Estimation of Intelligence Quotient in Healthy Individuals using Magnetic Resonance Imaging: A Systematic Review

Authors

  • Nur Liana Amran Universiti Sultan Zainal Abidin
  • Elza Azri Othman Universiti Sultan Zainal Abidin

DOI:

https://doi.org/10.33736/jcshd.5938.2023

Keywords:

intelligence quotient, IQ estimation, magnetic resonance imaging, fMRI, brain structures

Abstract

The intelligence quotient (IQ) is typically used to reflect human intelligence. Conventional IQ tests are commonly used to assess an individual’s level of intelligence. However, the reliability of these conventional methods remains controversial as they are vulnerable to bias and often yield inconsistent results. Interestingly, emerging evidence suggested that magnetic resonance imaging (MRI) may be an alternative method to estimate a person’s level of intelligence, as IQ is closely linked with the brain's structure. In this article, we systematically reviewed published studies on the estimation of IQ in healthy individuals using MRI. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was performed in the PubMed database. The literature search focused on studies reporting on brain structures associated with IQ in healthy individuals and the effects of brain structural changes on IQ. 22 studies met the eligibility criteria and were included in this review. Key brain regions associated with IQ are grey matter (GM), white matter (WM), caudate nucleus, left hemisphere, limbic system, frontoparietal (FP) cortices, and default-mode network (DMN). The critical effect of ageing on brain changes and its impact on IQ were also discussed. Overall, the findings suggested that brain structures play a significant role in IQ levels in healthy individuals. This systematic review highlights the potential use of MRI in estimating IQ by examining brain structures. Nonetheless, available MRI studies were limited by methodological issues. Future MRI investigations should include well-characterized groups of females and matched male healthy individuals while considering confounding factors such as types of IQ tests.  

References

REFERENCES

Abdul Wahab, N. S., Yahya, N., Yusoff, A. N., Zakaria, R., Thanabalan, J., Othman, E., Hong, S. B., Kumar, R. K. A., & Abdul Manan, H. (2022). Effects of different scan duration on brain effective connectivity among default mode network nodes. Diagnostics, 12(5), 1277. https://doi.org/10.3390/diagnostics12051277

Abiko, K., Shiga, T., Katoh, C., Hirata, K., Kuge Yuji, Kobayashi, K., Ikeda, S., & Ikoma, K. (2018). Relationship between intelligence quotient (IQ) and cerebral metabolic rate of oxygen in patients with neurobehavioural disability after traumatic brain injury. Brain Injury, 32(11), 1367-1372. https://doi.org/10.1080/02699052.2018.1496478

Bajaj, S., Raikes, A., Smith, R., Dailey, N. S., Alkozei, A., Vanuk, J. R., & Killgore, W. D. (2018). The relationship between general intelligence and cortical structure in healthy individuals. Neuroscience, 388, 36-44. https://doi.org/10.1016/j.neuroscience.2018.07.008

Baker, L. M., Laidlaw, D. H., Cabeen, R., Akbudak, E., Conturo, T. E., Correia, S., Tate, D. F., Heaps-Woodruff, J. M., Brier, M. R., Bolzenius, J., Salminen, L. E., Lane, E. M., McMichael, A. R., & Paul, R. H. (2017). Cognitive reserve moderates the relationship between neuropsychological performance and white matter fiber bundle length in healthy older adults. Brain Imaging and Behavior, 11(3), 632–639. https://doi.org/10.1007/s11682-016-9540-7

Banich, M. T., & Brown, W. S. (2000). A life-span perspective on interaction between the cerebral hemispheres. Developmental Neuropsychology, 18(1), 1–10. https://doi.org/10.1207/S15326942DN1801_1

Bechara, A., Damasio, H., & Damasio, A. R. (2003). Role of amygdala in decision-making. Annals of the New York Academy of Sciences, 985(1), 356-339. https://doi.org/10.1111/j.1749-6632.2003.tb07094.x

Bertola, L., Ávila, R. T., Bicalho, M. A. C., & Malloy-Diniz, L. F. (2019). Semantic memory, but not education or intelligence, moderates cognitive aging: a cross-sectional study. Brazilian Journal of Psychiatry, 41(6), 535–539. https://doi.org/10.1590/1516-4446-2018-0290

Braaten, E. B., & Norman, D. (2006). Intelligence (IQ) testing. Pediatric in Reviews, 27(11), 403-408. https://doi.org/10.1542/pir.27-11-403

Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control: rewarding, aversive, and alerting. Neuron, 68(5), 815–834. https://doi.org/10.1016/j.neuron.2010.11.022

Bubb, E. J., Metzler-Baddeley, C., & Aggleton, J. P. (2018). The cingulum bundle: Anatomy, function, and dysfunction. Neuroscience & Biobehavioral Reviews, 92, 104-127. https://doi.org/10.1016/j.neubiorev.2018.05.008

Chen, P. Y., Chen, C. L., Hsu, Y. C., & Tseng, W. Y. I. (2020). Fluid intelligence is associated with cortical volume and white matter tract integrity within multiple-demand system across adult lifespan. NeuroImage, 212, 116576. https://doi.org/10.1016/j.neuroimage.2020.116576

Chiao, J. Y., Harada, T., Komeda, H., Li, Z., Mano, Y., Saito, D. N., & Tanabe, H. C. (2020). Dynamic neural architecture for social knowledge retrieval. Proceedings of the National Academy of Sciences, 117(38), 23505-23515. https://doi.org/10.1073/pnas.2000198117

Chiu, Y. C., Jiang, J., & Egner, T. (2017). The caudate nucleus mediates learning of stimulus–control state associations. The Journal of Neuroscience, 37(4), 1028–1038. https://doi.org/10.1523/JNEUROSCI.0778-16.2016

Corballis M. C. (2014). Left brain, right brain: facts and fantasies. PLoS Biology, 12(1), e1001767. https://doi.org/10.1371/journal.pbio.1001767

Davis, N. J. (2021). Quantifying the trajectory of gyrification changes in the aging brain. European Journal of Neuroscience, 53(11), 3634-3636. https://doi.org/10.1111/ejn.15220

de Zubicaray, G. I., Rose, S. E., & McMahon, K. L. (2011). The structure and connectivity of semantic memory in the healthy older adult brain. NeuroImage, 54(2), 1488–1494. https://doi.org/10.1016/j.neuroimage.2010.08.058

DeSerisy, M., Ramphal, B., Pagliaccio, D., Raffanello, E., Tau, G., Marsh, R., Psoner, J., & Margolis, A. E. (2021). Frontoparietal and default mode network connectivity varies with age. Developmental Cognitive Neuroscience, 48, 100928. https://doi.org/10.1016/j.dcn.2021.100928

Deyoung C. G. (2013). The neuromodulator of exploration: A unifying theory of the role of dopamine in personality. Frontiers in Human Neuroscience, 7, 762. https://doi.org/10.3389/fnhum.2013.00762

Duncan, J., Chylinski, D., Mitchell, D. J., & Bhandari, A. (2017). Complexity and compositionality in fluid intelligence. Proceedings of the National Academy of Sciences of the United States of America, 114(20), 5295–5299. https://doi.org/10.1073/pnas.1621147114

Ferreira, D., Machado, A., Molina, Y., Nieto, A., Correia, R., Westman, E., & Barroso, J. (2017). Cognitive variability during middle-age: Possible association with neurodegeneration and cognitive reserve. Frontiers in Aging Neuroscience, 9, 188. https://doi.org/10.3389/fnagi.2017.00188

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), 9673–9678. https://doi.org/10.1073/pnas.0504136102

Friedman, N. P., & Robbins, T. W. (2022). The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology, 47(1), 72–89. https://doi.org/10.1038/s41386-021-01132-0

Ganuthula, V. R. R., & Sinha, S. (2019). The looking glass for intelligence quotient tests: The interplay of motivation, cognitive functioning, and affect. Frontiers in Psychology, 10, 2857. https://doi.org/10.3389/fpsyg.2019.02857

Giedd, J. N., & Rapoport, J. L. (2010). Structural MRI of pediatric brain development: What have we learned and where are we going? Neuron, 67(5), 728–734. https://doi.org/10.1016/j.neuron.2010.08.040

Grazioplene, R. G., G Ryman, S., Gray, J. R., Rustichini, A., Jung, R. E., & DeYoung, C. G. (2015). Subcortical intelligence: Caudate volume predicts IQ in healthy adults. Human Brain Mapping, 36(4), 1407–1416. https://doi.org/10.1002/hbm.22710

Han, S., Jiang, Y., Gu, H., Rao, H., Mao, L., Cui, Y., & Zhai, R. (2004). The role of human parietal cortex in attention networks. Brain, 127(3), 650-659. https://doi.org/10.1093/brain/awh071

Hartwigsen, G., Bengio, Y., & Bzdok, D. (2021). How does hemispheric specialization contribute to human-defining cognition. Neuron, 109(13), 2075–2090. https://doi.org/10.1016/j.neuron.2021.04.024

Hoffman, P., Cox, S. R., Dykiert, D., Muñoz Maniega, S., Valdés Hernández, M. C., Bastin, M. E., Wardlaw, J. M., & Deary, I. J. (2017). Brain grey and white matter predictors of verbal ability traits in older age: The Lothian birth cohort 1936. NeuroImage, 156, 394–402. https://doi.org/10.1016/j.neuroimage.2017.05.052

Huang, F., Fan, J., & Luo, J. (2015). The neural basis of novelty and appropriateness in processing of creative chunk decomposition. Neuroimage, 113, 122-132. https://doi.org/10.1016/j.neuroimage.2015.03.030

Jäncke, L., Sele, S., Liem, F., Oschwald, J., & Merillat, S. (2020). Brain aging and psychometric intelligence: A longitudinal study. Brain Structure & Function, 225(2), 519–536. https://doi.org/10.1007/s00429-019-02005-5

Jung, R., & Haier, R. (2007). The parieto-frontal integration theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135-154. https://doi.org/10.1017/S0140525X07001185

Kintz, S., & Wright, H. H. (2017). Semantic knowledge use in discourse: Influence of age. Discourse Processes, 54(8), 670–681. https://doi.org/10.1080/0163853X.2016.1150652

Lamballais, S., Vinke, E. J., Vernooij, M. W., Ikram, M. A., & L, R. (2020). Cortical gyrification in relation to age and cognition in older adults. NeuroImage, 212, 116637. https://doi.org/10.1016/j.neuroimage.2020.116637

Langer, N., Pedroni, A., Gianotti, L. R. R., Hänggi, J., Knoch, D., & Jäncke, L. (2012). Functional brain network efficiency predicts intelligence. Human Brain Mapping, 33(6), 1393-1406. https://doi.org/10.1002/hbm.21297

Li, Y., Xue, Y. Z., Zhao, W. T., Li, S. S., Li, J., & Xu, Y. (2021). Correlates of intelligence via resting-state functional connectivity of the amygdala in healthy adults. Brain Research, 1751, 147176. https://doi.org/10.1016/j.brainres.2020.147176

Li, H., Hirano, S., Furukawa, S., Nakano, Y., Kojima, K., Ishikawa, A., Tai, H., Horikoshi, T., Iimori, T., Uno, T., Matsuda, H., & Kuwabara, S. (2020). The relationship between the striatal dopaminergic neuronal and cognitive function with aging. Frontiers in Aging Neuroscience, 12, 41. https://doi.org/10.3389/fnagi.2020.00041

Liederman, J., & Meehan, P. (1986). When is between-hemisphere division of labor advantageous? Neuropsychologia, 24(6), 863–874. https://doi.org/10.1016/0028-3932(86)90086-2

Lockhart, S. N., & DeCarli, C. (2014). Structural imaging measures of brain aging. Neuropsychology Review, 24(3), 271–289. https://doi.org/10.1007/s11065-014-9268-3

Mazhirina, K. G., Mel'nikov, M. E., Pokrovskii, M. A., Petrovskii, E. D., Savelov, A. A., & Shtark, M. B. (2016). Raven's progressive matrices in the lexicon of dynamic mapping of the brain (MRI). Bulletin of Experimental Biology and Medicine, 160(6), 850-856. https://doi.org/10.1007/s10517-016-3325-2

Murman D. L. (2015). The impact of age on cognition. Seminars in Hearing, 36(3), 111–121. https://doi.org/10.1055/s-0035-1555115

Nakamura, K., & Hikosaka, O. (2006). Role of dopamine in the primate caudate nucleus in reward modulation of saccades. The Journal of Neuroscience, 26(20), 5360–5369. https://doi.org/10.1523/JNEUROSCI.4853-05.2006

Othman, E. A., Yusoff, A. N., Mohamad, M., Abdul Manan, H., Abd Hamid, A. I., Giampietro, V. (2020). Hemispheric lateralization of auditory working memory regions during stochastic resonance: an fMRI study. Journal of Magnetic Resonance Imaging, 51(6), 1821-1828. https://doi.org/10.1002/jmri.27016

Othman, E., Yusoff, A. N., Mohamad, M., Abdul Manan, H., Giampietro, V., Abd Hamid, A. I., Dzulkifli, M. A., Osman, S. S., Wan Burhanuddin, W. I. D. (2019). Low intensity white noise improves performance in auditory working memory task: an fMRI study. Heliyon, 5(9), e02444. https://doi.org/10.1016/j.heliyon.2019.e02444

Oschwald, J., Guye, S., & Liem, F. (2019). Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change. Reviews in the Neurosciences, 31(1), 1-57. https://doi.org/10.1515/revneuro-2018-0096.

Penke, L., Maniega, S. M., Bastin, M. E., Valdés Hernández, M. C., Murray, C., Royle, N. A., Starr, J. M., Wardlaw, J. M., & Deary, I. J. (2012). Brain white matter tract integrity as a neural foundation for general intelligence. Molecular Psychiatry, 17(10), 1026–1030. https://doi.org/10.1038/mp.2012.66

Posner, J., Park, C., & Wang, Z. (2014). Connecting the dots: A review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder. Neuropsychology Review, 24(1), 3-15. https://doi.org/10.1007/s11065-014-9251-z

Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676–682. https://doi.org/10.1073/pnas.98.2.676

Salthouse T. A. (2009). When does age-related cognitive decline begin? Neurobiology of Aging, 30(4), 507–514. https://doi.org/10.1016/j.neurobiolaging.2008.09.023

Salthouse T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428. https://doi.org/10.1037/0033-295x.103.3.403

Santarnecchi, E., Tatti, E., Rossi, S., Serino, V., & Rossi, A. (2015). Intelligence-related differences in the asymmetry of spontaneous cerebral activity. Human Brain Mapping, 36(9), 3586–3602. https://doi.org/10.1002/hbm.22864

Schaefer, A., & Gray, J. R. (2007). A role for the human amygdala in higher cognition. Reviews in the Neurosciences, 18(5), 355–363. https://doi.org/10.1515/revneuro.2007.18.5.355

Schenker, N. M., Desgouttes, A. M., & Semendeferi, K. (2005). Neural connectivity and cortical substrates of cognition in hominoids. Journal of Human Evolution, 49(5), 547–569. https://doi.org/10.1016/j.jhevol.2005.06.004

Schnack, H. G., van Haren, N. E., Brouwer, R. M., Evans, A., Durston, S., Boomsma, D. I., Kahn, R. S., & Hulshoff Pol, H. E. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral Cortex, 25(6), 1608–1617. https://doi.org/10.1093/cercor/bht357

Sheffield, J. M., Repovs, G., Harms, M. P., Carter, C. S., Gold, J. M., MacDonald, A. W., 3rd, Daniel Ragland, J., Silverstein, S. M., Godwin, D., & Barch, D. M. (2015). Fronto-parietal and cingulo-opercular network integrity and cognition in health and schizophrenia. Neuropsychologia, 73, 82–93. https://doi.org/10.1016/ j.neuropsychologia.2015.05.006

Shokri-Kojori, E., Bennett, I. J., Tomeldan, Z. A., Krawczyk, D. C., & Rypma, B. (2021). Estimates of brain age for gray matter and white matter in younger and older adults: Insights into human intelligence. Brain Research, 1763, 147431. https://doi.org/10.1016/j.brainres.2021.147431

Shuttleworth-Edwards, A. B., Kemp, R. D., Rust, A. L., Muirhead, J. G., Hartman, N. P., & Radloff, S. E. (2004). Cross-cultural effects on IQ test performance: a review and preliminary normative indications on WAIS-III test performance. Journal of Clinical and Experimental Neuropsychology, 26(7), 903–920. https://doi.org/10.1080/13803390490510824

Simic, G., Tkalcic, M., Vukic, V., Mulc, D., Spanic, E., Sagud, M., Olucha-Bordonau, F. E., Vuksic, M., & Hof, P. R. (2021). Understanding emotions: Origins and roles of the amygdala. Biomolecules, 11(6), 823. https://doi.org/10.3390/biom11060823

Tadayon, E., Pascual-Leone, A., & Santarnecchi, E. (2020). Differential contribution of cortical thickness, surface area, and gyrification to fluid and crystallized intelligence. Cerebral Cortex, 30(1), 215–225. https://doi.org/10.1093/cercor/bhz082

Wang, H., Zhou, H., Guo, Y., Gao, L., & Xu, H. (2021). Voxel-wise analysis of structural and functional MRI for lateralization of handedness in college students. Frontiers in Human Neuroscience, 15, 687965. https://doi.org/10.3389/fnhum.2021.687965

Wang, L., Wee, C. Y., Suk, H. I., Tang, X., & Shen, D. (2015). MRI-based intelligence quotient (IQ) estimation with sparse learning. PloS One, 10(3), e0117295. https://doi.org/10.1371/journal.pone.0117295

Wheater, E., Shenkin, S. D., Muñoz Maniega, S., Valdés Hernández, M., Wardlaw, J. M., Deary, I. J., Bastin, M. E., Boardman, J. P., & Cox, S. R. (2021). Birth weight is associated with brain tissue volumes seven decades later but not with MRI markers of brain ageing. NeuroImage: Clinical, 31, 102776. https://doi.org/10.1016/j.nicl.2021.102776

Downloads

Published

2023-09-30

How to Cite

Nur Liana Amran, & Othman, E. A. (2023). Estimation of Intelligence Quotient in Healthy Individuals using Magnetic Resonance Imaging: A Systematic Review. Journal of Cognitive Sciences and Human Development, 9(2), 1–19. https://doi.org/10.33736/jcshd.5938.2023