Abstract

Maturation of brain white matter pathways is an important factor in cognitive, behavioral, emotional and motor development during childhood and adolescence. In this study, we investigate white matter maturation as reflected by changes in anisotropy and white matter density with age. Thirty-four children and adolescents aged 6–19 years received diffusion-weighted magnetic resonance imaging scans. Among these, 30 children and adolescents also received high-resolution T1-weighed anatomical scans. A linear regression model was used to correlate fractional anisotropy (FA) values with age on a voxel-by-voxel basis. Within the regions that showed significant FA changes with age, a post hoc analysis was performed to investigate white matter density changes. With increasing age, FA values increased in prefrontal regions, in the internal capsule as well as in basal ganglia and thalamic pathways, the ventral visual pathways, and the corpus callosum. The posterior limb of the internal capsule, intrathalamic connections, and the corpus callosum showed the most significant overlaps between white matter density and FA changes with age. This study demonstrates that during childhood and adolescence, white matter anisotropy changes in brain regions that are important for attention, motor skills, cognitive ability, and memory. This typical developmental trajectory may be altered in individuals with disorders of development, cognition and behavior.

Introduction

Childhood and adolescence is a period of dynamic behavioral, cognitive and emotional development. This development parallels significant changes in white matter structure (Yakovlev, 1967; Pfefferbaum et al., 1994; Reiss et al., 1996; Paus et al., 1999; Schmithorst et al., 2002). Thicker myelin sheathes, increased axonal diameter and improved organization of white matter tracts can improve signal transduction, and may be critical factors in allowing cognitive, behavioral, and emotional development to take place in an optimal fashion.

Different methodologies have been used to study white matter development. Postmortem histological studies have described an increase in white matter myelination throughout childhood and adolescence. However, these studies are limited by small sample sizes (Yakovlev, 1967; Benes et al., 1994). Magnetic resonance imaging (MRI) analyses of brain development in vivo have consistently reported increases in white matter volume and density from childhood through adolescence (Reiss et al., 1996; Giedd et al., 1999a,b; Paus et al., 1999). These changes are typically attributed to increases in axon diameter, myelination or concentration of iron, depending on the method of analysis (Reiss et al., 1996; Steen et al., 1997; Paus et al., 1999, 2001). However, debate continues as to which of these processes contribute to white matter changes throughout development (Paus et al., 1999; Schmithorst et al., 2002).

Structural neuroimaging investigations attempting to elucidate regional patterns of white matter development have yielded conflicting results. A large longitudinal study of children and adolescents revealed linear increases in white matter volume that were not region specific (Giedd et al., 1999a). In contrast, in a volumetric whole-brain MRI study, Reiss et al. reported prominent regional increases in frontal white matter volume during childhood (Reiss et al., 1996). Paus et al. showed age-related increases in white matter density in tracts constituting putative corticospinal or thalamocortical tracts, and in left fronto-temporal pathways, but not in frontal regions (Paus et al., 1999).

Recently, investigators have turned to diffusion tensor imaging (DTI) to further investigate the development of white matter, and to clarify the conflicting data from other structural imaging modalities. DTI uses the magnetic resonance signal to visualize water movement within axons, which can help characterize axonal structure. While several DTI studies have consistently shown increases in anisotropy indices throughout infancy and the first decade of life (Mukherjee et al., 2001; McGraw et al., 2002; Ulug, 2002; Miller et al., 2003), only two DTI studies investigated white matter development beyond childhood (Klingberg et al., 1999; Schmithorst et al., 2002). The first was a region-of-interest (ROI)-based study, in which seven children were compared to five adults. This study suggested that adults had greater white matter anisotropy than children in frontal regions (Klingberg et al., 1999). In the second study, Schmithorst et al. reported increased anisotropy with age in corticospinal tracts, the left arcuate fasciculus, and the right inferior longitudinal fasciculus in 33 subjects ranging in age from 5 to 18 years (Schmithorst et al., 2002). In this study, trace (a measure of overall diffusivity) values, but not anisotropy, were observed to increase with age in prefrontal regions.

Inconsistent prefrontal findings in studies using DTI are intriguing, particularly because there is significant maturation of higher-order executive functions during childhood and adolescence (Casey et al., 1997, 2000; Luna et al., 2001; Kwon et al., 2002; McGivern et al., 2002; Tamm et al., 2002). Frontal regions, with their extensive cortical and subcortical connections, are crucial for these functions (Cummings, 1993; Fuster, 2000).

In this study, we sought to further delineate the spatial development of white matter pathways in typically developing children and adolescents using DTI. Our measure of analysis was fractional anisotropy (FA), an intravoxel measure that yields values between 0 (isotropic diffusion, i.e. diffusion that is equal in all directions) and 1 (maximum anisotropic diffusion, i.e. the hypothetical case of diffusion along one axis) (Basser and Pierpaoli, 1996; Pierpaoli and Basser, 1996). The degree of diffusion anisotropy in a voxel is determined by microstructural features of the tissue in that particular image voxel, including fiber diameter and density, the myelin sheath and other oriented macromolecular structures that hinder proton diffusion, as well as macrostructural features such as intravoxel fiber-tract coherence (Pierpaoli and Basser, 1996). In addition, we conducted a voxel-based morphometry analysis, in which we examined age-related changes in white matter signal intensity, also referred to as ‘white matter density’ (Paus et al., 1999).

Based on cognitive and behavioral data as well as previous imaging studies, we anticipated age-related changes in white matter within prefrontal regions, corticospinal tracts and left fronto-temporal tracts. A second objective of our study was to better define which structural changes occur in white matter throughout development. To address this goal, we studied overlaps and discrepancies between fractional anisotropy (FA) and white matter density changes with age. It was hypothesized that this analysis would help to differentiate between structural changes in white matter that affect both FA and white matter density (e.g. increasing myelination and axonal density and diameter) (Barkovich et al., 1988; Pierpaoli and Basser, 1996; Paus et al., 1999), and other structural changes that affect only FA values (e.g. fiber orientation).

Materials and Methods

Subjects

Typically developing participants were recruited as control subjects through newspaper advertisements and parent networks. After providing a complete description of the study to participating subjects and their caretakers, written informed consent was obtained under protocols approved by the Institutional Review Board of Stanford University. All subjects were right-handed, in good health and without evidence of neurological or psychiatric disorder, as assessed utilizing a comprehensive telephone screening instrument and scores in the non-significant range on the Child Behavior Checklist (Achenbach, 1991). Each participant was administered intelligence testing using age-appropriate Wechsler Scales of Intelligence. Subjects with a Full Scale IQ (FSIQ) score <80 or >130 were excluded from the study, as we wished the sample to be representative of the general population. In all, 34 (18 male) typically developing children and adolescents participated in this study (mean age = 12.7 ± 3.3; age range = 6–19 years; mean FSIQ = 114 ± 9.5).

Image Acquisition

Each participant was scanned on a 1.5 T whole body GE Signa Horizon scanner (GE Medical Systems, Milwaukee), and received a three-dimensional, high-resolution T1-weighted anatomic gradient- and RF-spoiled rapid gradient echo sequence with the following pulse sequence: repetition time (TR) = 35 ms; echo time (TE) = 6 ms, flip angle (α) = 45°, number of excitations (NEX) = 1, field of view = 24 cm, matrix size = 256 × 256, for 124 contiguous slices of 1.5 mm width. Magnetic resonance signal excitation and reception was performed using a quadrature birdcage coil provided by vendor.

DTI image acquisition was accomplished with single-shot spin-echo echo-planar imaging (EPI) sequence with diffusion sensitizing gradients applied on either side of the 180° refocusing pulse (Moseley et al., 1991; Basser et al., 1994). Imaging parameters for the diffusion-weighted sequence were as follows: field of view (FOV) = 24 cm, matrix size 128 × 128, TE/TR = 106/6000 ms, 18 or 19 axial-oblique slices, slice thickness 5 mm/skip 1 mm, and fractional k-space acquisition. A spectral-spatial excitation RF pulse was used to select water protons only and served to avoid ghost artifacts from lipid-bound protons. The scan was prescribed from the top of the brain and included only the most superior part of the cerebellum. Diffusion gradient duration was δ = 32 ms, diffusion weighting was b = 900 s/mm2. In addition, two reference measurements (b0-scans) were performed and averaged for each slice with the diffusion-encoding gradients turned off.

Diffusion was measured along six non-collinear directions: xy, xz, yz, −xy, −xz and −yz. This pattern was repeated four times for each slice with the sign of all diffusion gradients inverted for odd repetitions.

Image Processing and Statistical Analyses

Eddy current effects in the diffusion-weighted images (i.e. geometric distortions that vary from one diffusion direction to the next) were unwarped prior to averaging (de Crespigny and Moseley, 1998). Averaging of the four magnitude images efficiently removed the effect of gradient cross-terms between the diffusion sensitizing and imaging gradients (Neeman et al., 1991). Fractional anisotropy (FA) was calculated for each voxel according to methods described by Basser and Pierpaoli (1996) to produce an FA image. A T2-weighted image map was used to determine normalizing parameters subsequently applied to the fractional anisotropy images using Statistic Parametric Mapping software (SPM99 software, Wellcome, UK). Normalized FA images were smoothed with a 4 mm kernel to increase the signal-to-noise ratio. Subsequently, a linear regression model was used to correlate the FA maps with subject's age on a voxel-by-voxel basis. Finally, to determine the presence of significant clusters of differences, the joint expected probability distribution of the height and extent of Z-scores with height (Z > 2.33; P < 0.01) and extent (Z > 1.67; P < 0.05) thresholds, was used to correct for spatial correlation in the data (Poline et al., 1997). Using a spatially normalized, average T1-weighted image (SPGR scans) for all subjects a white matter cerebral mask was created and used to highlight changes in white matter tracts eliminating noise and edge effects.

In the secondary analyses, we examined white matter density changes with age within regions that showed significant increases in FA values with age. The procedure and template used to spatially normalize the T1-weighted anatomic images were identical to those used to normalize the B0 images as described above. The T1-weighted images were normalized into standard stereotactic space using a combined 12-parameter affine and non-linear transformation (7 × 8 × 7 nonlinear basis functions). These data were then segmented to create images representing white matter, gray matter and cerebrospinal fluid. The segmented white matter images were then smoothed using a 4 mm kernel. Finally, within ROIs in which FA values were significantly correlated with age, a linear regression model was used to correlate the white matter density images with the subjects' ages using voxel-by-voxel unpaired t-tests (using SPM99). To identify significant voxel clusters and minimize Type II errors, both height (P < 0.01) and extent (P < 0.05) thresholds were employed. This analysis was performed with 30 of the original 34 subjects, as an SPGR was not acquired for four subjects.

Results

FA Changes with Age

White matter showing increased FA values with age was seen in prefrontal regions, in the internal capsule, in pathways extending within the basal ganglia, and between the basal ganglia and the thalamus (Fig. 1A). Additional age-related increases in FA were observed in areas that appeared to correspond to cortico-thalamic and cortico-spinal tracts extending from sensory-motor regions, and in white matter of the ventral visual streams. Finally, age-related increases were observed in intrahemispheric tracts, which correspond to the location of the arcuate fasciculi extending from the frontal lobe to the superior and middle temporal gyrii and in the corpus callosum. No white matter regions were found to have significant decreases in FA values with age.

Figure 1.

(A) Voxels that showed significant correlation between FA values and age (axial view). (B) Regions in which there was a significant increase in FA values with age (shown in red), and areas in which there was significant increase in both FA values and white matter density with age (shown in yellow). Areas in which the correlation between white matter density and age was more significant than correlation between FA values and age are shown in green.

Figure 1.

(A) Voxels that showed significant correlation between FA values and age (axial view). (B) Regions in which there was a significant increase in FA values with age (shown in red), and areas in which there was significant increase in both FA values and white matter density with age (shown in yellow). Areas in which the correlation between white matter density and age was more significant than correlation between FA values and age are shown in green.

Figure 2.

A correlation graph between FA values in sample brain regions and age. FA values were generated from two points of peak correlation between FA and age described in Table 1. (A) Right DLPFC; (B) right middle frontal gyrus.

Figure 2.

A correlation graph between FA values in sample brain regions and age. FA values were generated from two points of peak correlation between FA and age described in Table 1. (A) Right DLPFC; (B) right middle frontal gyrus.

Table 1

Voxel coordinates in Talairach space and the associated Z-scores for the most significant voxel of each cluster in the statistical parametric map in which FA values were significantly correlated with age.

Location of significant FA differences
 
Cluster size in voxels
 
Talairach coordinates of most significant voxel
 
Z-score
 
Basal ganglia, thalamus 5762 8, −2, −10 5.94 
Sensory-motor strip 188 −4,−5,59 5.03 
Middle frontal gyrus adjacent to dorsolateral prefrontal cortex 241 −40,25,25 4.47 
Occipital pathway extending into the ventral visual pathway 432 −40,−72,7 4.46 
Occipital pathways extending into the parietal cortex and the ventral visual stream (adjacent to the middle and inferior occipital gyrus) 795 38,−73,17 4.44 
Dorsolateral prefrontal cortex (DLPFC) 249 46,23,26 4.40 
Superior temporal gyrus extending into the middle temporal gyrus. Around the superior temporal sulcus 316 −48,−33,3 4.32 
Left extrastriate regions 222 −22,−85,−3 3.98 
Adjacent to the interparietal sulcus bordering the angular gyrus 156 28,−56,45 3.96 
Deep parietal white matter in the centrum semiovale 172 28,−31,35 3.9 
Middle frontal gyrus 126 32,30,15 3.89 
Precuneus medial parietal 148 −18,−65,25 3.81 
Right middle occipital gyrus 127 28,−86,−2 3.8 
Inferior frontal gyrus extending into the orbitofrontal gyrus
 
177
 
28,48,−6
 
3.74
 
Location of significant FA differences
 
Cluster size in voxels
 
Talairach coordinates of most significant voxel
 
Z-score
 
Basal ganglia, thalamus 5762 8, −2, −10 5.94 
Sensory-motor strip 188 −4,−5,59 5.03 
Middle frontal gyrus adjacent to dorsolateral prefrontal cortex 241 −40,25,25 4.47 
Occipital pathway extending into the ventral visual pathway 432 −40,−72,7 4.46 
Occipital pathways extending into the parietal cortex and the ventral visual stream (adjacent to the middle and inferior occipital gyrus) 795 38,−73,17 4.44 
Dorsolateral prefrontal cortex (DLPFC) 249 46,23,26 4.40 
Superior temporal gyrus extending into the middle temporal gyrus. Around the superior temporal sulcus 316 −48,−33,3 4.32 
Left extrastriate regions 222 −22,−85,−3 3.98 
Adjacent to the interparietal sulcus bordering the angular gyrus 156 28,−56,45 3.96 
Deep parietal white matter in the centrum semiovale 172 28,−31,35 3.9 
Middle frontal gyrus 126 32,30,15 3.89 
Precuneus medial parietal 148 −18,−65,25 3.81 
Right middle occipital gyrus 127 28,−86,−2 3.8 
Inferior frontal gyrus extending into the orbitofrontal gyrus
 
177
 
28,48,−6
 
3.74
 

White Matter Density Changes with Age

Within the regions of interest derived from the FA analysis results, there were prominent white matter density increases with age in the internal capsule, in inter-thalamic pathways, and in the corpus callosum. Additional white matter density increases with age were observed in prefrontal regions, in the arcuate fasciculus, and in visual pathways (Fig. 1B, in yellow).

Discussion

We found significant and widespread developmental changes in white matter anisotrophy (FA values) in typically developing children and adolescents between 6 and 19 years of age. Our findings are consistent with previous studies that have reported age-related increases in white matter anisotropy in the arcuate fasciculus, in motor areas, and in the internal capsule (Paus et al., 1999; Schmithorst et al., 2002). In addition, our study replicated, in a larger sample, age-related FA increases in prefrontal regions (Klingberg et al., 1999). Age-related changes in white matter FA values also were observed in four brain pathways that may be important for our understanding of changing brain function during development: the corpus callosum, white matter tracts within the basal ganglia, between the basal ganglia and the thalamus, and in ventral-visual pathways.

White Matter Anisotropy Changes with Age in Prefrontal Regions

As children mature, they become more capable of executing tasks that require complex cognitive functioning. Contributing to this cognitive development are capabilities such as working memory, inhibition and attention. These capabilities improve with age (Berman and Friedman, 1995; Luciana and Nelson, 1998; Bedard et al., 2002; Luna et al., 2001; Kwon et al., 2002), and have been attributed at least partially to prefrontal circuitry (Filley, 2002; Kwon et al., 2002; Konishi et al., 2003). Synaptic proliferation and pruning, as well as ongoing myelination, are assumed to be important mechanisms that shape cognitive development (Huttenlocher, 1979; Greenough et al., 1987; McGivern et al., 2002; Sowell et al., 2004). Supporting this assumption, there is evidence for gray matter loss, beginning around puberty, in sensorimotor areas that spreads during late adolescence into ‘higher-order’ cortical regions, including the dorsolateral prefrontal cortex (Gogtay et al., 2004). In another study, frontal cortical thinning was related to improved ability to retain and retrieve verbal and spatial information (Sowell ER et al., 2001).

Our data suggest that beyond the processes that occur in the prefrontal cortex, cognitive improvement with age may be due to more coherent, or more myelinated, non-cortical white matter circuitry in prefrontal regions.

This typical brain development may be altered in developmental disorders that cause impairments in working memory, attention or inhibition (e.g. attention deficit hyperactivity disorder, Obssesive-Compulsive disorder, Tourette's syndrome, and schizophrenia) (Weinberger et al., 1986; Park and Holzman, 1992; Purcell et al., 1998; Cohen et al., 1999; Merriam et al., 1999; Rucklidge and Tannock, 2002; Stevens et al., 2002; Channon et al., 2003). Interestingly, prefrontal white matter has been implicated in these disorders (Mac Master et al., 1999; Foong et al., 2001; Hoptman et al., 2002; Kates et al., 2002).

White Matter Anisotropy Changes with Age in the Corpus Callosum

The corpus callosum, the largest white matter tract in the brain, functions to connect left and right cerebral hemispheres. Callosal fibers are important for motor and sensory integration, attention, memory, and general cognitive functioning (Njiokiktjien et al., 1994; Brown et al., 1999; Eliassen et al., 1999; McDonald et al., 2001; Tomaiuolo et al., 2001; Bookstein et al., 2002; Dorion et al., 2002).

In our study, we found overlapping changes in FA and white matter density with age in the body of the corpus callosum, which contains fibers important for connecting motor, sensory and auditory cortices (Pandya, 1986). The observed increases in FA values and white matter density may be related to the improved motor skills during development.

White Matter Anisotropy Changes with Age in the Basal Ganglia, and Between the Basal Ganglia and Thalamus

Previous research has shown that the striatum undergoes structural changes during childhood and adolescence (Sowell et al., 1999). These changes are thought to result from normal dendritic pruning, myelination, and changes in iron deposition. Our study complements these findings and suggests that white matter tracts originating or terminating in the basal ganglia and thalamus also show temporal maturation during childhood and adolescence. The basal ganglia and thalamus have extensive reciprocal connections with the frontal cortex and the anterior cingulate (Graybiel, 2000). These pathways have an important regulatory influence on the cortex and are known to be involved in emotional, behavioral, cognitive, and motor functions as well as in attention, learning and memory (Cummings, 1993; Graybiel, 2000; Herrero et al., 2002; Packard and Knowlton, 2002).

The increases in anisotropy with age we observed in the basal ganglia, thalamus and prefrontal cortex may reflect maturation of these basal-ganglia-thalamo-cortical pathways. Accordingly, during normal development, improvement in attention, motor skills, cognitive ability and memory, as well as in the modulation of behavior, may be a result of white matter maturation and improved signal transmission in brain circuits connecting the prefrontal cortex and the basal ganglia.

White Matter Anisotropy Changes with age in Ventral Visual Pathways

Although the visual system matures early in development, the functions of the ventral visual pathways, such as the establishment of form, face, and object representations, may develop throughout life (Neville and Bavelier, 2002). Neuropsychological evidence also suggests that visual−spatial integration, another important function of the ventral visual pathways, develops during late childhood and adolescence (Kovacs et al., 1999; Kovacs, 2000). Our study provides the first anatomical evidence of white matter changes during development localized to the ventral visual pathways that may correspond with this cognitive trajectory.

FA Changes versus White Matter Density Changes with Age

The underlying histological processes that lead to increases in white matter anisotropy with age are currently unknown. Changes in myelination, axonal diameter and density presumably affect both white matter density and anisotropy values. In contrast, changes in fiber orientation would probably affect white matter anisotropy but not white matter density. In this study, some regions showed overlap between anisotropy and density changes with age, while others had FA changes that were independent of the white matter density changes. This finding suggests that there are different histological processes acting in concert during white matter development.

Although white matter density changes with age corresponded, to some degree, with most major pathways that showed FA increases with age, the most significant overlap was observed in the internal capsule and in regions that correspond to pathways within the thalamus. This finding may reflect the fact that brain pathways are tightly packed in these areas, so that even small changes with age in myelin and fiber tract density result in a higher signal change for both FA and white matter density. Alternatively, this finding may reflect regional increases in axonal myelination, axonal diameter, or fiber tract density, or a combination of these processes. A caveat should be mentioned regarding regions of overlap that were observed in white matter bordering gray matter, as these findings may reflect subtle discrepancies in spatial normalization.

Many regions in which FA values increased with age did not show concurrent increases in white matter density (see Fig. 1B, red areas). This discrepancy may reflect a true difference between white matter anisotropy and white matter density changes with age. However, interpretation should be made with caution as regional differences in statistical power between the two methods may underlie some or all of these findings.

Interestingly, most areas in which FA values increased, but white matter density did not, tended to be in the periphery of the tracts, in regions that are closest to the gray matter/white matter borders. This could be explained by a neurodevelopmental sequence for which the peripheral changes in FA white matter values arise from fine-tuning of the direction and increased coherence of white matter tracts in association with brain maturation. Specifically, these data suggest that as neuronal connections are strengthened and pruned during development (Huttenlocher and Dabholkar, 1997), axons become more organized and coherent, resulting in increased anisotropy in those regions.

Future Directions and Conclusion

Future studies are needed to extend the findings presented here. First, while our study utilized a relatively large sample size, it also was cross-sectional. Longitudinal designs with even larger sample sizes will provide more accurate information as to the nature of white matter development in childhood and adolescence. In addition, future studies using new fiber tracking techniques in DTI will be useful in delineating the specific fiber tracts that show a significant change in FA values with age. Specifically, it would be important to investigate whether the basal ganglia, thalamic, and prefrontal pathways that showed FA maturation with age are continuous. Finally, further studies are needed to assess whether the observed white matter maturation is associated with changes in function.

In conclusion, this study provides new evidence of spatial and temporal development of white matter throughout childhood and adolescence. Our findings also suggest that particular histological phenomena underlie this development on a regional basis. Studies such as these will be helpful in eventually delineating how typical developmental trajectory is altered in individuals with disorders of development, cognition, and behavior.

The research presented in this manuscript was supported by NIH grant HD031715, HD40761 and the Sinclair Training Neuroimaging Fund.

References

Achenbach T (
1991
) Manual for the child behavior checklist/14–18 and 1991 profile. Burlington, VT: University of Vermont, Department of Psychiatry.
Barkovich AJ, Kjos BO, Jackson DE Jr, Norman D (
1988
) Normal maturation of the neonatal and infant brain: MR imaging at 1.5 T.
Radiology
 
166
:
173
–180.
Basser PJ, Pierpaoli C (
1996
) Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI.
J Magn Reson B
 
111
:
209
–219.
Basser PJ, Mattiello J, LeBihan D (
1994
) MR diffusion tensor spectroscopy and imaging.
Biophys J
 
66
:
259
–267.
Bedard AC, Nichols S, Barbosa JA, Schachar R, Logan GD, Tannock R (
2002
) The development of selective inhibitory control across the life span.
Dev Neuropsychol
 
21
:
93
–111.
Benes FM, Turtle M, Khan Y, Farol P (
1994
) Myelination of a key relay zone in the hippocampal formation occurs in the human brain during childhood, adolescence, and adulthood.
Arch Gen Psychiatry
 
51
:
477
–484.
Berman S, Friedman D (
1995
) The development of selective attention as reflected by event-related brain potentials.
J Exp Child Psychol
 
59
:
1
–31.
Bookstein FL, Streissguth AP, Sampson PD, Connor PD, Barr HM (
2002
) Corpus callosum shape and neuropsychological deficits in adult males with heavy fetal alcohol exposure.
Neuroimage
 
15
:
233
–251.
Brown WS, Jeeves MA, Dietrich R, Burnison DS (
1999
) Bilateral field advantage and evoked potential interhemispheric transmission in commissurotomy and callosal agenesis.
Neuropsychologia
 
37
:
1165
–1180.
Casey BJ, Trainor RJ, Orendi JL (
1997
) A developmental functional MRI study of prefrontal activation during performance of a go–no-go task.
J Cogn Neurosci
 
9
:
835
–847.
Casey BJ, Giedd JN, Thomas KM (
2000
) Structural and functional brain development and its relation to cognitive development.
Biol Psychol
 
54
:
241
–257.
Channon S, Pratt P, Robertson MM (
2003
) Executive function, memory, and learning in Tourette's syndrome.
Neuropsychology
 
17
:
247
–254.
Cohen JD, Barch DM, Carter C, Servan-Schreiber D (
1999
) Context-processing deficits in schizophrenia: converging evidence from three theoretically motivated cognitive tasks.
J Abnorm Psychol
 
108
:
120
–133.
Cummings JL (
1993
) Frontal-subcortical circuits and human behavior.
Arch Neurol
 
50
:
873
–880.
de Crespigny AJ, Moseley ME (
1998
) Eddy current induced image warping in diffusion weighted EPI. ISMRM 6th Meeting. Sydney.
Dorion AA, Sarazin M, Hasboun D, Hahn-Barma V, Dubois B, Zouaoui A, Marsault C, Duyme M (
2002
) Relationship between attentional performance and corpus callosum morphometry in patients with Alzheimer's disease.
Neuropsychologia
 
40
:
946
–956.
Eliassen JC, Baynes K, Gazzaniga MS (
1999
) Direction information coordinated via the posterior third of the corpus callosum during bimanual movements.
Exp Brain Res
 
128
:
573
–577.
Filley CM (
2002
) The neuroanatomy of attention.
Semin Speech Lang
 
23
:
89
–98.
Foong J, Symms MR, Barker GJ, Maier M, Woermann FG, Miller DH, Ron MA (
2001
) Neuropathological abnormalities in schizophrenia: evidence from magnetization transfer imaging.
Brain
 
124
:
882
–892.
Fuster JM (
2000
) Executive frontal functions.
Exp Brain Res
 
133
:
66
–70.
Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A, Paus T, Evans AC, Rapoport JL (
1999
a) Brain development during childhood and adolescence: a longitudinal MRI study.
Nat Neurosci
 
2
:
861
–863.
Giedd JN, Blumenthal J, Jeffries NO, Rajapakse JC, Vaituzis AC, Liu H, Berry YC, Tobin M, Nelson J, Castellanos FX (
1999
b) Development of the human corpus callosum during childhood and adolescence: a longitudinal MRI study.
Prog Neuropsychopharmacol Biol Psychiatry
 
23
:
571
–588.
Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, Nugent TF, 3rd, Herman DH, Clasen LS, Toga AW, Rapoport JL, Thompson PM (
2004
) Dynamic mapping of human cortical development during childhood through early adulthood.
Proc Natl Acad Sci USA
 
101
:
8174
–8179.
Graybiel AM (
2000
) The basal ganglia.
Curr Biol
 
10
:
R509
–R511.
Greenough WT, Black JE, Wallace CS (
1987
) Experience and brain development.
Child Dev
 
58
:
539
–559.
Herrero MT, Barcia C, Navarro JM (
2002
) Functional anatomy of thalamus and basal ganglia.
Childs Nerv Syst
 
18
:
386
–404.
Hoptman MJ, Volavka J, Johnson G, Weiss E, Bilder RM, Lim KO (
2002
) Frontal white matter microstructure, aggression, and impulsivity in men with schizophrenia: a preliminary study.
Biol Psychiatry
 
52
:
9
–14.
Huttenlocher PR (
1979
) Synaptic density in human frontal cortex — developmental changes and effects of aging.
Brain Res
 
163
:
195
–205.
Huttenlocher PR, Dabholkar AS (
1997
) Regional differences in synaptogenesis in human cerebral cortex.
J Comp Neurol
 
387
:
167
–178.
Kates WR, Frederikse M, Mostofsky SH, Folley BS, Cooper K, Mazur-Hopkins P, Kofman O, Singer HS, Denckla MB, Pearlson GD, Kaufmann WE (
2002
) MRI parcellation of the frontal lobe in boys with attention deficit hyperactivity disorder or Tourette syndrome.
Psychiatry Res
 
116
:
63
–81.
Klingberg T, Vaidya CJ, Gabrieli JD, Moseley ME, Hedehus M (
1999
) Myelination and organization of the frontal white matter in children: a diffusion tensor MRI study.
Neuroreport
 
10
:
2817
–2821.
Konishi S, Jimura K, Asari T, Miyashita Y (
2003
) Transient activation of superior prefrontal cortex during inhibition of cognitive set.
J Neurosci
 
23
:
7776
–7782.
Kovacs I (
2000
) Human development of perceptual organization.
Vision Res
 
40
:
1301
–1310.
Kovacs I, Kozma P, Feher A, Benedek G (
1999
) Late maturation of visual spatial integration in humans.
Proc Natl Acad Sci USA
 
96
:
12204
–12209.
Kwon H, Reiss AL, Menon V (
2002
) Neural basis of protracted developmental changes in visuo-spatial working memory.
Proc Natl Acad Sci USA
 
99
:
13336
–13341.
Luciana M, Nelson CA (
1998
) The functional emergence of prefrontally-guided working memory systems in four- to eight-year-old children.
Neuropsychologia
 
36
:
273
–293.
Luna B, Thulborn KR, Munoz DP, Merriam EP, Garver KE, Minshew NJ, Keshavan MS, Genovese CR, Eddy WF, Sweeney JA (
2001
) Maturation of widely distributed brain function subserves cognitive development.
Neuroimage
 
13
:
786
–793.
Mac Master FP, Keshavan MS, Dick EL, Rosenberg DR (
1999
) Corpus callosal signal intensity in treatment-naive pediatric obsessive compulsive disorders.
Prog Neuropsychopharmacol Biol Psychiatry
 
23
:
601
–612.
McDonald CR, Crosson B, Valenstein E, Bowers D (
2001
) Verbal encoding deficits in a patient with a left retrosplenial lesion.
Neurocase
 
7
:
407
–417.
McGivern RF, Andersen J, Byrd D, Mutter KL, Reilly J (
2002
) Cognitive efficiency on a match to sample task decreases at the onset of puberty in children.
Brain Cogn
 
50
:
73
–89.
McGraw P, Liang L, Provenzale JM (
2002
) Evaluation of normal age-related changes in anisotropy during infancy and childhood as shown by diffusion tensor imaging.
AJR Am J Roentgenol
 
179
:
1515
–1522.
Merriam EP, Thase ME, Haas GL, Keshavan MS, Sweeney JA (
1999
) Prefrontal cortical dysfunction in depression determined by Wisconsin Card Sorting Test performance.
Am J Psychiatry
 
156
:
780
–782.
Miller JH, McKinstry RC, Philip JV, Mukherjee P, Neil JJ (
2003
) Diffusion-tensor MR imaging of normal brain maturation: a guide to structural development and myelination.
AJR Am J Roentgenol
 
180
:
851
–859.
Moseley ME, Wendland MF, Kucharczyk J (
1991
) Magnetic resonance imaging of diffusion and perfusion.
Top Magn Reson Imaging
 
3
:
50
–67.
Mukherjee P, Miller JH, Shimony JS, Conturo TE, Lee BC, Almli CR, McKinstry RC (
2001
) Normal brain maturation during childhood: developmental trends characterized with diffusion-tensor MR imaging.
Radiology
 
221
:
349
–358.
Neeman M, Freyer JP, Sillerud LO (
1991
) A simple method for obtaining cross-term-free images for diffusion anisotropy studies in NMR microimaging.
Magn Reson Med
 
21
:
138
–143.
Neville H, Bavelier D (
2002
) Human brain plasticity: evidence from sensory deprivation and altered language experience.
Prog Brain Res
 
138
:
177
–188.
Njiokiktjien C, de Sonneville L, Vaal J (
1994
) Callosal size in children with learning disabilities.
Behav Brain Res
 
64
:
213
–218.
Packard MG, Knowlton BJ (
2002
) Learning and memory functions of the basal ganglia.
Annu Rev Neurosci
 
25
:
563
–593.
Pandya D (
1986
) Two hemispheres — one brain. New York: Alan Liss.
Park S, Holzman PS (
1992
) Schizophrenics show spatial working memory deficits.
Arch Gen Psychiatry
 
49
:
975
–982.
Paus T, Collins DL, Evans AC, Leonard G, Pike B, Zijdenbos A (
2001
) Maturation of white matter in the human brain: a review of magnetic resonance studies.
Brain Res Bull
 
54
:
255
–266.
Paus T, Zijdenbos A, Worsley K, Collins DL, Blumenthal J, Giedd JN, Rapoport JL, Evans AC (
1999
) Structural maturation of neural pathways in children and adolescents: in vivo study.
Science
 
283
:
1908
–1911.
Pfefferbaum A, Mathalon DH, Sullivan EV, Rawles JM, Zipursky RB, Lim KO (
1994
) A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood.
Arch Neurol
 
51
:
874
–887.
Pierpaoli C, Basser PJ (
1996
) Toward a quantitative assessment of diffusion anisotropy .
Magn Reson Med
 
36
:
893
–906 [erratum: Magn Reson Med 37:972].
Poline JB, Worsley KJ, Evans AC, Friston KJ (
1997
) Combining spatial extent and peak intensity to test for activations in functional imaging.
Neuroimage
 
5
:
83
–96.
Purcell R, Maruff P, Kyrios M, Pantelis C (
1998
) Neuropsychological deficits in obsessive-compulsive disorder: a comparison with unipolar depression, panic disorder, and normal controls.
Arch Gen Psychiatry
 
55
:
415
–423.
Reiss AL, Abrams MT, Singer HS, Ross JL, Denckla Mbyte (
1996
) Brain development, gender and IQ in children. A volumetric imaging study.
Brain
 
119
:
1763
–1774.
Rucklidge JJ, Tannock R (
2002
) Neuropsychological profiles of adolescents with ADHD: effects of reading difficulties and gender.
J Child Psychol Psychiatry
 
43
:
988
–1003.
Schmithorst VJ, Wilke M, Dardzinski BJ, Holland SK (
2002
) Correlation of white matter diffusivity and anisotropy with age during childhood and adolescence: a cross-sectional diffusion-tensor MR imaging study.
Radiology
 
222
:
212
–218.
Sowell ER, Delis D, Stiles J, Jernigan TL (
2001
) Improved memory functioning and frontal lobe maturation between childhood and adolescence: a structural MRI study.
J Int Neuropsychol Soc
 
7
:
312
–322.
Sowell ER, Thompson PM, Holmes CJ, Jernigan TL, Toga AW (
1999
) In vivo evidence for post-adolescent brain maturation in frontal and striatal regions.
Nat Neurosci
 
2
:
859
–861.
Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW (
2004
) Longitudinal mapping of cortical thickness and brain growth in normal children.
J Neurosci
 
24
:
8223
–8231.
Steen RG, Ogg RJ, Reddick WE, Kingsley PB (
1997
) Age-related changes in the pediatric brain: quantitative MR evidence of maturational changes during adolescence.
AJNR Am J Neuroradiol
 
18
:
819
–828.
Stevens J, Quittner AL, Zuckerman JB, Moore S (
2002
) Behavioral inhibition, self-regulation of motivation, and working memory in children with attention deficit hyperactivity disorder.
Dev Neuropsychol
 
21
:
117
–139.
Tamm L, Menon V, Reiss AL (
2002
) Maturation of brain function associated with response inhibition.
J Am Acad Child Adolesc Psychiatry
 
41
:
1231
–1238.
Tomaiuolo F, Nocentini U, Grammaldo L, Caltagirone C (
2001
) Interhemispheric transfer time in a patient with a partial lesion of the corpus callosum.
Neuroreport
 
12
:
1469
–1472.
Ulug AM (
2002
) Monitoring brain development with quantitative diffusion tensor imaging.
Devel Sci
 
5
:
286
–292.
Weinberger DR, Berman KF, Zec RF (
1986
) Physiologic dysfunction of dorsolateral prefrontal cortex in schizophrenia. I. Regional cerebral blood flow evidence.
Arch Gen Psychiatry
 
43
:
114
–124.
Yakovlev P (
1967
) Regional development of the brain in early life. Oxford: Blackwell Scientific.

Author notes

1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA, 2Program in Neuroscience, Stanford University, Stanford, CA 94305, USA, 3Neuroscience Institute, Stanford University, Stanford, CA 94305, USA, 4Department of Psychology, Children's Hospital of Orange County, Orange, CA 92868, USA and 5Lucas MRS/I Center Department of Radiology, Stanford University, Stanford, CA 94305, USA