Brain Structure and Function

, Volume 221, Issue 9, pp 4369–4382 | Cite as

Gray matter responsiveness to adaptive working memory training: a surface-based morphometry study

  • Francisco J. Román
  • Lindsay B. Lewis
  • Chi-Hua Chen
  • Sherif Karama
  • Miguel Burgaleta
  • Kenia Martínez
  • Claude Lepage
  • Susanne M. Jaeggi
  • Alan C. Evans
  • William S. Kremen
  • Roberto ColomEmail author
Original Article


Here we analyze gray matter indices before and after completing a challenging adaptive cognitive training program based on the n-back task. The considered gray matter indices were cortical thickness (CT) and cortical surface area (CSA). Twenty-eight young women (age range 17–22 years) completed 24 training sessions over the course of 3 months (12 weeks, 24 sessions), showing expected performance improvements. CT and CSA values for the training group were compared with those of a matched control group. Statistical analyses were computed using a ROI framework defined by brain areas distinguished by their genetic underpinning. The interaction between group and time was analyzed. Middle temporal, ventral frontal, inferior parietal cortices, and pars opercularis were the regions where the training group showed conservation of gray matter with respect to the control group. These regions support working memory, resistance to interference, and inhibition. Furthermore, an interaction with baseline intelligence differences showed that the expected decreasing trend at the biological level for individuals showing relatively low intelligence levels at baseline was attenuated by the completed training.


Cognitive training Brain plasticity Surface-based morphometry Cortical thickness Cortical surface area 



This research was supported by Grant PSI2010-20364 (Ministerio de Ciencia e Innovación, Spain). FJR is supported by BES-2011-043527 (Ministerio de Ciencia e Innovación, Spain). KM is supported by AP2008-00433 (Ministerio de Educación, Spain). MB was funded by the Spanish Ministerio de Economía y Competitividad (MINECO-FPDI-2013-17528). Also, WSK was supported by Grants R01 AG022381, AG018386A, and AG018384 (U.S. National Institute on Aging).

Supplementary material

429_2015_1168_MOESM1_ESM.docx (2.5 mb)
Supplementary material 1 (DOCX 2572 kb)


  1. Ad-Dab’bagh Y, Lyttelton O, Muehlboeck JS, Lepage C, Einarson D, Mok K, Ivanov O, Vincent RD, Lerch J, Fombonne E, Evans AC (2006) The CIVET image processing environment: A fully automated comprehensive pipeline for anatomical neuroimaging research. In: Corbetta M (ed) Proceedings of the 12th Annual Meeting of the Organization for Human Brain Mapping NeuroImage. Florence, ItalyGoogle Scholar
  2. Aron AR, Robbins TW, Poldrack RA (2004) Inhibition and the right inferior frontal cortex. Trends in cognitive sciences 8:170–177CrossRefPubMedGoogle Scholar
  3. Au J, Sheehan E, Tsai N, Duncan GJ, Buschkuehl M, Jaeggi SM (2014) Improving fluid intelligence with training on working memory: a meta-analysis. Psychonomic bulletin and review, pp 1–12Google Scholar
  4. Blanton RE, Levitt JG, Thompson PM, Narr KL, Capetillo-Cunliffe L, Nobel A, Toga AW (2001) Mapping cortical asymmetry and complexity patterns in normal children. Psychiatry Res Neuroimag 107:29–43CrossRefGoogle Scholar
  5. Bueti D, Lasaponara S, Cercignani M, Macaluso E (2012) Learning about time: plastic changes and interindividual brain differences. Neuron 75:725–737CrossRefPubMedGoogle Scholar
  6. Burgaleta M, Johnson W, Waber DP, Colom R, Karama S (2014) Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents. NeuroImage 84:810–819CrossRefPubMedGoogle Scholar
  7. Burgess GC, Gray JR, Conway AR, Braver TS (2011) Neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span. J Exp Psychol Gen 140:674–692CrossRefPubMedPubMedCentralGoogle Scholar
  8. Buschman TJ, Siegel M, Roy JE, Miller EK (2011) Neural substrates of cognitive capacity limitations. Proc Natl Acad Sci 108:11252–11255CrossRefPubMedPubMedCentralGoogle Scholar
  9. Cabeza R (2002) Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol Aging 17:85–100CrossRefPubMedGoogle Scholar
  10. Chance SA, Casanova MF, Switala AE, Crow TJ (2008) Auditory cortex asymmetry, altered minicolumn spacing and absence of ageing effects in schizophrenia. Brain 131:3178–3192CrossRefPubMedPubMedCentralGoogle Scholar
  11. Chen CH, Gutierrez ED, Thompson W, Panizzon MS, Jernigan TL, Eyler LT, Dale AM (2012) Hierarchical genetic organization of human cortical surface area. Science 335:1634–1636CrossRefPubMedPubMedCentralGoogle Scholar
  12. Chen CH, Fiecas M, Gutiérrez ED, Panizzon MS, Eyler LT, Vuoksimaa E, Kremen WS (2013) Genetic topography of brain morphology. Proc Natl Acad Sci 110:17089–17094CrossRefPubMedPubMedCentralGoogle Scholar
  13. Chklovskii DB, Mel BW, Svoboda K (2004) Cortical rewiring and information storage. Nature 431:782–788CrossRefPubMedGoogle Scholar
  14. Cohen J (1992) A power primer. Psychol Bull 112:155–159CrossRefPubMedGoogle Scholar
  15. Colom R, Jung RE, Haier RJ (2007) General intelligence and memory span: evidence for a common neuroanatomic framework. Cognit Neuropsychol 24:867–878CrossRefGoogle Scholar
  16. Colom R, Quiroga M, Solana AB, Burgaleta M, Román FJ, Privado J, Karama S (2012) Structural changes after videogame practice related to a brain network associated with intelligence. Intelligence 40:479–489CrossRefGoogle Scholar
  17. Colom R, Burgaleta M, Román FJ, Karama S, Álvarez-Linera J, Abad FJ, Haier RJ (2013a) Neuroanatomic overlap between intelligence and cognitive factors: morphometry methods provide support for the key role of the frontal lobes. Neuroimage 72:143–152CrossRefPubMedGoogle Scholar
  18. Colom R, Román FJ, Abad FJ, Shih PC, Privado J, Froufe M, Jaeggi SM (2013b) Adaptive n-back training does not improve fluid intelligence at the construct level: Gains on individual tests suggest that training may enhance visuospatial processing. Intelligence 41:712–727CrossRefGoogle Scholar
  19. Deary IJ, Weiss A, Batty GD (2010) Intelligence and personality as predictors of illness and death how researchers in differential psychology and chronic disease epidemiology are collaborating to understand and address health inequalities. Psychol Sci Publ Interest 11:53–79CrossRefGoogle Scholar
  20. Draganski B, Kherif F (2013) In vivo assessment of use-dependent brain plasticity—beyond the “one trick pony” imaging strategy. NeuroImage 73:255–259CrossRefPubMedGoogle Scholar
  21. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A (2004) Neuroplasticity: changes in grey matter induced by training. Nature 427:311–312CrossRefPubMedGoogle Scholar
  22. Draganski B, Gaser C, Kempermann G, Kuhn HG, Winkler J, Büchel C, May A (2006) Temporal and spatial dynamics of brain structure changes during extensive learning. J Neurosci 26(23):6314–6317CrossRefPubMedGoogle Scholar
  23. Duncan J, Owen AM (2000) Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci 23:475–483CrossRefPubMedGoogle Scholar
  24. Engvig A, Fjell AM, Westlye LT, Moberget T, Sundseth Ø, Larsen VA, Walhovd KB (2010) Effects of memory training on cortical thickness in the elderly. Neuroimage 52:1667–1676CrossRefPubMedGoogle Scholar
  25. Erickson KI (2013) Evidence for structural plasticity in humans: comment on Thomas and Baker (2012). NeuroImage 73:237–238CrossRefPubMedGoogle Scholar
  26. Feczko E, Augustinack JC, Fischl B, Dickerson BC (2009) An MRI-based method for measuring volume, thickness and surface area of entorhinal, perirhinal, and posterior parahippocampal cortex. Neurobiol Aging 30:420–431CrossRefPubMedGoogle Scholar
  27. Fields RD (2013) Changes in brain structure during learning: fact or artifact? Reply to Thomas and Baker. NeuroImage 73:260–264CrossRefPubMedGoogle Scholar
  28. Forstmann B, van den Wildenberg W, Ridderinkhof K (2008) Neural mechanisms, temporal dynamics, and individual differences in interference control. J Cogn Neurosci 20:1854–1865CrossRefPubMedGoogle Scholar
  29. Fu M, Zuo Y (2011) Experience-dependent structural plasticity in the cortex. Trends Neurosci 34:177–187CrossRefPubMedPubMedCentralGoogle Scholar
  30. Haier RJ, Karama S, Leyba L, Jung RE (2009) MRI assessment of cortical thickness and functional activity changes in adolescent girls following three months of practice on a visual-spatial task. BMC Res Notes 2:174CrossRefPubMedPubMedCentralGoogle Scholar
  31. Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT (2006) Brain connectivity related to working memory performance. J Neurosci 26:13338–13343CrossRefPubMedPubMedCentralGoogle Scholar
  32. Hensch TK (2004) Critical period regulation. Ann Rev Neurosci 27:549–579CrossRefPubMedGoogle Scholar
  33. Hill J, Inder T, Neil J, Dierker D, Harwell J, Van Essen D (2010) Similar patterns of cortical expansion during human development and evolution. Proc Natl Acad Sci 107:13135–13140CrossRefPubMedPubMedCentralGoogle Scholar
  34. Hsu NS, Buschkuehl M, Jonides J, Jaeggi SM (2013) Potential Mechanisms Underlying Working Memory Training and Transfer. Poster presented at the Psychonomic Society Annual Meeting, Toronto, OntarioGoogle Scholar
  35. Huttenlocher PR, Dabholkar AS (1997) Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol 387:167–178CrossRefPubMedGoogle Scholar
  36. Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Weiner MW (2008) The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 27:685–691CrossRefPubMedPubMedCentralGoogle Scholar
  37. Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ (2008) Improving fluid intelligence with training on working memory. Proc Natl Acad Sci 105:6829–6833CrossRefPubMedPubMedCentralGoogle Scholar
  38. Jaeggi SM, Buschkuehl M, Perrig WJ, Meier B (2010) The concurrent validity of the N-back task as a working memory measure. Memory 18:394–412CrossRefPubMedGoogle Scholar
  39. Jaeggi SM, Buschkuehl M, Jonides J, Shah P (2011) Short-and long-term benefits of cognitive training. Proc Natl Acad Sci 108:10081–10086CrossRefPubMedPubMedCentralGoogle Scholar
  40. Jaeggi SM, Buschkuehl M, Shah P, Jonides J (2014) The role of individual differences in cognitive training and transfer. Memory Cognition 42:464–480CrossRefPubMedGoogle Scholar
  41. Jung RE, Haier RJ (2007) The Parieto-frontal integration theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav Brain Sci 30:135–154CrossRefPubMedGoogle Scholar
  42. Karama S, Bastin ME, Murray C, Royle NA, Penke L, Maniega SM, Deary IJ (2014) Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age. Molecular psychiatry 19:555–559CrossRefPubMedGoogle Scholar
  43. Karnath HO (2001) New insights into the functions of the superior temporal cortex. Nat Rev Neurosci 2:568–576CrossRefPubMedGoogle Scholar
  44. Kim JS, Singh V, Lee JK, Lerch J, Ad-Dab’bagh Y, MacDonald D, Evans AC (2005) Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage 27:210–221CrossRefPubMedGoogle Scholar
  45. Kirkwood T, Bond J, May C, McKeith I, Teh M (2008) Foresight mental capital and wellbeing project. Mental capital through life: Future challenges. The Government Office for Science, LondonGoogle Scholar
  46. Kirwan CB, Stark CE (2007) Overcoming interference: An fMRI investigation of pattern separation in the medial temporal lobe. Learn Memory 14:625–633CrossRefGoogle Scholar
  47. Knudsen E (2004) Sensitive periods in the development of the brain and behavior. Cognit Neurosci J 16:1412–1425CrossRefGoogle Scholar
  48. Kwok V, Niu Z, Kay P, Zhou K, Mo L, Jin Z, Tan LH (2011) Learning new color names produces rapid increase in gray matter in the intact adult human cortex. Proc Natl Acad Sci 108:6686–6688CrossRefPubMedPubMedCentralGoogle Scholar
  49. la Fougère C, Grant S, Kostikov A, Schirrmacher R, Gravel P, Schipper HM, Thiel A (2011) Where in vivo imaging meets cytoarchitectonics: The relationship between cortical thickness and neuronal density measured with high-resolution [18F] flumazenil-PET. Neuroimage 56:951–960CrossRefPubMedGoogle Scholar
  50. Landi SM, Baguear F, Della-Maggiore V (2011) One week of motor adaptation induces structural changes in primary motor cortex that predict long-term memory 1 year later. J Neurosci 31:11808–11813CrossRefPubMedPubMedCentralGoogle Scholar
  51. Lerch JP, Yiu AP, Martinez-Canabal A, Pekar T, Bohbot VD, Frankland PW, Sled JG (2011) Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning. Neuroimage 54:2086–2095CrossRefPubMedGoogle Scholar
  52. Lustig C, Shah P, Seidler R, Reuter-Lorenz PA (2009) Aging, training, and the brain: a review and future directions. Neuropsychol Rev 19:504–522CrossRefPubMedPubMedCentralGoogle Scholar
  53. Lyttelton OC, Karama S, Ad-Dab’bagh Y, Zatorre RJ, Carbonell F, Worsley K, Evans AC (2009) Positional and surface area asymmetry of the human cerebral cortex. Neuroimage 46:895–903CrossRefPubMedGoogle Scholar
  54. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113CrossRefPubMedGoogle Scholar
  55. Østby Y, Tamnes CK, Fjell AM, Westlye LT, Due-Tønnessen P, Walhovd KB (2009) Heterogeneity in subcortical brain development: a structural magnetic resonance imaging study of brain maturation from 8 to 30 years. J Neurosci 29:11772–11782CrossRefPubMedGoogle Scholar
  56. Panizzon MS, Fennema-Notestine C, Eyler LT, Jernigan TL, Prom-Wormley E, Neale M, Kremen WS (2009) Distinct genetic influences on cortical surface area and cortical thickness. Cerebral Cortex, bhp026Google Scholar
  57. Paus T (2010) Growth of white matter in the adolescent brain: myelin or axon? Brain Cogn 72:26–35CrossRefPubMedGoogle Scholar
  58. Petanjek Z, Judaš M, Kostović I, Uylings HB (2008) Lifespan alterations of basal dendritic trees of pyramidal neurons in the human prefrontal cortex: a layer-specific pattern. Cereb Cortex 18:915–929CrossRefPubMedGoogle Scholar
  59. Prabhakaran V, Rypma B, Narayanan NS, Meier TB, Austin BP, Nair VA, Gabrieli JD (2011) Capacity-speed relationships in prefrontal cortex. PLoS One 6:e27504CrossRefPubMedPubMedCentralGoogle Scholar
  60. Rakic P (1988) Specification of cerebral cortical areas. Science 24:170–176CrossRefGoogle Scholar
  61. Raznahan A, Shaw P, Lalonde F, Stockman M, Wallace GL, Greenstein D, Giedd JN (2011) How does your cortex grow? J Neurosci 31:7174–7177CrossRefPubMedPubMedCentralGoogle Scholar
  62. Reuter-Lorenz PA, Jonides J, Smith EE, Hartley A, Miller A, Marshuetz C, Koeppe RA (2000) Age differences in the frontal lateralization of verbal and spatial working memory revealed by PET. J Cogn Neurosci 12:174–187CrossRefPubMedGoogle Scholar
  63. Rimol LM, Panizzon MS, Fennema-Notestine C, Eyler LT, Fischl B, Franz CE, Dale AM (2010) Cortical thickness is influenced by regionally specific genetic factors. Biol Psychiatry 67:493–499CrossRefPubMedGoogle Scholar
  64. Román FJ, Abad FJ, Escorial S, Burgaleta M, Martínez K, Álvarez-Linera J, Colom R (2014) Reversed hierarchy in the brain for general and specific cognitive abilities: A morphometric analysis. Hum Brain Mapp 35:3805–3818CrossRefPubMedGoogle Scholar
  65. Rottschy C, Langner R, Dogan I, Reetz K, Laird AR, Schulz JB, Eickhoff SB (2012) Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 60:830–846CrossRefPubMedGoogle Scholar
  66. Schnack HG, van Haren NEM, Brouwer RM, Evans A, Durston S, Hulshoff Pol HH (2015) Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cereb Cortex 25:1608–1617CrossRefPubMedGoogle Scholar
  67. Shaw P, Greenstein D, Lerch J, Clasen L, Lenroot R, Gogtay N, Giedd J (2006) Intellectual ability and cortical development in children and adolescents. Nature 440:676–679CrossRefPubMedGoogle Scholar
  68. Sur M, Rubenstein JL (2005) Patterning and plasticity of the cerebral cortex. Science 310:805–810CrossRefPubMedGoogle Scholar
  69. Takeuchi H, Sekiguchi A, Taki Y, Yokoyama S, Yomogida Y, Komuro N, Kawashima R (2010) Training of working memory impacts structural connectivity. J Neurosci 30:3297–3303CrossRefPubMedGoogle Scholar
  70. Thomas C, Baker CI (2013a) Teaching an adult brain new tricks: a critical review of evidence for training-dependent structural plasticity in humans. Neuroimage 73:225–236CrossRefPubMedGoogle Scholar
  71. Thomas C, Baker CI (2013b) On evidence, biases and confounding factors: Response to commentaries. NeuroImage 73:265–267CrossRefPubMedGoogle Scholar
  72. Thomas AG, Marrett S, Saad ZS, Ruff DA, Martin A, Bandettini PA (2009) Functional but not structural changes associated with learning: an exploration of longitudinal voxel-based morphometry (VBM). Neuroimage 48:117–125CrossRefPubMedPubMedCentralGoogle Scholar
  73. Thompson PM, Hayashi KM, Dutton RA, Chiang MC, Leow AD, Sowell ER, Toga AW (2007) Tracking Alzheimer’s disease. Ann N Y Acad Sci 1097:183–214CrossRefPubMedPubMedCentralGoogle Scholar
  74. Vendetti MS, Bunge SA (2014) Evolutionary and developmental changes in the lateral frontoparietal network: a little goes a long way for higher-level cognition. Neuron 84:906–917CrossRefPubMedPubMedCentralGoogle Scholar
  75. Vuoksimaa E, Panizzon MS, Chen CH, Fiecas M, Eyler LT, Fennema-Notestine C, Kremen WS (2015) The genetic association between neocortical volume and general cognitive ability is driven by global surface area rather than thickness. Cereb Cortex 25:2127–2137CrossRefPubMedGoogle Scholar
  76. Wierenga LM, Langen M, Oranje B, Durston S (2014) Unique developmental trajectories of cortical thickness and surface area. NeuroImage 87:120–126CrossRefPubMedGoogle Scholar
  77. Woollett K, Maguire EA (2011) Acquiring “the Knowledge” of London’s layout drives structural brain changes. Curr Biol 21:2109–2114CrossRefPubMedPubMedCentralGoogle Scholar
  78. World Medical Association (2008) Declaration of Helsinki—ethical principles for medical research involving human subjects, 59th WMA General Assembly. Seoul, KoreaGoogle Scholar
  79. Yassa MA, Stark CE (2011) Pattern separation in the hippocampus. Trends Neurosci 34:515–525CrossRefPubMedPubMedCentralGoogle Scholar
  80. Yassa MA, Mattfeld AT, Stark SM, Stark CE (2011) Age-related memory deficits linked to circuit-specific disruptions in the hippocampus. Proc Natl Acad Sci 108:8873–8878CrossRefPubMedPubMedCentralGoogle Scholar
  81. Zhang Q, Buschkuehl M, Bernat E, Jaeggi SM (2014) EEG power changes as a function of working memory training. Poster presented at the 26th Annual Convention of the Association for Psychological Science, San FranciscoGoogle Scholar
  82. Zhou D, Lebel C, Evans A, Beaulieu C (2013) Cortical thickness asymmetry from childhood to older adulthood. NeuroImage 83:66–74CrossRefPubMedGoogle Scholar
  83. Zhou D, Lebel C, Treit S, Evans A, Beaulieu C (2015) Accelerated longitudinal cortical thinning in adolescence. NeuroImage 104:138–145CrossRefPubMedGoogle Scholar
  84. Zilles K, Amunts K (2012) Segregation and wiring in the brain. Science 335:1582–1584CrossRefPubMedGoogle Scholar
  85. Zou Q, Ross TJ, Gu H, Geng X, Zuo XN, Hong LE, Yang Y (2013) Intrinsic resting-state activity predicts working memory brain activation and behavioral performance. Hum Brain Mapp 34:3204–3215CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Francisco J. Román
    • 1
  • Lindsay B. Lewis
    • 2
  • Chi-Hua Chen
    • 3
  • Sherif Karama
    • 2
  • Miguel Burgaleta
    • 4
  • Kenia Martínez
    • 1
    • 5
  • Claude Lepage
    • 2
  • Susanne M. Jaeggi
    • 6
  • Alan C. Evans
    • 2
  • William S. Kremen
    • 3
  • Roberto Colom
    • 1
    Email author
  1. 1.Universidad Autónoma de MadridMadridSpain
  2. 2.Montreal Neurological Institute (MNI)McGill UniversityMontrealCanada
  3. 3.University of CaliforniaSan DiegoUSA
  4. 4.Universidad Pompeu FabraBarcelonaSpain
  5. 5.Hospital Gregorio MarañonMadridSpain
  6. 6.University of California at IrvineIrvineUSA

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