Abstract
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.
Keywords
- 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.
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- 1.
It should be noted that the endophenotype approach relies on the assumption that the genetic basis of endophenotypes is easier to analyze than the categorical classification of an end-phenotype, such as a neuropsychiatric disorder. However, a systematic metanalysis of genetic association studies of endophenotypes showed that while endophenotypes measures may afford greater reliability, it should not be assumed that they will also demonstrate simpler genetic architecture (Flint and Munafo 2007). The added value of the endophenotype approach thus remains to be proven.
References
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.
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.
Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. NeuroImage, 11, 805–821.
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.
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.
Bigos, K. L., Weinberger, D. R. (2010). Imaging genetics—days of future past. NeuroImage, 53, 804–809.
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.
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.
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.
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.
Chen. R., Jiao, Y., & Herskovits, E. H. (2010). Structural MRI in autism spectrum disorder. Pediatric research, 69, 63–68.
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.
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.
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.
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.
Deary, I. J. (2012). Intelligence. Annual review of psychology, 63, 453–482.
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature reviews. Neuroscience, 11, 201–211.
Durston, S. (2010). Imaging genetics in ADHD. NeuroImage, 53, 832–838.
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.
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.
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.
Flint, J., & Munafo, M. R. (2007). The endophenotype concept in psychiatric genetics. Psychological medicine, 37, 163–180.
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.
Galton, F. (1888). Head growth in students at the University of Cambridge. Nature, 38, 14–15.
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.
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.
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.
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.
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.
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.
Gur, R. E., & Gur, R. C. (2010). Functional magnetic resonance imaging in schizophrenia. Dialogues in clinical neuroscience, 12, 333–343.
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.
Huettel, S. A. (2012). Event-related fMRI in cognition. NeuroImage, 62, 1152–1156.
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.
Jack, C. R. Jr. (2012). Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play. Radiology, 263, 344–361.
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.
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.
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.
Kanai, R., & Rees, G. (2011). The structural basis of inter-individual differences in human behavior and cognition. Nature reviews. Neuroscience, 12, 231–242.
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.
Kaymaz, N., & van Os, J. (2009). Heritability of structural brain traits an endophenotype approach to deconstruct schizophrenia. International review of neurobiology, 89, 85–130.
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.
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.
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.
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.
McCall, R. B. (1977). Childhood IQs as predictors of adult educational and occupational status. Science, 197, 482–483.
McDaniel, M. (2005). Big-brained people are smarter. Intelligence, 33, 337–346.
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.
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.
Meyer-Lindenberg, A. (2010). Imaging genetics of schizophrenia. Dialogues in clinical neuroscience, 12, 449–456.
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.
Norris, D. G. (2006). Principles of magnetic resonance assessment of brain function. Journal of magnetic resonance imaging, 23, 794–807.
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.
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.
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.
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.
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.
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.
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.
Reiman, E. M., & Jagust, W. J. (2012). Brain imaging in the study of Alzheimer’s disease. NeuroImage, 61, 505–516.
Repovs, G., Csernansky, J. G., Barch, D. M. (2011). Brain Network Connectivity in Individuals with Schizophrenia and Their Siblings. Biological Psychiatry, 69, 967–973.
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.
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.
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.
Serences, J. T., & Saproo, S. (2011). Computational advances towards linking BOLD and behavior. Neuropsychologia, 50, 435–446.
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.
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.
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.
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.
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.
Tomasi, D., & Volkow, N. D. (2011). Abnormal Functional Connectivity in Children with Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 71, 443–450.
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)
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.
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.
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.
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.
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.
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.
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Chavarría-Siles, I., Fernández, G., Posthuma, D. (2014). Brain Imaging and Cognition. In: Finkel, D., Reynolds, C. (eds) Behavior Genetics of Cognition Across the Lifespan. Advances in Behavior Genetics, vol 1. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7447-0_8
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