Brain Imaging and Cognition

  • Iván Chavarría-SilesEmail author
  • Guillén Fernández
  • Danielle Posthuma
Part of the Advances in Behavior Genetics book series (AIBG, volume 1)


Magnetic resonance imaging (MRI) of the brain allows us to study the morphology and function of the brain in a noninvasive way. The rapid introduction of high-resolution MRI scanners has been accompanied by a constant improvement of semiautomated statistical methods to quantify and systematically compare morphological and functional differences of diverse brain structures. These methods provide a powerful tool for characterizing individual differences in brain anatomy, connectivity, and functionality. Both structural and functional brain measures have been associated with cognitive, affective, and behavioral measures. Brain imaging genetics is the study of the effect that genetic variants may have on brain structure and function. Studying how genes can affect brain development and cognition has helped us to understand better the underlying biological mechanisms of cognitive traits and brain disorders. Genes associated with brain structure are of importance for cognitive functioning; and vice versa, genes associated with cognitive functioning are also of importance for the development of brain structures. This chapter provides an overview of the most commonly used imaging techniques to study brain anatomy, connectivity, and functionality. It also reviews how neuroimaging techniques have been used to elucidate the development of the brain across lifespan and its relation to cognitive function. Finally, it reviews some of the most consistent findings on the genetics of neuroimaging measures and the effect genetic variation can have on the brain in relation to cognition and in some neuropsychiatric disorders such as Autism, ADHD, Schizophrenia, and Alzheimer’s.


Autism Spectrum Disorder Autism Spectrum Disorder Diffusion Tensor Imaging Functional Connectivity Cortical Thickness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Alzheimer’s Association. Thies, W., & Bleiler, L. (2011). 2011 Alzheimer’s disease facts and figures. Alzheimer’s & dementia: the journal of the Alzheimer’s Association, 7, 208–244.CrossRefGoogle Scholar
  2. Assaf, Y., & Pasternak, O. (2008). Diffusion tensor imaging (DTI)-based white matter mapping in brain research: A review. Journal of molecular neuroscience: MN, 34, 51–61.PubMedCrossRefGoogle Scholar
  3. Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. NeuroImage, 11, 805–821.PubMedCrossRefGoogle Scholar
  4. Batty, G. D., Deary, I. J., Gottfredson, L. S. (2007). Premorbid (early life) IQ and later mortality risk: Systematic review. Annals of epidemiology, 4, 278–288.CrossRefGoogle Scholar
  5. Bartzokis, G., Lu, P. H., Heydari, P., Couvrette, A., Lee, G. J., Kalashyan, G., Freeman, F., Grinstead, J. W., Villablanca, P., Finn, J. P., Mintz, J., Alger, J. R., Altshuler, L. L. (2012). Multimodal magnetic resonance imaging assessment of white matter aging trajectories over the lifespan of healthy individuals. Biological psychiatry, 72, 1026–1034.CrossRefGoogle Scholar
  6. Bigos, K. L., Weinberger, D. R. (2010). Imaging genetics—days of future past. NeuroImage, 53, 804–809.PubMedCrossRefGoogle Scholar
  7. Blokland, G. A., McMahon, K. L., Thompson, P. M., Martin, N. G., de Zubicaray, G. I., & Wright, M. J. (2011). Heritability of working memory brain activation. The Journal of neuroscience: the official journal of the Society for Neuroscience, 31, 10882–10890.CrossRefGoogle Scholar
  8. Cascio, C. J., Gerig, G., & Piven, J. (2007). Diffusion tensor imaging: Application to the study of the developing brain. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 213–223.PubMedCrossRefGoogle Scholar
  9. Chavarría-Siles, I., Rijpkema, M., Lips, E., Arias-Vasquez, A., Verhage, M., Franke, B., Fernández, G., Posthuma, D. (2013). Genes encoding heterotrimeric G-proteins are associated with gray matter volume variations in the medial frontal cortex. Cerebral cortex, 23, 1025–1030.Google Scholar
  10. Chen, C. H., Gutierrez, E. D., Thompson, W., Panizzon, M. S., Jernigan, T. L., Eyler, L. T., Fennema-Notestine, C., Jak, A. J., Neale, M. C., Franz, C. E., Lyons, M. J., Grant, M. D., Fischl, B., Seidman, L. J., Tsuang, M. T., Kremen, W. S., Dale, A. M. (2012). Hierarchical genetic organization of human cortical surface area. Science, 335, 1634–1636.PubMedCrossRefGoogle Scholar
  11. Chen. R., Jiao, Y., & Herskovits, E. H. (2010). Structural MRI in autism spectrum disorder. Pediatric research, 69, 63–68.CrossRefGoogle Scholar
  12. Chiang, M. C., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Hickie, I., Toga, A. W., Wright, M. J., & Thompson, P. M. (2011). Genetics of white matter development: a DTI study of 705 twins and their siblings aged 12–29. NeuroImage, 54, 2308–2317.PubMedCrossRefGoogle Scholar
  13. Cole, D. M., Smith, S. M., Beckmann, C. F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in systems neuroscience, 4, 8.Google Scholar
  14. Colom, R., Haier, R. J., Head, K., Álvarez-Linera, J., Ángeles Quiroga, M., Chun Shih, P., et al. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the PFIT model. Intelligence, 37, 124–135.Google Scholar
  15. Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., Ke, X., Le Hellard, S., Christoforou, A., Luciano, M., mcghee, K., Lopez, L., Gow, A. J., Corley, J., Redmond, P., Fox, H. C., Haggarty, P., Whalley, L. J., mcneill, G., Goddard, M. E., Espeseth, T., Lundervold, A. J., Reinvang, I., Pickles, A., Steen, V. M., Ollier, W., Porteous, D. J., Horan, M., Starr, J. M., Pendleton, N., Visscher, P. M., Deary, I. J. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular psychiatry, 16, 996–1005.PubMedCrossRefGoogle Scholar
  16. Deary, I. J. (2012). Intelligence. Annual review of psychology, 63, 453–482.PubMedCrossRefGoogle Scholar
  17. Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature reviews. Neuroscience, 11, 201–211.PubMedGoogle Scholar
  18. Durston, S. (2010). Imaging genetics in ADHD. NeuroImage, 53, 832–838.PubMedCrossRefGoogle Scholar
  19. Ehrlich, S., Brauns, S., Yendiki, A., Ho, B. C., Calhoun, V., Schulz, S. C., Gollub, R. L., Sponheim, S. R. (2012). Associations of cortical thickness and cognition in patients with schizophrenia and healthy controls. Schizophrenia Bulletin, 38, 1050–1062.Google Scholar
  20. Finkel, D., Reynolds, C. A., McArdle, J. J., Pedersen, N. L. (2005). The longitudinal relationship between processing speed and cognitive ability: Genetic and environmental influences. Behavior genetics, 35, 535–549.PubMedCrossRefGoogle Scholar
  21. Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America, 97, 11050–11055.CrossRefGoogle Scholar
  22. Flint, J., & Munafo, M. R. (2007). The endophenotype concept in psychiatric genetics. Psychological medicine, 37, 163–180.PubMedCrossRefGoogle Scholar
  23. Fornito, A., & Bullmore, E. T. (2010). What can spontaneous fluctuations of the blood oxygenation-level-dependent signal tell us about psychiatric disorders? Current opinion in psychiatry, 23, 239–249.PubMedCrossRefGoogle Scholar
  24. Galton, F. (1888). Head growth in students at the University of Cambridge. Nature, 38, 14–15.CrossRefGoogle Scholar
  25. Geyer, S., Weiss, M., Reimann, K., Lohmann, G., & Turner, R. (2011). Microstructural Parcellation of the human cerebral cortex—from Brodmann’s post-mortem map to in vivo mapping with high-field magnetic resonance imaging. Frontiers in human neuroscience, 5, 19.PubMedCrossRefGoogle Scholar
  26. Giedd, J. N., & Rapoport, J. L. (2010). Structural MRI of pediatric brain development: what have we learned and where are we going? Neuron, 67, 728–734.PubMedCrossRefGoogle Scholar
  27. Glahn, D. C., Thompson, P. M., & Blangero, J. (2007). Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function. Human brain mapping, 28, 488–501.PubMedCrossRefGoogle Scholar
  28. Glahn, D. C., Almasy, L., Barguil, M., Hare, E., Peralta, J. M., Kent, J. W., Dassori, A., Contreras, J., Pacheco, A., Lanzagorta, N., Nicolini, H., Raventos, H., & Escamilla, M. A. (2010a). Neurocognitive endophenotypes for bipolar disorder identified in multiplex multigenerational families. Archives of General Psychiatry, 67, 168–177.CrossRefGoogle Scholar
  29. Glahn, D. C., Winkler, A. M., Kochunov, P., Almasy, L., Duggirala, R., Carless, M. A., Curran, J. C., Olvera, R. L., Laird, A. R., Smith, S. M., Beckmann, C. F., Fox, P. T., & Blangero, J. (2010b). Genetic control over the resting brain. Proceedings of the National Academy of Sciences of the United States of America, 107, 1223–1228.CrossRefGoogle Scholar
  30. 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, 21–36.PubMedCrossRefGoogle Scholar
  31. Gur, R. E., & Gur, R. C. (2010). Functional magnetic resonance imaging in schizophrenia. Dialogues in clinical neuroscience, 12, 333–343.PubMedGoogle Scholar
  32. Hebert, L. E., Scherr, P. A., Bienias, J. L., Bennett, D. A., Evans, D. A. (2003). Alzheimer disease in the US population: prevalence estimates using the 2000 census. Archives of neurology, 60, 1119–1122.PubMedCrossRefGoogle Scholar
  33. Huettel, S. A. (2012). Event-related fMRI in cognition. NeuroImage, 62, 1152–1156.Google Scholar
  34. Hulshoff Pol, H. E., Schnack, H. G., Posthuma, D., Mandl, R. C., Baare, W. F., van Oel, C., van Haren, N. E., Collins, D. L., Evans, A. C., Amunts, K., Burgel, U., Zilles, K., de Geus, E., Boomsma, D. I., & Kahn, R. S. (2006). Genetic contributions to human brain morphology and intelligence. The Journal of neuroscience : the official journal of the Society for Neuroscience, 26, 10235–10242.CrossRefGoogle Scholar
  35. Jack, C. R. Jr. (2012). Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play. Radiology, 263, 344–361.PubMedCrossRefGoogle Scholar
  36. Jones, D. K., Knösche, T. R., Turner, R. (2013). White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. NeuroImage, 73, 239–254.Google Scholar
  37. Joshi, A. A., Lepore, N., Joshi, S. H., Lee, A. D., Barysheva, M., Stein, J. L., McMahon, K. L., Johnson, K., de Zubicaray, G. I., Martin, N. G., Wright, M. J., Toga, A. W., & Thompson, P. M. (2011). The contribution of genes to cortical thickness and volume. Neuroreport, 22, 101–105.PubMedCrossRefGoogle Scholar
  38. Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. The Behavioral and brain sciences, 30, 135–154.PubMedCrossRefGoogle Scholar
  39. Kanai, R., & Rees, G. (2011). The structural basis of inter-individual differences in human behavior and cognition. Nature reviews. Neuroscience, 12, 231–242.PubMedCrossRefGoogle Scholar
  40. Karlsgodt, K. H., Kochunov, P., Winkler, A. M., Laird, A. R., Almasy, L., Duggirala, R., Olvera, R. L., Fox, P. T., Blangero, J., & Glahn, D. C. (2010). A multimodal assessment of the genetic control over working memory. The Journal of neuroscience: the official journal of the Society for Neuroscience, 30, 8197–8202.CrossRefGoogle Scholar
  41. Kaymaz, N., & van Os, J. (2009). Heritability of structural brain traits an endophenotype approach to deconstruct schizophrenia. International review of neurobiology, 89, 85–130.PubMedCrossRefGoogle Scholar
  42. Lebel, C., & Beaulieu, C. (2011). Longitudinal development of human brain wiring continues from childhood into adulthood. The Journal of neuroscience: the official journal of the Society for Neuroscience, 31, 10937–47.CrossRefGoogle Scholar
  43. Lenroot, R. K., Gogtay, N., Greenstein, D. K., Wells, E. M., Wallace, G. L., Clasen, L. S., Blumenthal, J. D., Lerch, J., Zijdenbos, A. P., Evans, A. C., Thompson, P. M., Giedd, J. N. (2007). Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage, 36, 1065–1073.CrossRefGoogle Scholar
  44. Liston, C., Cohen, M. M., Teslovich, T., Levenson, D., & Casey, B. J. (2011). Atypical prefrontal connectivity in attention-deficit/hyperactivity disorder: pathway to disease or pathological end point? Biological Psychiatry, 69, 1168–1177.PubMedCrossRefGoogle Scholar
  45. Mayes, S. D., & Calhoun, S. L. (2008). WISC-IV and WIAT-II profiles in children with high-functioning autism. Journal of autism and developmental disorders, 38, 428–439.PubMedCrossRefGoogle Scholar
  46. McCall, R. B. (1977). Childhood IQs as predictors of adult educational and occupational status. Science, 197, 482–483.PubMedCrossRefGoogle Scholar
  47. McDaniel, M. (2005). Big-brained people are smarter. Intelligence, 33, 337–346.CrossRefGoogle Scholar
  48. Mechelli, A., Price, C. J., Friston, K. J., Ashburner, J. (2005). Voxel-Based Morphometry of the Human Brain: Methods and Applications. Current Medical Imaging Reviews, 1, 105–113.CrossRefGoogle Scholar
  49. Meda, S. A., Koran, M. E., Pryweller, J. R., Vega, J. N., Thornton-Wells, T. A., Alzheimer’s Disease Neuroimaging, I. (2013). Genetic interactions associated with 12-month atrophy in hippocampus and entorhinal cortex in Alzheimer’s Disease Neuroimaging Initiative. Neurobiology of aging, 34, 1518.Google Scholar
  50. Meyer-Lindenberg, A. (2010). Imaging genetics of schizophrenia. Dialogues in clinical neuroscience, 12, 449–456.PubMedGoogle Scholar
  51. Minshew, N. J., & Keller, T. A. (2010). The nature of brain dysfunction in autism: functional brain imaging studies. Current opinion in neurology, 23, 124–130.PubMedCrossRefGoogle Scholar
  52. Norris, D. G. (2006). Principles of magnetic resonance assessment of brain function. Journal of magnetic resonance imaging, 23, 794–807.PubMedCrossRefGoogle Scholar
  53. Panizzon, M. S., Fennema-Notestine, C., Eyler, T., Jernigan, T. L., Prom-Wormley, E., Neale, M., Jacobson, K., Lyons, M. J., Grant, M. D., Franz, C. E., Xian, H., Tsuang, M., Fischl, B., Seidman, L., Dale, A., & Kremen, W. S. (2009). Distinct Genetic Influences on Cortical Surface and Cortical Thickness. Cerebral cortex (New York, N. Y.: 1991), 19, 2728–2735.CrossRefGoogle Scholar
  54. Park, J., Shedden, K., & Polk, T. A. (2012). Correlation and heritability in neuroimaging datasets: a spatial decomposition approach with application to an fMRI study of twins. NeuroImage, 59, 1132–1142.PubMedCrossRefGoogle Scholar
  55. Peelle, J. E., Cusack, R., Henson, R. N. (2012). Adjusting for global effects in voxel-based morphometry: gray matter decline in normal aging. NeuroImage, 60, 1503–1516.PubMedCrossRefGoogle Scholar
  56. Posthuma, D., de Geus, E. J., Neale, M. C., Hulshoff Pol, H. E., Baare, W. E. C., Kahn, R. S., & Boomsma, D. (2000). Multivariate genetic analysis of brain structure in an extended twin design. Behavior genetics, 30, 311–319.PubMedCrossRefGoogle Scholar
  57. Posthuma, D., de Geus, E. J., Baare, W. F., Hulshoff Pol, H. E., Kahn, R. S., & Boomsma, D. I. (2002). The association between brain volume and intelligence is of genetic origin. Nature neuroscience, 5, 83–84.PubMedCrossRefGoogle Scholar
  58. Posthuma, D., de Geus, E. J. C., Deary, I. J. (2009). The genetics of intelligence. In: Terry Goldberg & Daniel Weinberger (Eds.), The Genetics of Cognitive Neuroscience. MITT Press.Google Scholar
  59. Ramsden, S., Richardson, F. M., Josse, G., Thomas, M. S., Ellis, C., Shakeshaft, C., Seghier, M. L., Price, C. J. (2011). Verbal and non-verbal intelligence changes in the teenage brain. Nature, 479, 113–116.Google Scholar
  60. Reiman, E. M., & Jagust, W. J. (2012). Brain imaging in the study of Alzheimer’s disease. NeuroImage, 61, 505–516.PubMedCrossRefGoogle Scholar
  61. Repovs, G., Csernansky, J. G., Barch, D. M. (2011). Brain Network Connectivity in Individuals with Schizophrenia and Their Siblings. Biological Psychiatry, 69, 967–973.PubMedCrossRefGoogle Scholar
  62. Roberts, R. E., Anderson, E. J., Husain, M. (2013). White matter microstructure and cognitive Function. The Neuroscientist: a review journal bringing neurobiology, neurology and psychiatry, 19, 8–15.Google Scholar
  63. Ruano, D., Abecasis, G. R., Glaser, B., Lips, E. S., Cornelisse, L. N., de Jong, A. P., Evans, D. M., Davey, S. G., Timpson, N. J., Smit, A. B., Heutink, P., Verhage, M., & Posthuma, D. (2010). Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability. American journal of human genetics, 86, 113–125.PubMedCrossRefGoogle Scholar
  64. Rubia, K., Smith, A. B., Brammer, M. J., & Taylor, E. (2007). Temporal lobe dysfunction in medication-naive boys with attention-deficit/hyperactivity disorder during attention allocation and its relation to response variability. Biological Psychiatry, 62, 999–1006.PubMedCrossRefGoogle Scholar
  65. Serences, J. T., & Saproo, S. (2011). Computational advances towards linking BOLD and behavior. Neuropsychologia, 50, 435–446.PubMedCrossRefGoogle Scholar
  66. Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., et al. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440, 676–679.PubMedCrossRefGoogle Scholar
  67. Shenton, M. E., Whitford, T. J., & Kubicki, M. (2010). Structural neuroimaging in schizophrenia: from methods to insights to treatments. Dialogues in clinical neuroscience, 12, 317–332.PubMedGoogle Scholar
  68. Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences of the United States of America, 105, 12569–12574.PubMedCrossRefGoogle Scholar
  69. Taylor, W. D., Hsu, E., Krishnan, K. R., macfall, J. R. (2004). Diffusion tensor imaging: background, potential, and utility in psychiatric research. Biological psychiatry, 55, 201–207.PubMedCrossRefGoogle Scholar
  70. Thompson, P. M., Cannon, T. D., Narr, K. L., van Erp, T., Poutanen, V. P., Huttunen, M., Lonnqvist, J., Standertskjold-Nordenstam, C. G., Kaprio, J., Khaledy, M., Dail, R., Zoumalan, C. I., & Toga, A. W. (2001). Genetic influences on brain structure. Nature Neuroscience, 4, 1253–1258.PubMedCrossRefGoogle Scholar
  71. Tomasi, D., & Volkow, N. D. (2011). Abnormal Functional Connectivity in Children with Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 71, 443–450.PubMedCrossRefGoogle Scholar
  72. Turner, G. R., Spreng, R. N. (2012). Executive functions and neurocognitive aging: dissociable patterns of brain activity. Neurobiology of aging, 33, 826.e1–13. (Epub ahead of print)PubMedCrossRefGoogle Scholar
  73. Uddin, L. Q., Supekar, K. S., Ryali, S., & Menon, V. (2011). Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. The Journal of neuroscience: the official journal of the Society for Neuroscience, 31, 18578–18589.CrossRefGoogle Scholar
  74. Valera, E. M., Faraone, S. V., Murray, K. E., & Seidman, L. J. (2007). Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biological Psychiatry, 61, 1361–1369.PubMedCrossRefGoogle Scholar
  75. Wang, L., Su, L., Shen, H., & Hu, D. (2012). Decoding lifespan changes of the human brain using resting-state functional connectivity MRI. PLoS ONE, 7, 44530.CrossRefGoogle Scholar
  76. Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., Liu, E., Morris, J. C., Petersen, R. C., Saykin, A. J., Schmidt, M. E., Shaw, L., Siuciak, J. A., Soares, H., Toga, A. W., Trojanowski, J. Q., Alzheimer’s Disease Neuroimaging Initiative. (2012). The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimer’s & dementia : the journal of the Alzheimer’s Association, 8, 1–68.CrossRefGoogle Scholar
  77. Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biological Psychiatry, 57, 1336–1346.PubMedCrossRefGoogle Scholar
  78. Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., Duggirala, R., & Glahn, D. C. (2010). Cortical thickness or gray matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage, 53, 1135–1146.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2014

Authors and Affiliations

  • Iván Chavarría-Siles
    • 1
    Email author
  • Guillén Fernández
    • 2
  • Danielle Posthuma
    • 3
  1. 1.Department of PsychiatryMount Sinai School of MedicineNew YorkUSA
  2. 2.Donders Institute, for Brainm, Cognition, and BehaviorRadboud University Nijmegen Medical CenterNijmegenThe Netherlands
  3. 3.Center for Neurogenomics and Cognitive Research (CNCR) & Department of Medical GenomicsVrij University AmsterdamAmsterdamThe Netherlands

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