How Age-Related Changes in the Brain Affect Cognition

doi:10.1017/9781108552684.004

3 - How Age-Related Changes in the Brain Affect Cognition

from Part I - Models of Cognitive Aging

Published online by Cambridge University Press: 28 May 2020

Anders M. Fjell and

Kristine B. Walhovd

Edited by

Ayanna K. Thomas and

Angela Gutchess

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Ayanna K. Thomas: Affiliation:
Tufts University, Massachusetts
Angela Gutchess: Affiliation:
Brandeis University, Massachusetts

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Summary

Cognition changes with age, and the amount and trajectory of change varies across individuals and functions. In this review, we argue that three general principles characterize adult life-span changes in brain and cognition. (1) Dimensionality: Many features of brain and cognition in aging and neurodegenerative disease represent quantitative differences along a continuum and are not unique to pathology. (2) Early influences – developmental origins of health and disease: Genetic dispositions and early environmental factors, likely even from fetal life, can have lasting impact on the brain and cognition. (3) Influences from a multitude of environmental factors: Current brain state and cognitive function will be determined by a combination of early factors and later environmental influences, often in interaction. These principles entail a model of age-associated cognitive decline and dementia based on dimensions rather than categories, life span rather than aging, and multidimensional systems-vulnerability rather than one major “biomarker.”

Keywords

Aging birth weight cortical thickness development early factors Flynn effect genetic influences interventions magnetic resonance imaging memory navigation plasticity prenatal factors white matter microstructure

Type: Chapter
Information: The Cambridge Handbook of Cognitive Aging
A Life Course Perspective
, pp. 47 - 61

DOI: https://doi.org/10.1017/9781108552684.004 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2020

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References

Abel, K. M., Wicks, S., Susser, E. S., et al. (2010). Birth weight, schizophrenia, and adult mental disorder: Is risk confined to the smallest babies? Archives of General Psychiatry, 67(9), 923–930. http://dx.doi.org/10.1001/archgenpsychiatry.2010.100 Google Scholar

Bale, T. L. (2015). Epigenetic and transgenerational reprogramming of brain development. Nature Reviews Neuroscience, 16(6), 332–344. http://dx.doi.org/10.1038/nrn3818 CrossRef Google Scholar PubMed

Baltes, P. B., & Kliegl, R. (1992). Further testing of limits of cognitive plasticity: Negative age differences in a mnemonic skill are robust. Developmental Psychology, 28(1), 121–125. http://dx.doi.org/10.1037/0012-1649.28.1.121 CrossRef Google Scholar

Barnes, S. K., & Ozanne, S. E. (2011). Pathways linking the early environment to long-term health and lifespan. Progress in Biophysics and Molecular Biology, 106(1), 323–336. http://dx.doi.org/10.1016/j.pbiomolbio.2010.12.005 Google Scholar

Boyke, J., Driemeyer, J., Gaser, C., Büchel, C., & May, A. (2008). Training-induced brain structure changes in the elderly. Journal of Neuroscience, 28(28), 7031–7035. http://dx.doi.org/10.1523/JNEUROSCI.0742-08.2008 CrossRef Google Scholar PubMed

Bratsberg, B., & Rogeberg, O. (2018). Flynn effect and its reversal are both environmentally caused. Proceedings of the National Academy of Sciences USA, 115(26), 6674–6678. http://dx.doi.org/10.1073/pnas.1718793115 Google Scholar

Brehmer, Y., Kalpouzos, G., Wenger, E., & Lövdén, M. (2014). Plasticity of brain and cognition in older adults. Psychological Research, 78(6), 790–802. http://dx.doi.org/10.1007/s00426-014-0587-z Google Scholar

Brehmer, Y., Li, S. C., Müller, V., von Oertzen, T., & Lindenberger, U. (2007). Memory plasticity across the life span: Uncovering children’s latent potential. Developmental Psychology, 43(2), 465–478. http://dx.doi.org/10.1037/0012-1649.43.2.465 CrossRef Google Scholar PubMed

Brehmer, Y., Shing, Y. L., Heekeren, H. R., Lindenberger, U., & Bäckman, L. (2016). Training-induced changes in subsequent-memory effects: No major differences among children, younger adults, and older adults. NeuroImage, 131, 214–225. http://dx.doi.org/10.1016/j.neuroimage.2015.11.074 Google Scholar

Brehmer, Y., Westerberg, H., & Bäckman, L. (2012). Working-memory training in younger and older adults: Training gains, transfer, and maintenance. Frontiers in Human Neuroscience, 6, p. 63. http://dx.doi.org/10.3389/fnhum.2012.00063 CrossRef Google Scholar PubMed

Bürki, C. N., Ludwig, C., Chicherio, C., & de Ribaupierre, A. (2014). Individual differences in cognitive plasticity: An investigation of training curves in younger and older adults. Psychological Research, 78(6), 821–835. http://dx.doi.org/10.1007/s00426-014-0559-3 Google Scholar

Carretti, B., Borella, E., & De Beni, R. (2007). Does strategic memory training improve the working memory performance of younger and older adults? Experimental Psychology, 54(4), 311–320. http://dx.doi.org/10.1027/1618-3169.54.4.311 Google Scholar

Chang, L., Douet, V., Bloss, C., et al. (2016). Gray matter maturation and cognition in children with different APOE ε genotypes. Neurology, 87(6), 585–594. http://dx.doi.org/10.1212/WNL.0000000000002939 Google Scholar

Colom, R., Lluis-Font, J. M., & Andrés-Pueyo, A. (2005). The generational intelligence gains are caused by decreasing variance in the lower half of the distribution: Supporting evidence for the nutrition hypothesis. Intelligence, 33(1), 83–91. http://dx.doi.org/10.1016/j.intell.2004.07.010 Google Scholar

Corder, E. H., Saunders, A. M., Strittmatter, W. J., et al. (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science, 261(5123), 921–923. http://dx.doi.org/10.1126/science.8346443 Google Scholar

Dahlin, E., Nyberg, L., Bäckman, L., & Neely, A. S. (2008). Plasticity of executive functioning in young and older adults: Immediate training gains, transfer, and long-term maintenance. Psychology and Aging, 23(4), 720–730. http://dx.doi.org/10.1037/a0014296 Google Scholar

de Lange, A. M. G., Bråthen, A. C. S., Grydeland, H., et al. (2016). White matter integrity as a marker for cognitive plasticity in aging. Neurobiology of Aging, 47, 74–82. http://dx.doi.org/10.1016/j.neurobiolaging.2016.07.007 Google Scholar

de Lange, A. M. G., Bråthen, A. C. S., Rohani, D. A., et al. (2017). The effects of memory training on behavioral and microstructural plasticity in young and older adults. Human Brain Mapping, 38(11), 5666–5680. http://dx.doi.org/10.1002/hbm.23756 Google Scholar

de Lange, A. M. G., Bråthen, A. C. S., Rohani, D. A., Fjell, A. M., & Walhovd, K. B. (2018). The temporal dynamics of brain plasticity in aging. Cerebral Cortex, 28(5), 1857–1865. http://dx.doi.org/10.1093/cercor/bhy003 Google Scholar

Deary, I. J., Pattie, A., & Starr, J. M. (2013). The stability of intelligence from age 11 to age 90 years: The Lothian birth cohort of 1921. Psychological Science, 24(12), 2361–2368. http://dx.doi.org/10.1177/0956797613486487 CrossRef Google Scholar PubMed

Dekaban, A. S., & Sadowsky, D. (1978). Changes in brain weights during the span of human life: Relation of brain weights to body heights and body weights. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 4(4), 345–356. https://doi.org/10.1002/ana.410040410 Google Scholar

Draganski, B., Gaser, C., Busch, V., et al. (2004). Neuroplasticity: Changes in grey matter induced by training. Nature, 427(6972), 311–312. http://dx.doi.org/10.1038/427311a Google Scholar

Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2010). Effects of memory training on cortical thickness in the elderly. NeuroImage, 52(4), 1667–1676. http://dx.doi.org/10.1016/j.neuroimage.2010.05.041 Google Scholar

Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2012a). Memory training impacts short‐term changes in aging white matter: A longitudinal diffusion tensor imaging study. Human Brain Mapping, 33(10), 2390–2406. http://dx.doi.org/10.1002/hbm.21370 Google Scholar

Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2012b). Hippocampal subfield volumes correlate with memory training benefit in subjective memory impairment. NeuroImage, 61(1), 188–194. http://dx.doi.org/10.1016/j.neuroimage.2012.02.072 CrossRef Google Scholar PubMed

Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2014). Effects of cognitive training on gray matter volumes in memory clinic patients with subjective memory impairment. Journal of Alzheimer’s Disease, 41(3), 779–791. http://dx.doi.org/10.3233/JAD-131889 Google Scholar

Fjell, A. M., Grydeland, H., Krogsrud, S. K., et al. (2015). Development and aging of cortical thickness correspond to genetic organization patterns. Proceedings of the National Academy of Sciences USA, 112(50), 15462–15467. http://dx.doi.org/10.1073/pnas.1508831112 CrossRef Google Scholar PubMed

Fjell, A. M., & Walhovd, K. B. (2010). Structural brain changes in aging: Courses, causes and cognitive consequences. Reviews in the Neurosciences, 21(3), 187–222. https://doi.org/10.1515/REVNEURO.2010.21.3.187 Google Scholar

Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101(2), 171–191. http://dx.doi.org/10.1037/0033-2909.101.2.171 Google Scholar

Grady, C. (2012). The cognitive neuroscience of ageing. Nature Reviews Neuroscience, 13(7), 491. http://dx.doi.org/10.1038/nrn3256 Google Scholar

Haukvik, U. K., Rimol, L. M., Roddey, J. C., et al. (2014). Normal birth weight variation is related to cortical morphology across the psychosis spectrum. Schizophrenia Bulletin, 40(2), 410–419. http://dx.doi.org/10.1093/schbul/sbt005 CrossRef Google Scholar PubMed

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. http://dx.doi.org/10.1093/cercor/bhs231 Google Scholar

Hülür, G., Ram, N., Willis, S. L., Schaie, K. W., & Gerstorf, D. (2015). Cognitive dedifferentiation with increasing age and proximity of death: Within-person evidence from the Seattle Longitudinal Study. Psychology and Aging, 30(2), 311–323. http://dx.doi.org/10.1037/a0039260 Google Scholar

Jack, C. R., Bennett, D. A., Blennow, K., et al. (2018). NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s and Dementia, 14(4), 535–562. http://dx.doi.org/10.1016/j.jalz.2018.02.018 Google Scholar

Jagust, W. J. (2016). Early life sets the stage for aging. Proceedings of the National Academy of Sciences USA, 113(33), 9148–9150. http://dx.doi.org/10.1073/pnas.1609720113 Google Scholar

Karama, S., Bastin, M. E., Murray, C., et al. (2014). Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age. Molecular Psychiatry, 19(5), 555–559. http://dx.doi.org/10.1038/mp.2013.64 Google Scholar

Khan, W., Giampietro, V., Banaschewski, T., et al. (2017). A multi-cohort study of ApoE ɛ4 and Amyloid-β effects on the hippocampus in Alzheimer’s disease. Journal of Alzheimer’s Disease, 56(3), 1159–1174. http://dx.doi.org/10.3233/JAD-161097 CrossRef Google Scholar PubMed

Kivipelto, M., Solomon, A., Ahtiluoto, S., et al. (2013). The Finnish geriatric intervention study to prevent cognitive impairment and disability (FINGER): Study design and progress. Alzheimer’s and Dementia, 9(6), 657–665. http://dx.doi.org/10.1016/j.jalz.2012.09.012 Google Scholar

Kliegl, R., Smith, J., & Baltes, P. B. (1990). On the locus and process of magnification of age differences during mnemonic training. Developmental Psychology, 26(6), 894–904. http://dx.doi.org/10.1037/0012-1649.26.6.894 Google Scholar

Knickmeyer, R. C., Wang, J., Zhu, H., et al. (2013). Common variants in psychiatric risk genes predict brain structure at birth. Cerebral Cortex, 24(5), 1230–1246. http://dx.doi.org/10.1093/cercor/bhs401 Google Scholar

Kovacs, G. G., Adle-Biassette, H., Milenkovic, I., et al. (2014). Linking pathways in the developing and aging brain with neurodegeneration. Neuroscience, 269, 152–172. http://dx.doi.org/10.1016/j.neuroscience.2014.03.045 Google Scholar

Livingston, G., Sommerlad, A., Orgeta, V., et al. (2017). Dementia prevention, intervention, and care. Lancet, 390(10113), 2673–2734. http://dx.doi.org/10.1016/S0140-6736(17)31363-6 CrossRef Google Scholar PubMed

Lövdén, M., Bäckman, L., Lindenberger, U., Schaefer, S., & Schmiedek, F. (2010a). A theoretical framework for the study of adult cognitive plasticity. Psychological Bulletin, 136(4), 659–676. http://dx.doi.org/10.1037/a0020080 CrossRef Google Scholar

Lövdén, M., Bodammer, N. C., Kühn, S., et al. (2010b). Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia, 48(13), 3878–3883. http://dx.doi.org/10.1016/j.neuropsychologia.2010.08.026 Google Scholar

Lövdén, M., Schaefer, S., Noack, H., et al. (2012). Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiology of Aging, 33(3), 620e9–620e22. http://dx.doi.org/10.1016/j.neurobiolaging.2011.02.013 Google Scholar

Lyall, A. E., Shi, F., Geng, X., et al. (2015). Dynamic development of regional cortical thickness and surface area in early childhood. Cerebral Cortex, 25(8), 2204–2212. http://dx.doi.org/10.1093/cercor/bhu027 Google Scholar

Lyons, M. J., York, T. P., Franz, C. E., et al. (2009). Genes determine stability and the environment determines change in cognitive ability during 35 years of adulthood. Psychological Science, 20(9), 1146–1152. http://dx.doi.org/10.1111/j.1467-9280.2009.02425.x Google Scholar

Melka, M. G., Gillis, J., Bernard, M., et al. (2013). FTO, obesity and the adolescent brain. Human Molecular Genetics, 22(5), 1050–1058. http://dx.doi.org/10.1093/hmg/dds504 Google Scholar

Mosing, M. A., Madison, G., Pedersen, N. L., & Ullén, F. (2016). Investigating cognitive transfer within the framework of music practice: Genetic pleiotropy rather than causality. Developmental Science, 19(3), 504–512. http://dx.doi.org/10.1111/desc.12306 Google Scholar

Muller, M., Sigurdsson, S., Kjartansson, O., et al. (2014). Birth size and brain function 75 years later. Pediatrics, 134(4), 761–770. http://dx.doi.org/10.1542/peds.2014-1108 Google Scholar

Ngandu, T., Lehtisalo, J., Solomon, A., et al. (2015). A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial. Lancet, 385(9984), 2255–2263. http://dx.doi.org/10.1016/S0140-6736(15)60461-5 Google Scholar

Nyberg, L., & Pudas, S. (2019). Successful memory aging. Annual Review of Psychology, 70, 219–243. http://dx.doi.org/10.1146/annurev-psych-010418-103052 Google Scholar

Nyberg, L., Sandblom, J., Jones, S., et al. (2003). Neural correlates of training-related memory improvement in adulthood and aging. Proceedings of the National Academy of Sciences USA, 100(23), 13728–13733. http://dx.doi.org/10.1073/pnas.1735487100 Google Scholar

Pietschnig, J., & Voracek, M. (2015). One century of global IQ gains: A formal meta-analysis of the Flynn effect (1909–2013). Perspectives on Psychological Science, 10(3), 282–306. http://dx.doi.org/10.1177/1745691615577701 Google Scholar

Raz, N., Ghisletta, P., Rodrigue, K. M., Kennedy, K. M., & Lindenberger, U. (2010). Trajectories of brain aging in middle-aged and older adults: Regional and individual differences. NeuroImage, 51(2), 501–511. http://dx.doi.org/10.1016/j.neuroimage.2010.03.020 Google Scholar

Raz, N., Lindenberger, U., Rodrigue, K. M., et al. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15(11), 1676–1689. http://dx.doi.org/10.1093/cercor/bhi044 Google Scholar

Raznahan, A., Greenstein, D., Lee, N. R., Clasen, L. S., & Giedd, J. N. (2012). Prenatal growth in humans and postnatal brain maturation into late adolescence. Proceedings of the National Academy of Sciences USA, 109(28), 11366–11371. http://dx.doi.org/10.1073/pnas.1203350109 Google Scholar

Reitz, C., Tosto, G., Mayeux, R., & Luchsinger, J. A. (2012). Genetic variants in the fat and obesity associated (FTO) gene and risk of Alzheimer’s disease. PLoS One, 7(12), e50354. http://dx.doi.org/10.1371/journal.pone.0050354 Google Scholar

Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003). Longitudinal magnetic resonance imaging studies of older adults: A shrinking brain. Journal of Neuroscience, 23(8), 3295–3301. http://dx.doi.org/10.1523/JNEUROSCI.23-08-03295.2003 CrossRef Google Scholar PubMed

Salthouse, T. A. (2016). Continuity of cognitive change across adulthood. Psychonomic Bulletin and Review, 23(3), 932–939. http://dx.doi.org/10.3758/s13423-015-0910-8 Google Scholar

Schaie, K. W. (1994). The course of adult intellectual development. American Psychologist, 49(4), 304–313. http://dx.doi.org/10.1037/0003-066X.49.4.304 Google Scholar

Schmiedek, F., Lövdén, M., & Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: Findings from the COGITO study. Frontiers in Aging Neuroscience, 2, p. 27. http://dx.doi.org/10.3389/fnagi.2010.00027 Google Scholar

Scholz, J., Klein, M. C., Behrens, T. E., & Johansen-Berg, H. (2009). Training induces changes in white-matter architecture. Nature Neuroscience, 12(11), 1370–1371. http://dx.doi.org/10.1038/nn.2412 Google Scholar

Sexton, C. E., Walhovd, K. B., Storsve, A. B., et al. (2014). Accelerated changes in white matter microstructure during aging: A longitudinal diffusion tensor imaging study. Journal of Neuroscience, 34(46), 15425–15436. http://dx.doi.org/10.1523/JNEUROSCI.0203-14.2014 Google Scholar

Smith, A. D., Smith, S. M., De Jager, C. A., et al. (2010). Homocysteine-lowering by B vitamins slows the rate of accelerated brain atrophy in mild cognitive impairment: A randomized controlled trial. PLoS One, 5(9), e12244. http://dx.doi.org/10.1371/journal.pone.0012244 Google Scholar

Storsve, A. B., Fjell, A. M., Tamnes, C. K., et al. (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. http://dx.doi.org/10.1523/JNEUROSCI.0391-14.2014 CrossRef Google Scholar PubMed

Thompson, P. M. (2015). Cracking the brain’s genetic code. Proceedings of the National Academy of Sciences USA, 112(50), 15269–15270. http://dx.doi.org/10.1073/pnas.1520702112 Google Scholar

Tucker-Drob, E. M. (2011). Global and domain-specific changes in cognition throughout adulthood. Developmental Psychology, 47(2), 331–343. http://dx.doi.org/10.1037/a0021361 Google Scholar

Tucker-Drob, E. M., Brandmaier, A. M., & Lindenberger, U. (2019). Coupled cognitive changes in adulthood: A meta-analysis. Psychological Bulletin, 145(3), 273–301. http://dx.doi.org/10.1037/bul0000179 Google Scholar

Vemuri, P., Lesnick, T. G., Przybelski, S. A., et al. (2012). Effect of lifestyle activities on Alzheimer disease biomarkers and cognition. Annals of Neurology, 72(5), 730–738. http://dx.doi.org/10.1002/ana.23665 Google Scholar

Walhovd, K. B., Bjørnebekk, A., Haabrekke, K., et al. (2015). Child neuroanatomical, neurocognitive, and visual acuity outcomes with maternal opioid and polysubstance detoxification. Pediatric Neurology, 52(3), 326–332. http://dx.doi.org/10.1016/j.pediatrneurol.2014.11.008 Google Scholar

Walhovd, K. B., Fjell, A. M., Brown, T. T., et al. (2012a). Long-term influence of normal variation in neonatal characteristics on human brain development. Proceedings of the National Academy of Sciences USA, 109(49), 20089–20094. http://dx.doi.org/10.1073/pnas.1208180109 Google Scholar

Walhovd, K. B., Fjell, A. M., & Espeseth, T. (2014a). Cognitive decline and brain pathology in aging – need for a dimensional, lifespan and systems vulnerability view. Scandinavian Journal of Psychology, 55(3), 244–254. http://dx.doi.org/10.1111/sjop.12120 Google Scholar

Walhovd, K. B., Krogsrud, S. K., Amlien, I. K., et al. (2016). Neurodevelopmental origins of lifespan changes in brain and cognition. Proceedings of the National Academy of Sciences USA, 113(33), 9357–9362. http://dx.doi.org/10.1073/pnas.1524259113 Google Scholar

Walhovd, K. B., Moe, V., Slinning, K., et al. (2007). Volumetric cerebral characteristics of children exposed to opiates and other substances in utero. NeuroImage, 36(4), 1331–1344. http://dx.doi.org/10.1016/j.neuroimage.2007.03.070 Google Scholar

Walhovd, K. B., Moe, V., Slinning, K., et al. (2009). Effects of prenatal opiate exposure on brain development – a call for attention. Nature Reviews Neuroscience, 10(5), 390. http://dx.doi.org/10.1038/nrn2598-c1 CrossRef Google Scholar

Walhovd, K. B., Storsve, A. B., Westlye, L. T., et al. (2014b). Blood markers of fatty acids and vitamin D, cardiovascular measures, body mass index, and physical activity relate to longitudinal cortical thinning in normal aging. Neurobiology of Aging, 35(5), 1055–1064. http://dx.doi.org/10.1016/j.neurobiolaging.2013.11.011 CrossRef Google Scholar PubMed

Walhovd, K. B., Tamnes, C. K., Østby, Y., Due-Tønnessen, P., & Fjell, A. M. (2012b). Normal variation in behavioral adjustment relates to regional differences in cortical thickness in children. European Child and Adolescent Psychiatry, 21(3), 133–140. http://dx.doi.org/10.1007/s00787-012-0241-5 CrossRef Google Scholar PubMed

Walhovd, K. B., Watts, R., Amlien, I., & Woodward, L. J. (2012c). Neural tract development of infants born to methadone-maintained mothers. Pediatric Neurology, 47(1), 1–6. http://dx.doi.org/10.1016/j.pediatrneurol.2012.04.008 Google Scholar

Wenger, E., Schaefer, S., Noack, H., et al. (2012). Cortical thickness changes following spatial navigation training in adulthood and aging. NeuroImage, 59(4), 3389–3397. http://dx.doi.org/10.1016/j.neuroimage.2011.11.015 Google Scholar

Westlye, L. T., Reinvang, I., Rootwelt, H., & Espeseth, T. (2012). Effects of APOE on brain white matter microstructure in healthy adults. Neurology, 79(19), 1961–1969. http://dx.doi.org/10.1212/WNL.0b013e3182735c9c Google Scholar

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. http://dx.doi.org/10.1038/nn.3045 Google Scholar

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