Growth of white matter in the adolescent brain: Myelin or axon?

Brain and Cognition 72 (2010) 26–35

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Review Article

Growth of white matter in the adolescent brain: Myelin or axon? Tomáš Paus * Brain and Body Centre, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom Montreal Neurological Institute, McGill University, Montreal, Canada

a r t i c l e

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Article history: Accepted 10 June 2009 Available online 23 October 2009 Keywords: MRI DTI MTR g ratio Schizophrenia ADHD Cytoskeleton Axonal transport

a b s t r a c t White matter occupies almost half of the human brain. It contains axons connecting spatially segregated modules and, as such, it is essential for the smooth flow of information in functional networks. Structural maturation of white matter continues during adolescence, as reflected in age-related changes in its volume, as well as in its microstructure. Here I review recent observations obtained with magnetic resonance imaging in typically developing adolescents and point out some of the known variations in structural properties of white matter vis-à-vis brain function in health and disease. I conclude by refocusing the interpretations of MR-based studies of white matter from myelin to axon. Ó 2009 Elsevier Inc. All rights reserved.

1. Neural connectivity White matter occupies almost half of the human brain (Miller, Alston, & Corsellis, 1980, Blinkov & Glezer, 1968) and contains a staggering 176,000 km of myelinated axons (Marner, Nyengaard, Tang, & Pakkenberg, 2003). Smooth flow of neural impulses throughout the brain allows for information to be integrated across the many spatially segregated and functionally specialised modules. Structural and functional maturation of neural pathways connecting individual brain regions is therefore a condition sine qua non for the successful development of cognitive, motor and sensory functions from infancy, through childhood and adolescence, and into adulthood. Depending on the spatial scale, one can distinguish ‘‘local” and ‘‘distal” connectivity. The former refers to neuronal interactions occurring within a small space of several cubic millimetres, such as those involving communication between neurons residing in different layers of the same cortical column or between neurons located in neighbouring columns (Douglas & Martin, 2007). The latter relates to long-range projections leaving specific cortical or subcortical regions and entering other regions some distance away. Current techniques available for in vivo imaging of the human brain possess spatial resolution allowing one to study only the long-range connectivity. Depending on the particular imaging modality used, such studies can provide insights into either functional or structural connectivity and its development (reviewed in Paus, 2007). Given the importance of structural properties of white matter as a vehicle of * Address: Brain and Body Centre, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom. Fax: +44 115 8468274. E-mail address: [email protected] 0278-2626/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.bandc.2009.06.002

long-range connectivity, here I focus on maturational changes occurring in this brain compartment during adolescence. 2. White matter: volumes The initial MR studies of brain development, from birth to young adulthood, clearly showed age-related increases in both global and regional volumes of white matter (reviewed in Paus et al., 2001). In most such volumetric studies, brain tissue is classified into grey- and white-matter (WM) voxels in T1-weighted images and, subsequently, WM voxels are summed across broadly defined brain ‘‘compartments” (e.g. ‘‘cortical” or ‘‘lobar” WM) or a well-defined WM structure (e.g. corpus callosum). Using data acquired in the largest MR study of brain development to date (n = 897 scans obtained in 601 individuals; 5–25 years of age), Lenroot et al. (2007) described a quadratic age-related growth of WM volumes constituting the four cerebral lobes and a linear growth of the corpus callosum. The WM growth showed a clear sexual dimorphism in that it was steeper in boys compared with girls. We have replicated the latter finding in a large adolescent sample (n = 408, age 12–18 years) and shown that it can be, in part, attributed to the rise of testosterone levels, especially in male adolescents with an ‘‘efficient” (vis-à-vis transcriptional activity) variant of the androgen-receptor gene (Fig. 1; Perrin et al., 2008). 3. White matter: densities In the mid 1990s, several authors introduced voxel-wise approaches to analyse regional differences in grey- and white-matter

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Fig. 1. Relationship between bioavailable testosterone and relative volume of white matter in male adolescents with different variants of androgen-receptor (AR) gene. Long and short AR gene refers to the relatively high and low number of CAG repeats in Exon 1, which is believed to be inversely proportional to the AR transcriptional activity. Reprinted with permission from Perrin et al. (2008).

‘‘density” in adult patients with schizophrenia (Andreasen et al., 1994; Wright et al., 1995). We had applied a similar approach to evaluate age-related changes in WM density in typically developing children and adolescents and had found that WM density increases between the age of 4 and 17 years in the putative cortico-spinal tract (CST) and the left arcuate fasciculus (Paus et al., 1999). The voxelbased analyses of WM (or grey-matter) densities involve several steps, including: (a) non-linear registration of T1-weighted images to the template brain (e.g. MNI305); (b) classification of the brain tissue into grey and white matter; (c) blurring the binary WM or GM maps to create density maps with values of WM/GM ‘‘densities” varying on a continuum between 0 and 1; and (d) testing the statistical relationship between a variable of interest (e.g. age or sex) and WM/GM density voxel-wise, using a general linear model (with appropriate corrections for multiple comparisons). Using this approach in our adolescent sample (12–18 years of age), we have recently described a clear sex difference in WM density in the putative CST (Females > Males); as shown in Fig. 2, this sex difference arises from a qualitative difference in the relationship between age and WM density in males and females, respectively (Perrin et al., 2009). Rising levels of testosterone appear to contribute to the observed decrease in WM density during male adolescence (Herve et al., in press). We will come back to this issue in the final section on possible cellular mechanisms underlying changes in white matter during adolescence. As shown by Barnea-Goraly et al. (2005), white-matter density is likely to correlate with regional measures of white-matter properties obtained with other MR techniques, such as diffusion tensor imaging. I will now provide a brief overview of this MR approach, as well as other methods allowing one to investigate structural properties of white matter, or its ‘‘microstructure”. 4. White matter: microstructure The introduction of diffusion tensor imaging (DTI) in the mid 1990s opened up new avenues for in vivo studies of white-matter

Fig. 2. White-matter density in the (left) cortico-spinal tract plotted as a function of age in female and male adolescents. Reprinted with permission from Perrin et al. (2009).

microstructure (Le Bihan & Basser, 1995). This imaging technique allows one to estimate several parameters of water diffusion in live tissue, such as mean diffusivity (MD) and fractional anisotropy (FA). The latter parameter reflects the degree of directionality of water diffusion; voxels containing water moving predominantly along a single direction have higher FA. In white matter, FA is believed to depend on the microstructural features of fibre tracts, including the relative alignment of individual axons, their packing ‘‘density” (which affects the amount of interstitial water), and myelin content. But the hypothesised differences in myelination are perhaps too hastily considered as an explanation for age-related differences in FA, to the exclusion of other possible factors. Using the short T2-component signal to estimate myelin–water fraction, a recent study carried out in adult participants found a significant correlation between this measure of myelin content and FA across (1.5 T: r2 = 0.39; 3 T: r2 = 0.56) but not within various brain regions (Mädler, Drabycz, Kolind, Whittall, & MacKay, 2008). The latter finding raises the possibility that inter-individual variations in FA values observed in developmental studies in a particular brain region, such as the corpus callosum (Fig. 3), may not be related to myelination. After acquiring DTI data throughout the brain, values of FA and MD can be assessed in a number of ways. For example, one can average FA/MD values across all voxels constituting WM in the four cerebral lobes or that of major fibre tracts (e.g. Lebel, Walker, Leemans, Phillips, & Beaulieu, 2008). It is also possible to evaluate variations in FA/MD throughout the brain on a voxel-wise basis using, for example, tract-based spatial statistics (Smith et al., 2006). Several studies have described age-related increases and decreases in FA and MD, respectively (reviewed in Cascio, Gerig, & Piven, 2007). In one of the largest studies to date, Lebel et al. (2008; n = 202, 5–30 years) described maturational trajectories of FA and MD that differed significantly across various fibre tracts (Fig. 4).

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Fig. 3. Comparison of DTI-based values of fractional anisotropy and the short T2-based estimates of myelin-water fraction in 11 white-matter and grey-matter structures. (A) 1.5 T data obtained in 16 adult subjects (168 ROIs); (B) 3.0 T data obtained in six adult subjects (separate accounting for contra-lateral ROIs). Reprinted with permission from Mädler et al. (2008).

Others pointed out sex differences in developmental trajectories of FA during childhood and adolescence in certain fibre tracts, such as the arcuate fasciculus (Schmithorst, Holland, & Dardzinski, 2008; n = 106, 6–20 years; FA increasing in girls but decreasing in boys; no such difference for MD). Although FA and MD usually show an inverse relationship, this is far from uniform across the developing brain. For example, in the same group of children and adolescents (n = 31, 6–17 years), some fibre tracts (e.g. the inferior fronto-occipital fasciculus) showed concomitant age-related increase/decrease in FA/MD while other tracts (e.g. CST) showed only a decrease in MD without any change in FA (Eluvathingal, Hasan, Kramer, Fletcher, & Ewing-Cobbs, 2007). The above examples illustrate both the value of DTI-based studies of WM maturation during adolescence, as well as challenges related to their interpretation.

Diffusion tensor imaging is not the only method allowing one to study white-matter microstructure. As mentioned above, the short T2-component signal fraction provides highly specific estimates of myelin-associated water (Mädler et al., 2008). To our knowledge, however, this technique has not been used yet in a developmental context. Regular T2-relaxometry has been used in the past to show, for example, age-related increases of T2 values in the splenium but not the genu of the corpus callosum (Kim et al., 2007; n = 33, 3– 15 years). The same authors also calculated a genu-to-splenium ratio of MR signal from T2-weighted images and showed its gradual decrease between 3 and 16 years of age, with no changes thereafter (Kim et al., 2007; n  180, 3–20 years of age). Given that several studies found little age-related change during adolescence in the volume or FA values in the genu of the corpus callosum, the above

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Fig. 4. Magnitude and timing of developmental changes in fractional anisotropy (a) and mean diffusivity (b). Reprinted with permission from Lebel et al. (2008).

‘‘genu-to-splenium” approach is worth exploring in large-scale studies that have collected T2-weighted images. Magnetisation-transfer (MT) imaging is another MR technique employed in studies of the structural properties of white matter. This type of imaging is used most often in patients with neurological disorders, such as multiple sclerosis (Filippi & Rocca, 2007). Contrast in MT images reflects the interaction between free water and water bound to macromolecules (McGowan, 1999); the macromolecules of myelin are the dominant source of the MT signal in white matter (Kucharczyk, Macdonald, Stanisz, & Henkelman, 1994). This interpretation of MT ratio (MTR) is supported by post-mortem data that revealed a significant positive correlation

between myelin content and MTR (Schmierer, Scaravilli, Altmann, Barker, & Miller, 2004; Schmierer et al., 2008). Magnetisationtransfer data are acquired using a dual acquisition with and without an MT saturation pulse; MTR images are calculated as the percent signal-change between the two acquisitions (Pike, 1996). Mean MTR values can subsequently be summed across all WM voxels constituting, for example, the four lobes of the brain. Using this approach, we have demonstrated that MTR decreases with age during male but not female adolescence (Fig. 5; Perrin et al., 2008, 2009; n = 404, 12–18 years). This finding argues against age-related changes in myelination driving the observed increases in WM volumes during male adolescence described in the same sample

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(see Fig. 1). We will come back to this issue in the section on possible cellular mechanisms mediating changes in white matter during adolescence. 5. White matter: brain function in health and disease As pointed out in the opening paragraph, white matter is essential for the smooth flow of information in functional networks. Frank disruptions of WM pathways due to stroke, for example, lead to ‘‘disconnection” syndromes with cognitive/behavioural manifestations corresponding to the site of disruption (Geschwind, 1965). More recently, a functional variant of the disconnection concept has been introduced in the context of psychiatric disorders, especially schizophrenia (Friston & Frith, 1995). Although functional

‘‘disconnection” can be related to disruptions occurring at many levels (including the synapse), here we ask whether subtle variations in cognitive and behavioural traits, observed during typical development or in patients with psychiatric disorders, are associated with subtle variations in structural properties of longdistance connections: that is, with variations in white-matter microstructure. Using FA as an index of WM maturational state in typically developing children and adolescents, several groups have reported a positive relationship between various cognitive skills and this measure in different fibre tracts (e.g. Ashtari, Cervellione, et al., 2007; Ashtari, Cottone, et al., 2007 [arcuate fasciculus and the score on the information subtest of WISC/WAIS]; Fryer et al., 2008 [splenium and reading]; Muetzel et al., 2008 [corpus callo-

Fig. 5. Values of magnetisation-transfer ratio (MTR) and white matter constituting the frontal (A), parietal (B), temporal (C) and occipital (D) white matter plotted as a function of age in female and male adolescents. Reprinted with permission from Perrin et al. (2009).

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sum and bimanual coordination]; Mabbott, Noseworthy, Bouffet, Laughlin, & Rockel, 2006 [right frontal WM and visuo-spatial search]; Nagy, Westerberg, & Klingberg, 2004 [genu of the corpus callosum and working memory]; Schmithorst, Wilke, Dardzinski, & Holland, 2005 [multiple tracts/WM regions and full-scale IQ]; Olson et al., 2009 [multiple WM tracts and delay discounting]). It should be noted, however, that only in some of these reports did FA correlate with behaviour above and beyond the effect of age. Using maps of tissue density, we have shown a significant, albeit subtle, relationship between hemispheric asymmetry in the putative cortico-spinal tract and left–right asymmetry in tapping speed during male adolescence (Herve et al., in press). Taken as a whole, the above findings confirm the importance of the structural integrity of WM vis-à-vis cognitive abilities in typically developing children and adolescents. Although this structure–function relationship is often quite subtle in healthy individuals, disruptions or delays in the maturation of WM may have more pronounced consequences. What are the differences in white matter between typically developing adolescents and those with psychiatric disorders? The brain of patients with attention-deficit hyperactivity disorder (ADHD), one of the most common psychiatric disorders of childhood and adolescence, presents with a number of structural deviations from the typically developing brain. These include differences in the volume of WM in the frontal lobe and the corpus callosum (Krain & Castellanos, 2006) and in WM microstructure. Using DTI in relatively small samples (<20 patients), several groups also reported lower FA values in different WM structures in patients with ADHD, as compared with healthy subjects (Ashtari et al., 2005 [cerebral and cerebellar peduncles]; Hamilton et al., 2008 [cortico-spinal tract and superior longitudinal fasciculus], but Silk, Vance, Rinehart, Bradshaw, & Cunnington, 2008 [higher FA in lobar WM]). Similarly, lower FA was found in the white matter of patients (typically adolescent) with early-onset schizophrenia (Ashtari, Cervellione, et al., 2007; Ashtari, Cottone, et al., 2007 [inferior longitudinal fasciculus]; Douaud et al., 2007 [corpus callosum, arcuate fasciculus]; Kumra et al., 2004 [frontal and occipital WM]; Kyriakopoulos, Vyas, Barker, Chitnis, & Frangou, 2008

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[cerebellar peduncles, parietal lobe] but see Price, Bagary, Cercignani, Altmann, & Ron, 2005 for negative findings in the corpus callosum), as well as in children and adolescents with bipolar disorder (Frazier et al., 2007 [frontal WM, corpus callosum]). Furthermore, a slower growth of white matter in patients with childhood-onset schizophrenia was documented using tensor-based morphometry (Gogtay et al., 2008). Although the above reports indicate the presence of ‘‘abnormalities” in white matter in children and adolescents with a number of psychiatric disorders, caution should be exercised regarding their interpretation. The sample sizes are relatively small in the majority of these studies, the apparent focality may – in some cases – be simply related to statistical power in voxel-wise analyses and, most importantly, the biological meaning of these findings is unclear. We will now proceed to discuss the latter issue. 6. White matter: axon and myelin The total volume of white matter is determined by the number of axons, their calibre and the thickness of myelin sheath produced by the oligodendrocytes. Given the known elimination of axons in the early post-natal period (e.g. LaMantia & Rakic, 1990, 1994), age-related increases in the volume of white matter during brain development can be accounted for by increases in axonal calibre and/or thickness of the myelin sheath. Most but not all axons in the adult human brain have a sheath of insulation produced by oligodendrocytes and consisting chiefly of a lipo-protein called myelin; the multilayered structure of the myelin sheath is depicted in Fig. 6. Past research in experimental animals (e.g. LaMantia & Rakic, 1990, 1994) and post-mortem human brains (e.g. Benes, Turtle, Khan, & Farol, 1994) clearly established that myelination continues throughout childhood, adolescence and young adulthood. This process is most likely guided not only by a genetic timetable but also by experience (Fields, 2008). As pointed out above, however, the majority of in vivo MR studies of white matter, whether volumetric or DTI-based, do not pos-

Fig. 6. Myelin sheath surrounding an axon with inset depicting a close up of bilayer, including myelin basic protein (MBP), proteolipid protein (PLP), cyclic nucleotide phosphodiesterase (CNP), and myelin-associated glycoprotein (MAG). Reprinted with permission from Laule et al. (2007).

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sess the required specificity to allow for interpretations invoking solely variations in myelin content. We argue that both axonal calibre and myelin thickness should be considered. This argument arose from our recent findings that showed clear sexual dimorphism in the maturation of white matter during adolescence. During male adolescence only, we observed three phenomena: (1) an increase in white-matter volume; (2) a decrease in MTR in white matter; and (3) a decrease in the ‘‘whiteness” of the cortico-spinal tracts. Taken together, we believe that the most plausible explanation of these findings is a relative increase in axonal diameter or, more specifically, an increase in g ratio (see below). Given the relative paucity of discussion on axon rather than myelin when interpreting MR-based developmental studies, we will focus here on the axon and provide basic facts concerning the cytoskeleton, pointing out mechanisms involved in the control of axonal calibre, and presenting information about a key function of the axon, namely axonal transport. 7. Axonal calibre, cytoskeleton, and axonal transport Fig. 7 (LaMantia & Rakic, 1990) illustrates the wide variations in axonal morphology vis-à-vis axonal calibre and the thickness of myelin sheath. In the monkey corpus callosum (CC), unmyelinated axons, accounting for about 30% of all axons in the genu and less than 10% elsewhere in the CC, have a small calibre (0.1 lm), whereas the internal diameter of myelinated axons varies between 0.1 and 2.5 lm, with the largest axons (2.5 lm) found in the CC midbody (LaMantia & Rakic, 1990; see also Aboitiz, Scheibel, Fisher, & Zaidel, 1992 for similar findings in the human CC). The largest axons in the human brain are found in the internal capsule and, most likely, correspond to the cortico-spinal neurons (Lassek, 1942; Verhaart, 1950); the size of these axons increases from birth (1 lm), through childhood (12 lm at seven years of age) into adulthood (24 lm). These numbers correspond to the external diameter (i.e. calibre + myelin sheath) and, as such, reflect both the radial growth and increased myelination of cortico-spinal axons with age. In general, axons of large calibre have a thick myelin sheath. It turns out that the ratio between the internal (=calibre) and external (calibre + myelin sheath) diameter of an axon, the so-called g ratio, is relatively stable (0.65; Rushton, 1951). This observation, together with the small calibre of unmyelinated axons, suggests possible coupling between myelination and axonal calibre (see be-

low). But the range of g ratio varies considerably across axons of the same fibre tract (Berthold, Nilsson, & Rydmark, 1983), as a function of age (Berthold et al., 1983; Jeronimo, Jeronimo, Rodrigues Filho, Sanada, & Fazan, 2008), mouse strain (Little & Heath, 1994) or disease (Friede & Beuche, 1985). Importantly, g ratio appears to increase as a function of axon diameter, indicating a relatively thinner myelin sheath in large axons (Berthold et al., 1983; Chatzopoulou et al., 2008; Gillespie & Stein, 1983). Note, however, that most of the above morphometric studies of g ratio were carried out in peripheral nerves. A subtle shift in the g ratio could change the proportion of tissue occupied by axons and myelin, respectively. But whether or not the g ratio differs between males and females is unknown. The axonal cytoskeleton consists of neurofilaments (NF) and microtubules (MT), the former outnumbering the latter 5–10 times (Lee & Cleveland, 1996). Neurofilaments support the cylindrical structure of an axon and, as such, protect the bore from compressive stress, securing its unobstructed state (Kumar, Yin, Trapp, Paulaitis, & Hoh, 2002). Axonal calibre is influenced both by the number of NF and their spacing (Hoffman, Griffin, & Price, 1984; Hoffman et al., 1987; Kumar et al., 2002). The former is regulated by NF synthesis (gene expression) and the amount of NF undergoing (slow) axonal transport in an anterograde direction (Hoffman et al., 1984, 1987). The latter depends on the phosphorylation of NF sidearms, which leads to their expansion (perpendicular to the NF core oriented along the axon long-axis) and subsequent increase in interfilament distance (e.g. Garcia et al., 2003; Kumar et al., 2002). This process appears to be regulated by a protein synthesized by oligodendrocytes, namely myelin-associated glycoprotein (MAG); this ‘‘outside-in” signalling pathway provides a cellular mechanism for the coupling between myelination and axonal calibre (Garcia et al., 2003; Yin et al., 1998). Theoretically, sex differences in g ratio could emerge either through differences in the rate of synthesis and/or axonal transport of NF, or the rate of phosphorylation of the NF sidearms. For example, an amplification of the MAG-initiated cascade in males, as compared with females could result in a larger increase in axonal calibre per unit of added myelin and, hence, a higher g ratio. What are the possible functional consequences of variations in axonal calibre? First of all, it is well known that both axonal calibre and the thickness of the myelin sheath determine conduction velocity (Gillespie & Stein, 1983; Minwegen & Friede, 1984; Waxman, 1980). But, as mentioned above, axonal cytoskeleton also pro-

Fig. 7. Electron micrograms of axons in the monkey corpus callosum. Note wide variations in the axonal calibre and myelin thickness. (A) Asterisks indicate small unmyelinated axons. (B) A large myelinated axon in the midbody of the corpus callosum. Same magnification (8500) for both micrographs. Reprinted with permission from LaMantia and Rakic (1990).

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vides the necessary ‘‘infrastructure” for unhindered transport of various cargoes between the cell body and the synapse (Goldstein, Wang, & Schwarz, 2008; Grafstein & Forman, 1980; Mandelkow & Mandelkow, 2002; Shah & Cleveland, 2002). In this way, cytoskeleton (i.e. NF and MT) and motor proteins are essential contributors to a large number of cellular processes, such as cell metabolism (e.g. transport of mitochondria and glycolytic enzymes) and neurotransmission (e.g. transport of synaptic-vesicle precursors). Cargoes are moved along MT ‘‘tracts” either at slow (1 mm/day) or fast (100 mm/day) rates (Mandelkow & Mandelkow, 2002; Shah & Cleveland, 2002). Slow axonal transport mainly moves elements of axonal cytoskeleton and cytosolic proteins (Barry, Millecamps, Julien, & Garcia, 2007), while cargoes necessary for synaptic activity are moved at fast rates (Goldstein et al., 2008; Grafstein & Forman, 1980). The slow rates are the result of intermittent pausing in the motion rather than a slower speed of the motors. In fact, motor proteins that generate the motion are the same for both types of transport, namely proteins of kinesin superfamily (45+ kinesins) and dyneins (Mandelkow & Mandelkow, 2002; Shah & Cleveland, 2002; Shea & Flanagan, 2001; Shea, Jung, & Pant, 2003). Typically, motor domains (‘‘heads”) of a kinesin bind to a microtubule and ‘‘walk” along it while hydrolysing ATP. Light chains located on the opposite end of a kinesin contain ‘‘docking” elements that can bind various cargoes (see Fig. 8). Affinity of a cargo to the motor protein may be one of the mechanisms regulating axonal transport by determining the ‘‘duty cycle”, i.e. the time spent moving vs. motionless (Shah & Cleveland, 2002). Could the number and/or spatial arrangement of NF and MT also influence the rate of axonal transport? An interesting observation made recently suggests that there is a link between axonal calibre and the amount of cargo transported by an axon. Murayama, Weber, Saleem, Augath, and Logothetis (2006) showed that the rate of in vivo transport of Mn++ from the eye to the lateral geniculate nucleus of the monkey is faster in the magnocellular than parvocellular pathway, which differ in axonal calibre; Mn++ transport depends, at least in part, on kinesin-based processes (Bearer et al., 2007). In summary, axonal calibre represents one of the cellular elements undergoing changes during brain development. Axonal

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cytoskeleton may be a target of androgens and, as such, a possible substrate of the known sexual dimorphism in the maturation of white matter during puberty. There is limited evidence supporting this notion. Neuropathy is one of the key symptoms of Kennedy’s disease (or X-linked spinal and bulbar muscular atrophy), a condition caused by an expanded polyglutamine (polyQ) stretch in the androgen receptor [AR] (Brooks & Fischbeck, 1995). This effect might be mediated by a PolyQ-AR induced inhibition of kinesinmediated axonal transport (Morfini et al., 2006) or a decrease in the expression of dynactin 1, a motor for retrograde axonal transport (Katsuno et al., 2006). Initial in vitro studies also suggest that testosterone up-regulates a- and b-tubulin, the building blocks of microtubules (Butler, Leigh, & Gallo, 2001). Together, these observations point to the possible role of AR and testosterone in the cellular processes underlying axonal transport and, by extension, in the regulation of axonal calibre. Given the role of cytoskeleton in axonal transport, such hypothesised effects of androgens on the axon could also have a multitude of downstream consequences related, for example, to neurotransmission and cell metabolism. Finally, together with the thickness of the myelin sheath, changes in axonal calibre will also affect conduction velocity and, in turn, the synchronicity of synaptic events occurring in spatially segregated brain regions. 8. Conclusions and future directions It is clear that white matter continues to mature during adolescence. At a gross-morphological level, many MRI studies revealed age-related changes in a number of structural features of white matter, including its volume, ‘‘density”, fractional anisotropy and magnetisation-transfer ratio. Some of these age effects show significant sexual dimorphism indicating possible differential role of androgens and estrogens in WM maturation during puberty. Although the cellular mechanisms underlying such changes are largely unknown, both myelin and axon should be considered when interpreting results of MR-based developmental studies of white matter. The ratio between the axon diameter and fibre diameter, namely g ratio, may be a useful metrics in this context. We

Fig. 8. Kinesin domain structure and associated proteins. Reprinted with permission from Mandelkow and Mandelkow (2002).

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speculate that g ratio increases during male adolescence while it remains the same during female adolescence. Future work should consider various avenues for increasing the specificity of MR-based studies of white matter vis-à-vis axon and myelin and validating the new (and old) approaches in experimental models. It is likely that a combination of several MR sequences in the same individual may be necessary to achieve such an increased specificity. For example, myelin-sensitive sequences, such as MTR and ultra-short T2, are likely to provide good estimates of myelin fraction. Imaging slow intra-cellular diffusion of water, using diffusion-weighted imaging at high b-factor, might be a way to estimate axonal fraction. On the other hand, it is not likely that DTI-based measures of axial and radial diffusivity of water along and perpendicular to the axons, respectively, are specific enough to allow one to distinguish axon- and myelin-related processes. Although radial diffusivity is clearly lower in unmyelinated than myelinated WM of the shiverer mouse lacking the myelin basic protein (Ou, Sun, Liang, Song, & Gochberg, 2009), this and similar findings should be interpreted with caution. As demonstrated elegantly with real-time iontophoretic method using tetramethylammonium, which does not cross cellular membranes and therefore diffuses only in the extracellular space, it is the barrier of a cylindrical object that forces ions ‘‘to go around” and, consequently, slows their radial but not axial diffusion (Vorísek & Syková, 1997). Given that it is the size of the object rather than its composition (reflected, for example, in g ratio), both increases in myelin thickness and axon diameter would likely decrease radial diffusion. Only combination of in vivo and ex vivo methods would allow us to move forward in dissecting cellular processes underlying maturation of white matter in experimental models and, in turn, using this knowledge when interpreting findings obtained with multi-modal MRI in children and adolescents. Both myelin and axon are likely to influence the smooth flow of information across functional networks, the latter not only through conduction velocity but also via axonal transport of certain cellular elements of the neurotransmission-related machinery. Therefore, both the axon and its myelin sheath should be considered in studies of typical and atypical maturations of white matter during adolescence. Acknowledgments The author’s work is supported by the Canadian Institutes of Health Research, the Royal Society (United Kingdom), and the National Institutes of Health (United States). I am grateful to my collaborators, students, and research fellows for their intellectual contributions and stimulating discussions. In particular, the work on the maturation of white matter during adolescence benefited significantly from the many contributions made by Jennifer Perrin and Pierre-Yves Hervé. Finally, I express thanks to Dr. Brenda Milner for reviewing a draft of this manuscript. References Aboitiz, F., Scheibel, A. B., Fisher, R. S., & Zaidel, E. (1992). Fiber composition of the human corpus callosum. Brain Research, 598, 143–153. Andreasen, N. C., Arndt, S., Swayze, V., 2nd, Cizadlo, T., Flaum, M., O’Leary, D., et al. (1994). Thalamic abnormalities in schizophrenia visualized through magnetic resonance image averaging. Science, 266, 294–298. Ashtari, M., Cervellione, K. L., Hasan, K. M., Wu, J., McIlree, C., Kester, H., et al. (2007). White matter development during late adolescence in healthy males: A crosssectional diffusion tensor imaging study. Neuroimage, 35, 501–510. Ashtari, M., Cottone, J., Ardekani, B. A., Cervellione, K., Szeszko, P. R., Wu, J., et al. (2007). Disruption of white matter integrity in the inferior longitudinal fasciculus in adolescents with schizophrenia as revealed by fiber tractography. Archives of General Psychiatry, 64, 1270–1280. Ashtari, M., Kumra, S., Bhaskar, S. L., Clarke, T., Thaden, E., Cervellione, K. L., et al. (2005). Attention-deficit/hyperactivity disorder: A preliminary diffusion tensor imaging study. Biological Psychiatry, 57, 448–455.

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