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Original Research |
1 Department of Radiology, Duke University Medical Center, Box 3808, Durham, NC
27710.
2 Beaconbioscience, Doylestown, PA.
3 Department of Community and Family Medicine, Duke University Medical Center,
Durham, NC.
Received March 8, 2006;
accepted after revision March 28, 2007.
FOR YOUR INFORMATION
Abstract
 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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MATERIALS AND METHODS. Fifty-three children (35 boys) ranging in age from 1.5 weeks premature to 51.5 weeks (mean age, 22.9 weeks) underwent conventional MRI, diffusion imaging in three directions (b = 1,000 s/mm2), and diffusion tensor imaging with gradient encoding in six directions, all on a 1.5-T MRI system. Apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were measured in three deep WM structures (posterior limb of internal capsule, genu, and splenium of corpus callosum) and two peripheral WM regions (associational WM underlying prefrontal and posterior parietal cortex) with a standard region of interest (44 ± 4 cm2). ADC and FA were expressed as a percentage of corresponding values measured in a group of healthy young adults. Mean ADC and FA values for deep and peripheral WM were plotted against gestational age normalized to term. The data were fit best with a broken-line linear regression model with a breakpoint at 100 days. ADC and FA values at term were estimated according to the intercept of the initial linear period (before day 100) with day 0. The slope of the linear fits was used to determine the rate of WM maturation in both the early and the late (after day 100) periods. Multivariate analysis of variance tests were used to compare deep and peripheral WM structures at term and at representative early and late ages (days 30 and 200) and to compare rates of ADC and FA maturation in early and late periods within the first year.
RESULTS. At term, peripheral WM was less mature than deep WM according to results of extrapolation of ADC and FA values in the first 100 days of life to day 0 (p < 0.01). Mean ADC and FA value (percentage of mean adult value) for peripheral WM were 1.32 x 10-3 mm2/s (163%) and 0.16 (32%), respectively, and 1.09 x 10-3 mm2/s (143%) and 0.36 (54%), respectively, for deep WM. On day 30 and day 200, estimated mean ADC and FA continued to show greater diffusion (higher ADC) and less anisotropy (lower FA value) in peripheral WM (p <0.01). During the first year of postnatal life, both ADC and FA matured at higher rates before postnatal day 100 compared with a later time. Differences were observed in rates of maturation in the first 100 days when rates of decrease in ADC and increase in FA were compared between peripheral WM and deep WM; however, the maturational trends differed whether ADC or FA was examined. The early rate of ADC decrease (maturation) was twice as great for peripheral WM than for deep WM (p < 0.01) unexpectedly, but the opposite pattern was observed for FA. The early rate of FA increase (maturation) was approximately one half as great for peripheral WM as for deep WM (p = 0.01). Throughout the rest of the first year, no differences were observed in the rates of change in either index between peripheral WM and deep WM.
CONCLUSION. At term, both ADC and FA differ significantly in peripheral WM and deep WM, deep WM structures being more mature. Both deep WM and peripheral WM mature more rapidly during approximately the first 3 months in comparison with the rest of the first year. Unexpected differences in early (first 100 days) rates of maturation assessed with diffusion-weighted (ADC) and diffusion tensor (FA) imaging suggest that these two techniques may be sensitive to different aspects of WM maturation in the early perinatal period.
Keywords: anisotropy • diffusion tensor imaging • diffusion-weighted imaging • infants • maturation
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Our overall purpose was to use DWI and DTI to assess the postnatal maturation of WM in human infants in an attempt to relate developmental changes to changes in structure and composition of WM. Previous DWI and DTI studies have typically focused on premature infants, neonates in the first week of life, and children in the first decade of life [1, 2, 11-13]. Few studies have focused on maturational changes throughout the first year of life [14], when substantial neuroanatomic and functional maturation occurs [15]. The maturational changes in WM during this period were the focus of this study.
It is well established that in the brains of older children and adults, substantial differences exist between the diffusional properties of tissue in deep WM structures (long compact WM pathways of the forebrain) and those in peripheral WM structures (less compact, subcortical WM) [16]. We had two goals in studying maturational changes in WM of infants. The first goal was to determine whether such differences exist in term infants. Such differences between deep WM and peripheral WM, if they exist, are not well documented in the radiologic literature. We compared the water diffusion properties of long tracks of the deep forebrain with those of more peripheral WM regions that underlie associational cortex to examine disparate degrees of structural and functional maturity in this early phase of postnatal development [17, 18]. Our second goal was to study whether rate of change in diffusion characteristics generally thought to be representative of myelination (e.g., FA and ADC) differs between compact WM and peripheral WM during the first year of life. The results of one study [3] suggest that a difference may exist in the rate of increase in FA among peripheral WM structures in comparison with deep tracks in children 1-6 years old, peripheral WM having a slightly greater rate of increase. Our second goal was to determine whether this previously reported greater rate of maturation of peripheral WM could be confirmed and extended to the first postnatal year in a sample with greater temporal resolution. Measurements of rates of diffusional changes would be expected to increase our understanding of how and when different neural systems mature in infancy and early childhood. Furthermore, documentation of normative radiologic data against which abnormal brains can be measured would be valuable for assessment of infants and children with various CNS disorders, such as leukodystrophy.
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All infants underwent one MRI examination. The distribution of gestational ages at birth was as follows: 32 weeks (n = 1), 35 weeks (n = 1), 36 weeks (n = 2), 37 weeks (n = 3), 38 weeks (n = 6), 39 weeks (n = 2), 40 weeks (n = 34), and 41 weeks (n = 4). We consulted a pediatric neurologist at our institution, who was not an investigator in this study and who had 30 years of experience with multiple-institution infant studies, about the appropriateness of including premature infants in the study. For the purposes of our study, infants born at 38 weeks of gestation or later were considered so close to the standard postconceptual age at birth of 40 weeks that we did not consider the premature infants substantially different from infants born at 40 weeks. We were advised to include premature infants (who did not have adverse birth events and who had no subsequent neurologic disability on review of medical records over a period of years). These premature infants typically had undergone imaging many weeks after birth, and this factor minimized the effect of prematurity. The age at which imaging was performed, adjusted for premature birth, was 43 weeks for the infant born at 32 weeks; 30 weeks for the infant born at 35 weeks; 16 weeks and 46 weeks for the two infants born at 36 weeks; and 9, 21, and 32 weeks for the infants born at 37 weeks.
FA data were available for analysis in all 53 cases. ADC data derived from the three-direction diffusion imaging sequence were available for 51 infants. For two infants, however, ADCs were not available because the optical disks on which their data sets were stored became corrupted. All other pulse sequences had already been stored on our PACS network. Indications for clinical MRI were as follows: scalp nevus or other scalp or facial lesion (n = 14), increased head circumference (n = 6), possible tethered spinal cord (n = 4), non-accidental trauma (n = 4), suspected visual abnormality (n = 4), irritability (n = 4), sleep apnea (n = 2), suspected encephalocele (n = 2), hearing deficit (n = 2), mild hypertonia (n = 2), congenital cardiac or abdominal abnormality (n = 2), cranial nerve palsy (n = 1), tachypnea (n = 1), aspiration (n = 1), microcephaly (n = 1), abnormal movements (n = 1), cranial bruit (n = 1), and congenital nystagmus (n = 1).
The age of each subject in weeks at the time of imaging was available in the medical record. We normalized this age to gestational age at birth and expressed age at imaging as an adjusted postnatal age at the time of imaging. For infants born prematurely (before 40 weeks of gestation), adjusted postnatal age at imaging was calculated as follows: age in weeks at imaging - (40 weeks - gestational age at birth). Thus an infant who was born at 38 weeks of gestational age and underwent imaging 10 weeks after birth was assigned a normalized age of 8 weeks. For infants born after term (after 40 weeks of gestation), adjusted postnatal age at imaging was calculated as follows: age in weeks at imaging + (gestational age at birth - 40 weeks). Thus an infant who was born at 41 weeks of gestational age and underwent imaging 10 weeks after birth was assigned a normalized age of 11 weeks. Adjusted postnatal ages at imaging (reported in 4-week epochs) were as follows: -2 to 0 weeks (n = 1), 1-4 weeks (n = 4), 5-8 weeks (n = 5), 9-12 weeks (n = 7), 13-16 weeks (n = 6), 17-20 weeks (n = 4), 21-24 weeks (n = 1), 25-28 weeks (n = 5), 29-32 weeks (n = 4), 33-36 weeks (n = 4), 37-40 weeks (n = 0), 41-44 weeks (n = 7), 45-48 weeks (n = 3), and 49-52 weeks (n = 2).
Imaging Parameters
For each MRI examination, unenhanced transverse T1-weighted, transverse
intermediate-weighted, and transverse T2-weighted images were obtained in
addition to the DTI protocol. All imaging was performed with a 1.5-T clinical
MRI unit (Signa, GE Healthcare) with a standard head coil. Diffusion imaging
was performed in the transverse plane with a spin-echo echo-planar imaging
sequence and the following parameters: TR/TE, 12,000/100; inversion time,
2,200 milliseconds; diffusion gradient encoding in three orthogonal
directions; b = 1,000 s/mm2; field of view, 20 x 40 cm;
matrix size, 128 x 64; slice thickness, 5 mm; gap, 2.5 mm; number of
signals acquired, 1. ADC was calculated with the following equation:
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where G is the amplitude of the pulsed diffusion gradient, 
is the gyromagnetic ratio, 
is the interval between the diffusion
gradients, 
is the duration of the diffusion gradients,
S(G) is the signal strength with pulsed diffusion gradient
on, and S(0) is the signal strength with the pulsed diffusion
gradient off [16].
The DTI protocol consisted of the following single-shot spin-echo echo-planar sequence: 12,000/101; field of view, 22 cm2; matrix size, 128 x 64; 6-mm contiguous slices through entire brain; number of excitations, 2. Diffusion gradients were encoded in six directions with a b value of 1,000 s/mm2 and an additional image with no diffusion gradient (b = 0 s/mm2). Imaging was performed through the entire brain. The T2-weighted sequence parameters were 2,800/100; field of view, 22 cm2; matrix size, 256 (frequency direction) x 192 (phase direction); slice thickness, 5 mm; gap, 2.5 mm; number of excitations, 2. The T1-weighted sequence parameters were 500/14; field of view, 22 cm; matrix size, 256 (frequency direction) x 194 (phase direction); slice thickness, 5 mm; gap, 2.5 mm; number of excitations, 2.
Generation of ADC Maps and FA Maps
ADC and FA maps were generated with commercial (Functool, GE Healthcare)
and proprietary software. For computation of diffusion tensors, the raw
diffusion tensor data were transferred to an independent workstation
(Advantage Windows, GE Healthcare) and processed. The six independent elements
of the diffusion tensor, Dxx, Dyy, Dzz,
Dxy, Dxz, and Dyz, were statistically
calculated for each voxel with a previously described method and based on the
following equation:
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where bij is the component of the ith row and
jth column of the diffusion gradient matrix b, A(b) is the resulting
echo intensity for a gradient sequence with directions and magnitudes of the
diffusion-sensitizing gradients described by the b matrix, A(b = 0) is the
echo intensity when b is the zero matrix (no diffusion gradient), and
Dij is the corresponding component of the diffusion tensor matrix D
[19,
20]. After the elements of the
diffusion tensor were obtained, its eigenvalues were determined by
diagonalization of the tensor matrix. FA was chosen for the index of
anisotropy because it is rotationally invariant, provides excellent gray
matter to WM contrast, and has a high contrast-to-noise ratio
[21]. FA is also the most
widely used index of anisotropy in recent literature, which facilitates
comparison with data from previous studies and other investigators. FA
represents the anisotropic portion of total diffusion. The following equation
is used for FA:
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where Ei = the three eigenvalues and d = (E1 +E2 +E3) / 3 [16]. Values for FA range from 0 to 1, where 0 represents isotropic diffusion and 1 represents extremely anisotropic diffusion [16]. FA does not have a unit because it is a ratio of diffusion coefficients. The calculations for FA were performed for each voxel and displayed as an anisotropy map. One neuroradiologist with 7 years of neuroradiology experience conducted a visual and qualitative inspection of FA maps before quantitative analysis was performed.
Regions of Interest
Using Functool, a single observer blinded to age drew standard (44 ±
4 cm2) regions of interest (ROIs) on ADC and FA maps with reference
to corresponding T2-weighted images, DW images, and DTI images displayed side
by side (Fig. 1A,
1B,
1C,
1D,
1E,
1F,
1G,
1H,
1I,
1J,
1K,
1L). The analyst identified
the anatomic structures of interest on the T2-weighted images and the DW image
(for ADC measurement) and the DT image (for FA measurement). In the analysis
program we used, ROIs placed on the DW image were automatically simultaneously
placed on the corresponding site on the relevant ADC map. In a similar manner,
ROIs placed on one of the DTI images were automatically simultaneously placed
on the corresponding site on the relevant FA map.
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Analysis of ADC and FA Measurements
Mean ADC and FA values from the five ROIs for each subject were averaged
categorically so that for each infant, we obtained mean ADC and FA values for
deep and peripheral WM. Regression models were used for each set of diffusion
indexes (ADC and FA) against age. Both data sets were best fit with linear
regression models with fitted breakpoints at day 100. Using these models, we
obtained estimated ADC and FA values at term for deep and peripheral WM using
the intercept of the initial linear period (the fitted line before day 100)
with day 0 (day of birth adjusted for gestational age). We similarly obtained
estimated ADC and FA values at day 30 and day 200 from the early (before day
100) and late (after day 100) linear fits. These days were arbitrarily chosen
to provide additional cross-sectional time points for statistical evaluation
of differences between deep and peripheral WM in the early and late phases of
maturation during the first year. Corresponding values for deep and peripheral
WM structures and regions were evaluated with Student's t tests. For
assessment of possible differences in the rate of maturation in peripheral WM
and deep WM, the equality of the regression slopes was tested within the
regression model with multivariate analysis of variance.
To evaluate the relative maturation of infant ADCs and FA, the data were also expressed in terms of percentage of mean adult values. For ADC, the data were normalized to previously published adult values for the same structures and regions [22] (summary data provided by Gilmore JH, personal communication). For FA, the data were normalized to adult values obtained from a cohort of 16 healthy adults (eight women) 19-28 years old (mean, 23.4 years) who underwent imaging in a previous study [23]. To facilitate comparison with other studies in which diffusion data are grouped by postnatal month, we also present our ADC and FA measurements grouped into 12 intervals of 4 weeks beginning with day 0 (day of birth adjusted for gestational age).
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Although our chief interest was a categoric comparison of representative peripheral WM regions and deep WM structures, we did detect statistically significant differences in both ADC and FA at term within the deep WM group (p < 0.01) (Table 1). For the two peripheral WM regions sampled, there were no differences in absolute ADC or FA; however, after normalization to mean adult values, differences were detected (p < 0.01). Because we had no a priori hypothesis regarding differences within WM categories, no further analysis of these differences was pursued.
ADC and FA Throughout the First Year
Mean ADC and FA for peripheral WM and deep WM in each case were plotted
against age (normalized to term) at imaging (Figs.
2 and
3). Regression models were fit
to each distribution. The best fits were obtained with broken-line models with
breakpoints at day 100 (all p < 0.01). Thus the rate of change in
ADC and FA during the first year in both peripheral WM and deep WM was not
constant. Both diffusion indexes matured at a faster rate in approximately the
first 3 months (before day 100) compared with the rest of the year (all
p < 0.01). In particular, the rates of change for peripheral WM
were nearly fivefold and twofold greater for ADC and FA, respectively, before
day 100 than in the rest of the year. For deep WM, the rate of change between
early and later periods increased approximately twofold for ADC and threefold
for FA.
We next asked whether categoric differences existed between peripheral WM and deep WM in rates of change in ADC and FA in the early (before day 100) and later (after day 100) periods. In the early period, statistically significant differences were detected between peripheral WM and deep WM in both rate of ADC change and rate of FA change; however, discordant results were obtained with the two diffusion indexes. In the early period, ADC in peripheral WM decreased at approximately twice the rate of ADC in deep WM (rate of change for peripheral WM, -0.020 x 10-3 mm2/s/wk; rate of change for deep WM, -0.009 x 10-3 mm2/s/wk; p < 0.01). Unexpectedly, the opposite pattern was observed for FA. FA in peripheral WM in the early period increased at only approximately one half of the rate of FA in deep WM (rate of change for peripheral WM, 0.005/wk; rate of change for deep WM, 0.009/wk; p = 0.01). Throughout the late period, no differences were observed between peripheral WM and deep WM in rates of change in either ADC (rate of change for peripheral WM, -0.004 x 10-3 mm2/s/wk; rate of change for deep WM, -0.004 x 10-3 mm2/s/wk; p = 0.64) or FA (rate of change for peripheral WM, 0.002/wk; rate of change for deep WM, 0.003/wk; p =0.08).
In addition to estimating rates of change in ADC and FA in peripheral WM and deep WM during the first year, we used the regression models to estimate differences in absolute ADC and FA at two cross-sections through the distributions: one date in the early period (day 30) and one in the later period (day 200). The significant differences in ADC and FA between peripheral WM and deep WM present at term persisted at both time points. Peripheral WM regions maintained a higher ADC and less FA than deep WM structures (p < 0.01) (Figs. 2 and 3). To facilitate comparison of our data with values found in other studies of infant subjects, we grouped our sample into 12 intervals of 4 weeks beginning on day 0 (day of birth adjusted for gestational age). These grouped values for ADC and FA throughout the first year are reported in Tables 2 and 3.
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We examined the effect of gender on the analysis of ADC and FA maturation in peripheral WM and deep WM. We compared the age distribution in the two gender classes by means of a two-group Student's t test and found no statistically significant difference in age distribution (p > 0.3). We then tested ADC and FA for peripheral WM and deep WM in a general linear model including gender effects for level and slopes. No significant gender effects were detected (p >0.2).
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Status of Cerebral WM at Term
The first major finding in our study was that substantial differences exist
between the diffusional properties of peripheral WM and those of deep WM in
the infant brain, even at term gestation. The ADC in peripheral WM was
significantly higher and FA significantly less than the corresponding values
in deep WM (Table 1). Deep WM
structures had already achieved approximately one half of the mean adult ADC
and FA at this age, and peripheral WM remained barely discernible at the
thresholds of detection (Table
1). ADC and FA are thought to reflect a number of WM features,
including degree of myelination and volume of the extracellular compartment,
amount of extracellular water, changes in composition of the extracellular
matrix, density and organization of axons in WM structures, maturation of
neurofilaments and other constituents of the axonal cytoskeleton, and
maturation of voltage-gated conductances along axonal membranes
[9,
25-28].
At term, the deep structures we studied (portions of the corpus callosum and
internal capsule) are still immature with respect to each of these cytologic
and histologic features of WM
[5]. Nevertheless, each deep WM
structure is well established at this age, having been pioneered in the second
trimester by long axonal projections arranged into relatively compact parallel
bundles of axonal fascicles
[29-31].
In contrast, the peripheral WM of the prefrontal and posterior parietal cortex
that we sampled was much less developed at term, being only poorly myelinated
and sparsely populated by associational fibers that have yet to establish
widespread corticocortical connections
[10,
32]. These peripheral WM
structures consequently are likely to retain larger extracellular volumes with
more extracellular fluid. Therefore, our findings of a significantly greater
ADC (greater water diffusion) and less FA (more isotropic diffusion) in
peripheral WM than in deep WM are consistent with known microanatomic
characterizations.
One goal of our study was to characterize the status of peripheral WM and deep WM in terms of mean ADC and FA in the infant brain at term. Rather than obtaining a large sample of term neonatal infants, we used statistical methods to estimate mean diffusional indexes at this important developmental mark. To confirm the validity of this approach, it is important to compare our neonatal values with those obtained in previous studies of term infants. Because of the lack of consistent, well-defined ROI locations, it is a challenge to compare our mean ADC and FA findings in peripheral WM with the findings of previous studies. It is possible, however, to compare our data on deep WM structures with those from previous studies because of the greater consistency and precision with which deep structures can be resolved and assessed. For instance, in one study [13], the mean FA values in the first 2 months of life were 0.38 for the genu and 0.42 for the splenium, similar to the mean FA values of 0.37 for the genu and 0.38 for the splenium during the first month of life in our study. Likewise, in the other study, the mean FA in the first 2 months of life for the internal capsule (presumably the posterior limb, because the sample was acquired with the aim of including the corticospinal tract) was 0.45, only slightly greater than our value of 0.40 in the first month.
In another study [22], which differed from ours in the use of a 3-T MRI unit and 12 signals averaged in the DTI sequence, neonatal (mean age, 16 days) and adult ADC and FA were reported only in bar graphs, but we acquired mean values from the authors (Gilmore JH, personal communication). In that study, the ADCs and FA values were 1.22 x 10-3 mm2/s and 0.47 for the genu of the corpus callosum, 1.15 x 10-3 mm2/s and 0.63 for the splenium of the corpus callosum, and 1.05 x 10-3 mm2/s and 0.45 for the internal capsule. These ADCs are reasonably close to our values at term; however, the FA values are considerably higher than ours, especially for the corpus callosum (Table 1). The differences in field strength and DTI sequence might have contributed to the discrepant FA values between the studies. Further studies from several institutions are needed for definitive establishment of normative ADCs and FA values for peripheral WM regions and deep WM structures in term neurologically healthy infants. Our data are intended to provide a clinically meaningful contribution toward that end.
Changes in Cerebral WM Early in the First Year of Life
Additional findings of our study pertain to changes in diffusion indexes
that occur during the first year of life as WM continues to differentiate and
mature. The first of these findings was that ADC and FA change at a faster
rate in the first 3 months after birth at term gestation than in the rest of
the year. This observation is qualitatively similar to previous findings
[2,
25,
28,
33] of an exponential decline
in ADC in several peripheral WM and deep WM (and gray matter) structures, the
period of greatest change occurring before the end of the first month after
birth at term gestation. Our ADC (and FA) data were best fit with broken-line
linear regression models with a breakpoint at 100 days, indicating that the
period of rapid change in ADC and FA continues beyond the first month of
life.
When we compared the rates of change in ADC and FA for peripheral WM and deep WM during this early period of rapid maturation, we found statistically significant differences between WM categories. Results of a study by Forbes et al. [25] also suggested a difference between the rate of decrease in ADC in peripheral WM and the rate in deep WM in the first few months of life, although the rates of ADC change were not quantified. In that study, graphs indicated that the greatest rate of decline in ADC was in peripheral (anterior and posterior subcortical) WM, with less-rapid change occurring in deep WM (anterior and posterior limbs of internal capsule). Consistent with that suggestion, our findings showed that the ADC in peripheral WM decreased at approximately twice the rate it did in deep WM in the first 100 days after birth at term (steeper slope of the peripheral WM regression line in Fig. 2). Unexpectedly, we did not observe a comparable difference in the rates of change in FA between peripheral and deep WM in this same early period. Instead, the opposite pattern was observed: The rate of change in FA in peripheral WM was approximately one half of the rate of change in FA in deep WM (steeper slope of the deep WM regression line in Fig. 3).
That both ADC and FA change rapidly during the first 3 months of life attests to the accelerated progression of WM development in both peripheral WM regions and deep WM structures in the perinatal period. However, the disparities we observed in the rates of change in ADC and FA in deep WM and peripheral WM attests to the heterogeneous nature and differential progression of WM histogenesis and maturation in different parts of the forebrain. A recent report [14] of maturational changes in mean diffusivity and FA in the infant brain during the first 4 months of life described similar isolated changes in mean diffusivity and FA (presumably, disproportionate changes in one diffusional index not matched by complementary changes in the other). Taken together these findings raise the possibility that ADC and FA are sensitive to different aspects of WM maturation in this early period of rapid change [9, 27]. One possibility is that ADC is most sensitive to earlier molecular and histologic changes associated with the so-called "pre-myelin sheath" stage of cerebral myelination [34]. If so, the greater rate of ADC change in peripheral WM would reflect the relative delay in progression toward a larger number of mature myelin sheaths in peripheral WM than in deep WM in this phase of the perinatal period [5, 6]. This possibility does not exclude contributions from other factors that restrict the content of water and its diffusional patterns in WM, nor does it imply that early rapid changes in FA may not also reflect processes that occur before myelination [14, 26].
Results of histologic studies of peripheral WM in human infants suggest a variety of changes that can account for the rapid decrease in ADC (and rapid increase in FA) that we observed in the first 3 months, including an increase in number and density of axons and an increase in number and phosphorylation of axonal neurofilaments, in addition to proliferation and compaction of myelin sheaths and expression of myelin basic protein [10, 34]. In one such postmortem study [10], the results of which characterized the development of peripheral WM underlying parietal associational cortex in human infants, the period of the most rapid change occurred in two early time periods: 43-54 and 72-92 postconceptional weeks. The breakpoints at 100 days identified in our linear regression models (the inflection points when the rates of change in ADC and FA decreased) corresponded approximately to postconceptional week 54. Thus our early period of rapid change in diffusional indexes corresponds remarkably well to the progression of WM histogenesis and maturation, at least for peripheral WM in the parietal lobe. Further detailed postmortem investigations of developing WM in the human brain of the type undertaken by Haynes et al. [10] are necessary to understand the differences between the developmental progression of peripheral and deep WM and how such events relate to radiologic assessments of water diffusion in cerebral tissue.
Changes in Cerebral WM Later in the First Year of Life
After the first 3 months of life, we detected no differences between the
rate of change in ADC and FA in peripheral WM and the rate in deep WM. Both
types of WM had a slowed but steady progression in maturation toward adult
values throughout the rest of the first year. These findings are generally
consistent with those of previous reports of ADC and FA data on infants in the
first year of life [2,
25,
28,
33,
35]. However, methodologic
differences and variation in the size, shape, and precise placement of ROIs
make it difficult to compare ADC and FA for infants of any age and their rates
of change throughout early infancy. Nevertheless, some meaningful comparisons
can be made.
Other authors [3, 15] have found that ADC and FA in various WM structures mature in both preterm and term infants and have shown differences between the ADC and FA of earlier maturing tracts (e.g., components of the corpus callosum) and those of later maturing tracts (e.g., subcortical WM regions). In one early study, in which FA was measured with only two orthogonal diffusion gradients, the authors [1] concluded that changes in anisotropy occur only in the first 6 months of life. In subsequent studies, however, investigators [2, 3, 15] found that anisotropy increases in major WM regions well beyond that period. Our findings support the assertion that WM anisotropy continues to increase and ADC to decrease throughout the first year of life, although not at the same rate of change as in the first 3 months. This finding would be expected given that both marked developmental changes and alterations in signal intensity consistent with myelination continue throughout and beyond the first year of life [9]. For example, Forbes et al. [25] calculated ADC in the posterior limb of the internal capsule through the first year of life. The mean ADC at birth appeared to be approximately 1.00 x 10-3 mm2/s, similar to the mean value we found for the first month of life (0.95 x 10-3 mm2/s). In that study, the mean ADC in the posterior limb of internal capsule in the last 3 months of the first year of life appears to have been approximately 0.80 x 10-3 mm2/s (20% decrease), similar to the mean value of 0.78 x 10-3 mm2/s (18% decrease) over the same period in our study. Thus both the mean values and the rate of ADC decline throughout the first year in the two studies are similar. These changes compare relatively well with those found in a third study [2], in which the mean ADCs in the posterior limb of the internal capsule were approximately 1.00 x 10-3 mm2/s at birth and approximately 0.70 x 10-3 mm2/s (30% decrease) in the last 3 months of the first year of life.
When we compared changes in ADC of posterior WM with results from previous studies, we found similar rates of decrease in ADC throughout the first year. Forbes et al. [25] calculated ADC in posterior subcortical WM on the same slice that showed the basal ganglia, which was at a more inferior level than the one we used to calculate parietal ADC above the roof of the lateral ventricles. Nonetheless, some limited comparisons can be made. The mean ADC of posterior WM calculated by Forbes et al. appears to have been approximately 1.45 x 10-3 mm2/s in the first postnatal month compared with 1.28 x 10-3 mm2/s in our study during that period. The mean ADC of posterior WM in the last 3 months of the first year in the study by Forbes et al. appears to have decreased to approximately 1.05 x 10-3 mm2/s (27% decrease) compared with 0.91 x 10-3 mm2/s (29% decrease) in our study during that period. Thus the rate of decrease in ADC over the first year of life for ROIs admittedly placed at different locations in the posterior WM appears quite similar in multiple studies.
Implications for Relative Contributions of Axonal Growth and Myelination
By the end of the first year of life, mean ADCs in all WM regions were
within 27% of mean adult values. Although statistically significant
differences between the ADC of deep WM and that of peripheral WM were detected
at the day 200 cross section through our data set (and persisted throughout
the rest of the first year), it would be difficult to attribute any biologic
or clinical significance to such small differences between the ADCs of the WM
categories at this later stage of the first year
(Fig. 2). Rather, these
findings likely indicate that the molecular and microstructural changes in WM
that are responsible for the volume of extracellular water and the restriction
of water diffusion are substantially mature after 1 year. This inference can
be extended to diffusional anisotropy but only for deep WM. Each of the deep
WM structures assessed had achieved FA no less than 86% of mean adult values
by the end of the first year (Table
3), again indicating substantial maturation of diffusion
anisotropy in deep WM during the first year. In contrast, the two peripheral
WM regions had achieved only 69% and 54% of mean adult values for frontal and
parietal WM, respectively, by the end of the first year
(Table 3). This finding
suggests the ongoing development of molecular and microstructural factors that
promote anisotropic diffusion beyond the first year in this type of WM. It is
well known that the process of myelination in peripheral WM continues beyond
the first year
[4-6].
The ongoing consolidation and proliferation of myelin sheaths is one factor
that should contribute to the remaining increase in FA that occurs after the
first year and to ongoing changes in MR signal intensity thought to reflect
progressive myelination [9,
36].
A variety of factors other than myelination may contribute to ongoing increases in anisotropy late in the first year and thereafter, especially in peripheral WM underlying associational cortical regions. Supporting evidence is most clear in studies of animals deficient in myelin. In one study [37], anisotropy within WM was clearly present in myelin-deficient rats, although to a lesser degree than in control rats. The authors concluded that myelination is not a prerequisite for the development of anisotropy but that the presence of myelination increases the degree of anisotropy. In another study, the investigators [38] examined anisotropy in two directions, parallel to axonal fibers (axial diffusivity) and perpendicular to their length (radial diffusivity), in mice with incomplete myelin formation reflecting dysmyelination (shiverer mice). That study showed that axial diffusivity was maintained at normal levels in shiverer mice compared with control mice, suggesting that axonal fibers (and not solely myelination) are the main contribution to axial diffusivity in that animal model. The results of these animal studies raise the possibility that a significant factor in the ongoing increase in FA in peripheral WM is the continued addition of new axonal projections and collateral fibers. Interestingly, growth-associated protein 43 (GAP43), a marker of axonal growth and elongation, continues to be expressed at high levels, relative to the levels in adult samples, in parietal WM throughout the first year, although peak expression is in the preterm period [10]. This finding suggests that axonal growth and elongation in parietal WM continue at a substantial pace at least during the second half of the first year of postnatal life, when the mean FA of peripheral WM is only approximately one half of the normal adult value.
Limitations
Our study was subject to a number of limitations, which need to be
considered in assessing our findings. First, all ADCs and FA values were
single recordings made by single observers. Although the observers had
substantial experience in recording ADCs and FA values in the same structures
in previous studies, we cannot provide any estimate of intraobserver or
interobserver variability. Second, because our images were acquired for
clinical purposes in the care of infants who had normal findings on
conventional MRI studies and did not need subsequent imaging, we recorded data
at only a single time point for each child. Therefore, our data set reflects
trends in a sample of individuals, whereas serial recordings for individuals
might have produced different rates of ADC decreases and FA increases.
The third limitation was that coregistration of ADC and FA maps and spin-echo MR images was not performed, which might have resulted in more precise measurements of neuroanatomic regions and structures. However, one would not expect such inaccuracy to result in systematic bias of the data in one direction or another. Some authors [14, 28, 33] have contended that a lower b value (e.g., 700 or 900 s/mm2) than the one used in our acquisitions (1,000 s/mm2) may be more optimal for infants. However, a change in b value would be expected to affect ADC and FA relatively equally in all structures at all ages. Fourth, we obtained MR images with four units and did not measure variability within or between units. However, our results did not differ from those of other published studies on this account and, as with the other limitations, are not expected to have produced a systematic bias that would affect data interpretation. Fifth, our study was aimed at understanding categoric differences in the early status and maturation of peripheral WM and deep WM. We did not develop hypotheses aimed at particular WM regions and tracks. It is possible, therefore, that differences within categories of WM (including regions and structures that we did not measure) might have deviated from the average trends reported. Differences within the deep WM at term were detected statistically and will be the subject of a future investigation.
The last limitation was that a head coil specifically designed for neonatal brain imaging, used in some research environments, was not used in this study. The data obtained in this study may differ from results obtained with alternative coils and with better-designed pulse sequences and scanning protocols. However, our purpose was to assess the diffusional properties of WM in the infant brain by use of instrumentation, acquisition protocols, and imaging times representative of most standard clinical imaging environments. Even when the limitations are considered, our normative data on the status of WM in the term infant brain and the rates of change in ADC and FA that occur in the first year should be broadly applicable for clinicians and clinical investigators who must evaluate infants with suspected disorders of cerebral WM.
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) and peripheral (
) white matter values for
one subject. Figure 