10.1016/j.neuroimage.2015.03.078Google Scholar
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., et al. (2015). Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664–1671. https://doi.org/10.1038/nn.4135Google Scholar
Genc, E., Fraenz, C., Schluter, C., Friedrich, P., Hossiep, R., Voelkle, M. C., et al. (2018). Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature Communications, 9(1), 1905. https://doi.org/10.1038/s41467-018-04268-8Google Scholar
Glascher, J., Rudrauf, D., Colom, R., Paul, L. K., Tranel, D., Damasio, H., & Adolphs, R. (2010). Distributed neural system for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences, 107(10), 4705–4709. https://doi.org/10.1073/Pnas.0910397107Google Scholar
Glascher, J., Tranel, D., Paul, L. K., Rudrauf, D., Rorden, C., Hornaday, A., et al. (2009). Lesion mapping of cognitive abilities linked to intelligence. Neuron, 61(5), 681–691. https://doi.org/10.1016/j.neuron.2009.01.026Google Scholar
Goriounova, N., Heyer, D. B., Wilbers, R., Verhoog, M. B., Giugliano, M., Verbist, C., et al. (2018). Large and fast human pyramidal neurons associate with intelligence. eLife, 7, e41714, https://doi.org/10.7554/eLife.41714CrossRefGoogle ScholarPubMed
Green, S., Blackmon, K., Thesen, T., DuBois, J., Wang, X. Y., Halgren, E., & Devinsky, O. (2018). Parieto-frontal gyrification and working memory in healthy adults. Brain Imaging and Behavior, 12(2), 303–308.Google Scholar
Guntupalli, J. S., Feilong, M., & Haxby, J. V. (2018). A computational model of shared fine-scale structure in the human connectome. PLoS Computational Biology, 14(4), e1006120. https://doi.org/10.1371/journal.pcbi.1006120Google Scholar
Haier, R. J. (2009). Neuro-intelligence, neuro-metrics and the next phase of brain imaging studies. Intelligence, 37(2), 121–123. https://doi.org/10.1016/j.intell.2008.12.006Google Scholar
Haier, R. J. (2011). Biological basis of intelligence. In Sternberg, R. J. & Kaufman, A. S. (Eds.), Cambridge handbook of intelligence (pp. 351–368). Cambridge, UK: Cambridge University Press.Google Scholar
Haier, R. J. (2014). Increased intelligence is a myth (so far). Frontiers in Systems Neuroscience, 8. https://doi.org/10.3389/fnsys.2014.00034CrossRefGoogle ScholarPubMed
Haier, R. J. (2017). The neuroscience of intelligence. New York: Cambridge University Press.Google Scholar
Haier, R. J. (2018). A view from the brain. In Sternberg, R. J. (Ed.), The nature of human intelligence (pp. 167–182). New York: Cambridge University Press.Google Scholar
Haier, R. J. (2019 in press). Biological approaches to intelligence. In Sternberg, R. J. (Ed.), Human intelligence: An introduction (pp. 139–173). New York: Cambridge University Press.Google Scholar
Haier, R. J., Karama, S., Leyba, L., & Jung, R. E. (2009). MRI assessment of cortical thickness and functional activity changes in adolescent girls following three months of practice on a visual-spatial task. BMC Research Notes, 2. https://doi.org/10.1186/1756–0500-2–174Google Scholar
Haier, R. J., Siegel, B. V., Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., et al. (1988). Cortical glucose metabolic-rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12(2), 199–217.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. https://doi.org/10.1038/srep32328Google Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017a). Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 10–25. https://doi.org/10.1016/j.intell.2016.11.001Google 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. https://doi.org/10.1038/s41598-017-15795-7Google Scholar
Hill, W. D., Davies, G., van de Lagemaat, L. N., Christoforou, A., Marioni, R. E., Fernandes, C. P., et al. (2014). Human cognitive ability is influenced by genetic variation in components of postsynaptic signalling complexes assembled by NMDA receptors and MAGUK proteins. Translational Psychiatry, 4, e341. https://doi.org/10.1038/tp.2013.114CrossRefGoogle ScholarPubMed
Hunt, E. B. (2011). Human intelligence. Cambridge, UK: Cambridge University Press.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. New York: Elsevier.Google Scholar
Johnson, W., te Nijenhuis, J., & Bouchard, T. J. (2008). Still just 1 g: Consistent results from five test batteries. Intelligence, 36(1), 81–95.Google Scholar
Jung, R. E., Brooks, W. M., Yeo, R. A., Chiulli, S. J., Weers, D. C., & Sibbitt, W. L. (1999). Biochemical markers of intelligence: A proton MR spectroscopy study of normal human brain. Proceedings of the Royal Society B: Biological Sciences, 266(1426), 1375–1379.Google Scholar
Jung, R. E., Gasparovic, C., Chavez, R. S., Caprihan, A., Barrow, R., & Yeo, R. A. (2009). Imaging intelligence with proton magnetic resonance spectroscopy. Intelligence, 37(2), 192–198.Google Scholar
Jung, R. E., & Haier, R. J. (2007). The parieto-frontal integration theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154; discussion 154–187. https://doi.org/10.1017/S0140525X07001185Google Scholar
Jung, R. E., Haier, R. J., Yeo, R. A., Rowland, L. M., Petropoulos, H., Levine, A. S., et al. (2005). Sex differences in N-acetylaspartate correlates of general intelligence: An H-1-MRS study of normal human brain. Neuroimage, 26(3), 965–972.CrossRefGoogle Scholar
Krapohl, E., Patel, H., Newhouse, S., Curtis, C. J., von Stumm, S., Dale, P. S., et al. (2018). Multi-polygenic score approach to trait prediction. Molecular Psychiatry, 23(5), 1368–1374. https://doi.org/10.1038/mp.2017.163Google 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. https://doi.org/10.1016/j.neuroimage.2018.01.018Google Scholar
Langer, N., Pedroni, A., Gianotti, L. R., Hanggi, J., Knoch, D., & Jancke, L. (2012). Functional brain network efficiency predicts intelligence. Human Brain Mapping, 33(6), 1393–1406. https://doi.org/10.1002/hbm.21297Google Scholar
Lashley, K. S. (1964). Brain mechanisms and intelligence. New York: Hafner.Google Scholar
Li, Y., Liu, Y., Li, J., Qin, W., Li, K. C., Yu, C. S., et al. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5(5). https://doi.org/10.1371/journal.pcbi.1000395Google Scholar
Liao, X., Vasilakos, A. V., & He, Y. (2017). Small-world human brain networks: Perspectives and challenges. Neuroscience and Biobehavioral Reviews, 77, 286–300.CrossRefGoogle ScholarPubMed
Liu, J., Liao, X., Xia, M., & He, Y. (2018). Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns. Human Brain Mapping, 39(2), 902–915. https://doi.org/10.1002/hbm.23890Google Scholar
Neubauer, A. C., & Fink, A. (2003). Fluid intelligence and neural efficiency: Effects of task complexity and sex. Personality and Individual Differences, 35(4), 811–827.Google Scholar
Neubauer, A. C., & Fink, A. (2008). Intelligence and neural efficiency: A review and new data. International Journal of Psychophysiology, 69(3), 168–169.Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency: Measures of brain activation versus measures of functional connectivity in the brain. Intelligence, 37(2), 223–229.Google Scholar
Nikolaidis, A., Baniqued, P. L., Kranz, M. B., Scavuzzo, C. J., Barbey, A. K., Kramer, A. F., et al. (2017). Multivariate associations of fluid intelligence and NAA. Cerebral Cortex, 27(4), 2607–2616. https://doi.org/10.1093/cercor/bhw070Google Scholar
Paul, E. J., Larsen, R. J., Nikolaidis, A., Ward, N., Hillman, C. H., Cohena, N. J., et al. (2016). Dissociable brain biomarkers of fluid intelligence. Neuroimage, 137, 201–211. https://doi.org/10.1016/j.neuroimage.2016.05.037Google Scholar
Plomin, R., & von Stumm, S. (2018). The new genetics of intelligence. Nature Reviews Genetics, 19(3), 148–159. https://doi.org/10.1038/nrg.2017.104Google Scholar
Poldrack, R. A. (2015). Is “efficiency” a useful concept in cognitive neuroscience? Developmental Cognitive Neuroscience, 11, 12–17. https://doi.org/10.1016/j.dcn.2014.06.001Google Scholar
Ponsoda, V., Martinez, K., Pineda-Pardo, J. A., Abad, F. J., Olea, J., Roman, F. J., et al. (2017). Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis. Human Brain Mapping, 38(2), 803–816. https://doi.org/10.1002/hbm.23419Google Scholar
Rietveld, C. A., Esko, T., Davies, G., Pers, T. H., Turley, P., Benyamin, B., et al. (2014). Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proceedings of the National Academy of Sciences, 111(38), 13790–13794. https://doi.org/10.1073/pnas.1404623111Google Scholar
Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., et al. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340(6139), 1467–1471. https://doi.org/10.1126/science.1235488Google Scholar
Roth, B., Becker, N., Romeyke, S., Schafer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades: A meta-analysis. Intelligence, 53, 118–137.Google Scholar
Ryman, S. G., Yeo, R. A., Witkiewitz, K., Vakhtin, A. A., van den Heuvel, M., de Reus, M., et al. (2016). Fronto-parietal gray matter and white matter efficiency differentially predict intelligence in males and females. Human Brain Mapping, 37(11), 4006–4016. https://doi.org/10.1002/hbm.23291CrossRefGoogle ScholarPubMed
Santarnecchi, E., Emmendorfer, A., & Pascual-Leone, A. (2017a). Dissecting the parieto-frontal correlates of fluid intelligence: A comprehensive ALE meta-analysis study. Intelligence, 63, 9–28.Google Scholar
Santarnecchi, E., Emmendorfer, A., Tadayon, S., Rossi, S., Rossi, A., & Pascual-Leone, A. (2017b). Network connectivity correlates of variability in fluid intelligence performance. Intelligence, 65, 35–47.Google Scholar
Santarnecchi, E., Galli, G., Polizzotto, N. R., Rossi, A., & Rossi, S. (2014). Efficiency of weak brain connections support general cognitive functioning. Human Brain Mapping, 35(9), 4566–4582. https://doi.org/10.1002/hbm.22495Google Scholar
Santarnecchi, E., & Rossi, S. (2016). Advances in the neuroscience of intelligence: From brain connectivity to brain perturbation. Spanish Journal of Psychology, 19. https://doi.org/10.1017/sjp.2016.89Google Scholar
Schafer, E. W. (1982). Neural adaptability: A biological determinant of behavioral intelligence. International Journal of Neuroscience, 17(3), 183–191.CrossRefGoogle ScholarPubMed
Selzam, S., Krapohl, E., von Stumm, S., O’Reilly, P. F., Rimfeld, K., Kovas, Y., et al. (2018). Predicting educational achievement from DNA. Molecular Psychiatry, 23(1), 161. https://doi.org/10.1038/mp.2017.203Google Scholar
Shehzad, Z., Kelly, C., Reiss, P. T., Cameron Craddock, R., Emerson, J. W., McMahon, K., et al. (2014). A multivariate distance-based analytic framework for connectome-wide association studies. Neuroimage, 93(Pt. 1), 74–94. https://doi.org/10.1016/j.neuroimage.2014.02.024Google Scholar
Sniekers, S., Stringer, S., Watanabe, K., Jansen, P. R., Coleman, J. R. I., Krapohl, E., et al. (2017). Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics, 49(7), 1107–1112. https://doi.org/10.1038/ng.3869CrossRefGoogle Scholar
Song, M., Liu, Y., Zhou, Y., Wang, K., Yu, C. S., & Jiang, T. Z. (2009). Default network and intelligence difference. IEEE Transactions on Autonomous Mental Development, 1(2), 101–109. https://doi.org/10.1109/tamd.2009.2029312Google Scholar
Thompson, R., Crinella, F. M., & Yu, J. (1990). Brain mechanisms in problem solving and intelligence: A survey of the rat brain. New York: Plenum Press.Google Scholar
Vakhtin, A. A., Ryman, S. G., Flores, R. A., & Jung, R. E. (2014). Functional brain networks contributing to the parieto-frontal integration theory of intelligence. Neuroimage, 103, 349–354. https://doi.org/10.1016/j.neuroimage.2014.09.055Google Scholar
Valizadeh, S. A., Liem, F., Merillat, S., Hanggi, J., & Jancke, L. (2018). Identification of individual subjects on the basis of their brain anatomical features. Scientific Reports, 8(1), 5611. https://doi.org/10.1038/s41598-018-23696-6Google Scholar
van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. Journal of Neuroscience, 31(44), 15775–15786. https://doi.org/10.1523/JNEUROSCI.3539-11.2011Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Pol, H. E. H. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 7619–7624. https://doi.org/10.1523/jneurosci.1443-09.2009Google Scholar
Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., et al. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature Neuroscience, 21, 860–868.Google Scholar
Zhao, M., Kong, L., & Qu, H. (2014). A systems biology approach to identify intelligence quotient score-related genomic regions, and pathways relevant to potential therapeutic treatments. Scientific Reports, 4, 4176. https://doi.org/10.1038/srep04176Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org 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.

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.

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.

Available formats
×