Hostname: page-component-74656f9884-c52kr Total loading time: 0 Render date: 2026-11-07T09:53:27.592Z Has data issue: false hasContentIssue false
  • English
  • Français

Is activity regulation of late myelination a plastic mechanism in the human nervous system?

Published online by Cambridge University Press:  29 September 2009

Fredrik Ullén
Fredrik Ullén*
Affiliation:
Neuropediatric Research Unit Q2:07, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
*
Correspondence should be addressed to: Fredrik Ullén, Department of Women's and Children's Health, Karolinska Institutet, SE-171 76 Stockholm, Sweden email: Fredrik.Ullen@ki.se
Rights & Permissions [Opens in a new window]

Abstract

Studies on various animal models have established that neuronal activity can influence the myelination process. Are such mechanisms present in humans, and do they mediate experience-driven white matter plasticity not only during early development but also in adolescents and adults? While there is as yet no direct evidence for this, a number of findings – reviewed here – are consistent with this idea. First, postmortem and neuroimaging studies show that the human white matter development is a protracted process that continues well into adulthood. Second, developmental changes and individual differences in white matter structure are related to differences in neural activity and behavior. Finally, studies on effects of long-term training, in particular in musicians, show strong relations between training and white matter structure. I conclude by briefly discussing possible types of white matter plasticity that could underlie these findings, emphasizing a distinction between indirect myelination plasticity, where the myelin sheath grows in parallel with the axon itself, and direct myelination plasticity, where the myelin sheath thickness is modulated independently of axonal diameter.

Keywords

Plasticitywhite matterdevelopmenttrainingmyelin

Information

Type
Research Article
Information
Neuron Glia Biology , Volume 5 , Special Issue 1-2: NG2-Glia, Part I: NG2 Cells as Components of Neural Circuits , May 2009 , pp. 29 - 34
Copyright
Copyright © Cambridge University Press 2009

DEVELOPMENT OF WHITE MATTER IN HUMANS – HISTOLOGICAL DATA

Myelinated axons are a principal constituent of the white matter of the nervous systems of almost all vertebrates and are also found in some invertebrate taxa (Schweigreiter et al., Reference Schweigreiter, Roots, Bandtlow and Gould2006; Hartline and Colman, Reference Hartline and Colman2007). The process of myelination may be more or less extended in time and its detailed spatiotemporal characteristics show considerable variability between species (Nadon et al., Reference Nadon, Crotzer and Stewart1995; Hamano et al., Reference Hamano, Takeya, Iwasaki, Nakayama, Ohto and Okada1998; Nunez et al., Reference Nunez, Nelson, Pych, Kim and Juraska2000).

A classical method to study myelination is to use some type of myelin-specific staining of histological sections, e.g. Weigert hematoxylin or Fast Blue staining (Kiernan, Reference Kiernan2007). Since the pioneering work of Paul Flechsig (Reference Flechsig1920), such techniques have been employed in a number of human postmortem studies. These studies have established several basic characteristics of the myelination process (Kinney et al., Reference Kinney, Brody, Kloman and Gilles1988). The most general observations are that while myelination is rapid during the first years of infancy, human myelination is a process that takes place over many years, and that the timing of myelination differs widely between brain regions. These regional differences are seen both for the onset time, the rate and the termination of myelination (Lucas Keene and Hewer, Reference Lucas Keene and Hewer1931; Yakovlev and Lecours, Reference Yakovlev, Lecours and Minkowski1967; Gilles et al., Reference Gilles, Shankle, Dooling, Gilles, Leviton and Dooling1983; Brody et al., Reference Brody, Kinney, Kloman and Gilles1987; Kinney et al., Reference Kinney, Brody, Kloman and Gilles1988). Studies on larger samples indicate that there are also substantial individual differences in the timing of myelination in the same brain region (Gilles et al., Reference Gilles, Shankle, Dooling, Gilles, Leviton and Dooling1983). Concerning the specific temporal order of myelination of different brain regions, a few principles have been observed. While several exceptions can be found, there is an overall tendency for caudal regions of the central nervous system to myelinate earlier than rostral regions. Several spinal cord and brain stem structures thus finish their myelination during the first year, or even prenatally (Yakovlev and Lecours, Reference Yakovlev, Lecours and Minkowski1967; Brody et al., Reference Brody, Kinney, Kloman and Gilles1987), while, for example, the association cortex of the forebrain appears to continue its myelination into adulthood (Yakovlev and Lecours, Reference Yakovlev, Lecours and Minkowski1967; Benes, Reference Benes1989). Projection pathways typically myelinate before association pathways, and proximal regions tend to myelinate before distal regions (Kinney et al., Reference Kinney, Brody, Kloman and Gilles1988). Within the telencephalon myelination proceeds from the region of central sulcus towards the poles (Kinney et al., Reference Kinney, Brody, Kloman and Gilles1988). This general picture of the human myelination process is consistent with neurochemical and immunohistochemical studies, where the sequential appearance of specific myelin-associated lipids and proteins during myelination is characterized (see e.g. Kinney et al., Reference Kinney, Karthigasan, Borenshteyn, Flax and Kirschner1994; Haynes et al., Reference Haynes, Borenstein, Desilva, Folkerth, Liu and Volpe2005).

Importantly, studies of the microstructure of white matter have shown that the development of myelination is accompanied by a growth in thickness of the axons themselves. For example in the pyramidal tract a dramatic increase in the diameter of the thickest axons occurs during the period from birth to early adolescence (Verhaart, Reference Verhaart1950; Nathan and Smith, Reference Nathan and Smith1955; Eyre et al., Reference Eyre, Miller and Ramesh1991). One important consequence of this is that the conduction velocity increases so that the conduction delay is kept approximately constant in spite of body growth (Eyre et al., Reference Eyre, Miller and Ramesh1991). During development, the growth of the myelin sheath and the growth of the axon are carefully coordinated, to produce a nerve fiber where the ratio between axonal diameter and outer diameter of the myelin sheath (g-ratio) is optimal for maximal conduction velocity (Smith and Koles, Reference Smith and Koles1970; Friede and Bischhausen, Reference Friede and Bischhausen1982; Michailov et al., Reference Michailov, Sereda, Brinkmann, Fischer, Haug and Birchmeier2004).

NEUROIMAGING STUDIES OF HUMAN WHITE MATTER DEVELOPMENT

Histological studies thus show that white matter development, including myelination and axonal growth, continues into adulthood (Yakovlev and Lecours, Reference Yakovlev, Lecours and Minkowski1967; Benes, Reference Benes1989), but the majority of these studies have focussed on the prenatal and infant period. In the last decade, research on white matter development has been revolutionized by the appearance of a number of magnetic resonance (MR) imaging techniques that allow investigations on healthy individuals in vivo and the use of longitudinal designs. Several different such techniques have been employed for the study of white matter. The first developmental MR studies provided evaluations of changes in signal intensities in conventional T1- and T2-weighted MR images as a function of age (Barkovich et al., Reference Barkovich, Kjos, Jackson and Norman1988; Martin et al., Reference Martin, Kikinis, Zuerrer, Boesch, Briner and Kewitz1988; van der Knaap and Valk, Reference van der Knaap and Valk1990; for a review see Paus et al., Reference Paus, Collins, Evans, Leonard, Pike and Zijdenbos2001). In line with the postmortem literature, these studies demonstrated that T1 and T2 relaxation times in white matter change dramatically during the first 2–3 years of life, presumably reflecting the rapid myelination during infancy. After this age a pattern of MR intensities that qualitatively resembles the adult stage is seen.

MR studies of late myelination have benefited from the development of more sensitive, quantitative methods to analyze conventional MR images as well as entirely new MR techniques. These developments include, first, studies of regional and global white matter volume or density. Such studies can employ either manual delineation of anatomical structures or procedures involving automated segmentation of white and gray matter, such as voxel-based morphometry (Good et al., Reference Good, Johnsrude, Ashburner, Henson, Friston and Frackowiak2001). This work has confirmed the protracted nature of human white matter development. During the first years of infancy, increases in white matter volume are slow compared to increases in gray matter volume (Knickmeyer et al., Reference Knickmeyer, Gouttard, Kang, Evans, Wilber and Smith2008). Giedd et al. (Reference Giedd, Blumenthal, Jeffries, Castellanos, Liu and Zijdenbos1999) found a linear increase of white matter volume with age in all lobes throughout the studied age range (4–22 years), in study on a large sample (n = 145) using a mixed cross-sectional and longitudinal design. These results are in line with cross-sectional studies on the development of total white matter volume (Pfefferbaum et al., Reference Pfefferbaum, Mathalon, Sullivan, Rawles, Zipursky and Lim1994; Reiss et al., Reference Reiss, Abrams, Singer, Ross and Denckla1996; Iwasaki et al., Reference Iwasaki, Hamano, Okada, Horigome, Nakayama and Takeya1997) and white matter density in corticospinal and frontotemporal pathways (Paus et al., Reference Paus, Zijdenbos, Worsley, Collins, Blumenthal and Giedd1999). The increase in white matter volume during adolescence is seen in both sexes but is steeper in males (Perrin et al., Reference Perrin, Leonard, Perron, Pike, Pitiot and Richer2009). Studies on older adults indicate no substantial decrease in total white matter volume before age 60 (Good et al., Reference Good, Johnsrude, Ashburner, Henson, Friston and Frackowiak2001).

Another MR technique of relevance for white matter studies is magnetization transfer imaging (MTI), which relies on interaction between proton magnetization in macromolecular structures (e.g. myelin) and in free water (Wolff and Balaban, Reference Wolff and Balaban1989; Henkelman et al., Reference Henkelman, Stanisz and Graham2001). A common MTI measure is the magnetization transfer ratio (MTR). MTR is higher in white matter than in gray matter and modeling of data from MTI of the optic nerve indicates that it largely reflects the degree of myelination of the white matter (Stanisz et al., Reference Stanisz, Kecojevic, Bronskill and Henkelman1999). A few studies have reported increasing MTR values in human white matter during childhood and adolescence, with the most rapid increases taking place during infancy (Engelbrecht et al., Reference Engelbrecht, Rassek, Preiss, Wald and Modder1998; van Buchem et al., Reference van Buchem, Steens, Vrooman, Zwinderman, McGowan and Rassek2001).

Finally, the development of white matter can be studied with diffusion tensor imaging (DTI). DTI is an MR technique that measures the local magnitude and direction of water diffusion (Le Bihan, Reference Le Bihan2003). The presence of barriers such as axonal membranes and myelin sheaths will make the diffusion anisotropic, in the case of white matter more rapid along the nerve fibers than perpendicular to them. DTI has proven particularly sensitive to the more subtle developmental changes in white matter occurring after infancy (Lobel et al., Reference Lobel, Sedlacik, Gullmar, Kaiser, Reichenbach and Mentzel2009). Different DTI parameters can be used to analyze white matter microstructure. Among the more commonly used are the apparent diffusion coefficient (ADC), which is a measure of overall diffusion, and the fractional anisotropy (FA), which reflects the degree of anisotropy. Importantly, DTI studies have shown that both ADC and FA continue to change with age also during adolescence and early adulthood. FA and other measures of anisotropy increase with age in widespread white matter regions (Ben Bashat et al., Reference Ben Bashat, Ben Sira, Graif, Pianka, Hendler and Cohen2002; Engelbrecht et al., Reference Engelbrecht, Scherer, Rassek, Witsack and Modder2002; McGraw et al., Reference McGraw, Liang and Provenzale2002; Zhai et al., Reference Zhai, Lin, Wilber, Gerig and Gilmore2003; Barnea-Goraly et al., Reference Barnea-Goraly, Menon, Eckert, Tamm, Bammer and Karchemskiy2005; Lobel et al., Reference Lobel, Sedlacik, Gullmar, Kaiser, Reichenbach and Mentzel2009). ADC, in contrast, decreases with age (Engelbrecht et al., Reference Engelbrecht, Scherer, Rassek, Witsack and Modder2002; Zhai et al., Reference Zhai, Lin, Wilber, Gerig and Gilmore2003; Lobel et al., Reference Lobel, Sedlacik, Gullmar, Kaiser, Reichenbach and Mentzel2009). The increase in FA is presumably dependent on a number of factors apart from increased myelination, e.g. changes in axonal diameters, packing density and distribution of axonal macromolecules (Beaulieu, Reference Beaulieu2002; Lobel et al., Reference Lobel, Sedlacik, Gullmar, Kaiser, Reichenbach and Mentzel2009). The reduction in ADC with age probably reflects an overall decrease in white matter water content, which in turn may be related to myelination (Lobel et al., Reference Lobel, Sedlacik, Gullmar, Kaiser, Reichenbach and Mentzel2009).

CORRELATIONS BETWEEN WHITE MATTER DEVELOPMENT AND FUNCTION

The developmental changes in white matter structure discussed above parallel the development of function (Wozniak and Lim, Reference Wozniak and Lim2006; Giedd, Reference Giedd2008; Marsh et al., Reference Marsh, Gerber and Peterson2008). DTI, with its high sensitivity for differences in white matter microstructure, has been particularly useful for elucidating relationships between function and structure. Prefrontal increases in FA and white matter density parallel the development of higher cognitive functions such as general intelligence, working memory, attention and inhibition (Olesen et al., Reference Olesen, Nagy, Westerberg and Klingberg2003; Barnea-Goraly et al., Reference Barnea-Goraly, Menon, Eckert, Tamm, Bammer and Karchemskiy2005; Schmithorst et al., Reference Schmithorst, Wilke, Dardzinski and Holland2005). Increases in FA in the corpus callosum may reflect the development of cognitive functioning as well as sensorimotor control (Barnea-Goraly et al., Reference Barnea-Goraly, Menon, Eckert, Tamm, Bammer and Karchemskiy2005; Hasan et al., Reference Hasan, Karnali, Kramer, Papnicolaou, Fletcher and Ewing-Cobbs2008) and have recently been shown to be correlated with bimanual coordination ability (Muetzel et al., Reference Muetzel, Collins, Mueller, Schissel, Lim and Luciana2008). The gradual development of the pyramidal tracts and arcuate fasciculus parallels the development of manual fine motor skills and language (Paus et al., Reference Paus, Zijdenbos, Worsley, Collins, Blumenthal and Giedd1999).

Relations between individual differences in behavior and white matter structure are also found independently of age. General intelligence is related to higher FA (Schmithorst et al., Reference Schmithorst, Wilke, Dardzinski and Holland2005) as well as larger volume (Haier et al., Reference Haier, Jung, Yeo, Head and Alkire2004; Ullén et al., Reference Ullén, Forsman, Blom, Karabanov and Madison2008) in widespread white matter regions, including fronto-parietal association bundles. We have recently found that prefrontal white matter volume is positively related both to cognitive ability and millisecond stability in a simple, automatic timing task (Ullén et al., Reference Ullén, Forsman, Blom, Karabanov and Madison2008). A possible link between white matter structure and cognitive performance may thus be that white matter connectivity influences millisecond accuracy of neuronal activity, which in turn affects higher cognitive functions (Ullén et al., Reference Ullén, Forsman, Blom, Karabanov and Madison2008; Madison et al., Reference Madison, Forsman, Blom, Karabanov and Ullén2009). Other studies have demonstrated positive relations between FA in different white matter regions and performance in a wide range of tasks. Among demonstrated relations are: temporo-parietal FA and reading ability (Klingberg et al., Reference Klingberg, Hedehus, Temple, Salz, Gabrieli and Moseley2000; Niogi and McCandliss, Reference Niogi and McCandliss2005); FA in the anterior corona radiata and working memory capacity (Niogi and McCandliss, Reference Niogi and McCandliss2005); FA in visuospatial attention pathways and reaction time (Tuch et al., Reference Tuch, Salat, Wisco, Zaleta, Hevelone and Rosas2005); FA in the corpus callosum and bimanual coordination (Johansen-Berg et al., Reference Johansen-Berg, Della-Maggiore, Behrens, Smith and Paus2007; Bonzano et al., Reference Bonzano, Tacchino, Roccatagliata, Abbruzzese, Mancardi and Bove2008; Muetzel et al., Reference Muetzel, Collins, Mueller, Schissel, Lim and Luciana2008); and FA in the longitudinal fascicles and memory retrieval (Fuentemilla et al., Reference Fuentemilla, Camara, Munte, Kramer, Cunillera and Marco-Pallares2009). FA in parietal and frontal pathways was recently shown to be negatively related to visual evoked potential latency (Stufflebeam et al., Reference Stufflebeam, Witzel, Mikulski, Hamalainen, Temereanca and Barton2008).

Perhaps most relevant for the question of experience-driven white matter plasticity in humans are studies demonstrating relations between long-term training and white matter structure. Most of this work has been performed on young adult musicians (typically aged 18–45 years) where amount of practicing was estimated retrospectively. A number of studies have found that the corpus callosum is larger in musicians (Schlaug et al., Reference Schlaug, Jäncke, Huang, Staiger and Steinmetz1995; Öztürk et al., Reference Öztürk, Tascioglu, Aktekin, Kurtoglu and Erden2002; Lee et al., Reference Lee, Chen and Schlaug2003). This effect appears to be most pronounced in musicians that started musical training early (<7 years) indicating a practicing effect (Schlaug et al., Reference Schlaug, Jäncke, Huang, Staiger and Steinmetz1995). We found positive relations between FA and training in widespread white matter regions in professional concert pianists (Bengtsson et al., Reference Bengtsson, Nagy, Forsman, Forssberg, Skare and Ullén2005). The correlations with childhood practicing were particularly extensive and strong and included the pyramidal tract, which also had higher FA in the pianists than in a control group of non-musicians (Fig. 1). The finding of higher FA in the pyramidal tract of pianists was recently replicated by Han et al. (Reference Han, Yang, Lv, Zhu, He and Tang2009). Surprisingly, another recent study (Imfeld et al., Reference Imfeld, Oechslin, Meyer, Loenneker and Jancke2009) reported lower pyramidal tract FA values in musicians than in non-musicians. This finding is puzzling given other studies on musicians, as well as the large literature demonstrating increases in FA during development and positive relations between FA and performance (see above). One notable difference to our own study and the study by Han et al. is that Imfeld and coworkers included performers of a large variety of keyboard, string, woodwind and brass instruments in their musician group. Pianists perform a large amount of individual finger movements with both hands during practicing, but for many wind instruments the frequency of finger movements may not exceed that in other professions where, for example, computers are used extensively.

Fig. 1.

Long-term training affects white matter structure. Correlations between number of hours practiced in childhood and white matter anisotropy (FA) in the right internal capsule (A) in a group of concert pianists. The clusters are overlaid on the mean FA image from all participants. In the diagrams, points and vertical lines represent mean FA values and standard deviations for all voxels in the cluster in one individual. (B) Cluster of voxels in the right internal capsule with significantly higher FA values in the pianists than in a non-musician control group. The mean FA values for each control participant in this cluster are illustrated with red points in the diagram in (A). Modified from Bengtsson et al. (Reference Bengtsson, Nagy, Forsman, Forssberg, Skare and Ullén2005).

IS ACTIVITY-MODULATED MYELINATION A PLASTIC PROCESS IN HUMANS?

Studies on different animal model systems have established that the neural activity in maturing axons influences the myelination process (for reviews, see e.g. Markham and Greenough, Reference Markham and Greenough2004; Fields, Reference Fields2008). As summarized above, a large literature shows that human white matter development is a slow process and that white matter structure has strong relations to functional brain properties, behavior and training. These findings make it plausible that neural activity driven white matter plasticity plays a role also in the human brain.

Many intriguing questions remain regarding the nature and functional roles of white matter plasticity. As mentioned, normal human white matter development involves both myelination and changes in the structure of the axons themselves, including increases in axonal diameters. The ratio between axon diameter and outer myelin sheath diameter (g-ratio) is carefully regulated to have a mean of around 0.65, which is the theoretical optimum for conduction velocity (Smith and Koles, Reference Smith and Koles1970). However, the correlation between axonal thickness and myelin sheath thickness is far from perfect (Leenen et al., Reference Leenen, Meek, Posthuma and Nieuwenhuys1985), which naturally raises the question of the possible functional role of that portion of the variance in myelin sheath thickness that is unexplained by axonal thickness. When discussing myelination plasticity it is thus, from both a functional and mechanistic point of view, probably important to distinguish between what one could call indirect myelination plasticity, i.e. a concomitant change in size of both axon and myelin sheath with preserved g-ratio, and direct myelination plasticity, where the thickness of the myelin sheath would be regulated independently of the axon diameter. It seems highly plausible that indirect myelination plasticity occurs in the human nervous system. Axonal thicknesses increase during development (Verhaart, Reference Verhaart1950; Nathan and Smith, Reference Nathan and Smith1955; Eyre et al., Reference Eyre, Miller and Ramesh1991) and animal data indicate that new myelin wraps are added onto the axons as the animal grows (Berthold et al., Reference Berthold, Nilsson and Rydmark1983; Fields, Reference Fields2008). The functional consequence of indirect myelination plasticity would simply be to maintain a high axonal conduction velocity as the axon grows. Direct myelination plasticity is a more speculative but theoretically interesting possibility. It would imply that the regulatory mechanisms controlling myelin sheath thickness are adjusted in a systematic and functionally meaningful way, e.g. by a change in set-point, so that the g-ratio is altered. This in turn could have effects on both conduction speed and other electrical properties of the axons, such as its refractory period. Fields (Reference Fields2008) has suggested that such regulation of axonal properties by oligodendrocytes could serve, for example, to fine tune conduction velocities in order to influence spike timing and synchronization of neural firing.

In summary, there is strong indirect support for that white matter plasticity occurs in late myelinating axons in humans. Further work will be needed to understand the mechanisms and functional importance of such plasticity and how it changes during the life span.

Statement of interest

None.

References

REFERENCES

Barkovich, A.J., Kjos, B.O., Jackson, D.E.J. and Norman, D. (1988) Normal maturation of the neonatal and infant brain: MR imaging at 1.5 T. Radiology 166, 173–180.CrossRefGoogle ScholarPubMed
Barnea-Goraly, N., Menon, V., Eckert, M., Tamm, L., Bammer, R., Karchemskiy, A. et al. (2005) White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study. Cerebral Cortex 15, 1848–1854.CrossRefGoogle ScholarPubMed
Beaulieu, C. (2002) The basis of anisotropic water diffusion in the nervous system – a technical review. NMR in Biomedicine 15, 435–455.CrossRefGoogle ScholarPubMed
Ben Bashat, D., Ben Sira, L., Graif, M., Pianka, P., Hendler, T., Cohen, Y. et al. (2002) Normal white matter development from infancy to adulthood: comparing diffusion tensor and high b value diffusion weighted MR images. 10th Annual Meeting of the International-Society-for-Magnetic-Resonance-in-Medicine (ISMRM). John Wiley & Sons Inc, pp. 503–511.Google Scholar
Benes, F.M. (1989) Myelination of cortical-hippocampal relays during late adolescence. Schizophrenia in Bulletin 15, 585–593.CrossRefGoogle ScholarPubMed
Bengtsson, S.L., Nagy, Z., Forsman, L., Forssberg, H., Skare, S. and Ullén, F. (2005) Extensive piano practicing has regionally specific effects on white matter development. Nature Neuroscience 8, 1148–1150.CrossRefGoogle ScholarPubMed
Berthold, C.H., Nilsson, I. and Rydmark, M. (1983) Axon diameter and myelin sheath thickness in nerve-fibers of the ventral spinal root of the 7th lumbar nerve of the adult and developing cat. Journal of Anatomy 136, 483–508.Google Scholar
Bonzano, L., Tacchino, A., Roccatagliata, L., Abbruzzese, G., Mancardi, G.L. and Bove, M. (2008) Callosal contributions to simultaneous bimanual finger movements. Journal of Neuroscience 28, 3227–3233.CrossRefGoogle ScholarPubMed
Brody, B.A., Kinney, H.C., Kloman, A.S. and Gilles, F.H. (1987) Sequence of central nervous system myelination in human infancy. I. An autopsy study of myelination. Journal of Neuropathology and Experimental Neurology 46, 268–301.CrossRefGoogle ScholarPubMed
Engelbrecht, V., Rassek, M., Preiss, S., Wald, C. and Modder, U. (1998) Age-dependent changes in magnetization transfer contrast of white matter in the pediatric brain. American Journal of Neuroradiology 19, 1923–1929.Google ScholarPubMed
Engelbrecht, V., Scherer, A., Rassek, M., Witsack, H.J. and Modder, U. (2002) Diffusion-weighted MR imaging in the brain in children: findings in the normal brain and in the brain with white matter diseases. Radiology 222, 410–418.CrossRefGoogle ScholarPubMed
Eyre, J.A., Miller, S. and Ramesh, V. (1991) Constancy of central conduction delays during development in man: investigation of motor and sensory pathways. Journal of Physiology 434, 441–452.CrossRefGoogle Scholar
Fields, R.D. (2008) White matter in learning, cognition and psychiatric disorders. Trends in Neurosciences 31, 361–370.CrossRefGoogle ScholarPubMed
Flechsig, P.E. (1920) Anatomie des Menschlichen Gehirns und Ruckenmarks auf Myelogenetischer Grundlage. Thieme.Google Scholar
Friede, R.L. and Bischhausen, R. (1982) How are sheath dimensions affected by axon caliber and internode length? Brain Research 235, 335–350.CrossRefGoogle ScholarPubMed
Fuentemilla, L., Camara, E., Munte, T.F., Kramer, U.M., Cunillera, T., Marco-Pallares, J. et al. (2009) Individual differences in true and false memory retrieval are related to white matter brain microstructure. Journal of Neuroscience 29, 8698–8703.CrossRefGoogle ScholarPubMed
Giedd, J.N. (2008) The teen brain: insights from neuroimaging. Journal of Adolescent Health 42, 335–343.CrossRefGoogle ScholarPubMed
Giedd, J.N., Blumenthal, J., Jeffries, N.O., Castellanos, F.X., Liu, H., Zijdenbos, A. et al. (1999) Brain development during childhood and adolescence: a longitudinal MRI study. Nature Neuroscience 2, 861–863.CrossRefGoogle ScholarPubMed
Gilles, F.H., Shankle, W. and Dooling, E.C. (1983) Myelinated tracts: growth patterns. In Gilles, F.H., Leviton, A. & Dooling, E.C. (eds) The Developing Human Brain. Boston, UK: John Wright, PSG Inc., pp. 117–183.CrossRefGoogle Scholar
Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N.A., Friston, K.J. and Frackowiak, R.S.J. (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14, 21–36.CrossRefGoogle ScholarPubMed
Haier, R.J., Jung, R.E., Yeo, R.A., Head, K. and Alkire, M.T. (2004) Structural brain variation and general intelligence. NeuroImage 23, 425–433.CrossRefGoogle ScholarPubMed
Hamano, K., Takeya, T., Iwasaki, N., Nakayama, J., Ohto, T. and Okada, Y. (1998) A quantitative study of the progress of myelination in the rat central nervous system, using the immunohistochemical method for proteolipid protein. Developmental Brain Research 108, 287–293.CrossRefGoogle ScholarPubMed
Han, Y., Yang, H., Lv, Y.-T., Zhu, C.-Z., He, Y., Tang, H.-H. et al. (2009) Gray matter density and white matter integrity in pianists' brain: A combined structural and diffusion tensor MRI study. Neuroscience Letters 459, 3–6.CrossRefGoogle Scholar
Hartline, D.K. and Colman, D.R. (2007) Rapid conduction and evolution of giant axons and myelinated fibers. Current Biology 17, R29–R35.CrossRefGoogle ScholarPubMed
Hasan, K.M., Karnali, A., Kramer, L.A., Papnicolaou, A.C., Fletcher, J.M. and Ewing-Cobbs, L. (2008) Diffusion tensor quantification of the human midsagittal corpus callosum subdivisions across the lifespan. Brain Research 1227, 52–67.CrossRefGoogle ScholarPubMed
Haynes, R.L., Borenstein, N.S., Desilva, T.M., Folkerth, R.D., Liu, L.G., Volpe, J.J. et al. (2005) Axonal development in the cerebral white matter of the human fetus and infant. Journal of Comparative Neurology 484, 156–167.CrossRefGoogle ScholarPubMed
Henkelman, R.M., Stanisz, G.J. and Graham, S.J. (2001) Magnetization transfer in MRI: a review. NMR in Biomedicine 14, 57–64.CrossRefGoogle ScholarPubMed
Imfeld, A., Oechslin, M.S., Meyer, M., Loenneker, T. and Jancke, L. (2009) White matter plasticity in the corticospinal tract of musicians: A diffusion tensor imaging study. NeuroImage 46, 600–607.CrossRefGoogle ScholarPubMed
Iwasaki, N., Hamano, K., Okada, Y., Horigome, Y., Nakayama, J., Takeya, T. et al. (1997) Volumetric quantification of brain development using MRI. Neuroradiology 39, 841–846.CrossRefGoogle ScholarPubMed
Johansen-Berg, H., Della-Maggiore, V., Behrens, T.E., Smith, S.M. and Paus, T. (2007) Integrity of white matter in the corpus callosum correlates with bimanual co-ordination skills. NeuroImage 36 Suppl 2, T16–T21.CrossRefGoogle Scholar
Kiernan, J.A. (2007) Histochemistry of staining methods for normal and degenerating myelin in the central and peripheral nervous systems. Journal of Histotechnology 30, 87–106.CrossRefGoogle Scholar
Kinney, H.C., Brody, B.A., Kloman, A.S. and Gilles, F.H. (1988) Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants. Journal of Neuropathology Experimental Neurology 47, 217–234.CrossRefGoogle ScholarPubMed
Kinney, H.C., Karthigasan, J., Borenshteyn, N.I., Flax, J.D. and Kirschner, D.A. (1994) Myelination in the developing human brain - biochemical correlates. Neurochemistry Research 19, 983–996.CrossRefGoogle ScholarPubMed
Klingberg, T., Hedehus, M., Temple, E., Salz, T., Gabrieli, J.D.E., Moseley, M.E. et al. (2000) Microstructure of temporo-parietal white matter as a basis for reading ability: evidence from diffusion tensor magnetic resonance imaging. Neuron 25, 493–500.CrossRefGoogle ScholarPubMed
Knickmeyer, R.C., Gouttard, S., Kang, C.Y., Evans, D., Wilber, K., Smith, J.K. et al. (2008) A structural MRI study of human brain development from birth to 2 years. Journal of Neuroscience 28, 12176–12182.CrossRefGoogle ScholarPubMed
Le Bihan, D. (2003) Looking into the functional architecture of the brain with diffusion MRI. Nature Reviews. Neuroscience 4, 469–480.Google Scholar
Lee, D.J., Chen, Y. and Schlaug, G. (2003) Corpus callosum: musician and gender effects. NeuroReport 14, 205–209.CrossRefGoogle ScholarPubMed
Leenen, L.P.H., Meek, J., Posthuma, P.R. and Nieuwenhuys, R. (1985) A detailed morphometrical analysis of the pyramidal tract of the rat. Brain Research 359, 65–80.CrossRefGoogle ScholarPubMed
Lobel, U., Sedlacik, J., Gullmar, D., Kaiser, W.A., Reichenbach, J.R. and Mentzel, H.J. (2009) Diffusion tensor imaging: the normal evolution of ADC, RA, FA, and eigenvalues studied in multiple anatomical regions of the brain. Neuroradiology 51, 253–263.CrossRefGoogle ScholarPubMed
Lucas Keene, M.F. and Hewer, E.E. (1931) Some observations on myelination in the human central nervous system. Journal of Anatomy 66, 1–13.Google Scholar
Madison, G., Forsman, L., Blom, Ö., Karabanov, A. and Ullén, F. (2009) Correlations between general intelligence and components of serial timing variability. Intelligence 37, 68–75.CrossRefGoogle Scholar
Markham, J.A. and Greenough, W.T. (2004) Experience-driven brain plasticity: beyond the synapse. Neuron Glia Biology 1, 351–363.CrossRefGoogle ScholarPubMed
Marsh, R., Gerber, A.J. and Peterson, B.S. (2008) Neuroimaging studies of normal brain development and their relevance for understanding childhood neuropsychiatric disorders. Journal of the American Academy of Child and Adolescent Psychiatry 47, 1233–1251.CrossRefGoogle ScholarPubMed
Martin, E., Kikinis, R., Zuerrer, M., Boesch, C., Briner, J., Kewitz, G. et al. (1988) Developmental stages of human brain: an MR study. Journal of Computer Assisted Tomography 12, 917–922.CrossRefGoogle Scholar
McGraw, P., Liang, L.X. and Provenzale, J.M. (2002) Evaluation of normal age-related changes in anisotropy during infancy and childhood as shown by diffusion tensor imaging. American Journal of Roentgenology 179, 1515–1522.CrossRefGoogle ScholarPubMed
Michailov, G.V., Sereda, M.W., Brinkmann, B.G., Fischer, T.M., Haug, B., Birchmeier, C. et al. (2004) Axonal neuregulin-1 regulates myelin sheath thickness. Science 304, 700–703.CrossRefGoogle ScholarPubMed
Muetzel, R.L., Collins, P.F., Mueller, B.A., Schissel, A.M., Lim, K.O. and Luciana, M. (2008) The development of corpus callosum microstructure and associations with bimanual task performance in healthy adolescents. NeuroImage 39, 1918–1925.CrossRefGoogle ScholarPubMed
Nadon, N.L., Crotzer, D.R. and Stewart, J.R. (1995) Embryonic development of central nervous system myelination in a reptilian species, Eumeces fasciatus. Journal of Comparative Neurology 362, 433–440.CrossRefGoogle Scholar
Nathan, P.J. and Smith, M.C. (1955) Long descending tracts in man. 1. Review of present knowledge. Brain 78, 249–303.CrossRefGoogle Scholar
Niogi, S.N. and McCandliss, B.D. (2005) Left lateralized white matter microstructure accounts for individual differences in reading ability and disability Conference on Advances in Developmental Cognitive Neuroscience. Pergamon-Elsevier Science Ltd, pp. 2178–2188.Google Scholar
Nunez, J.L., Nelson, J., Pych, J.C., Kim, J.H.Y. and Juraska, J.M. (2000) Myelination in the splenium of the corpus callosum in adult male and female rats. Developmental Brain Research 120, 87–90.CrossRefGoogle ScholarPubMed
Olesen, P.J., Nagy, Z., Westerberg, H. and Klingberg, T. (2003) Combined analysis of DTI and fMRI data reveals a joint maturation of white and grey matter in a fronto-parietal network. Cognitive Brain Research 18, 48–57.CrossRefGoogle Scholar
Öztürk, A.H., Tascioglu, B., Aktekin, M., Kurtoglu, Z. and Erden, I. (2002) Morphometric comparison of the human corpus callosum in professional musicians and non-musicians by using in vivo magnetic resonance imaging. Journal of Neuroradiology 29, 29–34.Google ScholarPubMed
Paus, T., Collins, D.L., Evans, A.C., Leonard, G., Pike, B. and Zijdenbos, A. (2001) Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Research Bulletin 54, 255–266.CrossRefGoogle Scholar
Paus, T., Zijdenbos, A., Worsley, K., Collins, D.L., Blumenthal, J., Giedd, J.N. et al. (1999) Structural maturation of neural pathways in children and adolescents: in vivo study. Science 283, 1908–1911.CrossRefGoogle ScholarPubMed
Perrin, J.S., Leonard, G., Perron, M., Pike, G.B., Pitiot, A., Richer, L. et al. (2009) Sex differences in the growth of white matter during adolescence. NeuroImage 45, 1055–1066.CrossRefGoogle ScholarPubMed
Pfefferbaum, A., Mathalon, D.H., Sullivan, E.V., Rawles, J.M., Zipursky, R.B. and Lim, K.O. (1994) A quantitative magnetic-resonance-imaging study of changes in brain morphology from infancy to late adulthood. Archives of Neurology 51, 874–887.CrossRefGoogle ScholarPubMed
Reiss, A.L., Abrams, M.T., Singer, H.S., Ross, J.L. and Denckla, M.B. (1996) Brain development, gender and IQ in children. A volumetric imaging study. Brain 119, 1763–1774.CrossRefGoogle ScholarPubMed
Schlaug, G., Jäncke, L., Huang, Y., Staiger, J.F. and Steinmetz, H. (1995) Increased corpus callosum size in musicians. Neuropsychologia 33, 1047–1055.CrossRefGoogle ScholarPubMed
Schmithorst, V.J., Wilke, M., Dardzinski, B.J. and Holland, S.K. (2005) Cognitive functions correlate with white matter architecture in a normal pediatric population: A diffusion tensor MRI study. Human Brain Mapping 26, 139–147.CrossRefGoogle Scholar
Schweigreiter, R., Roots, B.I., Bandtlow, C.E. and Gould, R.M. (2006) Understanding myelination through studying its evolution. International Review of Neurobiology 73, 219–273.CrossRefGoogle ScholarPubMed
Smith, R.S. and Koles, Z.J. (1970) Myelinated nerve fibers: computed effect of myelin thickness on conduction velocity. American Journal of Physiology 219, 1256–1258.CrossRefGoogle ScholarPubMed
Stanisz, G.J., Kecojevic, A., Bronskill, M.J. and Henkelman, R.M. (1999) Characterizing white matter with magnetization transfer and T-2. Magnetic Resonance in Medicine 42, 1128–1136.3.0.CO;2-9>CrossRefGoogle ScholarPubMed
Stufflebeam, S.M., Witzel, T., Mikulski, S., Hamalainen, M.S., Temereanca, S., Barton, J.J.S. et al. (2008) A non-invasive method to relate the timing of neural activity to white matter microstructural integrity. NeuroImage 42, 710–716.CrossRefGoogle ScholarPubMed
Tuch, D.S., Salat, D.H., Wisco, J.J., Zaleta, A.K., Hevelone, N.D. and Rosas, H.D. (2005) Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention. Proceedings of the National Academy of Sciences of the U.S.A. 102, 12212–12217.CrossRefGoogle ScholarPubMed
Ullén, F., Forsman, L., Blom, Ö., Karabanov, A. and Madison, G. (2008) Intelligence and variability in a simple timing task share neural substrates in the prefrontal white matter. Journal of Neuroscience 28, 4238–4243.CrossRefGoogle Scholar
van Buchem, M.A., Steens, S.C.A., Vrooman, H.A., Zwinderman, A.H., McGowan, J.C., Rassek, M. et al. (2001) Global estimation of myelination in the developing brain on the basis of magnetization transfer imaging: A preliminary study. 7th Scientific Meeting and Exhibition of the International-Society-for-Magnetic-Resonance-in-Medicine. American Society of Neuroradiology, pp. 762–766.Google Scholar
van der Knaap, M.S. and Valk, J. (1990) MR imaging of the various stages of normal myelination during the first year of life. Neuroradiology 31, 459–470.CrossRefGoogle ScholarPubMed
Verhaart, W.J.C. (1950) Hypertrophy of pes pedunculi and pyramid as result of degeneration of contralateral corticofugal fiber tracts. Journal of Comparative Neurology 92, 1–15.CrossRefGoogle Scholar
Wolff, S.D. and Balaban, R.S. (1989) Magnetization transfer contrast (Mtc) and tissue water proton relaxation in vivo. Magnetic Resonance in Medicine 10, 135–144.CrossRefGoogle ScholarPubMed
Wozniak, J.R. and Lim, K.O. (2006) Advances in white matter imaging: A review of in vivo magnetic resonance methodologies and their applicability to the study of development and aging. Neuroscience Biobehavioral Review 30, 762–774.CrossRefGoogle Scholar
Yakovlev, P.I. and Lecours, A.-R. (1967) The myelogenetic cycles of regional maturation of the brain. In Minkowski, A. (ed) Regional Development of the Brain in Early Life. Oxford, UK: Blackwell Scientific Publications, pp. 3–65.Google Scholar
Zhai, G.H., Lin, W.L., Wilber, K.P., Gerig, G. and Gilmore, J.H. (2003) Comparisons of regional white matter diffusion in healthy neonates and adults performed with a 3.0-T head-only MR imaging unit. Radiology 229, 673–681.CrossRefGoogle Scholar
Figure 0

Fig. 1. Long-term training affects white matter structure. Correlations between number of hours practiced in childhood and white matter anisotropy (FA) in the right internal capsule (A) in a group of concert pianists. The clusters are overlaid on the mean FA image from all participants. In the diagrams, points and vertical lines represent mean FA values and standard deviations for all voxels in the cluster in one individual. (B) Cluster of voxels in the right internal capsule with significantly higher FA values in the pianists than in a non-musician control group. The mean FA values for each control participant in this cluster are illustrated with red points in the diagram in (A). Modified from Bengtsson et al. (2005).