Maturation of white matter in the human brain: a review of magnetic resonance studies

Brain Research Bulletin, Vol. 54, No. 3, pp. 255–266, 2001 Copyright © 2001 Elsevier Science Inc. Printed in the USA. All rights reserved 0361-9230/01/$–see front matter

PII S0361-9230(00)00434-2

Maturation of white matter in the human brain: A review of magnetic resonance studies T. Paus,* D. L. Collins, A. C. Evans, G. Leonard, B. Pike and A. Zijdenbos Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada [Accepted 26 October 2000] ABSTRACT: This review focuses on the maturation of brain white-matter, as revealed by magnetic resonance (MR) imaging carried out in healthy subjects. The review begins with a brief description of the nature of the MR signal and its possible biological underpinnings, and proceeds with a description of MR findings obtained in newborns, infants, children and adolescents. On MR images, a significant decrease in water content leads to a decrease of longitudinal relaxation times (T1) and transverse relaxation times (T2) and consequent “adult-like” appearance of T1-weighted and T2-weighted images becomes evident towards the end of the first year of life. Owing to the onset of myelination and the related increase of lipid content, MR images gradually acquire an exquisite grey-white matter contrast in a temporal sequence reflecting the time course of myelination. Albeit less pronounced, age-related changes in white matter continue during childhood and adolescence; white matter increases its overall volume and becomes more myelinated in a region-specific fashion. Detection of more subtle changes during this “late” phase of brain development is greatly aided by computational analyses of MR images. The review also briefly outlines future directions, including the use of novel MR techniques such as diffusion tensor imaging and magnetization transfer, as well as the suggestion for the concurrent use of experimental behavioral test-batteries, with structural MR imaging, to study developmental changes in structure-function relationships. © 2001 Elsevier Science Inc.

and with the combination of transcranial magnetic stimulation (TMS) and brain imaging (reviewed in [63]). In the child, studies of functional interactions are in their infancy. With the exception of age-related changes in EEG coherence (e.g., [81]) and in TMS-derived cortico-spinal [25,56,61] and transcallosal [37,74] conduction times, very little is known about inter-regional communication in the developing human brain. Because the smooth flow of information depends, to a great extent, on the structural properties of connecting pathways, MRI investigations of anatomic maturation in major fiber tracts provide information essential for the understanding of functional interactions in the developing human brain. Age-related changes in MR signals reflect the effects of a variety of biological factors. To facilitate the interpretation of the MR findings, we begin this review with a description of the nature of the MR signal and its possible biological underpinnings. We proceed with reviewing MR findings obtained in newborns and infants, followed by a review of MR findings obtained in children and adolescents. Finally future directions are outlined, including the use of diffusion tensor imaging to reveal major fiber tracts and the concurrent use of experimental behavioral test-batteries and structural MRI to study developmental changes in structure-function relationships. PRINCIPLES OF MRI

KEY WORDS: Children, Development, Connectivity, Cognition, Corpus Callosum, Myelination.

Nuclei that have an odd number of nucleons (protons and neutrons) possess both a magnetic moment and angular momentum (or spin). In the presence of an external magnetic field such nuclei precess (or wobble) around their axis at a rate proportional to the strength of the magnetic field, emitting electromagnetic energy in the process. The hydrogen atom contains only a single proton, and therefore precesses when exposed to a magnetic field. In the majority of MR imaging studies, precessing nuclei of hydrogen associated with water and fat are the source of signal. The signal is generated and measured in the following way. First, the subject is exposed to a large static magnetic field (B0) that preferentially aligns hydrogen nuclei along the direction of the applied field. In clinical scanners, the strength of B0 is most often 1.5 Tesla (T) (or 15,000 Gauss; for comparison, the Earth’s magnetic field is about 0.5 Gauss), and it is oriented horizontally pointing from head to toe along the long axis of the cylindrical magnet. Second, a pulse of electromagnetic energy is applied at a

INTRODUCTION The non-invasive nature of magnetic resonance imaging (MRI) has opened unique opportunities for in vivo investigation of the developing human brain. In the past decade, significant progress has been made in delineating changes in brain morphology, including those in grey and white matter, from birth to adolescence. In this review, we focus on MRI findings, obtained in healthy subjects, pertinent to the maturation of brain white matter. This emphasis is motivated by the interest in studies of functional inter-regional interactions, or functional connectivity, in the adult and developing human brain. In the adult, functional interactions are studied with a variety of tools, including positron emission tomography [27,53, 65], functional MRI [9,28], electroencephalography (EEG) [31],

* Address for correspondence: Toma´sˇ Paus, M.D., Ph.D. Montreal Neurological Institute, 3801 University Street, Montreal, Quebec, Canada, H3A 2B4. Fax: ⫹1-(514)-398-1338; E-mail: [email protected]

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FIG. 1. Illustration of longitudinal relaxation time (T1) and transverse relaxation time (T2). Radio-frequency (RF) excitations are applied with a repetition time (TR) (top) with each excitation producing a magnetic resonance signal (Mxy) that exponentially decays with a time constant T2 (bottom right). Between excitations, the magnetization also exponentially returns to its equilibrium state, pointing along the direction of the main magnetic field, with a time constant T1 (bottom left). By manipulating the TR and TE (time delay after excitation before the signal is measured; bottom right) the relative contributions of T1 and T2 can be manipulated to produce T1-, and T2-weighted images. If the effects of both T1 and T2 are minimised (long TR and short TE) the remaining contrast is simply the proton density weighted.

specific radiofrequency (RF) with an RF coil placed around (or near) the head. The frequency is selected to be the same as the frequency of precession of the imaged nuclei at a given strength of B0; for hydrogen, this “resonant” frequency is about 64 MHz at 1.5 T. The RF pulse rotates the precessing nuclei away from their axes, thus allowing one to measure, with a receiver coil, the time it takes for the nuclei to “relax” back to their original position pointing along B0. The spatial origin of the signal is determined using subtle position-related changes in B0 induced by gradient coils (these generate the knocking noise heard during scanning). Contrast in structural MR images is based on local differences in proton density, i.e., in the number of hydrogen nuclei per unit of tissue volume, or in either of the following two relaxation times: (1) longitudinal relaxation time (T1); and (2) transverse relaxation time (T2) (Fig. 1). T1 represents an exponential recovery of the total magnetization with time, and depends in a complex manner on the local structural pattern (lattice) assumed by the hydrogen nuclei; in brain, the more structured the tissue, the shorter T1. Due to local magnetic-field inhomogeneities, individual hydrogen nuclei precess at slightly different rates leading to their magnetic moments eventually pointing in different directions, with a concomitant decay of the total MR signal. T2 reflects the local rate of such “de-phasing” within a population of nuclei; in brain, the more structured the tissue, the more rapid the de-phasing and hence the shorter the T2. Quantitative measurements of T1 and T2 relaxation times are not carried out frequently, however. In the majority of MR studies, some combination of Proton Density-, T1- and/or T2-weighted images are acquired instead. In these acquisitions, MR signal is repeatedly measured (with a repetition time [TR]) at one time-point (so-called time-to-echo [TE]) after the application of each RF pulse. Local differences in relaxation times are reflected in the image contrast simply because, at a given TR/TE combination, the MR signal has already recovered (T1) or decayed (T2) more in regions with short T1 (or T2) and vice versa. For this reason, tissue with short T1 (white matter) shows high MR signal and appears bright on T1-weighted images, whereas tissue with

long T2 (grey matter) shows high signal and is bright on T2weighted images. While proton density reflects the amount of signal-emitting nuclei present in the tissue (primarily water), relaxation times and therefore tissue “brightness” on T1- and T2-weighted images depend on a variety of biological and structural properties of the brain tissue, all of which vary as a function of age. The most important factors are described below but it should be emphasised that the relationship between the composition and microstructure of the tissue and MR relaxation times is complex and still not fully understood. Content of water is one of the most important determinants influencing T1 in the brain; to simplify, the more water there is in a given tissue compartment, the longer T1 and the lower the signal on a T1-weighted image will be in that compartment. In the adult brain, T1 is the longest in cerebrospinal fluid, intermediate in grey matter, and the shortest in white matter. Content of [hydrophobic] lipids in white matter may also influence T1 through magnetic interactions with hydrogen nuclei of the lipids. Content of iron is another important factor influencing primarily local magnetic inhomogeneities; the higher the content of iron, the shorter T2. Finally, the anatomic arrangement of axons may influence the amount of interstitial water and, in turn, T1 values; more tightly bundled axons would have shorter T1 and therefore appear brighter on T1-weighted images. MRI IN NEWBORNS AND INFANTS (0 – 4 YEARS) The majority of early MR studies provide qualitative descriptions of T1- and/or T2-weighted images, focusing on the greywhite matter contrast and degree of myelination [3,4,7,14,23,36, 47,51,52,84]. In most studies, the scans were acquired for clinical reasons and later screened to include only subjects without neurological (or MR) abnormalities; the number of subjects varied from 34 to 120. In several studies, MRIs of premature (29 to 37 post-conception weeks) newborns were also described [8,13,40, 51,52]. The images were acquired with a variety of MR sequences

MATURATION OF WHITE MATTER and on magnets of different field strength (0.3–2.35 T). Qualitative evaluation of the images by visual inspection focused on signal differentiation between the white and grey matter (i.e., grey-white differentiation) and on MR correlates of myelination; various staging schemes were employed to capture major changes over the first 12 to 24 months of life. In the case of grey-white differentiation, the following developmental patterns are typically seen: (1) the infantile pattern (⬍6 months), which shows a reversal of the normal adult pattern of MR intensities, i.e., the newborn T2-weighted image looks like the adult T1-weighted image and vice versa for T1-weighted images; (2) the isointense pattern (8 –12 months), which is characterised by poor differentiation between white matter and grey matter; and (3) the early-adult pattern (⬎12 months), where grey-matter intensity is higher than that of white matter on T2-weighted images and vice versa on T1-weighted images. The exact time of the cross over from the “infantile” to the “adult” pattern varies across different brain regions and depends on the MR sequence, with T1-weighted images acquiring the “early-adult” pattern first. Clearly, the above changes are related to changes in relaxation times. Holland et al. [39] measured T1 and T2 at B0 ⫽ 0.35 T in 37 children (0 –14 years of age) in 13 regions of interest and found that both T1 and T2 were very high during the first week of life (T1 of 1615 ⫾ 120 ms in white matter and 1580 ⫾ 43 ms in grey matter; T2 of 91 ⫾ 6 ms in white matter and 88 ⫾ 8 ms in grey matter) and reached nearly adult values during the second year of life for T1 (white matter: 505 ⫾ 55 ms and grey matter: 840 ⫾ 90 ms) and third year of life for T2 (white matter: 53 ⫾ 35 ms and grey matter: 62 ⫾ 7 ms). The switch over in relaxation times of white and grey matter was observed during the first 6 months, first for T1 and then for T2. It is generally agreed that this rapid shortening of relaxation times, observed during the first 12 months, is related to a decrease in water content in both grey and white matter. In the case of myelination, qualitative evaluation of T2weighted or T1-weighted inversion-recovery (IR) images have consistently detected the following temporal order in which different brain structures first acquire a “myelinated” appearance (Fig. 2). Myelination is first observed in the pons and cerebellar peduncles (birth), followed by the posterior limb of the internal capsule, optic radiation and the splenium of the corpus callosum (1–3 months), then the anterior limb of the internal capsule, genu of the corpus callosum (6 months) and finally the white matter of the frontal, parietal and occipital lobes (8 –12 months) [4,7,14,22, 36]. Although the above temporal sequence is consistent across studies, the actual times vary widely depending on the particular grading scheme and MR sequence employed. Nevertheless, it is important to note that structures containing axons originating in the same brain region, such as the anterior limb of the internal capsule and the genu of corpus callosum, myelinate at about the same time. In addition to the qualitative assessment of MR images, several attempts have been made to quantify changes in the volume or intensity of white matter during the first two years of life. Barkowich and Kjos [3] measured the thickness and length of the corpus callosum in 63 neurological patients with normal MR (3 days to 12 months of age) and observed significant thickening of the genu, body and splenium with age; the splenium increased in thickness the most, with a suggestion of a developmental spur between 4 and 6 months of age. In the same study, the corpus callosum appeared isointense on T1-weighted images at birth, acquiring a “myelinated” appearance first in the splenium (4 months) and then in the genu (6 months); by the age of 8 months, “all the examined patients had a corpus callosum essentially identical in configuration and intensity to that of an adult” [3]. Hayakawa et al. [36] measured an area of the centrum semiovale, containing the bulk of cortical and subcortical white-matter, as it

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FIG. 2. Stages of myelination. Inversion-recovery images (TR ⫽ 2800 or 2400 ms, TI ⫽ 600 ms, B0 ⫽ 0.6 Tesla) of infant brains acquired at the following stages of myelination (top to bottom): I (1st month), II (2nd month), III (3rd– 6th month), IV (7th–9th month) and V (⬎9th month). Reprinted from van der Knaap and Valk [84] with permission.

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was acquiring a hypointense appearance on T2-weighted images. They described an initial rapid increase in the area of the centrum semiovale during the first 3 years of life followed by a steady and significant “growth” until adulthood. Finally, regional differences in cortical myelination akin to those first described in post-mortem studies by Flechsig [26] could be sufficiently robust to aid in identification of the perirolandic cortex on T2-weighted images [44,89]. The human brain undergoes rapid growth during the first 2 years of life. On MR images, a significant decrease in water content leads to a decrease of T1 and T2 relaxation times and consequent “adult-like” appearance of T1-weighted and T2weighted images towards the end of the first year of life. Due to the onset of myelination and the related increase of lipid content, MR images gradually acquire an exquisite grey-white matter contrast in a temporal sequence that reflects the time course of myelination. The initial MR studies of the early postnatal development of the human brain have the following main limitations: (1) qualitative rather than quantitative; (2) cross-sectional rather than longitudinal and (3) no data on the concurrent behavioral development of the subjects. MRI IN CHILDREN AND ADOLESCENTS (5–18 YEARS) Brain weight reaches adult values (about 1.45 kg) between 10 and 12 years of age. The fastest growth occurs during the first 3 years of life so that by the age of 5 years the infant’s brain weighs about 90% of the adult value [19]. Clearly, changes in brain morphology in childhood and adolescence are more subtle that those in the first 4 years of life. Qualitative evaluation of MR images is of little value at this point and the ability to obtain quantitative measurements is of the essence if we are to detect brain maturation during this “late” period of brain development. Several approaches have been used to obtain such measurements, including: (1) semi-automatic or automatic classification of brain tissue and subsequent “count” of image elements classified as a particular tissue type (e.g., white matter); (2) manual outlining of a structure of interest (e.g., the corpus callosum) on an MR image and the subsequent calculation of its volume/area; (3) voxel-wise analysis of local growth using deformation fields; and (4) voxelwise statistical analysis of white matter “density”. Classification of brain tissue into grey and white matter and into cerebrospinal fluid allowed several investigators to calculate absolute and/or relative brain volume occupied by white matter. In the most extensive study to date Giedd et al. [32] reported agerelated changes in 145 children and adolescents (age 4 –20 years) in volumes of grey and white matter of the frontal, parietal, temporal and occipital lobes; the study used a cross-sectional (n ⫽ 145) and longitudinal (65/145 subjects) design. Volumes were quantified by combining a technique using an artificial neural network to classify tissues based on voxel intensity with a technique performing non-linear registration to a template brain for which the four lobes had been manually defined [16]. A significant increase in the absolute volume of white matter was found in this study, with the volume increasing steadily across ages 4 –22 by about 12%; this increase was similar in the four different lobes but was steeper in boys than in girls. These recent findings are consistent with the previous observations of other authors, including the age-related increase in the cerebral white-matter/grey-matter ratio ([41]; 8 –10 years, n ⫽ 9, vs. 25–39 years, n ⫽ 15); the increase in absolute and relative volumes of “cortical” white matter ([69]; 3 months–30 years, n ⫽ 88); and increase in the relative volume of cerebral white-matter ([73]; 5–17 years, n ⫽ 85). In the Pfefferbaum et al. [69] cross-sectional study, cortical

FIG. 3. Volumes of cortical grey and white matter. Absolute volumes of cortical grey (top) and white (middle) matter and their ratio (bottom) calculated for 88 healthy volunteers aged 3 months to 30 years. The “cortical” region of interest was defined as the outer 45% of each magnetic resonance imaging slice. Reprinted from Pfefferbaum et al. [69] with permission.

white-matter continued to increase from birth to about age 20 years, after which it levelled off (Fig. 3). In the majority of the above studies, age-related increases in white-matter volume are accompanied by decreases in grey-matter volume. This observation is best illustrated in a study by Steen et al. ([79]; 4 –10 years, n ⫽ 19, 10 –20 years, n ⫽ 31, 20 –30 years, n ⫽ 20) who measured T1 relaxation times and used these to classify all voxels into those containing either “pure” grey or white matter. As can be seen in Fig. 4, the number of white-matter and grey-matter voxels increased and decreased with age, respectively,

MATURATION OF WHITE MATTER

FIG. 4. Longitudinal relaxation times (T1) and tissue classification. Top: T1 relaxation times measured in frontal white-matter in 70 subjects aged 4 to 30 years (B0 ⫽ 1.5 Tesla). Middle: Absolute number of brain pixels defined as pure white (diamonds; T1: 568 –712 ms) and grey (crosses; T1: 1160 –1440 ms) matter. Bottom: The inverse relationship between the number of grey and white-matter pixels (r ⫽ 0.64; R2 ⫽ 6.86; p ⬍ 0.0001). Reprinted from Steen et al. [79] with permission.

with the inverse relationship between the number of “white” and “grey” voxels (r ⫽ 0.64; R2 ⫽ 6.86, p ⬍ 0.0001 [79]). This finding raises the possibility that a decrease in (cortical) grey-matter volumes may, at least in part, reflect intracortical myelination and the resulting partial volume effects. The above described changes in the volume of white matter, calculated using various tissue-classification algorithms, reflect to

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FIG. 5. Area of the corpus callosum. Absolute values of the midsagittal area of the entire corpus callosum (top), the genu (middle) and the splenium (bottom) of the corpus callosum measured in 111 healthy children and adolescents. Reprinted from Giedd et al. [34] with permission.

a great extent subtle age-related changes in relaxation times. Steen et al. ([79] see above for sample size and age) measured T1 relaxation times in nine regions-of-interest (ROI), including the four placed in the white matter: (1) genu of the corpus callosum; (2) frontal white-matter; (3) optic radiation and (4) occipital whitematter. The mean values of T1 decreased from childhood (4 –10 years) to adulthood (20 –30 years) in all four white-matter structures. Significant decreases were also observed when comparing children with adolescents (10 –20 years) in all white-matter structures except the genu of the corpus callosum, and when comparing

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FIG. 6. Local growth of the corpus callosum. Six sets of deformation fields calculated for six individuals scanned twice each; in each subject, the deformation field specifies the value of local forces (expressed as “percent of local growth”) that are necessary to bring the anatomy measured at the first scan into alignment with the anatomy of the second (i.e., later) scan. Reprinted from Thompson et al. [82] with permission.

adolescents with young adults in all regions except occipital white matter. Transverse relaxation times (T2) were measured by Hassink et al. ([35]; 8 –10 years, n ⫽ 9 and 24 –25 years, n ⫽ 8) in eight ROIs, including the frontal subcortical white-matter, the corpus callosum and the genu of the internal capsule. Although limited by a small sample, significant decreases in T2 values were found from childhood to adulthood in all white-matter regions but the corpus callosum. These two quantitative studies clearly illustrate significant age-related changes in white matter, most likely caused by small but consistent increases in the degree of myelination. Volumetric assessment of age-related changes in white matter is often global rather than regional. This is due to the absence of readily detectable boundaries between different fiber tracts constituting the white matter of the four cerebral lobes. This is not the case for the corpus callosum, the largest fiber tract in the human brain interconnecting the cerebral cortex of the left and right hemispheres. The corpus callosum can be easily delineated on a mid-sagittal section and, therefore, its area can be measured with great precision. Even though the corpus callosum acquires an “adult” appearance by about the age of 1 year ([3], see above), its growth continues until early adulthood. In one of the first morphometric studies, Pujol et al. [72] measured the area of the corpus callosum in adolescents (11–19 years, n ⫽ 14), young adults (20 –29 years, n ⫽ 45) and older adults (30 – 61 years, n ⫽ 31)

using a longitudinal design; two scans were acquired in each subject with an average between-scan interval of 2 years. Not surprisingly, the largest growth in the 2-year period was observed in the youngest group (about 10% increase). But the area of the corpus callosum increased significantly even in the group of young adults (24 –25 years, about 5% increase over 2 years); no change was observed from the age of 27 years on. In a cross-sectional study, Giedd et al. ([34]; 4 –18 years, n ⫽ 114) confirmed the significant age-related increase in the total area of the corpus callosum. They were also able to demonstrate that the bulk of this growth occurs in the posterior half of the corpus callosum and, in particular, in the splenium (Fig. 5); no growth was observed in the genu. These findings were recently corroborated by the same authors who followed up their original sample and re-scanned a large number of the subjects (n ⫽ 75) at least once 2 years later [33]. Again, the growth of the splenium far outperformed that of any other subdivision of the corpus callosum. This finding is somewhat surprising in light of the caudal-to-rostral time sequence of myelination of this structure after birth (see above) and the presumed late development of the prefrontal cortex, a region inter-connected through the (rostral) genu of the corpus callosum. Further studies are necessary to establish whether changes in the splenium reflect possible ongoing maturation of the inferior temporal and occipital cortices, the main sources of the callosal fibers in the splenium [62].

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261 local anatomy of the subject’s brain in alignment with that of the template brain. Thompson et al. [82] re-scanned each subject within 1 to 4 years and, by subtracting local deformation fields obtained in each subject at Time 1 and Time 2, were able to visualise changes in the local growth of the corpus callosum. Consistent with the previous findings, changes in the genu were observed in the youngest subject (scanned at age 3 and 6 years) while changes in the splenium were most pronounced when rescanning during adolescence (Time 1: 9 years, Time 2: 13 years) (Fig. 6). In order to detect more subtle variations, however, group analysis of deformation fields and its statistical evaluation will be necessary. Chung et al. [15] have developed a novel statistical analysis of local growth that will allow investigators to evaluate the statistical significance of age-related changes in deformation fields throughout the brain. Subtle regional variations in white matter can also be evaluated using a voxel-by-voxel analysis of the images. This approach borrows from the concepts developed in the context of functional neuroimaging: it is based on the use of standardised stereotaxic space and voxel-based statistics. In a study of age-related changes in white matter, we ([66]; 4 –18 years, n ⫽ 111) have observed significant age-related changes in white-matter “density” in the posterior limb of the internal capsule and in the left arcuate fasciculus (Fig. 7); the former contains fibers connecting the motor cortex and spinal cord, and the latter those connecting the anterior (Broca’s) and posterior (Wernicke’s) language areas. The analysis of images obtained in 111 children and adolescents included the following steps: (1) non-linear transformation of images into standardised stereotaxic space to remove global and local differences in the size and shape of the individual brains; (2) classification of brain tissue into white matter, grey matter and cerebro-spinal fluid; (3) blurring of white-matter binary masks to generate 3-D maps of white-matter “density” and (4) correlation between voxel values of white-matter density and the subject’s age. Such voxel-based analyses of age-related variations in white- and grey-matter [78] densities complement the volumetric approach in allowing for subtle local differences to emerge in regions that may not be delineated as a single volume. However, relatively large numbers of individuals and rather conservative statistical criteria need to be applied to separate signal from noise in a reliable fashion. Overall, there is ample evidence that white matter continues to mature during childhood and adolescence, increasing its volume and becoming more myelinated. Most of the changes are occurring throughout this period of development, with no pronounced differences in the rate of maturation during puberty. The absence of such developmental “spurs” could, however, reflect the lack of high-density longitudinal data for this period of life. FUTURE DIRECTIONS

FIG. 7. Age-related changes in white-matter density. Age-related changes in white-matter density in the internal capsule (top) and the left arcuate fasciculus (middle, bottom). The thresholded maps of t-statistic values (t ⬎ 4.0) are superimposed on axial (capsule) and sagittal and coronal (arcuate) sections through the magnetic resonance image of a single subject. The images depict the exact brain locations that showed statistically significant correlations between white-matter density and the subject’s age (n ⫽ 111; age: 4 –17 years). Reprinted from Paus et al. [66] with permission.

The rostro-caudal wave of growth of the corpus callosum was recently demonstrated by a computational analysis of so called deformation fields ([82]; 3–15 years, n ⫽ 6). The 3-D deformation fields specify, at each voxel, the vector of forces applied to bring

Computational analysis of MR images clearly enhances our ability to detect subtle age-related changes in white matter, as measured with conventional T1- and T2-weighted imaging. The use of such techniques to analyse large and, preferably, longitudinal datasets will undoubtedly reveal new details about maturation of the human brain. It is also likely that, in the near future, our knowledge will be further expanded by measuring more directly distinct properties of white matter, such as those reflecting structural organisation of fiber tracts and myelination. Diffusion Tensor MR Imaging (DTI) is one of the novel techniques employed for in vivo tracking of fiber pathways in the human brain [46,70,83]. This technique provides local measures of water diffusion and diffusion anisotropy by sensitising the MR signal to random motion of water molecules in the brain. Because this motion is highest along the axons, fiber tracts consisting of axons running in the same 3-D

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PAUS ET AL. TABLE 1 TESTS OF SENSORY, MOTOR AND COGNITIVE ABILITIES SUITABLE FOR INFANTS (0 – 4 YEARS) System

Visual Auditory

Somatosensory

Cross-modal

Skeletomotor

Oculomotor

Executive

Test

Function

Structure

Form from motion Face perception Gap detection

High-level vision High-level vision Temporal resolution

MT/MST Occipito-temporal cortex A1, A2

[2] [54] [88]

Voice discrimination

High-level audition

A2, superior temporal sulcus

[5]

Discrimination (and recognition) of objects by touch; both hands Tactual-to-visual object recognition

High-level tactile

SI, SII, superior parietal

[76]

High-level tactile and visual

[75]

Preferential looking

Peg board

Fine-motor skills

[57]

Peg board

Reaching

Motor coordination

[38]

Symmetry of optokinetic nystagmus Smooth pursuit Self-ordered pointing

Optokinetic nystagmus Smooth pursuit Working memory, action monitoring Joint attention

Polymodal temporal cortex, insulaclaustrum M1, cortico-spinal tract, basal ganglia, cerebellum Frontal premotor, superior parietal Visual cortex

[49,80]

Videotape reaching for objects of 3 different sizes and shapes Observation or EOG

FEF, MT/MST Dorsolateral PFC

[1] [21,67]

Videotaping or EOG Observation

Medial PFC, orbitofrontal

[12]

At which (dull) toy is an experimenter looking with excitement Search for a small toy in a scale model of a room Subsequently, search for the toy in the life-size room Number of elements and difference between them Novelty preference (familiarise with one expression, test with the old and a novel expression) Preferential looking (hearing a vocal expression first, followed by two faces while the voice is still present) JTCI, Carey Temperament Scales

Social referencing

Reference

Model-reality transfer

Symbolic representation

Parietal, frontal cortex

[20]

Arithmetics

Numerical distance

Number sense

Inferior parietal cortex

[18]

Emotion

Categorisation of facial expressions

Face/emotion processing

Amygdala, orbitofrontal and medial frontal cortex

[11]

Matching vocal and facial expressions

Voice/face emotion processing

Amygdala, orbitofrontal, supramodal temporal cortex

[85]

Questionnaires

Temperament, personality

Amygdala, orbitofrontal and medial frontal cortex

[91,92]

Temperament/ Personality

Method

Observation Preferential looking Duration of looking at a visual stimulus associated with a given auditory stimulus Novelty preference (familiarise with one voice, test with the old and a novel voice). Proportion of exploration of novel shapes

MT, middle temporal; MST, medial superior temporal; FEF, frontal eye field; EOG, electro-oculogram; PFC, prefrontal cortex; JTCI, Junior Temperament Character Inventory.

orientation can be visualised; this visualisation is again aided greatly by computation analysis of DTI images [17]. In the developing brain, myelination of axons and their structural arrangement into “tight” bundles may underlie changes observed with this method (e.g., [42,55,60]). Myelination of white matter most likely drives many of the changes observed with the above-described MR techniques, and yet none of these techniques measures the amount of myelin directly. Another novel MR technique, termed magnetization-transfer (MT) imaging [87], may provide more myelinspecific information. MT imaging probes the interaction of the normally observed water signal with the hydrogen nuclei of large

macromolecules that are not directly observable because of their extremely short T2 relaxation times. In white matter, the MT effect is dominated by the lipids of myelin [45] and the technique has therefore been used extensively to study demyelination in diseases such as multiple sclerosis [24,30,71]. It is likely that large changes in myelination occurring during infancy can be quantified using this technique; it remains to be seen whether more subtle variations observed in white matter, for example, during adolescence would be measurable as well. As pointed out in the Introduction, the interest in studying developmental changes in brain structure is motivated by our

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263 TABLE 2

TESTS OF SENSORY, MOTOR AND COGNITIVE ABILITIES SUITABLE FOR SCHOOL-AGE CHILDREN AND ADOLESCENTS (5–18 YEARS) System

Visual

Test

Function

Structure

Reference

Method

Detection of radial-frequency patterns

Hyperacuity, from vision

V4

[6,86]

Face discrimination

High-level vision

Occipito-temporal cortex

[10]

Gap detection

Temporal resolution

A1, A2

[88]

Voice discrimination

High-level audition

A2, superior temporal sulcus

[5]

Somatosensory Discrimination of 3-D objects; uni-manual

High-level tactile

SI, SII, superior parietal

[75]

Cross-modal

Tactual-to-visual object discrimination

High-level tactile and visual

Polymodal temporal cortex, insula-claustrum

[75]

Skeletomotor

Peg board

Fine-motor skills

[57]

Bi-manual coordination tapping Smooth pursuit Pro/anti-saccade “Buttercup” test Simultaneity of tactile stimulation; across hands Simultaneity of visual stimulation; across hemifields Simultaneity of tactile stimulation; uni-manual Self-ordered pointing

Coordination

[48]

Thurstone Tapping aparatus

Smooth pursuit Saccades, Inhibition Articulation Inter-hemispheric transfer Inter-hemispheric transfer

M1, cortico-spinal tract, basal ganglia, cerebellum Frontal premotor, superior parietal, cerebellum FEF, V5 FEF, PFC, superior parietal M1, cortico-bulbar tract Mid-body of the corpus callosum Splenium of the corpus callosum

Identification of a deformed circle presented together with a nondeformed one Same-different judgements of faces differing only in global shape, configural spacing or local cues Detection of a gap between two tones Same-different judgements of male/female voices Same-different judgment of two non-sense shapes explored with one hand Same-different judgment of a non-sense shape (explored with one hand) and a visual display Peg board

[1] [58,64] [93] [59]

EOG EOG NEPSY Computer-controlled air-puff

Time perception

SI, cerebellum

[59]

Computer-controlled air-puff

Working memory, Response monitoring

Dorsolateral PFC

[68,77]

Word list

Verbal memory

Mesial temporal lobe (left)

[94]

Selection of a different drawing from the same array on each trial while keeping in mind those already selected and those to be selected Children’s Memory Scale

Arithmetics

Dot location Numerical distance

Location memory Number sense

Mesial temporal lobe (right) Inferior parietal cortex

[94] [18]

Language

Dichotic listening

Emotion

Categorization of facial expressions

Hemispheric specialization Face/emotion processing

Corpus callosum, posterior and [90] anterior speech areas Amygdala, oribitofrontal [43]

Matching vocal and facial expressions

Voice/face emotion processing

Amygdala, orbitofrontal, supramodal temporal cortex

[50]

Questionnaires

Temperament, personality

Amygdala, orbitofrontal and medial frontal cortex

[91,95]

Auditory

Oculomotor Orofacial Inter-hemispheric

Time perception Executive

Episodic memory

Temperament personality

FEF, frontal eye field; PFC, prefrontal cortex; EOG, electro-oculogram; NEO-PI, NEO Personality Inventory.

Children’s Memory Scale Judgements (estimates/not counts) on the number of dots Fused words Same-different judgement on faces differing in their emotional expressions Same-different judgement (hearing a vocal expression first, followed by two faces while the voice is still present) JTCI, NEO-PI

264

PAUS ET AL.

search for biological underpinnings of motor, sensory and cognitive development. Ultimately, one would like to combine assessment of brain structure with that of brain function, the latter with techniques such as event-related potentials and functional MRI. Although desirable, such combined structure-function imaging studies are technically challenging and highly demanding on subjects’ co-operation. As an alternative, we suggest that significant progress vis-a`-vis structure-function correlation can be made by acquiring, in the same individual, structural images and quantitative measures of sensory, motor and cognitive functions. For example, interpretation of age-related changes in white-matter density observed in the internal capsule and the left arcuate fasciculus in our MRI study [66] would have been greatly aided by knowledge of fine motor skills (tested, e.g., by Purdue Peg Board) and speech processing (tested, e.g., by a gap-detection auditory task) in the same group of individuals. Although correlative in nature, this approach to structure-function relationship would provide first approximation of the development processes co-occurring at the two levels of analysis. It should be emphasised, however, that meaningful correlational analysis of this nature may be better accomplished with the use of well-defined experimental rather than standardised clinical tests. We would argue that experimental tests are more likely to capture “building blocks” rather than “composites” of a given behavior and that, in turn, the “building blocks” are more likely to be linked to a single neural system and as such, more readily detectable during brain development. For example, reading single words represents a “composite” behavior, with specific aspects of visual (motion) and auditory (temporal envelope) processing being two basic “building blocks”. Current advances in cognitive neuroscience would allow investigators to select experimental tests suitable for a neural system of interest. To facilitate this approach, we have included a list of tests thought suitable for assessing sensory, motor and cognitive development in infants and pre-schoolers (Table 1) and school-aged children and adolescents (Table 2). In summary, future studies of brain development can benefit from the use of: (1) automatic data-processing methods; (2) concurrent assessment of behavior with tests derived from cognitiveneuroscience research; (3) employment of the longitudinal design; and (4) novel MR techniques, such as DTI and magnetization transfer. Knowledge of structural connectivity and its maturation, derived from computational analyses of MR images, will provide the stepping stone for future studies of functional interactions in the developing human brain.

5. 6. 7.

8. 9. 10. 11. 12. 13.

14.

15.

16.

17.

18. 19. 20.

ACKNOWLEDGEMENTS

21.

This work was supported in part by the International Consortium for Brain Mapping and Canadian Institutes of Health Research. We thank Drs. Dauphne Maurer and Kate Watkins for their help with the selection of experimental tests included in Tables1 and 2, and Drs. Jay Giedd and Judith Rapoport for the fruitful collaboration on MR studies of brain development.

22.

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