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Pediatric Imaging |
1 From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, St Louis, MO 63110. Received October 23, 2000; revision requested December 12; revision received February 12, 2001; accepted March 9. Supported in part by National Institutes of Health grant P50-NS06833. Address correspondence to P.M. (e-mail: mukherjeep@mir.wustl.edu).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
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MATERIALS AND METHODS: Retrospective analysis of normal MR examination findings in 153 subjects (age range, 1 day to 11 years) referred for clinical neuroimaging was performed. All studies included diffusion tensor-encoded echo-planar MR imaging. Isotropic diffusion coefficient (
) and diffusion anisotropy (A
) were measured in the corpus callosum, internal capsule, caudate nucleus, lentiform nucleus, and thalamus.
RESULTS: exhibited biexponential decay with age in gray and white matter regions, except for monoexponential decay in the genu of the corpus callosum. There was a steep nonlinear increase of A in white matter tracts that paralleled the time course of the decline in . In basal ganglia, only a small linear increase in A was observed in patients. A changes in the thalamus were intermediate between basal ganglia and white matter structures.
CONCLUSION: Changes in magnitude and anisotropy of water diffusion follow stereotypical time courses during brain development that can be empirically described with multiexponential regression models, which suggests that quantitative scalar parameters derived from diffusion-tensor MR imaging may provide clinically useful developmental milestones for brain maturity. Supplemental material: radiology.rsnajnls.org/cgi/content/full/2212001702/DC1.
Index terms: Anisotropy • Brain, diffusion, 10.12144 • Brain, growth and development, 10.92 • Brain, MR, 10.121411, 10.12144 • Children, central nervous system • Diffusion tensor • Magnetic resonance (MR), diffusion study, 10.12144, 10.92
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
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During the past 15 years, MR imaging techniques have been developed that are sensitive to the microscopic diffusion of water within tissues (4–7). Application of these techniques to the imaging of the brains of newborns and infants has demonstrated greater apparent diffusion coefficients (ADCs) and less spatial anisotropy of water diffusion than in the adult brain (8–13). The initial decrease in ADC and increase in anisotropy during the postnatal maturation of some brain regions predate the earliest T1- and T2-weighted signal intensity alterations on conventional MR images and are thought to represent the changes of "premyelination" (9,12–14). Later age-dependent changes in brain water diffusion have been attributed to decreasing total water content and progression of myelination (12). Further, these changes raise the possibility that diffusion MR imaging may be sensitive to brain development beyond the 2nd year of life, when changes demonstrated on conventional T1- and T2-weighted MR images are largely complete (3).
The diffusion tensor provides a mathematical description of the three-dimensional spatial diffusion of water protons within each imaging voxel, from which rotationally invariant scalar quantities measuring its overall magnitude, or ADC, and directionality, or anisotropy, can be derived (7,15,16). Knowledge of the age-dependent normal values of these parameters during brain development is important for the clinical assessment of altered water diffusion, caused by pathologic states such as ischemia, trauma, tumors, infection, inflammation, and demyelinating diseases, in children. If maturational changes in water diffusion continue beyond the first 2 years of life, when the human brain assumes an adult appearance on conventional T1- and T2-weighted MR images, then quantitative measurements of ADC and anisotropy might also prove valuable in the clinical assessment of brain maturation in older children. In this study, we characterize the maturational changes in water diffusion within the central gray matter nuclei and the central white matter pathways of the brain in children less than 12 years old by using diffusion-tensor MR imaging.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
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Our study included 153 children (95 boys, 58 girls; age range, 1 day to 11 years 1 month; mean age, 3.5 years). To limit the subjects to a manageable number for further detailed analysis, not all children more than 4 years old who met the inclusion criterion were enrolled in this study. Fifty-one of the 153 subjects were randomly selected from a group of approximately 400 children 4–12 years old who met the inclusion criterion. We considered this reduction in the sample size of older children acceptable because prior investigations have indicated that the greatest changes in brain water diffusion occur before 4 years of age (9,13).
Applying the two-tailed z test to the normal approximation to the binomial distribution (17), we determined that there was no statistically significant difference (z = 1.02, P = .31) between the gender ratio in this series and the gender ratio in the total population of 1,503 children (867 boys, 636 girls) less than 12 years old who underwent brain MR imaging examinations at our institution during the 15-month duration of the study. The 62.1% male composition of the study sample of 153 children is, however, significantly greater than the 50% proportion that would be expected in the general population (z = 2.91, P < .01).
MR Imaging Protocol
All examinations were performed on a 1.5-T system (Magnetom Vision; Siemens, Erlangen, Germany) with circularly polarized radio-frequency coils. The diffusion-tensor imaging protocol (3,000/97.4 [repetition time msec/echo time msec]) consisted of a single-shot multisection spin-echo echo-planar pulse sequence with a 24 x 24-cm field of view, 5-mm section thickness, and a 1-mm gap between sections (18,19). Four tetrahedrally oriented diffusion-weighted MR images (b value, 1,012.4 sec/mm2), three orthogonally oriented diffusion-weighted MR images (b value, 337.5 sec/mm2), and a reference T2-weighted signal intensity MR image (b value, 0.0 sec/mm2) were obtained at each transverse section.
Fourteen transverse sections were acquired in 35 seconds with a 96 x 128-voxel matrix (2.50 x 1.88 x 5.00-mm voxels), interpolated to a 192 x 256-pixel matrix. All images were realigned in two dimensions, by using a combination of intra- and crossmodality affine realignment procedures, to correct for image displacements and linear stretch and/or shear caused by eddy currents (18). For technical reasons, the diffusion-tensor MR images must be oriented in the transverse plane relative to the magnet bore. The diffusion-tensor MR images were, therefore, not necessarily acquired in register with those of the conventional MR sequences in the clinical neuroimaging protocol, which were oriented along the plane parallel to the anterior commissure and posterior commissure (AC-PC line).
For each pixel, the elements of the diffusion tensor were derived from this combination of tetrahedral and perpendicular diffusion measurements (18,19). The reference T2-weighted intensity image was not included in the diffusion tensor calculations because of the presence of artifact that arose from spurious free induction decay signal in some studies. The isotropic diffusion coefficient () (Appendix; Eq [E1]; [20,21]; radiology.rsnajnls.org/cgi/content/full/2212001702/DC1), a measure of the directionally averaged magnitude of diffusion with units of millimeters squared per second, and the dimensionless diffusion anisotropy (A) (Appendix, Eq [E2]) were computed from the diffusion tensor.
Region of Interest Analysis
On the diffusion-tensor MR images, regions of interest (ROIs) were defined by using a software program (ANALYZEAVW, Mayo Foundation, Rochester, Minn). In each patient study, bilateral ROIs were manually traced by one of two authors (P.M., J.H.M.) for three gray matter and four white matter structures on a single transverse section through the level of the basal ganglia. The four white matter regions were the anterior and posterior limbs of the internal capsule and the genu and splenium of the corpus callosum. These white matter structures were chosen because they exhibit visible anisotropy in newborns and are, thus, easily identified on diffusion-tensor MR images obtained in patients throughout the age range examined in this study. The three gray matter regions were the head of the caudate nucleus, the lentiform nucleus (comprising the putamen and the globus pallidus), and the thalamus. The selected gray matter regions are located adjacent to the white matter ROIs and can, therefore, be localized by using the white matter tracts as landmarks.
The ROI sizes and positions, relative to the structure being sampled, corresponded to regions 3, 4, 5, 7, 8, and 10 in figure 3 of Shimony et al (18), except that the ROI corresponding to region 4 in Shimony et al was placed more anteriorly to sample the anterior limb of the internal capsule, not its genu. The ROIs for the genu of the corpus callosum, a structure not studied by Shimony et al, were of homologous size and position to those placed on the splenium of the corpus callosum. All ROIs were confirmed to be correctly placed anatomically by the consensus of a board-certified radiologist (P.M.) and an attending neuroradiologist (R.C.M.) with a certificate of added qualification in neuroradiology. In a small minority of patient studies, not all of the seven structures were included on the same transverse section. In these cases, the additional ROIs were drawn on the adjacent transverse section that included the structure of interest.
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Placement of ROIs in cortical gray matter or subcortical and deep white matter was not attempted because of the technical difficulties involved in selecting homologous regions of gray matter and white matter for all ages. This is particularly problematic in the youngest subjects, who have no visible anisotropy in these areas. In addition, because of the differences in plane of section between the conventional MR images of the clinical protocol and the diffusion-tensor MR images, there were no coregistered anatomic images available to serve as a guide.
Data Analysis
The mean and mean A of the pixel values in each ROI were computed for each subject. Values obtained from the left and right ROIs for each structure were averaged. In two regions, the anterior limb of the internal capsule and the lentiform nucleus, the ROI data from one subject each were based only on the mean and mean A values of the right-sided region, because of the presence of image artifact in the left-sided region. The resulting graphs of A versus subject age in the basal ganglia were empirically fit with linear regression models. Graphs of A versus age in the thalamus and in white matter tracts and graphs of versus age in all seven regions were empirically fit with multiexponential regression by using monoexponential (Appendix, Eq [E3], [), biexponential (Appendix, Eq [E4]), and triexponential (Appendix, Eq [E5]) functions.
Levenberg-Marquardt least squares minimization was used to determine the best-fitting values of the nonlinear model parameters and their standard errors (22). The 
2 statistic, describing the goodness of fit of each model, and the R2 statistic, measuring the proportion of the variance in the data explained by the model, were calculated. The nonlinear model that best represented the data was determined by comparison of the 2 statistics for the mono-, bi-, and triexponential regressions by using the F test (Appendix; Eq [E6]; [23,24]). Statistical inference testing among different brain regions, involving comparisons of the fitted parameters of the optimal nonlinear model, was performed with the two-population two-tailed t test with Bonferroni correction for multiple pairwise comparisons.
Data modeling and plots were produced with technical analysis software (ORIGIN, version 6.0; Microcal Software, Northampton, Mass). Illustrative figures were prepared with graphics software (PHOTOSHOP, version 5.0; Adobe Systems, San Jose, Calif) to create montages and to adjust size, brightness, contrast, and orientation.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
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showed decreases with postnatal maturation throughout the brain, including both gray matter and white matter (Fig 1). Images of the rotationally invariant diffusion anisotropy A showed large age-dependent increases in white matter tracts but comparatively little change in gray matter (Fig 2).
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values from a representative white matter structure, the posterior limb of the internal capsule (Fig 3a), and a representative gray matter structure, the lentiform nucleus (Fig 3b), confirmed the visual impressions gained from Figure 1. Although the steepest decline in occurs before age 2, the trend continues well into later childhood. Biexponential regression (Appendix, Eq [E4]) provided a better fit to the age-varying data than monoexponential regression (Appendix, Eq [E3]) in six of the seven structures (Table 1), and the improvement in goodness of fit was statistically significant (P < .05) in these six areas. This nonlinear model accounted for 72%–86% of the variance in the data in the six regions. Monoexponential regression provided the best fit to the data from the genu of the corpus callosum, accounting for 73% of the total variance. The triexponential model (Appendix, Eq [E5]) did not improve the goodness of fit more than that of the biexponential model in any of the studied regions.
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value (
) among five of the studied brain areas: the anterior and posterior limbs of the internal capsule, the splenium of the corpus callosum, the thalamus, and the lentiform nucleus. The fitted values of in these five regions varied from 0.64 to 0.71 · 10-3 mm2/sec. in the head of the caudate nucleus was (0.76 ± 0.01) · 10-3 mm2/sec, a value significantly greater than those in the internal capsule or lentiform nucleus (P < .05) but not those in the corpus callosum or the thalamus. Monoexponential regression of the data from the genu of the corpus callosum yielded a value of (0.79 ± 0.02) · 10-3 mm2/sec, which was significantly greater (P < .05) than that in the other regions, except for the caudate head, thalamus, and splenium of the corpus callosum.
There was no statistically significant (P > .05) variation among the fitted decay rate constants 
fast and slow in the six brain regions modeled with biexponential regression. Values of fast and slow in the thalamus and in the splenium of the corpus callosum were larger than those in the other structures; however, the uncertainties in these fitted parameters were also greater. Thus, no statistically significant differences were found. There were also no statistically significant differences (P > .05) in the values of the fitted amplitude parameters Afast and Aslow among the six brain regions modeled with biexponential regression, with one exception. Afast in the caudate head was significantly greater (P < .05) than that in the posterior limb of the internal capsule. The time course of the age-dependent changes in within the genu of the corpus callosum could not be compared with those in the other six regions because of differences in the best-fitting nonlinear model.
Three types of age-dependent variation in A were observed: (a) a steep nonlinear increase with age in central white matter tracts, specifically the corpus callosum and the internal capsule (Fig 4a); (b) a small linear increase with age in the basal ganglia, specifically the caudate head and the lentiform nucleus (Fig 4b); and (c) a nonlinear increase in the thalamus that was intermediate in magnitude between the white matter regions and the basal ganglia (Fig 4c). In the lentiform nucleus, linear regression yielded a slope of 0.0067 ± 0.0009 (SD) per year, with a y intercept of 0.13 ± 0.004. The correlation coefficient (Pearson r) was 0.51 ± 0.03, which was statistically significant (P < .001). In the caudate head, the slope of the regression line was 0.0073 ± 0.0009 per year, and the y intercept was 0.12 ± 0.004. The correlation coefficient (0.56 ± 0.03) also was statistically significant (P < .001).
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during maturation of white matter tracts was not as strong as that for the age-dependent decay of . Overall, the nonlinear regression accounted for 65%–73% of the variance in the A data from white matter structures (Table 2), which is less than that for the data. The posterior limb of the internal capsule was the white matter region with the greatest R2 value and, therefore, with the least variability in A measurements that was not explained by the regression analysis. For the posterior limb of the internal capsule, the biexponential model provided the best approximation to the A data, with a statistically significant improvement in goodness of fit compared with the monoexponential function (P < .01).
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2 with the addition of a second exponential to the regression analysis.
All of the white matter structures demonstrated large increases in A with age, and these increases reached an asymptote during the first decade of life. The asymptotic (adult) values of A derived from the nonlinear regression (Table 2) were significantly less in the internal capsule than in the corpus callosum (P < .01). A, was also significantly less in the anterior limb than in the posterior limb of the internal capsule (P < .01). No statistically significant difference in A, was found between the genu and the splenium of the corpus callosum (P > .05). A, values of all of the white matter tracts were significantly greater than that of the thalamus (P < .01).
The time course of the double-exponential rise of A was similar to that of the decline in in the posterior limb of the internal capsule (Tables 1, 2). The rapid decay constants, fast, of the two processes were not significantly different (P > .05), nor were the slow decay constants, slow (P > .05). Similarly, the rate constant 1 describing the monoexponential decay of in the genu of the corpus callosum was not significantly different from that describing the growth of A in this white matter structure (P > .05). The rate constants describing the changes in and A for the other five brain regions could not be directly compared because of differences in the model that best fit the data.
The thalamus showed a larger age-dependent rise in A than the other gray matter regions but not as large as that of the white matter tracts (Fig 4). The time course of A in the thalamus was best described by a monoexponential process (Table 2), which provided a goodness of fit that was better than that of the simple linear model used in the other gray matter regions (P < .01). There was no benefit to adding a second exponential in terms of reducing the 2 value.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
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, as the majority of anisotropy information lies in the off-diagonal elements of the diffusion tensor (18), to which orthogonal diffusion encoding in three or fewer directions is not sensitive. Moreover, computation of the entire diffusion tensor, requiring at least six diffusion-encoding directions, is needed to generate rotationally invariant measures of anisotropy that are not affected by variables such as head tilt and orientation of white matter fibers.
The data from the youngest and oldest subjects in our investigation are in excellent agreement with quantitative diffusion MR imaging findings in newborns (11,12) and adults (7,25,26), respectively. This concordance at both extremes supports the accuracy of our measurements in the entire age range studied. In those brain regions where R2 of the fitted function exceeded 80% (Table 1), approached its adult (asymptotic) value at approximately age 6 years (Fig 3). In these regions, there was also strong statistical evidence for a biexponential process governing the maturational decline of . There was no statistically significant variation among these regions in either the fast or slow decay constants.
However, measurements from areas adjacent to ventricles, such as the corpus callosum and thalamus, exhibited greater noise that resulted in R2 values of 80% or less (Table 1). This increased noise may arise from cerebrospinal fluid pulsations transmitted from the ventricles (27–29). The longer rate constants found in these structures were not considered to indicate a statistically significant difference compared with those found in regions not bordering on ventricles, because of greater uncertainty in the measurements. Partial volume averaging with cerebrospinal fluid (30) may account for the larger values found in the caudate head and genu of the corpus callosum than in the other regions.
The fast exponential component of the maturational decline in the magnitude of water diffusion may reflect the increasing concentration of macromolecules in the developing brain. In white matter, this mechanism is thought to produce the rapid changes in T1- and T2-weighted signal intensity that correlate with progression of myelination during the first 2 years of life (3). The shift from free-water protons to macromolecule-bound protons can also explain the rapid monoexponential decay in absolute T2 relaxation times and in the development of magnetization transfer contrast that occur at these same ages (31). A plausible mechanism for the slow component of decay might be ongoing reductions in the total water content of the brain, which decreases by 14%–18% from birth to adulthood (32).
Prolonged time courses for human brain maturation, extending beyond the 1st decade of life, have previously been demonstrated by analyses of age-dependent changes in T2-weighted signal intensity (33), absolute T1 relaxation times (34), and white matter "density" assessed from T1-weighted gradient echo MR images (35). Baratti et al (36) discovered a strong correlation between alterations in T2 relaxation times and in the trace of the diffusion tensor during feline brain maturation. Further investigation with quantitative measurements of T2 and the diffusion tensor in the same subjects is needed to resolve whether T2 and diffusion are also strongly concordant during human brain maturation.
We found three distinct patterns of maturational changes in A: a small linear increase in the gray matter of the basal ganglia, a large nonlinear increase in white matter structures, and an intermediate pattern in the thalamus (Fig 4). The greater growth of anisotropy in the thalamus than in the basal ganglia can be attributed to its greater fraction of internal white matter tracts. The small A increases in the basal ganglia may also reflect maturation of internal white matter pathways, as cortical gray matter anisotropy in adults is statistically indistinguishable from zero (18). Alternatively, the small anisotropy increase in the basal ganglia may result from increasing noise bias (18), as the overall magnitude of water diffusion declines with age. The large maturational increases in A that we report in the internal capsule and the corpus callosum are most likely due to the progression of myelination. Except for the anterior limb of the internal capsule, these projectional and commissural white matter pathways are already partially myelinated at birth; hence, the postnatal growth of anisotropy in these structures does not represent "premyelination" (9, 12,14).
The age-dependent rise in A in these white matter tracts paralleled the decline in . In the posterior limb of the internal capsule, the biexponential model provided a better fit to the A data than the monoexponential model (P < .01), and the time constants that described the decay of and the growth of A were statistically indistinguishable (P > .05), which implied a similar time course for both processes. However, in other white matter tracts, there was no statistical benefit of the biexponential model for anisotropy growth over the monoexponential model. The probable explanation for this discrepancy is greater scatter in the A data, as manifest from the lower R2 values of the nonlinear regression in these other white matter regions (Table 2). A measurements are more sensitive than measurements to noise, such as artifacts from motion or spatial distortions and misregistration, as well as to inconsistent ROI placement. The reason for this last factor is that A values throughout the brain become increasingly heterogeneous with age, whereas rapidly becomes homogeneous (Figs 1, 2).
Since our method of ROI analysis followed that of Shimony et al (18), we did not expect variability in A due to inconsistent ROI position to be any greater than in that study. We also note that sources of random measurement error in and A would serve to reduce the effectiveness of nonlinear regression and to obscure age dependencies in the data but would not introduce artifactual relationships.
The relative magnitudes of the asymptotic anisotropy (A,) that we found in central gray and white matter structures (Table 2) agree with the normative standards derived by Shimony et al (18) from diffusion-tensor imaging in normal adult volunteers. For example, both studies show that the commissural white matter of the corpus callosum displays greater anisotropy than the projectional white matter of the internal capsule and that the thalamus has greater anisotropy than the basal ganglia. However, the quantitative A, values in this study are 30%–40% greater than the corresponding adult A values (18). We attribute this systematic error to the lower signal-to-noise levels obtained with the rapid 35-second clinical diffusion-tensor sequence used in this investigation, compared with the optimized technique of Shimony et al (18) in which longer examination times, signal averaging, and cardiac gating to reduce noise from cerebrospinal fluid pulsatility were used. Since low signal-to-noise ratios cause a proportionately larger overestimation of A in areas of low anisotropy (18), these percentage errors may be greater in the younger subjects of this study.
Also, the maximum b factor of greater than 1,000 sec/mm2 used in this study and that of Shimony et al (18) is tailored for older children and adults, whereas maximum b values of 800–900 sec/mm2 would be optimal for newborns and infants (12,28). The age dependence of these systematic errors may distort the shape of the resulting time course curve. The data presented herein are most comparable with measurements obtained with a similar number and range of b values, since altering these parameters can have a statistically significant influence on estimates of trace and anisotropy (37). In particular, our data are not applicable to measurements performed with b factors greatly exceeding 1,000 sec/mm2 (38).
Other limitations of our study stem from its design as a cross-sectional retrospective study of patients referred for clinical neuroimaging, which introduces inevitable selection biases. The greater proportion of boys than girls in this series reflects the referral patterns at our institution but is not representative of the general population. There were no coregistered anatomic images available to allow for precise localization and evaluation of the cortical gray matter and subcortical white matter of the cerebral hemispheres in the entire age range of subjects. The cross-sectional nature of this study may obscure trends that would be evident with a longitudinal design, such as periods of particularly rapid brain maturation that occur at different ages in different subjects. It was noteworthy in this cross-sectional study that the variability among subjects in both and A appeared to increase with age, largely during the first 2 years of life (Figs 3, 4). This finding needs to be confirmed by findings of a longitudinal study, in which individual developmental trajectories would be predicted to diverge from more similar values of and A at birth to less similar values with increasing age.
In summary, we examined the evolution of brain water diffusion during the 1st decade of life in the major commissural and projectional white matter tracts, as well as in central gray matter nuclei, using diffusion-tensor MR imaging. We found that changes in the magnitude and anisotropy of water diffusion follow stereotypical time courses during brain development that can be empirically described with multiexponential regression models, which suggests that quantitative scalar parameters derived from diffusion-tensor MR imaging may provide clinically useful developmental milestones for brain maturity.
The microstructural integrity of white matter tracts, assessed with diffusion-tensor imaging, has been shown to correlate with cognitive functions in adults, such as reading ability (39). Diffusion-tensor MR imaging can also be used in vivo to track white matter fibers between arbitrary regions of the human brain (40). Since the onset of function in maturing brain regions is thought to parallel the progress of such factors as myelination (41) and connectivity (35), diffusion-tensor MR imaging can help elucidate the mechanisms governing both structural and functional aspects of brain development.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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= diffusion anisotropy,
ADC = apparent diffusion coefficient,
= isotropic diffusion coefficient,
ROI = region of interest Author contributions: Guarantors of integrity of entire study, P.M., R.C.M.; study concepts and design, B.C.P.L., P.M., R.C.M.; literature research, P.M., R.C.M.; clinical studies, B.C.P.L., C.R.A., P.M., R.C.M.; data acquisition, T.E.C., P.M.; data analysis/interpretation, J.S.S., P.M., R.C.M., J.H.M.; statistical analysis, J.S.S., P.M., R.C.M.; manuscript preparation, P.M.; manuscript definition of intellectual content, B.C.P.L., J.S.S., T.E.C., P.M., R.C.M.; manuscript editing, P.M., R.C.M.; manuscript revision/review, B.C.P.L., C.R.A., J.S.S., T.E.C., P.M., R.C.M.; manuscript final version approval, P.M., R.C.M.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
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L.M. Nagae, A.H. Hoon Jr., E. Stashinko, D. Lin, W. Zhang, E. Levey, S. Wakana, H. Jiang, C.C. Leite, L.T. Lucato, et al. Diffusion Tensor Imaging in Children with Periventricular Leukomalacia: Variability of Injuries to White Matter Tracts AJNR Am. J. Neuroradiol., August 1, 2007; 28(7): 1213 - 1222. [Abstract] [Full Text] [PDF]
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 J. M. Provenzale, L. Liang, D. DeLong, and L. E. White Diffusion Tensor Imaging Assessment of Brain White Matter Maturation During the First Postnatal Year Am. J. Roentgenol., August 1, 2007; 189(2): 476 - 486. [Abstract] [Full Text] [PDF]
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C. Juhasz, E.M. Haacke, J. Hu, Y. Xuan, M. Makki, M.E. Behen, M. Maqbool, O. Muzik, D.C. Chugani, and H.T. Chugani Multimodality Imaging of Cortical and White Matter Abnormalities in Sturge-Weber Syndrome AJNR Am. J. Neuroradiol., May 1, 2007; 28(5): 900 - 906. [Abstract] [Full Text] [PDF]
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 T. D. Warner, M. Behnke, F. D. Eyler, K. Padgett, C. Leonard, W. Hou, C. W. Garvan, I. M. Schmalfuss, and S. J. Blackband Diffusion Tensor Imaging of Frontal White Matter and Executive Functioning in Cocaine-Exposed Children Pediatrics, November 1, 2006; 118(5): 2014 - 2024. [Abstract] [Full Text] [PDF]
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K. Yamada, H. Matsuzawa, M. Uchiyama, I. L. Kwee, and T. Nakada Brain Developmental Abnormalities in Prader-Willi Syndrome Detected by Diffusion Tensor Imaging Pediatrics, August 1, 2006; 118(2): e442 - e448. [Abstract] [Full Text] [PDF]
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 D. J. Mabbott, M. D. Noseworthy, E. Bouffet, C. Rockel, and S. Laughlin Diffusion tensor imaging of white matter after cranial radiation in children for medulloblastoma: Correlation with IQ Neuro-oncol, July 1, 2006; 8(3): 244 - 252. [Abstract] [Full Text] [PDF]
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 C. van Pul, J. Buijs, A. Vilanova, F. G. Roos, and P. F. F. Wijn Infants with Perinatal Hypoxic Ischemia: Feasibility of Fiber Tracking at Birth and 3 Months Radiology, July 1, 2006; 240(1): 203 - 214. [Abstract] [Full Text] [PDF]
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P. Ward, S. Counsell, J. Allsop, F. Cowan, Y. Shen, D. Edwards, and M. Rutherford Reduced Fractional Anisotropy on Diffusion Tensor Magnetic Resonance Imaging After Hypoxic-Ischemic Encephalopathy Pediatrics, April 1, 2006; 117(4): e619 - e630. [Abstract] [Full Text] [PDF]
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 P.-L. Khong, L. H.T. Leung, A. S.M. Fung, D. Y.T. Fong, D. Qiu, D. L.W. Kwong, G.-C. Ooi, G. McAlanon, G. Cao, and G. C.F. Chan White Matter Anisotropy in Post-Treatment Childhood Cancer Survivors: Preliminary Evidence of Association With Neurocognitive Function J. Clin. Oncol., February 20, 2006; 24(6): 884 - 890. [Abstract] [Full Text] [PDF]
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N. Barnea-Goraly, V. Menon, M. Eckert, L. Tamm, R. Bammer, A. Karchemskiy, C. C. Dant, and A. L. Reiss White Matter Development During Childhood and Adolescence: A Cross-sectional Diffusion Tensor Imaging Study Cereb Cortex, December 1, 2005; 15(12): 1848 - 1854. [Abstract] [Full Text] [PDF]
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 B. Thomas, M. Eyssen, R. Peeters, G. Molenaers, P. Van Hecke, P. De Cock, and S. Sunaert Quantitative diffusion tensor imaging in cerebral palsy due to periventricular white matter injury Brain, November 1, 2005; 128(11): 2562 - 2577. [Abstract] [Full Text] [PDF]
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N. Rollins Semilobar Holoprosencephaly Seen with Diffusion Tensor Imaging and Fiber Tracking AJNR Am. J. Neuroradiol., September 1, 2005; 26(8): 2148 - 2152. [Abstract] [Full Text] [PDF]
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P.-L. Khong, L. H. T. Leung, G. C. F. Chan, D. L. W. Kwong, W. H. S. Wong, G. Cao, and G.-C. Ooi White Matter Anisotropy in Childhood Medulloblastoma Survivors: Association with Neurotoxicity Risk Factors Radiology, August 1, 2005; 236(2): 647 - 652. [Abstract] [Full Text] [PDF]
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 A. Drobyshevsky, S.-K. Song, G. Gamkrelidze, A. M. Wyrwicz, M. Derrick, F. Meng, L. Li, X. Ji, B. Trommer, D. J. Beardsley, et al. Developmental Changes in Diffusion Anisotropy Coincide with Immature Oligodendrocyte Progression and Maturation of Compound Action Potential J. Neurosci., June 22, 2005; 25(25): 5988 - 5997. [Abstract] [Full Text] [PDF]
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N. Rollins, T. Reyes, and J. Chia Diffusion Tensor Imaging in Lissencephaly AJNR Am. J. Neuroradiol., June 1, 2005; 26(6): 1583 - 1586. [Abstract] [Full Text] [PDF]
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 M Symms, H R Jager, K Schmierer, and T A Yousry A review of structural magnetic resonance neuroimaging J. Neurol. Neurosurg. Psychiatry, September 1, 2004; 75(9): 1235 - 1244. [Abstract] [Full Text] [PDF]
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B. S. M. ter Rahe, C. B. L. M. Majoie, E. M. Akkerman, G. J. den Heeten, B. T. Poll-The, and P. G. Barth Peroxisomal Biogenesis Disorder: Comparison of Conventional MR Imaging with Diffusion-Weighted and Diffusion-Tensor Imaging Findings AJNR Am. J. Neuroradiol., June 1, 2004; 25(6): 1022 - 1027. [Abstract] [Full Text] [PDF]
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G. Zhai, W. Lin, K. P. Wilber, G. Gerig, and J. H. Gilmore Comparisons of Regional White Matter Diffusion in Healthy Neonates and Adults Performed with a 3.0-T Head-only MR Imaging Unit Radiology, December 1, 2003; 229(3): 673 - 681. [Abstract] [Full Text] [PDF]
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Y. Arzoumanian, M. Mirmiran, P.D. Barnes, K. Woolley, R.L. Ariagno, M.E. Moseley, B.E. Fleisher, and S.W. Atlas Diffusion Tensor Brain Imaging Findings At Term-equivalent Age May Predict Neurologic Abnormalities in Low Birth Weight Preterm Infants AJNR Am. J. Neuroradiol., September 1, 2003; 24(8): 1646 - 1653. [Abstract] [Full Text] [PDF]
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A. Mazumdar, P. Mukherjee, J. H. Miller, H. Malde, and R. C. McKinstry Diffusion-Weighted Imaging of Acute Corticospinal Tract Injury Preceding Wallerian Degeneration in the Maturing Human Brain AJNR Am. J. Neuroradiol., June 1, 2003; 24(6): 1057 - 1066. [Abstract] [Full Text] [PDF]
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M. J. Rivkin Opening the Window Into Brain Development in Children More Widely With Magnetic Resonance Imaging Pediatrics, June 1, 2003; 111(6): 1432 - 1433. [Full Text] [PDF]
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A. Righini, E. Bianchini, C. Parazzini, P. Gementi, L. Ramenghi, C. Baldoli, U. Nicolini, F. Mosca, and F. Triulzi Apparent Diffusion Coefficient Determination in Normal Fetal Brain: A Prenatal MR Imaging Study AJNR Am. J. Neuroradiol., May 1, 2003; 24(5): 799 - 804. [Abstract] [Full Text] [PDF]
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J. F. L. Schneider, K. A. Il'yasov, E. Boltshauser, J. Hennig, and E. Martin Diffusion Tensor Imaging in Cases of Adrenoleukodystrophy: Preliminary Experience as a Marker for Early Demyelination? AJNR Am. J. Neuroradiol., May 1, 2003; 24(5): 819 - 824. [Abstract] [Full Text] [PDF]
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J. H. Miller, R. C. McKinstry, J. V. Philip, P. Mukherjee, and J. J. Neil Diffusion-Tensor MR Imaging of Normal Brain Maturation: A Guide to Structural Development and Myelination Am. J. Roentgenol., March 1, 2003; 180(3): 851 - 859. [Full Text] [PDF]
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 M. P. Goldberg and B. R. Ransom New Light on White Matter Stroke, February 1, 2003; 34(2): 330 - 332. [Full Text] [PDF]
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P. McGraw, L. Liang, and J. M. Provenzale Evaluation of Normal Age-Related Changes in Anisotropy During Infancy and Childhood as Shown by Diffusion Tensor Imaging Am. J. Roentgenol., December 1, 2002; 179(6): 1515 - 1522. [Abstract] [Full Text] [PDF]
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P. Mukherjee, J. H. Miller, J. S. Shimony, J. V. Philip, D. Nehra, A. Z. Snyder, T. E. Conturo, J. J. Neil, and R. C. McKinstry Diffusion-Tensor MR Imaging of Gray and White Matter Development during Normal Human Brain Maturation AJNR Am. J. Neuroradiol., October 1, 2002; 23(9): 1445 - 1456. [Abstract] [Full Text] [PDF]
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 R. C. McKinstry, J. H. Miller, A. Z. Snyder, A. Mathur, G. L. Schefft, C. R. Almli, J. S. Shimony, S. I. Shiran, and J. J. Neil A prospective, longitudinal diffusion tensor imaging study of brain injury in newborns Neurology, September 24, 2002; 59(6): 824 - 833. [Abstract] [Full Text] [PDF]
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