Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-22T06:47:20.692Z Has data issue: false hasContentIssue false
  • English
  • Français

Mind over matter – what do we know about neuroplasticity in adults?

Published online by Cambridge University Press:  02 January 2014

Vyara Valkanova ,
Rocio Eguia Rodriguez  and
Klaus P. Ebmeier
Vyara Valkanova
Affiliation:
Department of Psychiatry, University of Oxford, Oxford, UK
Rocio Eguia Rodriguez
Affiliation:
Department of Psychiatry, University of Nuevo León, Monterrey, Mexico
Klaus P. Ebmeier*
Affiliation:
Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
*
Correspondence should be addressed to: Klaus P. Ebmeier, Professor of Old Age Psychiatry, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, UK. Phone: +44 1865 226469; Fax: +44 1865 793101. Email: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Background:

An increasing number of studies have examined the effects of training of cognitive and other tasks on brain structure, using magnetic resonance imaging.

Methods:

Studies combining cognitive and other tasks training with longitudinal imaging designs were reviewed, with a view to identify paradigms potentially applicable to treatment of cognitive impairment.

Results:

We identified 36 studies, employing training as variable as juggling, working memory, meditation, learning abstract information, and aerobic exercise. There were training-related structural changes, increases in gray matter volume, decreases, increases and decreases in different regions, or no change at all. There was increased integrity in white matter following training, but other patterns of results were also reported.

Conclusions:

Questions still to be answered are: Are changes due to use-dependent effects or are they specific to learning? What are the underlying neural correlates of learning, the temporal dynamics of changes, the relations between structure and function, and the upper limits of improvement? How can gains be maintained? The question whether neuroplasticity will contribute to the treatment of dementia will need to be posed again at that stage.

Keywords

magnetic resonance imagingdiffusion tensor imagingfunctional MRIcognitive trainingworking memoryaerobic exercisegray matterwhite matter
Type
Review Article
Information
International Psychogeriatrics , Volume 26 , Issue 6 , June 2014 , pp. 891 - 909
Copyright
Copyright © International Psychogeriatric Association 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alexander, A. L., Lee, J. E., Lazar, M. and Field, A. S. (2007). Diffusion tensor imaging of the brain. Neurotherapeutics, 4, 316–329.Google Scholar
Bellander, M. et al. (2011). Preliminary evidence that allelic variation in the LMX1A gene influences training-related working memory improvement. Neuropsychologia, 49, 1938–1942.Google Scholar
Bengtsson, S. L., Nagy, Z., Skare, S., Forsman, L., Forssberg, H. and Ullen, F. (2005). Extensive piano practicing has regionally specific effects on white matter development. Nature Neuroscience, 8, 1148–1150.CrossRefGoogle ScholarPubMed
Bezzola, L., Merillat, S., Gaser, C. and Jancke, L. (2011). Training-induced neural plasticity in golf novices. Journal of Neuroscience, 31, 12444–12448.CrossRefGoogle ScholarPubMed
Bisdas, S., Bohning, D. E., Besenski, N., Nicholas, J. S. and Rumboldt, Z. (2008). Reproducibility, interrater agreement, and age-related changes of fractional anisotropy measures at 3T in healthy subjects: effect of the applied b-value. AJNR: American Journal of Neuroradiology, 29, 1128–1133.CrossRefGoogle ScholarPubMed
Boyke, J., Driemeyer, J., Gaser, C., Buchel, C. and May, A. (2008). Training-induced brain structure changes in the elderly. Journal of Neuroscience, 28, 7031–7035.Google Scholar
Bueti, D., Lasaponara, S., Cercignani, M. and Macaluso, E. (2012). Learning about time: plastic changes and interindividual brain differences. Neuron, 75, 725–737.CrossRefGoogle ScholarPubMed
Burzynska, A. Z. et al. (2010). Age-related differences in white matter microstructure: region-specific patterns of diffusivity. NeuroImage, 49, 2104–2112.Google Scholar
Ceccarelli, A., Rocca, M. A., Pagani, E., Falini, A., Comi, G. and Filippi, M. (2009). Cognitive learning is associated with gray matter changes in healthy human individuals: a tensor-based morphometry study. NeuroImage, 48, 585–589.Google Scholar
Ciccarelli, O. et al. (2003). From diffusion tractography to quantitative white matter tract measures: a reproducibility study. NeuroImage, 18, 348–359.Google Scholar
Colcombe, S. J. et al. (2006). Aerobic exercise training increases brain volume in aging humans. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 61, 1166–1170.Google Scholar
Colom, R. et al. (2012). Structural changes after videogame practice related to a brain network associated with intelligence. Intelligence, 40, 479–489.Google Scholar
Cummings, B. J. et al. (2005). Human neural stem cells differentiate and promote locomotor recovery in spinal cord-injured mice. Proceedings of the National Academy of Sciences of the United States of America, 102, 14069–14074.Google Scholar
Danielian, L. E., Iwata, N. K., Thomasson, D. M. and Floeter, M. K. (2010). Reliability of fiber tracking measurements in diffusion tensor imaging for longitudinal study. NeuroImage, 49, 1572–1580.Google Scholar
Draganski, B. and Kherif, F. (2013). In vivo assessment of use-dependent brain plasticity – beyond the “one trick pony” imaging strategy. NeuroImage, 73, 255–259; discussion 265–257.CrossRefGoogle ScholarPubMed
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U. and May, A. (2004). Neuroplasticity: changes in grey matter induced by training. Nature, 427, 311–312.Google Scholar
Draganski, B. et al. (2006). Temporal and spatial dynamics of brain structure changes during extensive learning. Journal of Neuroscience, 26, 6314–6317.CrossRefGoogle ScholarPubMed
Driemeyer, J., Boyke, J., Gaser, C., Buchel, C. and May, A. (2008). Changes in gray matter induced by learning – revisited. PLoS One, 3, e2669.CrossRefGoogle ScholarPubMed
Engvig, A. et al. (2010). Effects of memory training on cortical thickness in the elderly. NeuroImage, 52, 1667–1676.Google Scholar
Engvig, A. et al. (2012). Memory training impacts short-term changes in aging white matter: a longitudinal diffusion tensor imaging study. Human Brain Mapping, 33, 2390–2406.Google Scholar
Erickson, K. I. (2013). Evidence for structural plasticity in humans: comment on Thomas and Baker (2012). NeuroImage, 73, 237–238; discussion 265–237.CrossRefGoogle Scholar
Erickson, K. I. et al. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences of the United States of America, 108, 3017–3022.CrossRefGoogle ScholarPubMed
Eriksson, P. S. et al. (1998). Neurogenesis in the adult human hippocampus. Nature Medicine, 4, 1313–1317.Google Scholar
Fields, R. D. (2011). Imaging learning: the search for a memory trace. Neuroscientist, 17, 185–196.Google Scholar
Fields, R. D. (2013). Changes in brain structure during learning: fact or artifact? Reply to Thomas and Baker. NeuroImage, 73, 260–264; discussion 265–267.Google Scholar
Golestani, N., Paus, T. and Zatorre, R. J. (2002). Anatomical correlates of learning novel speech sounds. Neuron, 35, 997–1010.Google Scholar
Gryga, M. et al. (2012). Bidirectional gray matter changes after complex motor skill learning. Frontiers in Systems Neuroscience, 6, 37.Google Scholar
Han, X. et al. (2006). Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. NeuroImage, 32, 180–194.Google Scholar
Heiervang, E., Behrens, T. E., Mackay, C. E., Robson, M. D. and Johansen-Berg, H. (2006). Between session reproducibility and between subject variability of diffusion MR and tractography measures. NeuroImage, 33, 867–877.CrossRefGoogle ScholarPubMed
Hempel, A. et al. (2004). Plasticity of cortical activation related to working memory during training. American Journal of Psychiatry, 161, 745–747.Google Scholar
Holzel, B. K. et al. (2011). Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Research, 191, 36–43.Google Scholar
Ilg, R. et al. (2008). Gray matter increase induced by practice correlates with task-specific activation: a combined functional and morphometric magnetic resonance imaging study. Journal of Neuroscience, 28, 4210–4215.Google Scholar
Ishibashi, T. et al. (2006). Astrocytes promote myelination in response to electrical impulses. Neuron, 49, 823–832.Google Scholar
Jaeggi, S. M., Buschkuehl, M., Jonides, J. and Shah, P. (2011). Short- and long-term benefits of cognitive training. Proceedings of the National Academy of Sciences of the United States of America, 108, 10081–10086.CrossRefGoogle Scholar
Jbabdi, S., Behrens, T. E. and Smith, S. M. (2010). Crossing fibres in tract-based spatial statistics. NeuroImage, 49, 249–256.Google Scholar
Johansen-Berg, H. (2012). The future of functionally-related structural change assessment. NeuroImage, 62, 1293–1298.CrossRefGoogle ScholarPubMed
Jolles, D. D., Grol, M. J., Van Buchem, M. A., Rombouts, S. A. and Crone, E. A. (2010). Practice effects in the brain: changes in cerebral activation after working memory practice depend on task demands. NeuroImage, 52, 658–668.Google Scholar
Jovicich, J. et al. (2013). Brain morphometry reproducibility in multi-center 3T MRI studies: a comparison of cross-sectional and longitudinal segmentations. NeuroImage, 83, 472–484.Google Scholar
Karbach, J. and Kray, J. (2009). How useful is executive control training? Age differences in near and far transfer of task-switching training. Developmental Science, 12, 978–990.Google Scholar
Kelly, A. M. and Garavan, H. (2005). Human functional neuroimaging of brain changes associated with practice. Cerebral Cortex, 15, 1089–1102.Google Scholar
Kheirbek, M. A. and Hen, R. (2013). (Radio)active neurogenesis in the human hippocampus. Cell, 153, 1183–1184.Google Scholar
Kwok, V. et al. (2011). Learning new color names produces rapid increase in gray matter in the intact adult human cortex. Proceedings of the National Academy of Sciences of the United States of America, 108, 6686–6688.Google Scholar
Landi, S. M., Baguear, F. and Della-Maggiore, V. (2011). One week of motor adaptation induces structural changes in primary motor cortex that predict long-term memory one year later. Journal of Neuroscience, 31, 11808–11813.Google Scholar
Lövdén, M. et al. (2010). Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia, 48, 3878–3883.CrossRefGoogle ScholarPubMed
Lövdén, M. et al. (2012). Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiology of Aging, 33, 620.e9–22.Google Scholar
Lövdén, M., Wenger, E., Martensson, J., Lindenberger, U. and Backman, L. (2013). Structural brain plasticity in adult learning and development. Neuroscience & Biobehavioral Reviews, 37, 2296–2310.Google Scholar
Madden, D. J. et al. (2009). Cerebral white matter integrity mediates adult age differences in cognitive performance. Journal of Cognitive Neuroscience, 21, 289–302.Google Scholar
Madden, D. J., Bennett, I. J., Burzynska, A., Potter, G. G., Chen, N. K. and Song, A. W. (2012). Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochimica et Biophysica Acta, 1822, 386–400.CrossRefGoogle ScholarPubMed
Maguire, E. A. et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America, 97, 4398–4403.Google Scholar
Mårtensson, J. et al. (2012). Growth of language-related brain areas after foreign language learning. NeuroImage, 63, 240–244.Google Scholar
May, A. (2011). Experience-dependent structural plasticity in the adult human brain. Trends in Cognitive Sciences, 15, 475–482.Google Scholar
Mozolic, J. L., Hayasaka, S. and Laurienti, P. J. (2010). A cognitive training intervention increases resting cerebral blood flow in healthy older adults. Frontiers in Human Neuroscience, 4, 16.Google Scholar
Park, D. C. and Bischof, G. N. (2013). The aging mind: neuroplasticity in response to cognitive training. Dialogues in Clinical Neuroscience, 15, 109–119.Google Scholar
Park, D. C. and Reuter-Lorenz, P. (2009). The adaptive brain: aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196.Google Scholar
Pascual-Leone, A., Amedi, A., Fregni, F. and Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401.Google Scholar
Pfefferbaum, A., Adalsteinsson, E. and Sullivan, E. V. (2003). Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain. Journal of Magnetic Resonance Imaging, 18, 427–433.Google Scholar
Sagi, Y., Tavor, I., Hofstetter, S., Tzur-Moryosef, S., Blumenfeld-Katzir, T. and Assaf, Y. (2012). Learning in the fast lane: new insights into neuroplasticity. Neuron, 73, 1195–1203.Google Scholar
Schmidt-Wilcke, T., Rosengarth, K., Luerding, R., Bogdahn, U. and Greenlee, M. W. (2010). Distinct patterns of functional and structural neuroplasticity associated with learning Morse code. NeuroImage, 51, 1234–1241.CrossRefGoogle ScholarPubMed
Scholz, J., Klein, M. C., Behrens, T. E. and Johansen-Berg, H. (2009). Training induces changes in white-matter architecture. Nature Neuroscience, 12, 1370–1371.Google Scholar
Song, S. K. et al. (2005). Demyelination increases radial diffusivity in corpus callosum of mouse brain. NeuroImage, 26, 132–140.Google Scholar
Spalding, K. L. et al. (2013). Dynamics of hippocampal neurogenesis in adult humans. Cell, 153, 1219–1227.Google Scholar
Takeuchi, H. et al. (2010). Training of working memory impacts structural connectivity. Journal of Neuroscience, 30, 3297–3303.Google Scholar
Takeuchi, H. et al. (2011a). Effects of training of processing speed on neural systems. Journal of Neuroscience, 31, 12139–12148.Google Scholar
Takeuchi, H. et al. (2011b). Working memory training using mental calculation impacts regional gray matter of the frontal and parietal regions. PLoS One, 6, e23175.CrossRefGoogle ScholarPubMed
Tang, Y. Y., Lu, Q., Geng, X., Stein, E. A., Yang, Y. and Posner, M. I. (2010). Short-term meditation induces white matter changes in the anterior cingulate. Proceedings of the National Academy of Sciences of the United States of America, 107, 15649–15652.Google Scholar
Tang, Y. Y., Lu, Q., Fan, M., Yang, Y. and Posner, M. I. (2012). Mechanisms of white matter changes induced by meditation. Proceedings of the National Academy of Sciences of the United States of America, 109, 10570–10574.CrossRefGoogle ScholarPubMed
Taubert, M. et al. (2010). Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections. Journal of Neuroscience, 30, 11670–11677.Google Scholar
Thomas, C. and Baker, C. I. (2012). Remodeling human cortex through training: comment on May. Trends in Cognitive Science, 16, 96–97; author reply 97–98.Google Scholar
Thomas, C. and Baker, C. I. (2013). Teaching an adult brain new tricks: a critical review of evidence for training-dependent structural plasticity in humans. NeuroImage, 73, 225–236.Google Scholar
Thomas, A. G., Marrett, S., Saad, Z. S., Ruff, D. A., Martin, A. and Bandettini, P. A. (2009). Functional but not structural changes associated with learning: an exploration of longitudinal voxel-based morphometry (VBM). NeuroImage, 48, 117–125.Google Scholar
Vollmar, C. et al. (2010). Identical, but not the same: intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0T scanners. NeuroImage, 51, 1384–1394.CrossRefGoogle Scholar
Wang, X. et al. (2008). Longitudinal MRI evaluations of human global cortical thickness over minutes to weeks. Neuroscience Letters, 441, 145–148.Google Scholar
Wang, J. Y., Abdi, H., Bakhadirov, K., Diaz-Arrastia, R. and Devous, M. D., Sr. (2012). A comprehensive reliability assessment of quantitative diffusion tensor tractography. NeuroImage, 60, 1127–1138.Google Scholar
Wenger, E. et al. (2012). Cortical thickness changes following spatial navigation training in adulthood and aging. NeuroImage, 59, 3389–3397.Google Scholar
Wonderlick, J. S. et al. (2009). Reliability of MRI-derived cortical and subcortical morphometric measures: effects of pulse sequence, voxel geometry, and parallel imaging. NeuroImage, 44, 1324–1333.Google Scholar
Woollett, K. and Maguire, E. A. (2011). Acquiring “the Knowledge” of London's layout drives structural brain changes. Current Biology, 21, 2109–2114.Google Scholar
Xu, T. et al. (2009). Rapid formation and selective stabilization of synapses for enduring motor memories. Nature, 462, 915–919.Google Scholar
Zatorre, R. J., Fields, R. D. and Johansen-Berg, H. (2012). Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nature Neuroscience, 15, 528–536.Google Scholar