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12 - Functional Brain 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

Functional brain imaging studies of intelligence have tackled the following questions: What happens in our brains when we solve tasks from an intelligence test? And are there differences between people? Do people with higher scores on an intelligence test show different patterns of brain activation while working on cognitive tasks than people with lower scores? Answering these questions can contribute to improving our understanding of the biological bases of intelligence. To investigate these questions, researchers have used different methods for quantifying patterns of brain activation changes and their association with cognitive processing – including electroencephalography (EEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The results of this research allow us to delineate those parts of the brain that are important for intelligence – either in the sense that they are activated when people solve tasks commonly used to test intelligence or in the sense that functional differences in these regions are associated with individual differences in intelligence. From the fact that some of our abilities – like our abilities to see, hear, feel, and move – can quite specifically be traced back to the contributions of distinct brain regions (namely the visual, auditory, somatosensory, and motor cortex) – one might derive the expectation that there must be another part of the brain responsible for higher cognitive functioning and intelligence. But, as the following review will show, there is no single “seat” of intelligence in our brain. Instead, intelligence is associated with a distributed set of brain regions.

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Publisher: Cambridge University Press
Print publication year: 2021

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References

Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Science, 22(1), 820.CrossRefGoogle ScholarPubMed
Barbey, A. K., Colom, R., & Grafman, J. (2013). Dorsolateral prefrontal contributions to human intelligence. Neuropsychologia, 51(7), 13611369.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, 485494.Google Scholar
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), 11541164. 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, 1027. doi: 10.1016/j.intell.2015.04.009.Google Scholar
Basten, U., Stelzel, C., & Fiebach, C. J. (2011). Trait anxiety modulates the neural efficiency of inhibitory control. Journal of Cognitive Neuroscience, 23(10), 31323145. doi: 10.1162/jocn_a_00003.Google Scholar
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), 571588. doi: 10.3758/s13415–012-0100-3.Google Scholar
Basten, U., Stelzel, C., & Fiebach, C. J. (2013). Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network. Intelligence, 41(5), 517528. 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), 541546. doi: 10.1002/ana.410230603.Google Scholar
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), 674692. doi: 10.1037/a0024695.Google Scholar
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), 365376. doi: 10.1038/nrn3475.Google Scholar
Cabeza, R., & Nyberg, L. (2000). Imaging cognition II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12(1), 147. doi: 10.1162/08989290051137585.Google Scholar
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 122. 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), 739749. 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), 1032310329. doi: 10.1523/JNEUROSCI.3259-08.2008.Google Scholar
Cole, M. W., & Schneider, W. (2007). The cognitive control network: Integrated cortical regions with dissociable functions. NeuroImage, 37(1), 343360. 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), 306324. 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.Google Scholar
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.Google 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, 3243.Google 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), 38293837. doi: 10.1016/j.neuroimage.2011.11.051.Google Scholar
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), 883892. doi: 10.1037/a0016615.Google Scholar
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), 1107311078. doi: 10.1073/pnas.0704320104.Google Scholar
Duncan, J. (1995). Attention, intelligence, and the frontal lobes. In Gazzaniga, M. S. (ed.), The cognitive neurosciences (pp. 721733). 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), 457460. doi: 10.1126/science.289.5478.457.Google Scholar
Duncan, J. (2005). Frontal lobe function and general intelligence: Why it matters. Cortex, 41(2), 215217. 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), 172179. doi: 10.1016/j.tics.2010.01.004.Google Scholar
Duncan, J., Burgess, P., & Emslie, H. (1995). Fluid intelligence after frontal lobe lesions. Neuropsychologia, 33(3), 261268. doi: 10.1016/0028-3932(94)00124-8.Google Scholar
Duncan, J., Emslie, H., Williams, P., Johnson, R., & Freer, C. (1996). Intelligence and the frontal lobe: The organization of goal-directed behavior. Cognitive Psychology, 30(3), 257303. 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), 331342. doi: 10.1016/j.neuroimage.2012.04.053.Google Scholar
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), 963979. doi: 10.1093/brain/122.5.963.Google Scholar
Euler, M. J., Weisend, M. P., Jung, R. E., Thoma, R. J., & Yeo, R. A. (2015). Reliable activation to novel stimuli predicts higher fluid intelligence. NeuroImage, 114, 311319. doi: 10.1016/j.neuroimage.2015.03.078.Google Scholar
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), 555564. 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.Google Scholar
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), 112124.Google Scholar
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), 47054709. doi: 10.1073/pnas.0910397107.CrossRefGoogle ScholarPubMed
Goel, V., & Dolan, R. J. (2001). Functional neuroanatomy of three-term relational reasoning. Neuropsychologia, 39(9), 901909.Google Scholar
Goel, V., Gold, B., Kapur, S., & Houle, S. (1998). Neuroanatomical correlates of human reasoning. Journal of Cognitive Neuroscience, 10(3), 293302. doi: 10.1162/089892998562744.Google Scholar
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), 422439. doi: 10.1016/j.brainresbull.2006.02.009.Google Scholar
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), 8998. doi: 10.1016/S0167–8760(03)00095-3.Google Scholar
Gray, J. R., Chabris, C. F., & Braver, T. S. (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience, 6(3), 316322. doi: 10.1038/nn1014.Google Scholar
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), 13011305. doi: 10.1016/j.cub.2016.03.021.Google Scholar
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), 199217. doi: 10.1016/0160-2896(88)90016-5.Google 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), 415426. 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, 9931004.Google Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017a). Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 1025. doi: 10.1016/j.intell.2016.11.001.Google 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.Google Scholar
Ioannidis, J. P. A. (2008). Why most discovered true associations are inflated. Epidemiology, 19(5), 640648. doi: 10.1097/EDE.0b013e31818131e7.Google Scholar
Jaušovec, N. (2000). Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: An EEG study. Intelligence, 28(3), 213237. doi: 10.1016/S0160–2896(00)00037-4.Google 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), 6574. doi: 10.1016/S0278–2626(03)00263-X.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. doi: 10.1017/S0140525X07001185.Google Scholar
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, 186198. 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), 203212.Google Scholar
Kruschwitz, J. D., Waller, L., Daedelow, L. S., Walter, H., & Veer, I. M. (2018). General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set. NeuroImage, 171, 323331. doi: 10.1016/j.neuroimage.2018.01.018.Google 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), 578586. 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.Google Scholar
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), 394408. doi: 10.1162/089892903321593117.Google Scholar
Mennes, M., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2013). Making data sharing work: The FCP/INDI experience. NeuroImage, 82, 683691. doi: 10.1016/j.neuroimage.2012.10.064.Google Scholar
Miller, D. I., & Halpern, D. F. (2014). The new science of cognitive sex differences. Trends in Cognitive Sciences, 18(1), 3745. doi: 10.1016/j.tics.2013.10.011.Google Scholar
Miller, E. M. (1994). Intelligence and brain myelination: A hypothesis. Personality and Individual Differences, 17(6), 803832. doi: 10.1016/0191-8869(94)90049-3.Google Scholar
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., … Smith, S. M. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19(11), 15231536. doi: 10.1038/nn.4393.Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews, 33(7), 10041023. doi: 10.1016/j.neubiorev.2009.04.001.Google Scholar
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), 515536. doi: 10.1016/S0160–2896(02)00091-0.Google Scholar
Neubauer, A. C., Freudenthaler, H. H., & Pfurtscheller, G. (1995). Intelligence and spatiotemporal patterns of event-related desynchronization (ERD). Intelligence, 20(3), 249266. doi: 10.1016/0160-2896(95)90010-1.Google 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), 217225. doi: 10.1016/j.cogbrainres.2005.05.011.Google Scholar
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), 5574. doi: 10.1016/j.actpsy.2003.11.005.Google Scholar
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), 16811694. doi: 10.1016/j.clinph.2005.03.013.Google Scholar
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), 241268. doi: 10.3758/s13415–011-0083-5.Google Scholar
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), 583587. doi: 10.1016/j.cogbrainres.2005.08.004.Google Scholar
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), 565575. doi: 10.1080/01688638808402795.Google Scholar
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, 201211.Google Scholar
Penke, L., Maniega, S. M., Bastin, M. E., Valdés Hernández, M. C., Murray, C., Royle, N. A., … Deary, I. J. (2012). Brain white matter tract integrity as a neural foundation for general intelligence. Molecular Psychiatry, 17(10), 10261030. doi: 10.1038/mp.2012.66.Google Scholar
Pfurtscheller, G., & Aranibar, A. (1977). Event-related cortical desynchronization detected by power measurements of scalp EEG. Electroencephalography and Clinical Neurophysiology, 42(6), 817826. doi: 10.1016/0013-4694(77)90235-8.Google Scholar
Poldrack, R.A. (2015). Is “efficiency” a useful concept in cognitive neuroscience? Developments in Cognitive Neuroscience, 11, 1217.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), 115127. doi: 10.1037/0894-4105.15.1.115.Google Scholar
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), 4363. 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, 928. doi: 10.1016/j.intell.2017.04.008.Google 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, 3547. doi: 10.1016/j.intell.2017.10.002.Google Scholar
Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201293. 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.Google Scholar
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), 42434258. doi: 10.1007/s00429–018-1747-5.Google Scholar
Toffanin, P., Johnson, A., de Jong, R., & Martens, S. (2007). Rethinking neural efficiency: Effects of controlling for strategy use. Behavioral Neuroscience, 121(5), 854870. doi: 10.1037/0735-7044.121.5.854.Google Scholar
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.Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 76197624. doi: 10.1523/JNEUROSCI.1443-09.2009.Google Scholar
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), 6279. doi: 10.1016/j.neuroimage.2013.05.041.Google Scholar
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), 200204. doi: 10.1038/s41562–017-0282-3.Google 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), 1489914902. doi: 10.1073/pnas.1007928107.Google Scholar
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), 294298. doi: 10.1111/j.1745-6924.2009.01127.x.CrossRefGoogle ScholarPubMed
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), 489496. 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), 11001122. doi: 10.1177/1745691617693393.Google Scholar
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), 11251165. doi: 10.1152/jn.00338.2011.Google Scholar

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To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

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