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-jkksz Total loading time: 0 Render date: 2024-12-25T21:18:05.388Z Has data issue: false hasContentIssue false

7 - It’s about Time

Towards a Longitudinal Cognitive Neuroscience of Intelligence

from Part II - Theories, Models, and Hypotheses

Published online by Cambridge University Press:  11 June 2021

By
Rogier A. Kievit  and
Ivan L. Simpson-Kent
Edited by
Aron K. Barbey ,
Sherif Karama  and
Richard J. Haier
Aron K. Barbey
Affiliation:
University of Illinois, Urbana-Champaign
Sherif Karama
Affiliation:
McGill University, Montréal
Richard J. Haier
Affiliation:
University of California, Irvine
Get access

Summary

The search for the biological properties that underlie intelligent behavior has held the scientific imagination at least since the pre-Socratic philosophers. Early hypotheses posited a crucial role for the heart (Aristotle; Gross, 1995), the ventricles (Galen; Rocca, 2009), and the “Heat, Moisture, and Driness” of the brain (Huarte, 1594). The advent of neuroimaging technology such as EEG, MEG, and MRI has provided more suitable tools to scientifically study the relationship between mind and brain. To date, many hundreds of studies have examined the association between brain structure and function on the one hand and individual differences in general cognitive abilities on the other. Both qualitative and quantitative reviews have summarized the cross-sectional associations between intelligence and brain volume (Pietschnig, Penke, Wicherts, Zeiler, & Voracek, 2015), as well as more network- and imaging-specific hypotheses which suggest a key role for the frontoparietal system in supporting individual differences in intelligence (Basten, Hilger, & Fiebach, 2015; Deary, Penke, & Johnson, 2010; Jung & Haier, 2007). These findings are bolstered by converging evidence from lesion studies (Barbey, Colom, Paul, & Grafman, 2014), cognitive abilities in disorders associated with physiological abnormalities (Kail, 1998), and the neural signatures associated with the rapid acquisition of new skills (Bengtsson et al., 2005).

Type
Chapter
Information
The Cambridge Handbook of Intelligence and Cognitive Neuroscience , pp. 123 - 146
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

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

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.

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
×