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10 - Diffusion-Weighted Imaging of Intelligence

from Part III - Neuroimaging Methods and Findings

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
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Summary

Since the dawn of intelligence research, it has been of considerable interest to establish a link between intellectual ability and the various properties of the brain. In the second half of the nineteenth century, scientists such as Broca and Galton were among the first to utilize craniometry in order to investigate relationships between different measures of head size and intellectual ability (Deary, Penke, & Johnson, 2010; Galton, 1888). However, since craniometry can at best provide a very coarse estimate of actual brain morphometry and adequate methods for intelligence testing were not established at that time, respective efforts were not particularly successful in producing insightful evidence. About 100 years later, technical developments in neuroscientific research, such as the introduction of magnetic resonance imaging (MRI), enabled scientists to assess a wide variety of the brain’s structural properties in vivo and relate them to cognitive capacity. One of the most prominent and stable findings from this line of research is that bigger brains tend to perform better at intelligence-related tasks. Meta-analyses comprising a couple of thousand individuals have reported correlation coefficients in the range of .24–.33 for the association between overall brain volume and intelligence (McDaniel, 2005; Pietschnig, Penke, Wicherts, Zeiler, & Voracek, 2015). A common biological explanation for this association is the fact that individuals with more cortical volume are likely to possess more neurons (Pakkenberg & Gundersen, 1997) and thus more computational power to engage in problem-solving and logical reasoning.

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Publisher: Cambridge University Press
Print publication year: 2021

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References

Allin, M. P. G., Kontis, D., Walshe, M., Wyatt, J., Barker, G. J., Kanaan, R. A. A., … Nosarti, C. (2011). White matter and cognition in adults who were born preterm. PLoS One, 6(10), e24525.CrossRefGoogle ScholarPubMed
Atkinson, D. S., Abou-Khalil, B., Charles, P. D., & Welch, L. (1996). Midsagittal corpus callosum area, intelligence and language in epilepsy. Journal of Neuroimaging, 6(4), 235239.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 820.Google Scholar
Basser, P. J., & Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of Magnetic Resonance, Series B, 111(3), 209219.Google Scholar
Beaulieu, C. (2002). The basis of anisotropic water diffusion in the nervous system – A technical review. NMR in Biomedicine, 15(7–8), 435455.Google Scholar
Behrens, T. E., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage, 34(1), 144155.CrossRefGoogle ScholarPubMed
Behrens, T. E., Woolrich, M. W., Jenkinson, M., Johansen-Berg, H., Nunes, R. G., Clare, S., … Smith, S. M. (2003). Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine, 50(5), 10771088.CrossRefGoogle ScholarPubMed
Booth, T., Bastin, M. E., Penke, L., Maniega, S. M., Murray, C., Royle, N. A., … Hernández, M. (2013). Brain white matter tract integrity and cognitive abilities in community-dwelling older people: The Lothian Birth Cohort, 1936. Neuropsychology, 27(5), 595607.Google Scholar
Campbell, J. S. W., & Pike, G. B. (2014). Potential and limitations of diffusion MRI tractography for the study of language. Brain and Language, 131, 6573.Google Scholar
Catani, M., & Thiebaut de Schotten, M. (2008). A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex, 44(8), 11051132.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
Cremers, L. G. M., de Groot, M., Hofman, A., Krestin, G. P., van der Lugt, A., Niessen, W. J., … Ikram, M. A. (2016). Altered tract-specific white matter microstructure is related to poorer cognitive performance: The Rotterdam Study. Neurobiology of Aging, 39, 108117.CrossRefGoogle ScholarPubMed
Deary, I. J., Bastin, M. E., Pattie, A., Clayden, J. D., Whalley, L. J., Starr, J. M., & Wardlaw, J. M. (2006). White matter integrity and cognition in childhood and old age. Neurology, 66(4), 505512.CrossRefGoogle ScholarPubMed
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201211.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Ferrer, E., Whitaker, K. J., Steele, J. S., Green, C. T., Wendelken, C., & Bunge, S. A. (2013). White matter maturation supports the development of reasoning ability through its influence on processing speed. Developmental Science, 16(6), 941951.Google Scholar
Filley, C. (2012). The behavioral neurology of white matter. New York: Oxford University Press.Google Scholar
Fischer, F. U., Wolf, D., Scheurich, A., & Fellgiebel, A. (2014). Association of structural global brain network properties with intelligence in normal aging. PLoS One, 9(1), e86258.Google Scholar
Galton, F. (1888). Head growth in students at the University of Cambridge. Nature, 38(996), 1415.Google Scholar
Genc, 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.Google Scholar
Genc, E., Fraenz, C., Schlüter, C., Friedrich, P., Voelkle, M. C., Hossiep, R., & Güntürkün, O. (2019). The neural architecture of general knowledge. European Journal of Personality, 33(5), 589605.Google Scholar
Goriounova, N. A., Heyer, D. B., Wilbers, R., Verhoog, M. B., Giugliano, M., Verbist, C., … Verberne, M. (2018). Large and fast human pyramidal neurons associate with intelligence. eLife, 7(1), e41714.Google Scholar
Goriounova, N. A., & Mansvelder, H. D. (2019). Genes, cells and brain areas of intelligence. Frontiers in Human Neuroscience, 13, 14.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.CrossRefGoogle ScholarPubMed
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), 1023510242.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., Mitchell, D. J., Taylor, J. R., Duncan, J., Tyler, L. K., … Cusack, R. (2014). Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking. Nature Communications, 5, 5658.CrossRefGoogle ScholarPubMed
Kim, D. J., Davis, E. P., Sandman, C. A., Sporns, O., O’Donnell, B. F., Buss, C., & Hetrick, W. P. (2016). Children’s intellectual ability is associated with structural network integrity. NeuroImage, 124, 550556.CrossRefGoogle ScholarPubMed
Koenis, M. M. G., Brouwer, R. M., Swagerman, S. C., van Soelen, I. L. C., Boomsma, D. I., & Hulshoff Pol, H. E. (2018). Association between structural brain network efficiency and intelligence increases during adolescence. Human Brain Mapping, 39(2), 822836.Google Scholar
Kontis, D., Catani, M., Cuddy, M., Walshe, M., Nosarti, C., Jones, D., … Allin, M. (2009). Diffusion tensor MRI of the corpus callosum and cognitive function in adults born preterm. Neuroreport, 20(4), 424428.Google Scholar
Kuznetsova, K. A., Maniega, S. M., Ritchie, S. J., Cox, S. R., Storkey, A. J., Starr, J. M., … Bastin, M. E. (2016). Brain white matter structure and information processing speed in healthy older age. Brain Structure and Function, 221(6), 32233235.Google Scholar
Le Bihan, D. (2003). Looking into the functional architecture of the brain with diffusion MRI. Nature Reviews Neuroscience, 4(6), 469480.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), e1000395.Google Scholar
Luders, E., Narr, K. L., Bilder, R. M., Thompson, P. M., Szeszko, P. R., Hamilton, L., & Toga, A. W. (2007). Positive correlations between corpus callosum thickness and intelligence. NeuroImage, 37(4), 14571464.Google Scholar
Ma, J., Kang, H. J., Kim, J. Y., Jeong, H. S., Im, J. J., Namgung, E., … Oh, J. K. (2017). Network attributes underlying intellectual giftedness in the developing brain. Scientific Reports, 7(1), 11321.Google Scholar
MacKay, A. L., & Laule, C. (2016). Magnetic resonance of myelin water: An in vivo marker for myelin. Brain Plasticity, 2(1), 7191.CrossRefGoogle Scholar
Malpas, C. B., Genc, S., Saling, M. M., Velakoulis, D., Desmond, P. M., & O’Brien, T. J. (2016). MRI correlates of general intelligence in neurotypical adults. Journal of Clinical Neuroscience, 24, 128134.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
Mori, S. (2007). Introduction to diffusion tensor imaging. Oxford: Elsevier.Google Scholar
Morris, D. M., Embleton, K. V., & Parker, G. J. M. (2008). Probabilistic fibre tracking: Differentiation of connections from chance events. NeuroImage, 42(4), 13291339.Google Scholar
Muetzel, R. L., Mous, S. E., van der Ende, J., Blanken, L. M. E., van der Lugt, A., Jaddoe, V. W. V., … White, T. (2015). White matter integrity and cognitive performance in school-age children: A population-based neuroimaging study. NeuroImage, 119, 119128.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
Neubauer, A., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33(7), 10041023.CrossRefGoogle ScholarPubMed
Nusbaum, F., Hannoun, S., Kocevar, G., Stamile, C., Fourneret, P., Revol, O., & Sappey-Marinier, D. (2017). Hemispheric differences in white matter microstructure between two profiles of children with high intelligence quotient vs. controls: A tract-based spatial statistics study. Frontiers in Neuroscience, 11, 173.Google Scholar
Ocklenburg, S., Anderson, C., Gerding, W. M., Fraenz, C., Schluter, C., Friedrich, P., … Genc, E. (2018). Myelin water fraction imaging reveals hemispheric asymmetries in human white matter that are associated with genetic variation in PLP1. Molecular Neurobiology, 56(6), 39994012.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), 312320.3.0.CO;2-K>CrossRefGoogle ScholarPubMed
Penke, L., Maniega, S. M., Bastin, M. E., Hernandez, M. C. V., 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), 10261030.CrossRefGoogle ScholarPubMed
Penke, L., Maniega, S. M., Murray, C., Gow, A. J., Hernandez, M. C., Clayden, J. D., … Deary, I. J. (2010). A general factor of brain white matter integrity predicts information processing speed in healthy older people. Journal of Neuroscience, 30(22), 75697574.CrossRefGoogle ScholarPubMed
Pietschnig, J., Penke, L., Wicherts, J. M., Zeiler, M., & Voracek, M. (2015). Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? Neuroscience and Biobehavioral Reviews, 57, 411432.CrossRefGoogle ScholarPubMed
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.Google Scholar
Ryman, S. G., Yeo, R. A., Witkiewitz, K., Vakhtin, A. A., van den Heuvel, M., de Reus, M., … Jung, R. E. (2016). Fronto-Parietal gray matter and white matter efficiency differentially predict intelligence in males and females. Human Brain Mapping, 37(11), 40064016.CrossRefGoogle ScholarPubMed
Sampaio-Baptista, C., Khrapitchev, A. A., Foxley, S., Schlagheck, T., Scholz, J., Jbabdi, S., … Thomas, N. (2013). Motor skill learning induces changes in white matter microstructure and myelination. Journal of Neuroscience, 33(50), 1949919503.Google Scholar
Schmithorst, V. J. (2009). Developmental sex differences in the relation of neuroanatomical connectivity to intelligence. Intelligence, 37(2), 164173.Google Scholar
Schmithorst, V. J., Wilke, M., Dardzinski, B. J., & Holland, S. K. (2005). Cognitive functions correlate with white matter architecture in a normal pediatric population: A diffusion tensor MRI study. Human Brain Mapping, 26(2), 139147.Google Scholar
Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., … Matthews, P. M. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage, 31(4), 14871505.Google Scholar
Tamnes, C. K., Østby, Y., Walhovd, K. B., Westlye, L. T., Due‐Tønnessen, P., & Fjell, A. M. (2010). Intellectual abilities and white matter microstructure in development: A diffusion tensor imaging study. Human Brain Mapping, 31(10), 16091625.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
Tuch, D. S. (2004). Q-ball imaging. Magnetic Resonance in Medicine, 52(6), 13581372.Google Scholar
Urger, S. E., De Bellis, M. D., Hooper, S. R., Woolley, D. P., Chen, S. D., & Provenzale, J. (2015). The superior longitudinal fasciculus in typically developing children and adolescents: Diffusion tensor imaging and neuropsychological correlates. Journal of Child Neurology, 30(1), 920.Google Scholar
Wang, Y., Adamson, C., Yuan, W., Altaye, M., Rajagopal, A., Byars, A. W., & Holland, S. K. (2012). Sex differences in white matter development during adolescence: A DTI study. Brain Research, 1 478, 115.Google Scholar
Wen, W., Zhu, W., He, Y., Kochan, N. A., Reppermund, S., Slavin, M. J., … Sachdev, P. (2011). Discrete neuroanatomical networks are associated with specific cognitive abilities in old age. Journal of Neuroscience, 31(4), 12041212.Google Scholar
Wiseman, S. J., Booth, T., Ritchie, S. J., Cox, S. R., Muñoz Maniega, S., Valdés Hernández, M., … Deary, I. J. (2018). Cognitive abilities, brain white matter hyperintensity volume, and structural network connectivity in older age. Human Brain Mapping, 39(2), 622632.CrossRefGoogle ScholarPubMed
Wolff, S. D., & Balaban, R. S. (1989). Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo. Magnetic Resonance in Medicine, 10(1), 135144.Google Scholar
Yu, C., Li, J., Liu, Y., Qin, W., Li, Y., Shu, N., … Li, K. (2008). White matter tract integrity and intelligence in patients with mental retardation and healthy adults. NeuroImage, 40(4), 15331541.Google Scholar
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 10001016.Google Scholar

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