Diffusion-Tensor Imaging Assessment of White Matter Maturation in Childhood and Adolescence

Transcription

1 Neuroradiology/Head and Neck Imaging Original Research Moon et al. White Matter Diffusion-Tensor Imaging Neuroradiology/Head and Neck Imaging Original Research Won-Jin Moon 1 James M. Provenzale 2,3 Basar Sarikaya 4,5 Yon Kwon Ihn 6 John Morlese 7 Steven Chen 2 Michael D. DeBellis 8 Moon WJ, Provenzale J, Sarikaya B, et al. Keywords: adolescence, apparent diffusion coefficient, children, diffusion-tensor imaging, fractional anisotropy DOI: /AJR Received December 26, 2010; accepted after revision February 23, Supported by National Institutes of Health grants K24 MH071434, K24 DA028773, RO1-MH61744, R01-AA12479, and RO1-MH63407 (M. D. DeBellis) and by Konkuk University. 1 Department of Radiology, Konkuk University School of Medicine, Konkuk University Medical Center, Seoul, South Korea. 2 Department of Radiology, Duke University Medical Center, Box 3808, Durham, NC Address correspondence to J. M. Provenzale (prove001@mc.duke.edu). 3 Departments of Radiology, Oncology and Biomedical Engineering, Emory University School of Medicine, Atlanta, GA. 4 Department of Radiology, TDV 29 Mayis Hastanesi, Ankara, Turkey. 5 Department of Radiology, University of Minnesota, Minneapolis MN. 6 Department of Radiology, St. Vincent s Hospital, The Catholic University of Korea, Suwon, Korea. 7 Department of Radiology, Leicester Royal Infirmary, Leicester, UK. 8 Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham NC. AJR 2011; 197: X/11/ American Roentgen Ray Society Diffusion-Tensor Imaging Assessment of White Matter Maturation in Childhood and Adolescence OBJECTIVE. The purpose of this study was to test a first hypothesis that fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values continue to change in late childhood and adolescence and a second hypothesis that less mature white matter (WM) regions have a higher rate of change than WM regions that are relatively more mature. SUBJECTS AND METHODS. Eighty-seven healthy children (50 girls, 37 boys; mean age, 11.2 ± 3.6 years; range, years) underwent six-direction diffusion-tensor imaging with a 3-T MRI system. Three neuroradiologists independently drew regions of interest in 10 WM regions and measured FA and ADC values. To test the first hypothesis, we correlated these values with subject age by linear regression analysis (p < 0.05). To test the second hypothesis, we determined whether regions with lower FA and higher ADC in the 4- to 7-year old group had a higher slope of FA increase and ADC decrease over the entire age range. For this assessment, we used linear regression analysis (p < 0.05) and curve fitting. RESULTS. In the test of the first hypothesis, increases in FA with age were noted in all WM regions and were statistically significant in six regions. Decreases in ADC values with age were noted in all brain regions except the genu of the corpus callosum. In all other regions except the splenium of the corpus callosum, the decreases were statistically significant. In the test of the second hypothesis, the relation between FA in the 4- to 7-year-old subjects and the FA increase in the entire sample was best described with a linear equation. The rate of age-related FA increase tended to be greater with lower initial FA (r = 0.384, p = 0.271). The relation between ADC in the 4- to 7-year-old subjects and ADC decrease in the entire population was best described with a second-order equation. The rate of age-related ADC decrease tended to be greater with higher initial ADC (r = 0.846, p = 0.001). For ADC values of 100 or less at age 4 7 years, the rate of ADC change with age tended to be decrease as initial ADC increased. CONCLUSION. In general, both hypotheses were verified. Overall, FA values continue to increase and ADC values continue to decrease during childhood and adolescence. The most rapid changes were found in WM regions that were least mature in the first few years of the study period. T he human brain develops continually from infancy to adolescence. Important developmental processes occur in terms of fine motor, affective, and cognitive functions during this period even though sensory development is almost complete at 5 7 years [1]. During this period, maturation of white matter (WM) is vital for development of neural pathways connecting individual brain regions [2]. Diffusion-tensor imaging (DTI) is a well-recognized method for assessing WM development. Two primary indexes that can be measured with DTI are fractional anisotropy (FA) and apparent diffusion coefficient (ADC) val- ues. FA refers to the tendency for microscopic water motion to proceed in one direction (anisotropic diffusion) as opposed to randomly (isotropic diffusion). ADC values indicate the rate of microscopic water motion regardless of directionality. DTI studies have shown that in infancy and early childhood, FA values increase and ADC values decrease with age in most WM regions. These changes are thought to reflect, at least in part, progressive myelination [3, 4]. Most studies show that the most prominent changes occur within the first 3 4 years of life [5 7]. Although DTI changes in early childhood are relatively well recorded, 704 AJR:197, September 2011

2 White Matter Diffusion-Tensor Imaging such changes in late childhood and adolescence are poorly documented. When available, findings have been contradictory [1, 8 11], perhaps because of variability in subject populations and relatively small sample sizes. In addition, most previous studies have been conducted with clinical 1.5-T MRI systems, which are liable to limitations of resolution and image quality due to long acquisition time and the accompanying increased likelihood of patient motion. Our goal was to study WM maturation in healthy children and adolescents (age range, 4 17 years) as reflected in changes in DTI parameters assessed with a 3-T MRI system. Our study had two hypotheses. First we hypothesized that just as FA values increase and ADC values decrease in early childhood, such changes would continue in late childhood and adolescence. Our second hypothesis was that WM regions that were relatively less mature (as reflected by lower FA and higher ADC values) early in the period we studied (ages 4 7 years) would have a higher rate of change than WM regions that are relatively more mature. The second hypothesis was based on findings in a previous study [12]. In that study, we found that the rate of FA increase during the first 6 years of life is higher in WM regions that have lower initial FA values (e.g., peripheral, noncompact WM) than in regions that have higher initial FA values (e.g., deep, compact WM regions such as the corpus callosum). Subjects and Methods This prospective study included 87 healthy subjects (50 girls, 37 boys; mean age, 11.2 ± 3.6 [SD] years; range, years) without a history of relevant medical, neurologic, or psychiatric illness. The mean age of the girls was 10.5 ± 3.3 years and of the boys was 12.0 ± 3.9 years. The age difference between girls and boys was not significant (p = 0.059, Student t test). The age distribution of the participants, which was obtained by use of the Kolmogorov-Smirnov z test, did not differ significantly between the sexes (p = 0.174). Control subjects were recruited from the same surrounding community through institutional review board approved advertisements at schools and pediatric clinics. Children and adolescents assented, and legal guardians provided informed consent before participation. The cognitive measures are described later. However, only imaging features were analyzed in this study. The children participated in a detailed clinical research assessment that included the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime version (K-SADS-PL) [13]. This semistructured interview was administered with caregivers and subjects. We also used archival records as sources of information. The K-SADS-PL was modified to include additional information about life events, including traumatic events from the Child and Adolescent Psychiatric Assessment [14]; disorders not present in the K-SADS-PL; a structured scale to quantify symptom frequency; and algorithms to determine Axis I psychiatric disorders based on Diagnostic and Statistical Manual, fourth edition (DSM-IV) criteria. Disorders were assigned a severity score of mild, moderate, or severe. This modified version is available on request. Cognitive testing was conducted to establish that controls had no DSM-IV Axis I disorders or learning disabilities. Exclusion criteria were Full Scale Intelligence Quotient less than 70; disability that made a comprehensive interview of the child difficult; relevant medical illness, head injury, or neurologic disorder; autism or pervasive developmental disorder; birth weight less than 5 pounds (2.3 kg) or severe prenatal compromise with neonatal ICU stay; and current or lifetime alcohol or substance use disorder (defined as DSM-IV abuse or dependence). The local university hospital institutional review board approved the study. Legal guardians gave informed consent, and children assented before participation. MRI Technique MRI was performed with a 3-T system (Trio, version VA 24 software, Siemens Healthcare) in the department of radiology at our institution. Diffusion-weighted images were acquired with a single-shot echo-planar imaging pulse sequence. Imaging parameters were TR/TE, 7200/90; bandwidth, 1346 Hz/pixel; acquisition matrix, ; FOV, 220 mm 2 ; slice thickness, 3 mm, contiguous. We used a long turbo spin-echo technique with a TE of 158 ms and a short turbo spin-echo with a TE of 24 ms rather than a double spin-echo sequence. All axial slices were acquired parallel to the anterior commissure posterior commissure line. Images were acquired with diffusion weighting in each of six directions, all with a b value (diffusion-weighting factor) of An image with no diffusion weighting (b = 0) was acquired for reference. Four separate acquisitions were performed with a standard six-direction Siemens Trio scheme. A threshold was applied to the data to remove the skull tissue, and smoothing was performed with a kernel. The corresponding directions were averaged (over the acquisitions, so for a given direction we averaged the four acquisitions for that direction after thresholding and smoothing). The diffusion tensor eigenvalues were calculated in each voxel for calculation of the mean diffusivity (ADC) and the FA value in each voxel by established methods [15]. The ADC and FA maps were aligned to a standard template by finding the transformation that aligned the b = 0 image to the template and applying that transformation to the ADC and FA maps. A neuroradiologist reviewed the images and excluded clinically significant abnormalities. The subjects tolerated the procedure well. No sedation was used. Data Processing Raw image data were transferred in DICOM format and converted into Analyze 7.1 format (Mayo Foundation). Diffusion tensor matrices from sets of the seven diffusion-weighted images were generated, and the three eigenvalues and eigenvectors were calculated by matrix diagonalization. FA and ADC maps were generated with custom scripts with Matlab software (The MathWorks) and stored as img files. Maps were generated according to the following standard algorithms: ADC = ln (s b = 1000 / s b = 0 ) / ( b) where s b = 1000 was obtained by averaging the six diffusion-weighted images. FA = 3/2 (λ 1 <λ>) 2 + (λ 2 <λ>) 2 + (λ 3 <λ>) 2 λ λ 2 + 3λ 2 where λ n is the eigenvalue describing the diffusion tensor and <λ> = (λ 1 + λ2 + λ 3 ) / 3. Each ADC and FA map was realigned to the b = 0 images by rigid coregistration and smoothed with a gaussian kernel of 3 mm at full width half-maximum. No eddy current correction was used. As a result, some degree of eddy current induced distortion remained on the images. Region of Interest Placement Polygonal or rectangular regions of interest (ROIs) were drawn on the slice of the FA map according to the foregoing criteria by three neuroradiologists, who each measured 33% of the ROIs. Each neuroradiologist had at least 3 years of experience in interpretation of MR images. The FA values in each ROI were measured by a single observer. To avoid inclusion of CSF or unintended gray matter in the ROIs, b = 0 images and ADC maps were assessed when the FA map was visualized to confirm the position of an ROI. Each ROI size was approximately 60 mm 2. ROIs were placed at various locations within the preestablished boundaries (described later) until the highest FA value was found and recorded. Each region was measured at least three times to yield the highest FA value. AJR:197, September

3 Moon et al. Region of Interest Analysis Average ADC and FA values were measured for 10 brain regions by ROI analysis with medical image processing, analysis, and visualization software (MIPAV, National Institutes of Health). The software enabled work on both the FA and ADC maps of the same subject at the same time and also allowed drawing of an identical ROI on the ADC map corresponding to the ROI indicating maximum the FA value for that particular region. This task was performed by copying the ROI drawn on the FA map (indicating the maximum FA value of that slice) and pasting it on the ADC map. The following 10 ROIs were chosen for analysis: genu of the corpus callosum, splenium of the corpus callosum, anterior limb of the internal capsule, posterior limb of the internal capsule, centrum semiovale, middle cerebellar peduncle, pericallosal frontal WM, superior frontal WM, inferior parietal WM, and superior parietal WM. All measurements were performed in both hemispheres. A template for selection of the slice and placement of an ROI is depicted in Figure 1. At the first three axial sections just superior to lateral ventricle, two lines in the coronal plane were drawn to divide the entire brain into three equal parts: an anterior segment, a middle segment, and a posterior segment. The ROI for the superior frontal WM was drawn in the subcortical WM of the superior frontal gyrus in the anterior segment. The ROI for the centrum semiovale was drawn in the central WM of the middle segment, care being taken not to include the cingulum in the medial aspect. The ROI for the superior parietal WM was drawn in the WM of the posterior segment, within the WM of the superior parietal lobule. A The ROI for the pericallosal frontal WM was drawn on the axial section on which the anterior-posterior extent of the genu and splenium was maximal. The readers drew a line in the parasagittal plane that was tangential to the lateral margin of the frontal horn of the lateral ventricle and another line in the coronal plane that was tangential to the posterior limit of the genu of the corpus callosum. The ROI for the pericallosal frontal WM was drawn in the region of the frontal WM that was bordered by these lines. In the same slice, the readers drew a line in the parasagittal plane that was tangential to the lateral margin of the atrium of the lateral ventricle and another line in the coronal plane that was tangential to the posterior limit of the atrium. The ROI for the inferior parietal WM was drawn in the parietal WM in a region bordered by these lines. The ROIs for the genu of the corpus callosum and the splenium of the corpus callosum were chosen in the same slice. For the ROI of the middle cerebellar peduncle, the readers drew a line in the parasagittal plane tangential to the lateral margin of the fourth ventricle on the axial slice in which the size of the fourth ventricle was maximal. The ROI was placed in the WM of the middle cerebellar peduncle just lateral to this line on that slice and on the adjacent two slices to obtain the maximum FA value for this region. Measurements of Intraobserver and Interobserver Variability Data were analyzed with statistical software (SPSS version 12, SPSS). Intraobserver and interobserver variability measurements were obtained for two of these neuroradiologists; the third neuroradiologist had left our institution at the time these comparisons were performed. The two neuroradiologists measured FA values in three ROIs (genu of corpus callosum, central WM, and pericallosal frontal WM) in 10 subjects twice at least 1 week apart. Intraobserver variability was measured for each reader for the two datasets using the Pearson test. Two comparisons of interobserver variability were performed. The first set of observations for each reader was compared with that of the other reader, again by Pearson test. The same process was performed for the second set. Comparison of Mean Fractional Anisotropy and Apparent Diffusion Coefficient Values With Age To test our first hypothesis, paired Student t tests were performed for comparison of FA values in the two hemispheres, and the same test was performed for ADC values. Mean ROI values of corresponding regions of both hemispheres were averaged. We evaluated normality of the data distribution using the Kolmogorov-Smirnov test, which allowed us to use a paired Student t test and Pearson correlation as statistical tests. Diffusion-Tensor Imaging Values as a Function of Age We assessed change in FA values as a function of age in each brain region by determining slopes of the rate of change for each using linear regression analysis with statistical significance defined as p < Similarly, we assessed change in ADC values as a function of age in each brain region by Fig. 1 Ten regions of interest used in study overlaid on fractional anisotropy maps. A, Axial diffusion-tensor image through subcortical white matter regions shows regions of interest for superior frontal white matter (1), centrum semiovale (2), and superior parietal white matter (3). B, Axial diffusion-tensor image through level of corpus callosum shows regions of interest for pericallosal frontal white matter (4), inferior parietal white matter (5), genu of corpus callosum (6), anterior limb of internal capsule (7), posterior limb of internal capsule (8), and splenium of corpus callosum (9). C, Axial diffusion-tensor image through cerebellum shows region of interest for middle cerebellar peduncle (10). B C 706 AJR:197, September 2011

4 White Matter Diffusion-Tensor Imaging determining slopes of rate of change for each using linear regression analysis with statistical significance defined as p < Diffusion-Tensor Imaging Values Versus Age According to Major Fiber Orientation To compare FA changes in WM regions with one another, we designated WM regions according to whether they contained major fiber pathways that were predominantly right-left, anteriorposterior, or cranial-caudal in orientation. These groups consisted of the genu of the corpus callosum, splenium of the corpus callosum, and middle cerebellar peduncle in the right-left orientation group; the pericallosal frontal WM and inferior parietal WM in the anterior-posterior orientation group; and the anterior limb of the internal capsule, posterior limb of the internal capsule, centrum semiovale, superior frontal WM, and superior parietal WM in the cranial-caudal orientation group. We also compared ADC changes in WM regions according to major fiber orientation. Diffusion-Tensor Imaging Values According to Compactness of White Matter Regions Similar to the comparison of DTI values and age according to major fiber orientation, we divided WM regions into compact and noncompact groups. The compact WM groups consisted of the genu of the corpus callosum, splenium of the corpus callosum, posterior limb of the internal capsule, anterior limb of the internal capsule, centrum semiovale, and middle cerebellar peduncle. The noncompact WM groups consisted of the superior frontal WM, superior parietal WM, pericallosal frontal WM, and inferior parietal WM. We then compared the mean slope of the compact WM groups and that of the noncompact group. We also compared ADC changes in WM regions according to the same compact versus noncompact WM designations as for FA changes. As in the analysis of the FA values, we compared the mean slope of the compact WM groups and that of the noncompact group. Influence of Degree of White Matter Maturity in Early Childhood on Later Diffusion-Tensor Imaging Changes To test our second hypothesis, we set out to determine the influence of degree of maturation of a WM region in early childhood on subsequent changes later in childhood. The average FA value at age 4 7 years for each ROI was plotted against the slope of change of FA values for the entire study sample for that ROI by the Pearson correlation test and linear regression analysis. We also compared mean ADC value in each WM region in children 4 7 years old against the slope of change of ADC values for the entire sample. We then calculated functions by comparing R 2 values to determine which function would best describe the data. Results Measurements of Intraobserver and Interobserver Variability Intraobserver measurements were for the genu of corpus callosum, for central WM, and for pericallosal frontal WM for observer 1 and for the genu of corpus callosum, for central WM, and for the pericallosal frontal WM for observer 2 (all p < 0.01 except for the last value, which was p < 0.02). The first set of interobserver measurements was for the corpus callosum, for central WM, and for the pericallosal frontal WM. The second set of interobserver measurements was for the corpus callosum, for central WM, and (p = 0.03) for the pericallosal frontal WM (all p < 0.01 unless otherwise noted). Assessment of Hemispheric Asymmetry of Mean Fractional Anisotropy and Apparent Diffusion Coefficient Values Mean FA values ranged from 0.41 ± 0.04 in the pericallosal frontal WM to 0.61 ± 0.06 in the splenium of the corpus callosum (Table 1). In general, no significant differences were seen with regard to hemispheric asymmetry. However, a small but significant degree of hemispheric asymmetry was found in three brain regions. FA was greater on the right in the genu of the corpus callosum (p < 0.001) and greater on the left in the splenium of the corpus callosum (p = 0.03) and the middle cerebellar peduncle (p = 0.043). Mean ADC values ranged from 0.80 ± mm 2 /s in the superior frontal WM to 1.12 ± mm 2 /s in the genu of the corpus callosum (Table 2). A small but significant degree of hemispheric asymmetry was found in only one brain region, the inferior parietal WM (p = 0.014). Diffusion-Tensor Imaging Values as a Function of Age Increases in FA with age were noted in all brain regions and were statistically significant in the following six regions: anterior limb of the internal capsule, posterior limb of the internal capsule, centrum semiovale, pericallosal frontal WM, superior frontal WM, and superior parietal WM (p < 0.05) (Table 3). The agerelated increase in FA was the greatest in the centrum semiovale followed by, in order, the posterior limb of the internal capsule, anterior limb of the internal capsule, superior parietal WM, superior frontal WM, and pericallosal TABLE 1: Fractional Anisotropy Values Averaged Across Both Hemispheres and in Each Hemisphere Fractional Anisotropy Region Average Left Right p Genu of corpus callosum 0.54 ± ± ± 0.07 < a Splenium of corpus callosum 0.61 ± ± ± a Anterior limb of the internal capsule 0.42 ± ± ± Posterior limb of the internal capsule 0.55 ± ± ± Middle cerebellar peduncle 0.59 ± ± ± a Centrum semiovale 0.54 ± ± ± Pericallosal frontal white matter 0.41 ± ± ± Inferior parietal white matter 0.43 ± ± ± Superior frontal white matter 0.46 ± ± ± Superior parietal white matter 0.43 ± ± ± Note Values are mean ± SD. Statistically significant difference between hemispheres. AJR:197, September

5 Moon et al. TABLE 2: Mean Apparent Diffusion Coefficient Values Averaged Across Hemispheres and in Each Hemisphere (n = 87) Apparent Diffusion Coefficient (10 3 mm 2 /s) Region Average Left Right p Genu of corpus callosum 1.12 ± ± ± Splenium of corpus callosum 1.06 ± ± ± Anterior limb of the internal capsule 0.82 ± ± ± Posterior limb of the internal capsule 0.84 ± ± ± Middle cerebellar peduncle 0.85 ± ± ± Centrum semiovale 0.85 ± ± ± Pericallosal frontal white matter 0.86 ± ± ± Inferior parietal white matter 0.87 ± ± ± a Superior frontal white matter 0.80 ± ± ± Superior parietal white matter 0.82 ± ± ± Note Values are mean ± SD. Statistically significant difference between hemispheres frontal WM (Table 3). In contrast, the genu of the corpus callosum and splenium of the corpus callosum exhibited little change, and the middle cerebellar peduncle and inferior parietal WM had only a moderate rate of change (Fig. 2). Decreases in ADC values with age were noted in all brain regions except for the genu of the corpus callosum, which had a mild increase in ADC value (Table 3). In all other regions except the splenium of the corpus callosum, the decreases were statistically significant (Table 3). The ADC decrease with age was the greatest in the centrum semiovale followed by the posterior limb of the internal capsule, pericallosal frontal WM, superior parietal WM, superior frontal WM, inferior parietal WM, middle cerebellar peduncle, and anterior limb of the internal capsule (Fig. 3). Diffusion-Tensor Imaging Values Versus Age According to Major Fiber Orientation Comparison of FA changes in WM regions according to major fiber orientation showed a mean slope of for the rightleft orientation group, for the anteriorposterior orientation group, and for the cranial-caudal orientation group. Comparison of ADC changes in WM regions according to major fiber orientation showed a mean slope of for the right-left orientation group, for the anterior-posterior orientation group, and for the cranialcaudal orientation group. Therefore, the lowest rate of change for both the FA changes and ADC changes was seen in the right-left orientation group. Whereas the FA changes were substantially lower for the anterior-posterior orientation group compared with the cranial-caudal orientation group, the changes in the two groups were similar for mean ADC values. Diffusion-Tensor Imaging Values According to Compactness of White Matter Regions In general, mean FA values were higher in regions generally considered to contain compact WM, that is, the genu of the corpus callosum, splenium of the corpus callosum, posterior limb of the internal capsule, and middle cerebellar peduncle (mean FA value, 0.57) compared with regions generally considered to have less compact WM, that is, the centrum semiovale, pericallosal frontal WM, anterior limb of the internal capsule, superior frontal WM, superior parietal WM, and inferior parietal WM (mean FA value, 0.45). The mean slope of FA change in compact WM (0.345) was slightly higher than in noncompact WM (0.314). TABLE 3: Results of Linear Regression Analysis of Fractional Anisotropy and Apparent Diffusion Coefficient With Age Fractional Anisotropy vs Age Apparent Diffusion Coefficient vs Age Region r R 2 Slope p r R 2 Slope p Genu of corpus callosum Splenium of corpus callosum Anterior limb of the internal capsule a < a Posterior limb of the internal capsule a < a < Middle cerebellar peduncle a Centrum semiovale a < a Pericallosal frontal white matter a a < Inferior parietal white matter a < Superior frontal white matter a a < Superior parietal white matter a < a < a Statistically significant. 708 AJR:197, September 2011

6 White Matter Diffusion-Tensor Imaging TABLE 4: Mean Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) Values for Baseline Values in Younger Patients and Slopes of FA, FA Increase, and ADC Decrease Throughout Age Range Apparent Diffusion Fractional Anisotropy Coefficient (10 3 mm 2 /s) Region Mean, 4 7 y Slope, 4 17 y Mean, 4 7 y Slope, 4 17 y Genu of corpus callosum Splenium of corpus callosum Anterior limb of the internal capsule Posterior limb of the internal capsule Middle cerebellar peduncle Centrum semiovale Pericallosal frontal white matter Inferior parietal white matter Superior frontal white matter Superior parietal white matter Unlike the FA data, no substantial differences in mean ADC values were seen between compact WM regions and noncompact WM regions. Instead, all WM regions had relatively similar mean ADC values except for the two regions in the corpus callosum. For compact and noncompact WM groups, the mean slope of ADC change in compact WM ( 0.029) was lower than in noncompact WM ( 0.052). Influence of Degree of White Matter Maturity in Early Childhood on Later Diffusion-Tensor Imaging Changes The mean FA values and ADC values during the early age period (age 4 7 years) and the slope of FA increase over the entire age range are listed in Table 4. With regression analysis and curve fitting, the relation between FA increase and FA during the early age period was Fractional Anisotropy Value Age (m) best described with a linear equation (Fig. 4). Given the fitting curve, the rate of age-related FA change tended to be greater with lower initial FA (r = 0.384, p = 0.271). Unlike the FA changes, the relation between ADC decrease and initial ADC at age 4 7 years was best fit with a second-order equation (r = 0.846, p = 0.001) (Fig. 5). For ADC values of mm 2 /s or less at age 4 7 years, the rate of ADC change with age tended to be greater with higher ADC values. For initial ADC values greater than 100 (found in the genu and splenium of the corpus callosum), the rate of ADC change with age was approximately zero. Discussion The major findings in our study are as follows. First, we found that during the period ADC ( 10 5 mm 2 /s) from early childhood to late adolescence, FA values increased in almost all WM regions studied; these findings are consistent with continued brain maturation. Changes in FA values in WM have been reported to be due to various factors. Whereas increases in myelination initially were thought to be the primary factor contributing to increases in FA during brain development, axonal factors such as increases in axonal diameter [2], axonal packing [1], and axonal pruning [16] are increasingly being recognized as contributing factors. The same, or similar, factors also likely contribute to changes in ADC values. In most of the WM regions examined in our study, the FA changes with age were statistically significant. The largest differences were seen in the anterior limb of the internal capsule, posterior limb of the internal capsule, and the centrum semiovale. The second major finding in our study is that WM ADC values were lower in older children than in younger children, with the exception of the genu of the corpus callosum; these findings are again consistent with continued brain maturation. The largest differences were seen in the posterior limb of the internal capsule, centrum semiovale, and pericallosal frontal WM. These differences were statistically significant for all brain regions studied. Therefore, our first hypothesis, that FA and ADC changes continue well past early childhood, was verified. These findings indicate that microstructural changes consistent with WM development continue well beyond the first few years of life and into adolescence. In conglomerate, these findings are evidence in support of our first hypothesis. The third major finding in our study is that, in general, the degree of maturity of WM re Age (m) Fig. 2 Graph shows relation between fractional anisotropy value and age for superior frontal white matter region of interest. Slope = 0.309x + 42, R 2 = Fig. 3 Graph shows relation between age and apparent diffusion coefficient (ADC) value for superior frontal white matter region of interest. Slope = 0.048x , R 2 = AJR:197, September

7 Moon et al. gions (as reflected in FA and ADC values in the youngest children in our sample) was inversely proportional to the degree of change in those values in the entire sample. These findings are discussed in greater detail later. Change in FA Value Initial FA Value Comparison With Previous Studies Our study population consisted of a large number of healthy subjects spanning childhood and adolescence. Compared with populations in most previous studies [8, 11, 17 20], our patient population is larger and provides a more continuous sample across the age periods mentioned. Reported FA and ADC values often differ substantially from one study to another because of differences in specific patient populations, image acquisition, data analysis, and whether ROIs are placed or analysis of entire fiber tracts is performed. The FA and ADC values in various WM regions recorded in our patients generally correlated well with those in previous studies [8, 17]. For instance, compared with the results in a study of 32 healthy children 8 12 years old, the FA and ADC values for most WM regions in our study differed 5 15%, a relatively small difference [8]. As in our study, the FA values in deep WM regions such as the corpus callosum and posterior limb of the internal capsule were substantially higher than in subcortical WM regions [8, 17]. These relative differences between regions in previous studies can be taken to validate our measurements regardless of relatively minor differences in absolute values. Our findings of generally continued FA increase and ADC decrease beyond early childhood can be compared with those in one study that, like our study, was conducted with six gradient directions in the DTI imaging protocol [8]. That study differed in methods and findings in a number of ways. First, the sample in that study had a less extensive age range (8 12 years). As in our study, the highest FA values were found in the genu and splenium of the corpus callosum and in the posterior limb of the internal capsule [8]. However, unlike our finding, an increase in FA in the posterior limb of the internal capsule was not found to be statistically significant. It is unclear why such differences exist between the two studies, but the smaller age range in the other study may account in part for these differences. Comparison of Corpus Callosum With Other White Matter Regions The relatively small changes in FA and ADC values within the genu and splenium of the corpus callosum differed substantially from the more profound changes in all other brain regions studied. These relatively minor changes have been reported in most other studies of brain maturation [11, 18 20]. Interestingly, our findings in the corpus callosum are at variance with those of another study, in which 32 healthy children 8 12 years old underwent DTI [8]. That study showed statistically significant increases in FA in the genu and splenium over the range of ages studied. Our findings are also at variance with substantial FA increases in the corpus callosum found in another study of Fig. 4 Graph of change in fractional anisotropy (FA) in relation to initial FA at age 4 7 years shows rate of age-related change in FA tends to be greater with lower initial FA. Slope = x , R 2 = Slope of ADC vs Age children and adolescents [9]. However, that study differed from ours in two important ways. First, the FA increases in that study appear to have been most prominent in the body of the corpus callosum, a region that we did not study. Second, that study used voxel-based analysis rather than placement of ROIs. Voxel-based morphometry has some benefits relative to techniques of placement of ROIs. As some investigators have noted, however, voxel-based morphometry has disadvantages due to difficulties related to the processes of nonlinear spatial normalization and smoothing [21]. Validation of Our Second Hypothesis It is well recognized that the genu and splenium of the corpus callosum are among the earliest structures to appear mature on T1- and T2-weighted images [22]. Studies have shown that FA values in the corpus callosum of preterm infants and during the first few years of postnatal life are among the highest among all brain regions [12, 23]. Furthermore, histologic studies have shown that by the end of the first year of life, mature myelin is found in approximately 90% of corpus callosum specimens, as opposed to less than 50% in WM of peripheral brain regions [24, 25]. These imaging and histologic findings appear to indicate that the corpus callosum is one of the earliest brain regions to reach a maturational stage close to that of the adult. Mean ADC Value Fig. 5 Graph shows relation between initial apparent diffusion coefficient (ADC) in subjects at lower end of age range (4 7 years) and subsequent slope of decrease in ADC value across entire age range (4 17 years). Mean ADC value for each of 10 regions of interest in 4- to 7-year age range proceeds from lowest value of mm 2 /s in anterior limb of internal capsule to value of mm 2 /s in genu of corpus callosum. Relation is best fit with second-order equation (r = 0.846, p = 0.001). Data point for anterior limb of internal capsule shows slope of 0.027, and that for genu of corpus callosum shows slope of For ADC values of 100 or less at age 4 7 years, rate of ADC change with age tends to decrease as initial ADC increases. For initial ADC values greater than 100 (found in genu and splenium of corpus callosum), rate of ADC change with age is approximately zero. 710 AJR:197, September 2011

8 White Matter Diffusion-Tensor Imaging Thus the rate of microstructural change in late childhood and adolescence would be expected to be slower than in other, less mature WM regions, which was explored in our second hypothesis. Our second hypothesis was that brain regions that appeared most mature by the end of the first few years of our study period (ages 4 7 years) would have a lower rate of change in DTI parameters during the remainder of the study period. We did find a general negative correlation between FA values during the age period 4 7 years and the rate of FA change over the entire age period (Fig. 4). Similarly, a positive correlation was seen between ADC values at the end of the first few years of the age range studied and subsequent ADC decrease; however, because of two outlier values, the relation was best fit by a second-order equation (Fig. 5). Thus our second hypothesis was also verified. It appears that a trend exists for WM regions that are less mature in the first few years of life to undergo FA increase and ADC decrease at a higher rate than regions that are initially more mature. In a previous study [12], we found that the rate of increase in FA values in the first year of life is higher in noncompact WM regions (which have lower initial FA values) relative to compact WM regions. Our results in the current study suggest that regional variation in the rate of diffusion change with age may be related not only to concomitant functional maturation but also to preexisting microanatomic conditions in the WM. These conditions include, among others, degree of fiber organization, amount of myelination, density of axonal packing at the earlier stage of childhood, and mixture of fiber population (i.e., crossing fibers) [20, 26]. Analysis of White Matter Regions According to Predominant Fiber Direction We found differences in FA increases, and concomitant ADC decreases, according to the most prominent fiber orientations in sites where ROIs were located. The greatest FA increase and ADC decrease were found in regions with pathways oriented in the cranial-caudal direction. Individually, two regions having the greatest degree of both FA increase and ADC decrease were the posterior limb of the internal capsule and the centrum semiovale. These regions are both cranial-caudal in orientation and contain, among other entities, components of motor pathways. Our findings differ from those in a previous study [11] of children and adolescents by virtue of the high rate of FA change in the centrum semiovale in our subjects; a previous study showed no change in FA values in that WM region. That study was similar to ours in the use of six-direction DTI. Interestingly, although that study did not show substantial changes in FA, prominent ADC changes were found, as in our study. As noted earlier, the cranial-caudal projections showed, as a group, the highest degree of FA and ADC change. Because these projections consist largely of motor and sensory pathways, our findings suggest that the greatest degree of maturation of WM pathways in childhood and adolescence is in pathways concerned with motor and sensory function. Nonetheless, we recognize that during this period of development, substantial maturation is occurring in association fibers connecting the frontal lobes with the temporal and parietal lobes, that is, WM pathways oriented along the anterior-posterior axis, such as the superior longitudinal fasciculus, inferior longitudinal fasciculus, and cingulum. These structures are concerned with higher-order functions such as regulating motor behavior, choosing between competing motor tasks, and retrieval of spatial information [27]. It is likely that the results of our analysis did not reflect maturation in these important pathways because the sites of ROI placement were not representative of the important anterior-posterior structures. In future studies, we intend to avoid this difficulty by using tractography-based ROIs to evaluate specific WM tracts. Analysis of White Matter Regions According to White Matter Compactness Decreases in ADC values were greater in noncompact WM regions than in compact WM regions. Our findings therefore are similar to those of a previous study [18] showing that the greatest degree of ADC decrease was found in subcortical WM regions compared with deep WM regions. Whereas ADC decreases differed in compact and noncompact WM regions, FA increases did not differ. A similar lack of difference in increase in FA between subcortical and deep WM regions (even in the presence of differences in ADC decreases) was found in another study [18]. Relevance of White Matter Regions to Developing Brain Functions Two of the regions in which we placed ROIs, the pericallosal frontal WM and the inferior parietal WM, are functionally closely related to the language cortex. Specifically, the pericallosal frontal WM would be expected to be related to the motor language area and inferior parietal WM related to the sensory language area [28]. Because much of language function develops before the age of the children in our study, one would expect that changes in FA values and ADC with age might not have been pronounced in our study. That was indeed the case; the number of FA changes in the pericallosal frontal WM and inferior parietal WM was relatively low, and that of ADC changes was somewhat higher. It is well known that increases in myelination can produce relatively little increase in FA (while still producing a decrease in ADC values) if a region contains many crossing fibers (as opposed to fibers that are oriented in parallel). It is possible that the fiber population in the pericallosal frontal WM and the inferior parietal WM is characterized by a higher number of crossing fibers than are other regions and therefore induces less FA change than expected from actual WM maturation [20, 26]. The superior frontal WM is subjacent to the dorsolateral prefrontal cortex, which is thought to be responsible for higher cortical functions such as working memory, inhibition, and attention [28, 29]. This WM region would be expected to develop substantially during childhood and adolescence. Notably, as in other peripheral WM regions, the change in FA in superior frontal WM was greater than in many deep WM regions. A previous DTI study [30] did not show age-related correlation between FA in this region and associated pathways. However, studies have shown increased FA in the dorsolateral prefrontal pathways [1, 9]. Our finding of an age-related FA increase in the superior frontal WM supports the latter observations [1, 9, 31]. Limitations As in any study, a number of limitations in our investigation are evident. First, we did not include subjects younger than 4 years, which would have allowed us to assess WM development throughout the temporal spectrum of childhood. Second, although we performed our study with the highest field strength routinely used for human imaging (3 T), we performed DTI with six diffusion gradients for DTI. Many studies now are conducted with a larger number of diffusion-encoding directions, which provide a more representative measurement of the diffusion tensor. Nonetheless, a 2009 study [32] AJR:197, September

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