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Part III - Neuroimaging Methods and Findings

Published online by Cambridge University Press:  11 June 2021

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
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The Cambridge Handbook of Intelligence and Cognitive Neuroscience , pp. 189 - 324
Publisher: Cambridge University Press
Print publication year: 2021

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References

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), 235–239.CrossRefGoogle ScholarPubMed
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20.CrossRefGoogle ScholarPubMed
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), 209–219.CrossRefGoogle ScholarPubMed
Beaulieu, C. (2002). The basis of anisotropic water diffusion in the nervous system – A technical review. NMR in Biomedicine, 15(7–8), 435–455.CrossRefGoogle ScholarPubMed
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), 144–155.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), 1077–1088.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), 595–607.CrossRefGoogle ScholarPubMed
Campbell, J. S. W., & Pike, G. B. (2014). Potential and limitations of diffusion MRI tractography for the study of language. Brain and Language, 131, 65–73.CrossRefGoogle Scholar
Catani, M., & Thiebaut de Schotten, M. (2008). A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex, 44(8), 1105–1132.CrossRefGoogle ScholarPubMed
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), 2212–2224.CrossRefGoogle ScholarPubMed
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, 108–117.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), 505–512.CrossRefGoogle ScholarPubMed
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201–211.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, 71–78.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), 941–951.Google ScholarPubMed
Filley, C. (2012). The behavioral neurology of white matter. New York: Oxford University Press.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Galton, F. (1888). Head growth in students at the University of Cambridge. Nature, 38(996), 14–15.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.CrossRefGoogle ScholarPubMed
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), 589–605.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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, 372–383.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), 10235–10242.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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, 550–556.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), 822–836.CrossRefGoogle ScholarPubMed
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), 424–428.CrossRefGoogle ScholarPubMed
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), 3223–3235.CrossRefGoogle ScholarPubMed
Le Bihan, D. (2003). Looking into the functional architecture of the brain with diffusion MRI. Nature Reviews Neuroscience, 4(6), 469–480.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.CrossRefGoogle ScholarPubMed
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), 1457–1464.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
MacKay, A. L., & Laule, C. (2016). Magnetic resonance of myelin water: An in vivo marker for myelin. Brain Plasticity, 2(1), 71–91.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, 128–134.CrossRefGoogle ScholarPubMed
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), 337–346.CrossRefGoogle 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), 1329–1339.CrossRefGoogle ScholarPubMed
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, 119–128.CrossRefGoogle ScholarPubMed
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), 2163–2171.CrossRefGoogle ScholarPubMed
Neubauer, A., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33(7), 1004–1023.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.CrossRefGoogle ScholarPubMed
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), 3999–4012.CrossRefGoogle ScholarPubMed
Pakkenberg, B., & Gundersen, H. J. G. (1997). Neocortical neuron number in humans: Effect of sex and age. Journal of Comparative Neurology, 384(2), 312–320.3.0.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), 1026–1030.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), 7569–7574.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, 411–432.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, 105–116.CrossRefGoogle 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), 4006–4016.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), 19499–19503.CrossRefGoogle ScholarPubMed
Schmithorst, V. J. (2009). Developmental sex differences in the relation of neuroanatomical connectivity to intelligence. Intelligence, 37(2), 164–173.CrossRefGoogle ScholarPubMed
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), 139–147.CrossRefGoogle 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), 1487–1505.CrossRefGoogle ScholarPubMed
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), 1609–1625.CrossRefGoogle ScholarPubMed
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), 293–303.CrossRefGoogle Scholar
Tuch, D. S. (2004). Q-ball imaging. Magnetic Resonance in Medicine, 52(6), 1358–1372.CrossRefGoogle ScholarPubMed
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), 9–20.CrossRefGoogle ScholarPubMed
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, 1–15.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), 1204–1212.CrossRefGoogle ScholarPubMed
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), 622–632.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), 135–144.CrossRefGoogle ScholarPubMed
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), 1533–1541.CrossRefGoogle ScholarPubMed
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), 1000–1016.CrossRefGoogle ScholarPubMed

References

Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage Publications, Inc.Google Scholar
Aleman-Gomez, Y., Janssen, J., Schnack, H., Balaban, E., Pina-Camacho, L., Alfaro-Almagro, F., … Desco, M. (2013). The human cerebral cortex flattens during adolescence. Journal of Neuroscience, 33(38), 15004–15010.CrossRefGoogle ScholarPubMed
Andreasen, N. C., Flaum, M., Swayze, V., 2nd, O’Leary, D. S., Alliger, R., Cohen, G., … Yuh, W. T. (1993). Intelligence and brain structure in normal individuals. American Journal of Psychiatry, 150(1), 130–134.Google ScholarPubMed
Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry – The methods. Neuroimage, 11(6 Pt 1), 805–821.CrossRefGoogle ScholarPubMed
Aydin, K., Ucar, A., Oguz, K. K., Okur, O. O., Agayev, A., Unal, Z., Yilmaz, S., and Ozturk, C. (2007). Increased gray matter density in the parietal cortex of mathematicians: A voxel-based morphometry study. AJNR American Journal of Neuroradiology, 28(10), 1859–1864.CrossRefGoogle ScholarPubMed
Bajaj, S., Raikes, A., Smith, R., Dailey, N. S., Alkozei, A., Vanuk, J. R., & Killgore, W. D. S. (2018). The relationship between general intelligence and cortical structure in healthy individuals. Neuroscience, 388, 36–44.CrossRefGoogle ScholarPubMed
Bassett, D. S., Bullmore, E., Verchinski, B. A., Mattay, V. S., Weinberger, D. R., & Meyer-Lindenberg, A. (2008). Hierarchical organization of human cortical networks in health and schizophrenia. Journal of Neuroscience, 28(37), 9239–9248.CrossRefGoogle 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.CrossRefGoogle Scholar
Bedford, S. A., Park, M. T. M., Devenyi, G. A., Tullo, S., Germann, J., Patel, R., … Consortium, Mrc Aims (2020). Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder. Molecular Psychiatry, 25(3), 614–628.CrossRefGoogle ScholarPubMed
Bjuland, K. J., Løhaugen, G. C., Martinussen, M., & Skranes, J. (2013). Cortical thickness and cognition in very-low-birth-weight late teenagers. Early Human Development, 89(6), 371–380.CrossRefGoogle ScholarPubMed
Bourgeois, J. P., Goldman-Rakic, P. S., & Rakic, P. (1994). Synaptogenesis in the prefrontal cortex of rhesus monkeys. Cerebral Cortex, 4(1), 78–96.CrossRefGoogle ScholarPubMed
Breslau, N., Chilcoat, H. D., Susser, E. S., Matte, T., Liang, K.-Y., & Peterson, E. L. (2001). Stability and change in children’s intelligence quotient scores: A comparison of two socioeconomically disparate communities. American Journal of Epidemiology, 154(8), 711–717.CrossRefGoogle ScholarPubMed
Brouwer, R. M., Hedman, A. M., van Haren, N. E. M., Schnack, H. G., Brans, R. G. H., Smit, D. J. A., … Hulshoff Pol, H. E. (2014). Heritability of brain volume change and its relation to intelligence. Neuroimage, 100, 676–683.CrossRefGoogle ScholarPubMed
Budde, J., Shajan, G., Scheffler, K., & Pohmann, R. (2014). Ultra-high resolution imaging of the human brain using acquisition-weighted imaging at 9.4T. Neuroimage, 86, 592–598.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Burgaleta, M., MacDonald, P. A., Martínez, K., Román, F. J., Álvarez-Linera, J., Ramos González, A., … Colom, R. (2014). Subcortical regional morphology correlates with fluid and spatial intelligence. Human Brain Mapping, 35(5), 1957–1968.CrossRefGoogle ScholarPubMed
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376.CrossRefGoogle ScholarPubMed
Chance, S. A., Casanova, M. F., Switala, A. E., & Crow, T. J. (2008). Auditory cortex asymmetry, altered minicolumn spacing and absence of ageing effects in schizophrenia. Brain, 131(Pt 12), 3178–3192.CrossRefGoogle Scholar
Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J., & Evans, A. C. (2008). Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cerebral Cortex, 18(10), 2374–2381.CrossRefGoogle ScholarPubMed
Chklovskii, D. B., Mel, B. W., & Svoboda, K. (2004). Cortical rewiring and information storage. Nature, 431(7010), 782–788.CrossRefGoogle ScholarPubMed
Choi, Y. Y., Shamosh, N. A., Cho, S. H., DeYoung, C. G., Lee, M. J., Lee, J. M., … Lee, K. H. (2008). Multiple bases of human intelligence revealed by cortical thickness and neural activation. Journal of Neuroscience, 28(41), 10323–10329.CrossRefGoogle ScholarPubMed
Cocosco, C. A., Zijdenbos, A. P., & Evans, A. C. (2003). A fully automatic and robust brain MRI tissue classification method. Medical Image Analysis, 7(4), 513–527.CrossRefGoogle ScholarPubMed
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18(2), 192–205.CrossRefGoogle ScholarPubMed
Colom, R., Burgaleta, M., Román, F. J., Karama, S., Alvarez-Linera, J., Abad, F. J., … Haier, R. J. (2013). Neuroanatomic overlap between intelligence and cognitive factors: Morphometry methods provide support for the key role of the frontal lobes. Neuroimage, 72, 143–152.CrossRefGoogle ScholarPubMed
Colom, R., Haier, R. J., Head, K., Álvarez-Linera, J., Quiroga, M. Á., Shih, P. C., & Jung, R. E. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence, 37(2), 124–135.CrossRefGoogle Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006). Distributed brain sites for the g-factor of intelligence. Neuroimage, 31(3), 1359–1365.CrossRefGoogle ScholarPubMed
DeFelipe, J. (2011). The evolution of the brain, the human nature of cortical circuits, and intellectual creativity. Frontiers in Neuroanatomy, 5, 29.CrossRefGoogle ScholarPubMed
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
Ducharme, S., Albaugh, M. D., Nguyen, T. V., Hudziak, J. J., Mateos-Perez, J. M., Labbe, A., … Brain Development Cooperative Group (2016). Trajectories of cortical thickness maturation in normal brain development – The importance of quality control procedures. Neuroimage, 125, 267–279.CrossRefGoogle ScholarPubMed
Eickhoff, S. B., Constable, R. T., & Yeo, B. T. T. (2018). Topographic organization of the cerebral cortex and brain cartography. Neuroimage, 170, 332–347.CrossRefGoogle ScholarPubMed
Escorial, S., Román, F. J., Martínez, K., Burgaleta, M., Karama, S., & Colom, R. (2015). Sex differences in neocortical structure and cognitive performance: A surface-based morphometry study. Neuroimage, 104, 355–365.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Evans, A. C., & Brain Development Cooperative Group (2006). The NIH MRI study of normal brain development. Neuroimage, 30(1), 184–202.CrossRefGoogle ScholarPubMed
Evans, A. C., Janke, A. L., Collins, D. L., & Baillet, S. (2012). Brain templates and atlases. Neuroimage, 62(2), 911–922.CrossRefGoogle ScholarPubMed
Fjell, A. M., Westlye, L. T., Amlien, I., Tamnes, C. K., Grydeland, H., Engvig, A., … Walhovd, K. B. (2015). High-expanding cortical regions in human development and evolution are related to higher intellectual abilities. Cerebral Cortex, 25(1), 26–34.CrossRefGoogle ScholarPubMed
Flashman, L. A., Andreasen, N. C., Flaum, M., & Swayze, V. W. (1997). Intelligence and regional brain volumes in normal controls. Intelligence, 25(3), 149–160.CrossRefGoogle Scholar
Frangou, S., Chitins, X., & Williams, S. C. (2004). Mapping IQ and gray matter density in healthy young people. Neuroimage, 23(3), 800–805.CrossRefGoogle ScholarPubMed
Ganjavi, H., Lewis, J. D., Bellec, P., MacDonald, P. A., Waber, D. P., Evans, A. C., … Brain Development Cooperative Group (2011). Negative associations between corpus callosum midsagittal area and IQ in a representative sample of healthy children and adolescents. PLoS One, 6(5), e19698.CrossRefGoogle Scholar
Gautam, P., Anstey, K. J., Wen, W., Sachdev, P. S., & Cherbuin, N. (2015). Cortical gyrification and its relationships with cortical volume, cortical thickness, and cognitive performance in healthy mid-life adults. Behavioural Brain Research, 287, 331–339.CrossRefGoogle ScholarPubMed
Goh, S., Bansal, R., Xu, D., Hao, X., Liu, J., & Peterson, B. S. (2011). Neuroanatomical correlates of intellectual ability across the life span. Developmental Cognitive Neuroscience, 1(3), 305–312.CrossRefGoogle ScholarPubMed
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage, 14(1 Pt 1), 21–36.CrossRefGoogle ScholarPubMed
Green, S., Blackmon, K., Thesen, T., DuBois, J., Wang, X., Halgren, E., & Devinsky, O. (2018). Parieto-frontal gyrification and working memory in healthy adults. Brain Imaging Behavior, 12(2), 303–308.CrossRefGoogle ScholarPubMed
Gregory, M. D., Kippenhan, J. S., Dickinson, D., Carrasco, J., Mattay, V. S., Weinberger, D. R., & Berman, K. F. (2016). Regional variations in brain gyrification are associated with general cognitive ability in humans. Current Biology, 26(10), 1301–1305.CrossRefGoogle ScholarPubMed
Gur, R. C., Turetsky, B. I., Matsui, M., Yan, M., Bilker, W., Hughett, P., & Gur, R. E. (1999). Sex differences in brain gray and white matter in healthy young adults: Correlations with cognitive performance. Journal of Neuroscience, 19(10), 4065–4072.CrossRefGoogle ScholarPubMed
Haier, R. J. (2016). The neuroscience of intelligence. Cambridge University Press.Google Scholar
Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2004). Structural brain variation and general intelligence. Neuroimage, 23(1), 425–433.CrossRefGoogle ScholarPubMed
Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2005). The neuroanatomy of general intelligence: Sex matters. Neuroimage, 25(1), 320–327.CrossRefGoogle ScholarPubMed
Haier, R. J., Karama, S., Colom, R., Jung, R., & Johnson, W. (2014). Yes, but flaws remain. Intelligence, 46, 341–344.CrossRefGoogle Scholar
He, Y., Chen, Z. J., & Evans, A. C. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 17(10), 2407–2419.CrossRefGoogle ScholarPubMed
Hogstrom, L. J., Westlye, L. T., Walhovd, K. B., & Fjell, A. M. (2013). The structure of the cerebral cortex across adult life: Age-related patterns of surface area, thickness, and gyrification. Cerebral Cortex, 23(11), 2521–2530.CrossRefGoogle ScholarPubMed
Huttenlocher, P. R. (1990). Morphometric study of human cerebral cortex development. Neuropsychologia, 28(6), 517–527.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Kabani, N., Le Goualher, G., MacDonald, D., & Evans, A. C. (2001). Measurement of cortical thickness using an automated 3-D algorithm: A validation study. Neuroimage, 13(2), 375–380.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Karama, S., Bastin, M. E., Murray, C., Royle, N. A., Penke, L., Muñoz Maniega, S., … Deary, I. J. (2014). Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age. Molecular Psychiatry, 19(5), 555–559.CrossRefGoogle ScholarPubMed
Karama, S., Colom, R., Johnson, W., Deary, I. J., Haier, R., Waber, D. P., … Brain Development Cooperative Group (2011). Cortical thickness correlates of specific cognitive performance accounted for by the general factor of intelligence in healthy children aged 6 to 18. Neuroimage, 55(4), 1443–1453.CrossRefGoogle ScholarPubMed
Kennedy, D. N., Lange, N., Makris, N., Bates, J., Meyer, J., & Caviness, V. S. Jr. (1998). Gyri of the human neocortex: An MRI-based analysis of volume and variance. Cerebral Cortex, 8(4), 372–384.CrossRefGoogle Scholar
Khundrakpam, B. S., Reid, A., Brauer, J., Carbonell, F., Lewis, J., Ameis, S., … Brain Development Cooperative Group (2013). Developmental changes in organization of structural brain networks. Cerebral Cortex, 23(9), 2072–2085.CrossRefGoogle ScholarPubMed
Kim, J. S., Singh, V., Lee, J. K., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., … Evans, A. C. (2005). Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage, 27(1), 210–221.CrossRefGoogle ScholarPubMed
la Fougere, C., Grant, S., Kostikov, A., Schirrmacher, R., Gravel, P., Schipper, H. M., … Thiel, A. (2011). Where in-vivo imaging meets cytoarchitectonics: The relationship between cortical thickness and neuronal density measured with high-resolution [18F]flumazenil-PET. Neuroimage, 56(3), 951–960.CrossRefGoogle ScholarPubMed
Lemaitre, H., Goldman, A. L., Sambataro, F., Verchinski, B. A., Meyer-Lindenberg, A., Weinberger, D. R., & Mattay, V. S. (2012). Normal age-related brain morphometric changes: Nonuniformity across cortical thickness, surface area and gray matter volume? Neurobiology of Aging, 33(3), 617.e1–617.e9.CrossRefGoogle ScholarPubMed
Lenroot, R. K., Gogtay, N., Greenstein, D. K., Wells, E. M., Wallace, G. L., Clasen, L. S., … Giedd, J. N. (2007). Sexual dimorphism of brain developmental trajectories during childhood and adolescence. Neuroimage, 36(4), 1065–1073.CrossRefGoogle ScholarPubMed
Lerch, J. P., & Evans, A. C. (2005). Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage, 24(1), 163–173.CrossRefGoogle Scholar
Lerch, J. P., Worsley, K., Shaw, W. P., Greenstein, D. K., Lenroot, R. K., Giedd, J., & Evans, A. C. (2006). Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage, 31(3), 993–1003.CrossRefGoogle ScholarPubMed
Li, W., Yang, C., Shi, F., Wu, S., Wang, Q., Nie, Y., & Zhang, X. (2017). Construction of individual morphological brain networks with multiple morphometric features. Frontiers in Neuroanatomy, 11, 34.CrossRefGoogle ScholarPubMed
Lo, C. Y., He, Y., & Lin, C. P. (2011). Graph theoretical analysis of human brain structural networks. Reviews Neuroscience, 22(5), 551–563.CrossRefGoogle ScholarPubMed
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), 1457–1464.CrossRefGoogle ScholarPubMed
Luders, E., Narr, K. L., Thompson, P. M., & Toga, A. W. (2009). Neuroanatomical correlates of intelligence. Intelligence, 37(2), 156–163.CrossRefGoogle ScholarPubMed
Luders, E., Thompson, P. M., Narr, K. L., Zamanyan, A., Chou, Y. Y., Gutman, B., … Toga, A. W. (2011). The link between callosal thickness and intelligence in healthy children and adolescents. Neuroimage, 54(3), 1823–1830.CrossRefGoogle ScholarPubMed
Lyttelton, O. C., Karama, S., Ad-Dab’bagh, Y., Zatorre, R. J., Carbonell, F., Worsley, K., & Evans, A. C. (2009). Positional and surface area asymmetry of the human cerebral cortex. Neuroimage, 46(4), 895–903.CrossRefGoogle ScholarPubMed
MacDonald, D., Kabani, N., Avis, D., & Evans, A. C. (2000). Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage, 12(3), 340–356.CrossRefGoogle ScholarPubMed
MacDonald, P. A., Ganjavi, H., Collins, D. L., Evans, A. C., & Karama, S. (2014). Investigating the relation between striatal volume and IQ. Brain Imaging and Behavior, 8(1), 52–59.CrossRefGoogle ScholarPubMed
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), 337–346.CrossRefGoogle Scholar
Menary, K., Collins, P. F., Porter, J. N., Muetzel, R., Olson, E. A., Kumar, V., … Luciana, M. (2013). Associations between cortical thickness and general intelligence in children, adolescents and young adults. Intelligence, 41(5), 597–606.CrossRefGoogle Scholar
Modroño, C., Navarrete, G., Nicolle, A., González-Mora, J. L., Smith, K. W., Marling, M., & Goel, V. (2019). Developmental grey matter changes in superior parietal cortex accompany improved transitive reasoning. Thinking & Reasoning, 25(2), 151–170.CrossRefGoogle ScholarPubMed
Moffitt, T. E., Caspi, A., Harkness, A. R., & Silva, P. A. (1993). The natural history of change in intellectual performance: Who changes? How much? Is it meaningful? Journal of Child Psychology and Psychiatry, 34(4), 455–506.CrossRefGoogle ScholarPubMed
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), 2163–2171.CrossRefGoogle ScholarPubMed
Panizzon, M. S., Fennema-Notestine, C., Eyler, L. T., Jernigan, T. L., Prom-Wormley, E., Neale, M., … Kremen, W. S. (2009). Distinct genetic influences on cortical surface area and cortical thickness. Cerebral Cortex, 19(11), 2728–2735.CrossRefGoogle ScholarPubMed
Paradiso, S., Andreasen, N. C., O’Leary, D. S., Arndt, S., & Robinson, R. G. (1997). Cerebellar size and cognition: Correlations with IQ, verbal memory and motor dexterity. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 10(1), 1–8.Google ScholarPubMed
Paul, E. J., Larsen, R. J., Nikolaidis, A., Ward, N., Hillman, C. H., Cohen, N. J., … Barbey, A. K. (2016). Dissociable brain biomarkers of fluid intelligence. Neuroimage, 137, 201–211.CrossRefGoogle ScholarPubMed
Paus, T., Zijdenbos, A., Worsley, K., Collins, D. L., Blumenthal, J., Giedd, J. N., … Evans, A. C. (1999). Structural maturation of neural pathways in children and adolescents: In vivo study. Science, 283(5409), 1908–1911.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 & Biobehavioral Reviews, 57, 411–432.CrossRefGoogle ScholarPubMed
Rakic, P. (1988). Specification of cerebral cortical areas. Science, 241(4862), 170–176.CrossRefGoogle ScholarPubMed
Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., … Giedd, J. N. (2011). How does your cortex grow? The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 31(19), 7174–7177.CrossRefGoogle ScholarPubMed
Regis, J., Mangin, J. F., Ochiai, T., Frouin, V., Riviere, D., Cachia, A., … Samson, Y. (2005). “Sulcal root” generic model: A hypothesis to overcome the variability of the human cortex folding patterns. Neurologia Medico-Chirurgica (Tokyo), 45(1), 1–17.CrossRefGoogle ScholarPubMed
Reiss, A. L., Abrams, M. T., Singer, H. S., Ross, J. L., & Denckla, M. B. (1996). Brain development, gender and IQ in children. A volumetric imaging study. Brain, 119(Pt 5), 1763–1774.CrossRefGoogle ScholarPubMed
Reuter, M., Tisdall, M. D., Qureshi, A., Buckner, R. L., van der Kouwe, A. J. W., & Fischl, B. (2015). Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage, 107, 107–115.CrossRefGoogle ScholarPubMed
Riahi, F., Zijdenbos, A., Narayanan, S., Arnold, D., Francis, G., Antel, J., & Evans, A. C. (1998). Improved correlation between scores on the expanded disability status scale and cerebral lesion load in relapsing-remitting multiple sclerosis. Results of the application of new imaging methods. Brain, 121(Pt 7), 1305–1312.CrossRefGoogle ScholarPubMed
Richman, D. P., Stewart, R. M., Hutchinson, J. W., & Caviness, V. S. Jr. (1975). Mechanical model of brain convolutional development. Science, 189(4196), 18–21.Google ScholarPubMed
Rilling, J. K., & Insel, T. R. (1999). The primate neocortex in comparative perspective using magnetic resonance imaging. Journal of Human Evolution, 37(2), 191–223.CrossRefGoogle ScholarPubMed
Ritchie, S. J., Booth, T., Valdes Hernandez, M. D., Corley, J., Maniega, S. M., Gow, A. J., … Deary, I. J. (2015). Beyond a bigger brain: Multivariable structural brain imaging and intelligence. Intelligence, 51, 47–56.CrossRefGoogle ScholarPubMed
Riva, D., & Giorgi, C. (2000). The cerebellum contributes to higher functions during development: Evidence from a series of children surgically treated for posterior fossa tumours. Brain, 123(5), 1051–1061.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Roth, G., & Dicke, U. (2005). Evolution of the brain and intelligence. Trends in Cognitive Sciences, 9(5), 250–257.CrossRefGoogle ScholarPubMed
Rushton, J. P., & Ankney, C. D. (2009). Whole brain size and general mental ability: A review. International Journal of Neuroscience, 119(5), 691–731.CrossRefGoogle ScholarPubMed
Sanabria-Diaz, G., Melie-Garcia, L., Iturria-Medina, Y., Aleman-Gomez, Y., Hernandez-Gonzalez, G., Valdes-Urrutia, L., … Valdes-Sosa, P. (2010). Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks. Neuroimage, 50(4), 1497–1510.CrossRefGoogle ScholarPubMed
Schmahmann, J. D. (2004). Disorders of the cerebellum: Ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. The Journal of Neuropsychiatry and Clinical Neurosciences, 16(3), 367–378.CrossRefGoogle ScholarPubMed
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, 39(16), 3028–3040.CrossRefGoogle ScholarPubMed
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), 4743–4752.CrossRefGoogle ScholarPubMed
Schoenemann, P. T., Budinger, T. F., Sarich, V. M., & Wang, W. S. Y. (2000). Brain size does not predict general cognitive ability within families. Proceedings of the National Academy of Sciences, 97(9), 4932–4937.CrossRefGoogle Scholar
Schulte, T., & Muller-Oehring, E. M. (2010). Contribution of callosal connections to the interhemispheric integration of visuomotor and cognitive processes. Neuropsychology Review, 20(2), 174–190.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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. The Journal of Neuroscience, 24(38), 8223.CrossRefGoogle ScholarPubMed
Stonnington, C. M., Tan, G., Klöppel, S., Chu, C., Draganski, B., Jack, C. R. Jr., … Frackowiak, R. S. (2008). Interpreting scan data acquired from multiple scanners: a study with Alzheimer’s disease. Neuroimage, 39(3), 1180–1185.CrossRefGoogle ScholarPubMed
Storsve, A. B., Fjell, A. M., Tamnes, C. K., Westlye, L. T., Overbye, K., Aasland, H. W., & Walhovd, K. B. (2014). Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: Regions of accelerating and decelerating change. Journal of Neuroscience, 34(25), 8488–8498.CrossRefGoogle ScholarPubMed
Stucht, D., Danishad, K. A., Schulze, P., Godenschweger, F., Zaitsev, M., & Speck, O. (2015). Highest resolution in vivo human brain MRI using prospective motion correction. PLoS One, 10(7), e0133921.CrossRefGoogle ScholarPubMed
Sur, M., & Rubenstein, J. L. (2005). Patterning and plasticity of the cerebral cortex. Science, 310(5749), 805–810.CrossRefGoogle ScholarPubMed
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(1).Google Scholar
Tamnes, C. K., Fjell, A. M., Østby, Y., Westlye, L. T., Due-Tønnessen, P., Bjørnerud, A., & Walhovd, K. B. (2011). The brain dynamics of intellectual development: Waxing and waning white and gray matter. Neuropsychologia, 49(13), 3605–3611.CrossRefGoogle ScholarPubMed
Thompson, P. M., Hayashi, K. M., Dutton, R. A., Chiang, M.-C., Leow, A. D., Sowell, E. R., … Toga, A. W. (2007). Tracking Alzheimer’s disease. Annals of the New York Academy of Science, 1 097, 183–214.CrossRefGoogle Scholar
Thompson, P. (2020). ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Biological Psychiatry, 87(9, Suppl), S56.CrossRefGoogle Scholar
Turin, G. (1960). An introduction to matched filters. IRE Transactions on Information Theory, 6(3), 311–329.CrossRefGoogle Scholar
Van Essen, D. C. (2005). A population-average, landmark- and surface-based (PALS) atlas of human cerebral cortex. Neuroimage, 28(3), 635–662.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Watson, P. D., Paul, E. J., Cooke, G. E., Ward, N., Monti, J. M., Horecka, K. M., … Barbey, A. K. (2016). Underlying sources of cognitive-anatomical variation in multi-modal neuroimaging and cognitive testing. Neuroimage, 129, 439–449.CrossRefGoogle ScholarPubMed
Westerhausen, R., Friesen, C. M., Rohani, D. A., Krogsrud, S. K., Tamnes, C. K., Skranes, J. S., … Walhovd, K. B. (2018). The corpus callosum as anatomical marker of intelligence? A critical examination in a large-scale developmental study. Brain Structure and Function, 223(1), 285–296.CrossRefGoogle Scholar
Westlye, L. T., Walhovd, K. B., Dale, A. M., Bjørnerud, A., Due-Tønnessen, P., Engvig, A., … Fjell, A. M. (2009). Life-span changes of the human brain white matter: Diffusion tensor imaging (DTI) and volumetry. Cerebral Cortex, 20(9), 2055–2068.CrossRefGoogle ScholarPubMed
Wickett, J. C., Vernon, P. A., & Lee, D. H. (2000). Relationships between factors of intelligence and brain volume. Personality and Individual Differences, 29(6), 1095–1122.CrossRefGoogle Scholar
Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., … Glahn, D. C. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage, 53(3), 1135–1146.CrossRefGoogle ScholarPubMed
Winkler, A. M., Sabuncu, M. R., Yeo, B. T., Fischl, B., Greve, D. N., Kochunov, P., … Glahn, D. C. (2012). Measuring and comparing brain cortical surface area and other areal quantities. Neuroimage, 61(4), 1428–1443.CrossRefGoogle ScholarPubMed
Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., & Evans, A. C. (1996). A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping, 4(1), 58–73.3.0.CO;2-O>CrossRefGoogle ScholarPubMed
Xie, Y., Chen, Y. A., & De Bellis, M. D. (2012). The relationship of age, gender, and IQ with the brainstem and thalamus in healthy children and adolescents: A magnetic resonance imaging volumetric study. Journal of Child Neurology, 27(3), 325–331.CrossRefGoogle ScholarPubMed
Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2012). Plasticity in gray and white: Neuroimaging changes in brain structure during learning. Nature Neuroscience, 15(4), 528–536.CrossRefGoogle ScholarPubMed
Zijdenbos, A. P., Lerch, J. P., Bedell, B. J., & Evans, A. C. (2005). Brain imaging in drug R&D. Biomarkers 10(Suppl 1), S58–S68.CrossRefGoogle ScholarPubMed
Zilles, K., Armstrong, E., Schleicher, A., & Kretschmann, H. J. (1988). The human pattern of gyrification in the cerebral cortex. Anatomy and Embryology (Berlin), 179(2), 173–179.CrossRefGoogle ScholarPubMed

References

Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Science, 22(1), 8–20.CrossRefGoogle ScholarPubMed
Barbey, A. K., Colom, R., & Grafman, J. (2013). Dorsolateral prefrontal contributions to human intelligence. Neuropsychologia, 51(7), 1361–1369.CrossRefGoogle ScholarPubMed
Barbey, A. K., Colom, R., Paul, E. J., & Grafman, J. (2014). Architecture of fluid intelligence and working memory revealed by lesion mapping. Brain Structure and Function, 219, 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(4), 1154–1164. doi: 10.1093/brain/aws021.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.CrossRefGoogle Scholar
Basten, U., Stelzel, C., & Fiebach, C. J. (2011). Trait anxiety modulates the neural efficiency of inhibitory control. Journal of Cognitive Neuroscience, 23(10), 3132–3145. doi: 10.1162/jocn_a_00003.CrossRefGoogle ScholarPubMed
Basten, U., Stelzel, C., & Fiebach, C. J. (2012). Trait anxiety and the neural efficiency of manipulation in working memory. Cognitive, Affective, & Behavioral Neuroscience, 12(3), 571–588. doi: 10.3758/s13415–012-0100-3.CrossRefGoogle ScholarPubMed
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. doi: 10.1016/j.intell.2013.07.006.CrossRefGoogle Scholar
Berent, S., Giordani, B., Lehtinen, S., Markel, D., Penney, J. B., Buchtel, H. A., … Young, A. B. (1988). Positron emission tomographic scan investigations of Huntington’s disease: Cerebral metabolic correlates of cognitive function. Annals of Neurology, 23(6), 541–546. doi: 10.1002/ana.410230603.CrossRefGoogle ScholarPubMed
Burgess, G. C., Gray, J. R., Conway, A. R. A., & Braver, T. S. (2011). Neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span. Journal of Experimental Psychology: General, 140(4), 674–692. doi: 10.1037/a0024695.CrossRefGoogle ScholarPubMed
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376. doi: 10.1038/nrn3475.CrossRefGoogle ScholarPubMed
Cabeza, R., & Nyberg, L. (2000). Imaging cognition II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12(1), 1–47. doi: 10.1162/08989290051137585.CrossRefGoogle ScholarPubMed
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22. doi: 10.1037/h0046743.CrossRefGoogle Scholar
Chang, L. J., Yarkoni, T., Khaw, M. W., & Sanfey, A. G. (2013). Decoding the role of the insula in human cognition: Functional parcellation and large-scale reverse inference. Cerebral Cortex, 23(3), 739–749. doi: 10.1093/cercor/bhs065.CrossRefGoogle ScholarPubMed
Choi, Y. Y., Shamosh, N. A., Cho, S. H., DeYoung, C. G., Lee, M. J., Lee, J.-M., … Lee, K. H. (2008). Multiple bases of human intelligence revealed by cortical thickness and neural activation. Journal of Neuroscience, 28(41), 10323–10329. doi: 10.1523/JNEUROSCI.3259-08.2008.CrossRefGoogle ScholarPubMed
Cole, M. W., & Schneider, W. (2007). The cognitive control network: Integrated cortical regions with dissociable functions. NeuroImage, 37(1), 343–360. doi: 10.1016/j.neuroimage.2007.03.071.CrossRefGoogle ScholarPubMed
Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting system of the human brain: From environment to theory of mind. Neuron, 58(3), 306–324. doi: 10.1016/j.neuron.2008.04.017.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.CrossRefGoogle ScholarPubMed
Daugherty, A. M., Sutton, B. P., Hillman, C. H., Kramer, A. F., Cohen, N. J., & Barbey, A. K. (2020). Individual differences in the neurobiology of fluid intelligence predict responsiveness to training: Evidence from a comprehensive cognitive, mindfulness meditation, and aerobic fitness intervention. Trends in Neuroscience and Education, 18, 100123. doi: 10.1016/j.tine.2019.100123.CrossRefGoogle Scholar
Daugherty, A. M., Zwilling, C., Paul, E. J., Sherepa, N., Allen, C., Kramer, A. F., … Barbey, A. K. (2018). Multi-modal fitness and cognitive training to enhance fluid intelligence. Intelligence, 66, 32–43.CrossRefGoogle Scholar
Derrfuss, J., Vogt, V. L., Fiebach, C. J., von Cramon, D. Y., & Tittgemeyer, M. (2012). Functional organization of the left inferior precentral sulcus: Dissociating the inferior frontal eye field and the inferior frontal junction. NeuroImage, 59(4), 3829–3837. doi: 10.1016/j.neuroimage.2011.11.051.CrossRefGoogle ScholarPubMed
DeYoung, C. G., Shamosh, N. A., Green, A. E., Braver, T. S., & Gray, J. R. (2009). Intellect as distinct from openness: Differences revealed by fMRI of working memory. Journal of Personality and Social Psychology, 97(5), 883–892. doi: 10.1037/a0016615.CrossRefGoogle ScholarPubMed
Dosenbach, N. U. F., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A. T., … Petersen, S. E. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences, 104(26), 11073–11078. doi: 10.1073/pnas.0704320104.CrossRefGoogle ScholarPubMed
Duncan, J. (1995). Attention, intelligence, and the frontal lobes. In Gazzaniga, M. S. (ed.), The cognitive neurosciences (pp. 721–733). Cambridge, MA: The MIT Press.Google Scholar
Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., … Emslie, H. (2000). A neural basis for general intelligence. Science, 289(5478), 457–460. doi: 10.1126/science.289.5478.457.CrossRefGoogle ScholarPubMed
Duncan, J. (2005). Frontal lobe function and general intelligence: Why it matters. Cortex, 41(2), 215–217. doi: 10.1016/S0010–9452(08)70896-7.CrossRefGoogle ScholarPubMed
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172–179. doi: 10.1016/j.tics.2010.01.004.CrossRefGoogle ScholarPubMed
Duncan, J., Burgess, P., & Emslie, H. (1995). Fluid intelligence after frontal lobe lesions. Neuropsychologia, 33(3), 261–268. doi: 10.1016/0028-3932(94)00124-8.CrossRefGoogle ScholarPubMed
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. doi: 10.1006/cogp.1996.0008.CrossRefGoogle ScholarPubMed
Ebisch, S. J., Perrucci, M. G., Mercuri, P., Romanelli, R., Mantini, D., Romani, G. L., … Saggino, A. (2012). Common and unique neuro-functional basis of induction, visualization, and spatial relationships as cognitive components of fluid intelligence. NeuroImage, 62(1), 331–342. doi: 10.1016/j.neuroimage.2012.04.053.CrossRefGoogle ScholarPubMed
Esposito, G., Kirkby, B. S., Van Horn, J. D., Ellmore, T. M., & Berman, K. F. (1999). Context-dependent, neural system-specific neurophysiological concomitants of ageing: Mapping PET correlates during cognitive activation. Brain: A Journal of Neurology, 122(Pt 5), 963–979. doi: 10.1093/brain/122.5.963.CrossRefGoogle ScholarPubMed
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. doi: 10.1016/j.neuroimage.2015.03.078.CrossRefGoogle ScholarPubMed
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673. doi: 10.1073/pnas.0504136102.CrossRefGoogle ScholarPubMed
Geake, J. G., & Hansen, P. C. (2005). Neural correlates of intelligence as revealed by fMRI of fluid analogies. NeuroImage, 26(2), 555–564. doi: 10.1016/j.neuroimage.2005.01.035.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Ghatan, P. H., Hsieh, J. C., Wirsén-Meurling, A., Wredling, R., Eriksson, L., Stone-Elander, S., … Ingvar, M. (1995). Brain activation induced by the perceptual maze test: A PET study of cognitive performance. NeuroImage, 2(2), 112–124.CrossRefGoogle ScholarPubMed
Gläscher, 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. doi: 10.1073/pnas.0910397107.CrossRefGoogle ScholarPubMed
Goel, V., & Dolan, R. J. (2001). Functional neuroanatomy of three-term relational reasoning. Neuropsychologia, 39(9), 901–909.CrossRefGoogle ScholarPubMed
Goel, V., Gold, B., Kapur, S., & Houle, S. (1998). Neuroanatomical correlates of human reasoning. Journal of Cognitive Neuroscience, 10(3), 293–302. doi: 10.1162/089892998562744.CrossRefGoogle ScholarPubMed
Grabner, R. H., Neubauer, A. C., & Stern, E. (2006). Superior performance and neural efficiency: The impact of intelligence and expertise. Brain Research Bulletin, 69(4), 422–439. doi: 10.1016/j.brainresbull.2006.02.009.CrossRefGoogle ScholarPubMed
Grabner, R. H., Stern, E., & Neubauer, A. C. (2003). When intelligence loses its impact: Neural efficiency during reasoning in a familiar area. International Journal of Psychophysiology, 49(2), 89–98. doi: 10.1016/S0167–8760(03)00095-3.CrossRefGoogle Scholar
Gray, J. R., Chabris, C. F., & Braver, T. S. (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience, 6(3), 316–322. doi: 10.1038/nn1014.CrossRefGoogle ScholarPubMed
Gregory, M. D., Kippenhan, J. S., Dickinson, D., Carrasco, J., Mattay, V. S., Weinberger, D. R., & Berman, K. F. (2016). Regional variations in brain gyrification are associated with general cognitive ability in humans. Current Biology, 26(10), 1301–1305. doi: 10.1016/j.cub.2016.03.021.CrossRefGoogle ScholarPubMed
Haier, R. (2016). The neuroscience of intelligence (Cambridge fundamentals of neuroscience in psychology). Cambridge University Press. doi: 10.1017/9781316105771.Google Scholar
Haier, R. J., Siegel, B. V., Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., … Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12(2), 199–217. doi: 10.1016/0160-2896(88)90016-5.CrossRefGoogle Scholar
Haier, R. J., Siegel, B., Tang, C., Abel, L., & Buchsbaum, M. S. (1992). Intelligence and changes in regional cerebral glucose metabolic rate following learning. Intelligence, 16(3–4), 415–426. do: 10.1016/0160-2896(92)90018-M.CrossRefGoogle Scholar
Hammer, R., Paul, E. J., Hillman, C. H., Kramer, A. F., Cohen, N. J., & Barbey, A. K. (2019). Individual differences in analogical reasoning revealed by multivariate task-based functional brain imaging. Neuroimage, 184, 993–1004.CrossRefGoogle ScholarPubMed
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. doi: 10.1016/j.intell.2016.11.001.CrossRefGoogle 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. doi: 10.1038/s41598–017-15795-7.CrossRefGoogle ScholarPubMed
Ioannidis, J. P. A. (2008). Why most discovered true associations are inflated. Epidemiology, 19(5), 640–648. doi: 10.1097/EDE.0b013e31818131e7.CrossRefGoogle ScholarPubMed
Jaušovec, N. (2000). Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: An EEG study. Intelligence, 28(3), 213–237. doi: 10.1016/S0160–2896(00)00037-4.CrossRefGoogle Scholar
Jaušovec, N., & Jaušovec, K. (2004). Differences in induced brain activity during the performance of learning and working-memory tasks related to intelligence. Brain and Cognition, 54(1), 65–74. doi: 10.1016/S0278–2626(03)00263-X.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Kievit, R. A., Davis, S. W., Griffiths, J., Correia, M. M., Cam-Can, , & Henson, R. N. (2016). A watershed model of individual differences in fluid intelligence. Neuropsychologia, 91, 186–198. doi: 10.1016/j.neuropsychologia.2016.08.008.CrossRefGoogle ScholarPubMed
Knauff, M., Mulack, T., Kassubek, J., Salih, H. R., & Greenlee, M. W. (2002). Spatial imagery in deductive reasoning: A functional MRI study. Brain Research Cognitive Brain Research, 13(2), 203–212.CrossRefGoogle ScholarPubMed
Kruschwitz, J. D., Waller, L., Daedelow, L. S., Walter, H., & Veer, I. M. (2018). General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set. NeuroImage, 171, 323–331. doi: 10.1016/j.neuroimage.2018.01.018.CrossRefGoogle Scholar
Lee, K. H., Choi, Y. Y., Gray, J. R., Cho, S. H., Chae, J.-H., Lee, S., & Kim, K. (2006). Neural correlates of superior intelligence: Stronger recruitment of posterior parietal cortex. NeuroImage, 29(2), 578–586. doi: 10.1016/j.neuroimage.2005.07.036.CrossRefGoogle ScholarPubMed
Lipp, I., Benedek, M., Fink, A., Koschutnig, K., Reishofer, G., Bergner, S., … Neubauer, A. C. (2012). Investigating neural efficiency in the visuo-spatial domain: An FMRI study. PLoS One, 7(12), e51316. doi: 10.1371/journal.pone.0051316.CrossRefGoogle ScholarPubMed
McKiernan, K. A., Kaufman, J. N., Kucera-Thompson, J., & Binder, J. R. (2003). A parametric manipulation of factors affecting task-induced deactivation in functional neuroimaging. Journal of Cognitive Neuroscience, 15(3), 394–408. doi: 10.1162/089892903321593117.CrossRefGoogle ScholarPubMed
Mennes, M., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2013). Making data sharing work: The FCP/INDI experience. NeuroImage, 82, 683–691. doi: 10.1016/j.neuroimage.2012.10.064.CrossRefGoogle ScholarPubMed
Miller, D. I., & Halpern, D. F. (2014). The new science of cognitive sex differences. Trends in Cognitive Sciences, 18(1), 37–45. doi: 10.1016/j.tics.2013.10.011.CrossRefGoogle ScholarPubMed
Miller, E. M. (1994). Intelligence and brain myelination: A hypothesis. Personality and Individual Differences, 17(6), 803–832. doi: 10.1016/0191-8869(94)90049-3.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews, 33(7), 1004–1023. doi: 10.1016/j.neubiorev.2009.04.001.CrossRefGoogle ScholarPubMed
Neubauer, A. C., Fink, A., & Schrausser, D. G. (2002). Intelligence and neural efficiency: The influence of task content and sex on the brain–IQ relationship. Intelligence, 30(6), 515–536. doi: 10.1016/S0160–2896(02)00091-0.CrossRefGoogle Scholar
Neubauer, A. C., Freudenthaler, H. H., & Pfurtscheller, G. (1995). Intelligence and spatiotemporal patterns of event-related desynchronization (ERD). Intelligence, 20(3), 249–266. doi: 10.1016/0160-2896(95)90010-1.CrossRefGoogle Scholar
Neubauer, A. C., Grabner, R. H., Fink, A., & Neuper, C. (2005). Intelligence and neural efficiency: Further evidence of the influence of task content and sex on the brain–IQ relationship. Cognitive Brain Research, 25(1), 217–225. doi: 10.1016/j.cogbrainres.2005.05.011.CrossRefGoogle ScholarPubMed
Neubauer, A. C., Grabner, R. H., Freudenthaler, H. H., Beckmann, J. F., & Guthke, J. (2004). Intelligence and individual differences in becoming neurally efficient. Acta Psychologica, 116(1), 55–74. doi: 10.1016/j.actpsy.2003.11.005.CrossRefGoogle ScholarPubMed
Neuper, C., Grabner, R. H., Fink, A., & Neubauer, A. C. (2005). Long-term stability and consistency of EEG event-related (de-)synchronization across different cognitive tasks. Clinical Neurophysiology, 116(7), 1681–1694. doi: 10.1016/j.clinph.2005.03.013.CrossRefGoogle ScholarPubMed
Niendam, T. A., Laird, A. R., Ray, K. L., Dean, Y. M., Glahn, D. C., & Carter, C. S. (2012). Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cognitive, Affective, & Behavioral Neuroscience, 12(2), 241–268. doi: 10.3758/s13415–011-0083-5.CrossRefGoogle ScholarPubMed
O’Boyle, M. W., Cunnington, R., Silk, T. J., Vaughan, D., Jackson, G., Syngeniotis, A., & Egan, G. F. (2005). Mathematically gifted male adolescents activate a unique brain network during mental rotation. Cognitive Brain Research, 25(2), 583–587. doi: 10.1016/j.cogbrainres.2005.08.004.CrossRefGoogle ScholarPubMed
Parks, R. W., Loewenstein, D. A., Dodrill, K. L., Barker, W. W., Yoshii, F., Chang, J. Y., … Duara, R. (1988). Cerebral metabolic effects of a verbal fluency test: A PET scan study. Journal of Clinical and Experimental Neuropsychology, 10(5), 565–575. doi: 10.1080/01688638808402795.CrossRefGoogle ScholarPubMed
Paul, E. J., Larsen, R. J., Nikolaidis, A., Ward, N., Hillman, C. H., Cohen, N. J., … Barbey, A. K. (2016). Dissociable brain biomarkers of fluid intelligence. Neuroimage, 137, 201–211.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Pfurtscheller, G., & Aranibar, A. (1977). Event-related cortical desynchronization detected by power measurements of scalp EEG. Electroencephalography and Clinical Neurophysiology, 42(6), 817–826. doi: 10.1016/0013-4694(77)90235-8.CrossRefGoogle ScholarPubMed
Poldrack, R.A. (2015). Is “efficiency” a useful concept in cognitive neuroscience? Developments in Cognitive Neuroscience, 11, 12–17.CrossRefGoogle Scholar
Prabhakaran, V., Rypma, B., & Gabrieli, J. D. E. (2001). Neural substrates of mathematical reasoning: A functional magnetic resonance imaging study of neocortical activation during performance of the necessary arithmetic operations test. Neuropsychology, 15(1), 115–127. doi: 10.1037/0894-4105.15.1.115.CrossRefGoogle ScholarPubMed
Prabhakaran, V., Smith, J. A. L., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. E. (1997). Neural substrates of fluid reasoning: An fMRI study of neocortical activation during performance of the Raven’s progressive matrices test. Cognitive Psychology, 33(1), 43–63. doi: 10.1006/cogp.1997.0659.CrossRefGoogle ScholarPubMed
Santarnecchi, E., Emmendorfer, A., & Pascual-Leone, A. (2017). Dissecting the parieto-frontal correlates of fluid intelligence: A comprehensive ALE meta-analysis study. Intelligence, 63, 9–28. doi: 10.1016/j.intell.2017.04.008.CrossRefGoogle Scholar
Santarnecchi, E., Emmendorfer, A., Tadayon, S., Rossi, S., Rossi, A., & Pascual-Leone, A. (2017). Network connectivity correlates of variability in fluid intelligence performance. Intelligence, 65, 35–47. doi: 10.1016/j.intell.2017.10.002.CrossRefGoogle Scholar
Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–293. doi: 10.2307/1412107.CrossRefGoogle Scholar
Sripada, C., Angstadt, M., & Rutherford, S. (2018). Towards a “treadmill test” for cognition: Reliable prediction of intelligence from whole-brain task activation patterns. BioRxiv, 412056. doi: 10.1101/412056.CrossRefGoogle Scholar
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., … Collins, R. (2015). UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Medicine, 12(3), e1001779. doi: 10.1371/journal.pmed.1001779.CrossRefGoogle ScholarPubMed
Takeuchi, H., Taki, Y., Nouchi, R., Yokoyama, R., Kotozaki, Y., Nakagawa, S., … Kawashima, R. (2018). General intelligence is associated with working memory-related brain activity: New evidence from a large sample study. Brain Structure and Function, 223(9), 4243–4258. doi: 10.1007/s00429–018-1747-5.CrossRefGoogle ScholarPubMed
Toffanin, P., Johnson, A., de Jong, R., & Martens, S. (2007). Rethinking neural efficiency: Effects of controlling for strategy use. Behavioral Neuroscience, 121(5), 854–870. doi: 10.1037/0735-7044.121.5.854.CrossRefGoogle ScholarPubMed
Turner, B. O., Paul, E. J., Miller, M. B., & Barbey, A. K. (2018). Small sample sizes reduce the replicability of task-based fMRI studies. Communications Biology, 1, 62. doi: 10.1038/s42003-018-0073-z.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80(15), 62–79. doi: 10.1016/j.neuroimage.2013.05.041.CrossRefGoogle ScholarPubMed
Woolgar, A., Duncan, J., Manes, F., & Fedorenko, E. (2018). Fluid intelligence is supported by the multiple-demand system not the language system. Nature Human Behaviour, 2(3), 200–204. doi: 10.1038/s41562–017-0282-3.CrossRefGoogle Scholar
Woolgar, A., Parr, A., Cusack, R., Thompson, R., Nimmo-Smith, I., Torralva, T., … Duncan, J. (2010). Fluid intelligence loss linked to restricted regions of damage within frontal and parietal cortex. Proceedings of the National Academy of Sciences, 107(33), 14899–14902. doi: 10.1073/pnas.1007928107.CrossRefGoogle ScholarPubMed
Yarkoni, T. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical power – Commentary on Vul et al. (2009). Perspectives on Psychological Science, 4(3), 294–298. doi: 10.1111/j.1745-6924.2009.01127.x.CrossRefGoogle Scholar
Yarkoni, T., Poldrack, R. A., Van Essen, D. C., & Wager, T. D. (2010). Cognitive neuroscience 2.0: Building a cumulative science of human brain function. Trends in Cognitive Sciences, 14(11), 489–496. doi: 10.1016/j.tics.2010.08.004.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., … Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. doi: 10.1152/jn.00338.2011.Google ScholarPubMed

References

Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., & Sporns, O. (2009). Modeling the impact of lesions in the human brain. PLoS Computational Biology, 5(6), e1000408.CrossRefGoogle ScholarPubMed
Baddeley, A. D., & Hitch, G. (1974). Working memory. In Bower, G. H. (ed.) Psychology of learning and motivation, 8th ed. (pp. 47–89). New York: Academic Press.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20.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.CrossRefGoogle ScholarPubMed
Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. The Neuroscientist, 12(6), 512–523.CrossRefGoogle ScholarPubMed
Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R. (2009). Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences of the United States of America, 106(28), 11747–11752.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.CrossRefGoogle Scholar
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016). Optimally controlling the human connectome: The role of network topology. Scientific Reports, 6, 30770.CrossRefGoogle ScholarPubMed
Bohlken, M. M., Brouwer, R. M., Mandl, R. C. W., Hedman, A. M., van den Heuvel, M. P., van Haren, N. E. M., … Hulshoff Pol, H. E. (2016). Topology of genetic associations between regional gray matter volume and intellectual ability: Evidence for a high capacity network. Neuroimage, 124(Pt A), 1044–1053.CrossRefGoogle ScholarPubMed
Breakspear, M. (2017). Dynamic models of large-scale brain activity. Nature Neuroscience, 20(3), 340–352.CrossRefGoogle ScholarPubMed
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.CrossRefGoogle ScholarPubMed
Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13(5), 336–349.CrossRefGoogle ScholarPubMed
Cabral, J., Kringelbach, M. L., & Deco, G. (2017). Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms. Neuroimage, 160, 84–96.CrossRefGoogle Scholar
Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014). The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262–274.CrossRefGoogle ScholarPubMed
Cao, M., Wang, Z., & He, Y. (2015). Connectomics in psychiatric research: Advances and applications. Neuropsychiatric Disease and Treatment, 11, 2801–2810.Google ScholarPubMed
Cattell, R. B. (1971). Abilities: Their structure, growth and action. Boston, MA: Houghton Mifflin.Google Scholar
Cattell, R. B., & Horn, J. D. (1978). A check on the theory of fluid and crystallized intelligence with description of new subtest designs. Journal of Educational Measurement, 15(3), 139–164.CrossRefGoogle Scholar
Chen, T., Cai, W., Ryali, S., Supekar, K., & Menon, V. (2016). Distinct global brain dynamics and spatiotemporal organization of the salience network. PLoS Biology, 14(6), e1002469.CrossRefGoogle ScholarPubMed
Chen, J. E., Chang, C., Greicius, M. D., & Glover, G. H. (2015). Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics. Neuroimage, 111, 476–488.CrossRefGoogle ScholarPubMed
Chen, Y., Spagna, A., Wu, T., Kim, T. H., Wu, Q., Chen, C., … Fan, J. (2019). Testing a cognitive control model of human intelligence. Scientific Reports, 9, 2898.CrossRefGoogle ScholarPubMed
Cohen, J. R. (2018). The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity. Neuroimage, 180(Pt B), 515–525.CrossRefGoogle ScholarPubMed
Cohen, J. R., & D’Esposito, M. (2016). The segregation and integration of distinct brain networks and their relationship to cognition. The Journal of Neuroscience, 36(48), 12083–12094.CrossRefGoogle Scholar
Cohen, J. R., Gallen, C. L., Jacobs, E. G., Lee, T. G., & D’Esposito, M. (2014). Quantifying the reconfiguration of intrinsic networks during working memory. PLoS One, 9(9), e106636.CrossRefGoogle ScholarPubMed
Cole, M. W., Ito, T., Bassett, D. S., & Schultz, D. H. (2016). Activity flow over resting-state networks shapes cognitive task activations. Nature Neuroscience, 19(12), 1718–1726.CrossRefGoogle ScholarPubMed
Cole, M. W., Ito, T., & Braver, T. S. (2015). Lateral prefrontal cortex contributes to fluid intelligence through multinetwork connectivity. Brain Connectivity, 5(8), 497–504.CrossRefGoogle ScholarPubMed
Cole, M. W., Laurent, P., & Stocco, A. (2013). Rapid instructed task learning: A new window into the human brain’s unique capacity for flexible cognitive control. Cognitive, Affective & Behavioral Neuroscience, 13(1), 1–22.CrossRefGoogle ScholarPubMed
Cole, M. W., Pathak, S., & Schneider, W. (2010). Identifying the brain’s most globally connected regions. Neuroimage, 49(4), 3132–3148.CrossRefGoogle ScholarPubMed
Cole, M. W., Yarkoni, T., Repovš, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. The Journal of Neuroscience, 32(26), 8988–8999.CrossRefGoogle ScholarPubMed
Conway, A. R. A., Getz, S. J., Macnamara, B., & Engel de Abreu, P. M. J. (2011). Working memory and intelligence. In Sternberg, R. J., & Kaufman, S. B. (eds.), The Cambridge handbook of intelligence (pp. 394–418). New York: Cambridge University Press.CrossRefGoogle Scholar
Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences, 7(12), 547–552.CrossRefGoogle ScholarPubMed
Deco, G., & Corbetta, M. (2011). The dynamical balance of the brain at rest. The Neuroscientist, 17(1), 107–123.CrossRefGoogle ScholarPubMed
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2013). Resting brains never rest: Computational insights into potential cognitive architectures. Trends in Neurosciences, 36(5), 268–274.CrossRefGoogle ScholarPubMed
Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O., & Kötter, R. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. Proceedings of the National Academy of Sciences of the United States of America, 106(25), 10302–10307.CrossRefGoogle ScholarPubMed
Deco, G., & Kringelbach, M. L. (2014). Great expectations: Using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron, 84(5), 892–905.CrossRefGoogle ScholarPubMed
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015). Rethinking segregation and integration: Contributions of whole-brain modelling. Nature Reviews Neuroscience, 16(7), 430–439.CrossRefGoogle ScholarPubMed
Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. Proceedings of the National Academy of Sciences of the United States of America, 95(24), 14529–14534.CrossRefGoogle ScholarPubMed
Dosenbach, N. U. F., Fair, D. A., Cohen, A. L., Schlaggar, B. L., & Petersen, S. E. (2008). A dual-networks architecture of top-down control. Trends in Cognitive Sciences, 12(3), 99–105.CrossRefGoogle ScholarPubMed
Dubin, M. (2017). Imaging TMS: Antidepressant mechanisms and treatment optimization. International Review of Psychiatry, 29(2), 89–97.CrossRefGoogle ScholarPubMed
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, 20170284.CrossRefGoogle ScholarPubMed
Duncan, J. (2001). An adaptive coding model of neural function in prefrontal cortex. Nature Reviews Neuroscience, 2(11), 820–829.CrossRefGoogle ScholarPubMed
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172–179.CrossRefGoogle ScholarPubMed
Ekman, M., Derrfuss, J., Tittgemeyer, M., & Fiebach, C. J. (2012). Predicting errors from reconfiguration patterns in human brain networks. Proceedings of the National Academy of Sciences of the United States of America, 109(41), 16714–16719.CrossRefGoogle ScholarPubMed
Elton, A., & Gao, W. (2014). Divergent task-dependent functional connectivity of executive control and salience networks. Cortex, 51, 56–66.CrossRefGoogle ScholarPubMed
Elton, A., & Gao, W. (2015). Task-related modulation of functional connectivity variability and its behavioral correlations. Human Brain Mapping, 36(8), 3260–3272.CrossRefGoogle ScholarPubMed
Euler, M. J. (2018). Intelligence and uncertainty: Implications of hierarchical predictive processing for the neuroscience of cognitive ability. Neuroscience and Biobehavioral Reviews, 94, 93–112.CrossRefGoogle ScholarPubMed
Finc, K., Bonna, K., Lewandowska, M., Wolak, T., Nikadon, J., Dreszer, J., … Kühn, S. (2017). Transition of the functional brain network related to increasing cognitive demands. Human Brain Mapping, 38(7), 3659–3674.Google ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience, 16(3), 159–172.CrossRefGoogle ScholarPubMed
Fox, M. D., Buckner, R. L., Liu, H., Chakravarty, M. M., Lozano, A. M., & Pascual-Leone, A. (2014). Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proceedings of the National Academy of Sciences of the United States of America, 111(41), E4367–E4375.CrossRefGoogle ScholarPubMed
Friedman, N. P., & Miyake, A. (2017). Unity and diversity of executive functions: Individual differences as a window on cognitive structure. Cortex, 86, 186–204.CrossRefGoogle ScholarPubMed
Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., Defries, J. C., & Hewitt, J. K. (2006). Not all executive functions are related to intelligence. Psychological Science, 17(2), 172–179.CrossRefGoogle ScholarPubMed
Gallen, C. L., & D’Esposito, M. (2019). Modular brain network organization: A biomarker of cognitive plasticity. Trends in Cognitive Sciences, 23(4), 293–304.CrossRefGoogle Scholar
Gallen, C. L., Turner, G. R., Adnan, A., & D’Esposito, M. (2016). Reconfiguration of brain network architecture to support executive control in aging. Neurobiology of Aging, 44, 42–52.CrossRefGoogle Scholar
Garlick, D. (2002). Understanding the nature of the general factor of intelligence: The role of individual differences in neural plasticity as an explanatory mechanism. Psychological Review, 109(1), 116–136.CrossRefGoogle ScholarPubMed
Girn, M., Mills, C., & Christoff, K. (2019). Linking brain network reconfiguration and intelligence: Are we there yet? Trends in Neuroscience and Education, 15, 62–70.CrossRefGoogle ScholarPubMed
Gläscher, 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 of the United States of America, 107(10), 4705–4709.CrossRefGoogle ScholarPubMed
Godwin, D., Barry, R. L., & Marois, R. (2015). Breakdown of the brain’s functional network modularity with awareness. Proceedings of the National Academy of Sciences of the United States of America, 112(12), 3799–3804.CrossRefGoogle ScholarPubMed
Gonzalez-Castillo, J., & Bandettini, P. A. (2018). Task-based dynamic functional connectivity: Recent findings and open questions. Neuroimage, 180(Pt B), 526–533.CrossRefGoogle ScholarPubMed
Goodkind, M., Eickhoff, S. B., Oathes, D. J., Jiang, Y., Chang, A., Jones-Hagata, L. B., … Etkin, A. (2015). Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry, 72(4), 305–315.CrossRefGoogle ScholarPubMed
Gordon, E. M., Stollstorff, M., & Vaidya, C. J. (2012). Using spatial multiple regression to identify intrinsic connectivity networks involved in working memory performance. Human Brain Mapping, 33(7), 1536–1552.CrossRefGoogle ScholarPubMed
Goschke, T. (2014). Dysfunctions of decision-making and cognitive control as transdiagnostic mechanisms of mental disorders: Advances, gaps, and needs in current research. International Journal of Methods in Psychiatric Research, 23(Suppl 1), 41–57.CrossRefGoogle ScholarPubMed
Gratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., … Petersen, S. E. (2018). Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron, 98(2), 439–452.e5.CrossRefGoogle ScholarPubMed
Gratton, C., Lee, T. G., Nomura, E. M., & D’Esposito, M. (2013). The effect of theta-burst TMS on cognitive control networks measured with resting state fMRI. Frontiers in Systems Neuroscience, 7, 124.CrossRefGoogle ScholarPubMed
Gratton, C., Nomura, E. M., Perez, F., & D’Esposito, M. (2012). Focal brain lesions to critical locations cause widespread disruption of the modular organization of the brain. Journal of Cognitive Neuroscience, 24(6), 1275–1285.CrossRefGoogle ScholarPubMed
Gratton, C., Sun, H., & Petersen, S. E. (2018). Control networks and hubs. Psychophysiology, 55(3), e13032.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., … Bassett, D. S. (2015). Controllability of structural brain networks. Nature Communications, 6, 8414.CrossRefGoogle ScholarPubMed
Guimerà, R., Mossa, S., Turtschi, A., & Amaral, L. A. N. (2005). The worldwide air transportation network: Anomalous centrality, community structure, and cities’ global roles. Proceedings of the National Academy of Sciences of the United States of America, 102(22), 7794–7799.CrossRefGoogle ScholarPubMed
Haier, R. J., Siegel, B. V., Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., … Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12, 199–217.CrossRefGoogle Scholar
Hart, M. G., Ypma, R. J. F., Romero-Garcia, R., Price, S. J., & Suckling, J. (2016). Graph theory analysis of complex brain networks: New concepts in brain mapping applied to neurosurgery. Journal of Neurosurgery, 124(6), 1665–1678.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Honey, C. J., Kötter, R., Breakspear, M., & Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences of the United States of America, 104(24), 10240–10245.CrossRefGoogle ScholarPubMed
Hutchison, R. M., & Morton, J. B. (2015). Tracking the brain’s functional coupling dynamics over development. The Journal of Neuroscience, 35(17), 6849–6859.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin & Review, 9(4), 637–671.CrossRefGoogle Scholar
Kenett, Y. N.,