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Various studies investigating effects of aging (O’Sullivan, et al. 2001a; Rose, et al. 2000), depression (Steffens, et al. 2002), multiple sclerosis (Edwards, et al. 2001; Rovaris, et al. 2002), or neurological abnormalities (Mulhern, et al. 2001; O’Sullivan, et al. 2001b) have demonstrated a connection between white matter properties and cognition. Executive function has been shown to correlate with age-related declines in diffusion anisotropy and increases in mean diffusivity (O’Sullivan, et al. 2001a), and reading ability was correlated with a measure of fiber tract organization in a group of healthy and reading-impaired adults (Klingberg, et al. 2000). Reduced integrity of the association white matter fiber tracts was seen in patients with Alzheimer’s Disease (Rose, et al. 2000) relative to normal controls. A study performed on geriatric depression patients (Steffens, et al. 2002) displayed a correlation between white matter lesions and performance impairment in daily living activities. In patients with multiple sclerosis, a significant correlation was demonstrated between white matter diffusion properties and measures of verbal fluency and spatial recall (Rovaris, et al. 2002), and global white matter volume was shown (Edwards, et al. 2001) to correlate with cognitive performance as well. Patients with ischemic leukoariosis (O’Sullivan, et al. 2001b) displayed diffusion-related changes in normal white matter relative to normal controls which correlated with executive function, and inadequate development of normal-appearing white matter (Mulhern, et al. 2001) was demonstrated to account for a significant part of the association of age at the time of craniospinal irradiation and IQ in survivors of childhood medulloblastoma.
In the normal pediatric population, however, while much is known about normal development of white matter, there is limited data available to relate changes in the development and maturation of white matter to measures of cognitive function. Diffusion anisotropy has been observed to increase with age during childhood and adolescence in the pyramidal tract, as well as association areas (Schmithorst, et al. 2002), while age-related decreases in mean diffusivity were observed throughout the white matter. Overall white matter volume increases and region-specific myelination have been demonstrated (Paus, et al. 2001) throughout the developmental period. A recent study comparing normal adults who had studied a musical instrument since childhood with those who had not (Schmithorst and Wilke 2002) demonstrated diffusion-related changes in both white matter association areas and the pyramidal tract, which were hypothesized to correspond to the cognitive and motor effects, respectively, of musical training. Moreover, specific deficits in reading ability have been linked to deficits in local fiber tract organization in a group of healthy and reading-impaired adults (Klingberg, et al. 2000). These results indicate that significant developmental changes occur in white matter during childhood and adolescence, which may be related to cognitive function. In the current study we wish to test the hypothesis that measures of cognitive function, such as IQ, will correlate with diffusion tensor imaging parameters, such as anisotropy and mean diffusivity, in the normal pediatric population. Diffusion tensor imaging (DTI) allows for the imaging of white matter microstructure in vivo: as the diffusion of water along axons is less restricted than diffusion perpendicular to the axon direction, the microstructure of white matter connections can be visualized non-invasively.
DTI data was successfully obtained from forty-seven children (42 Caucasian, 2 Asian, 3 Multi-Ethnic, 13M, 34F, mean age = 11.0 +/− 3.3 years, 41 right-handed, 6 left-handed). Institutional review board and informed consent were obtained for all subjects. Exclusion criteria were: previous neurological illness; learning disability; head trauma with loss of consciousness; current or past use of psychostimulant medication; pregnancy; IQ less than 80, measured via the Wechsler Intelligence Scale for Children, Full-Scale IQ, Third Edition (WISC-IQ); birth at 37 weeks gestational age or earlier; or abnormal findings at a routine neurological examination performed by an experienced pediatric neurologist. The anatomical scans were read as normal by a pediatric neuroradiologist. The age and gender breakdown of the study participants is shown in Table 1 All DTI scans were visually inspected for gross motion artifacts. In addition to the 47 successful scans, datasets from 15 subjects (all Caucasian) were excluded due to motion artifacts, and datasets from 2 subjects (Caucasian) were excluded due to technical problems. The included subjects, as a group, had IQ scores within the high end of the normal range (Full-Scale IQ: 114.4 +/− 13.4; Performance IQ: 111.2 +/− 13.8; Verbal IQ: 115.2 +/− 13.6).
All images were acquired on a Bruker 3T Medspec system (Bruker, Ettlingen, Germany). A 24-slice, diffusion-weighted, spin-echo, echo-planar imaging (EPI) scan was acquired in the transverse plane with the following parameters: TR = 6070 ms, TE = 87 ms, FOV = 19.2 × 25.6 cm, slice thickness = 5 mm, matrix = 64 × 128, Δ = 40 ms, δ = 18 ms, diffusion gradient strength = 30 mT/m (resulting in a b-value of 710 s/mm2). Three scans were acquired with no diffusion weighting, and 25 diffusion-weighted scans were acquired, each with different diffusion directions determined using an electrostatic repulsive model (Jones, et al. 1999). In addition, a fluid-attenuated inversion recovery (FLAIR)-EPI scan with the same scan parameters and no diffusion weighting, and a T1-weighted Modified Driven Equilibrium Fourier Transform (MDEFT) (Duewll, et al. 1996) whole-brain anatomical scan, were also acquired.
Geometric distortion due to gradient eddy currents was minimized using an automated gradient preemphasis adjustment routine (Schmithorst and Dardzinski 2002). Any residual distortion due to gradient eddy currents was corrected for by co-registering the diffusion-weighted images to the FLAIR-EPI scan with no diffusion weighting using a Levenberg-Marquardt iterative least-squares algorithm. Geometric distortion due to B0 inhomogeneity was corrected for using a multi-echo reference scan (Schmithorst, et al. 2001).
In each subject, only the voxels determined to be in white matter, using the segmentation procedure available in SPM99 (Wellcome Department of Cognitive Neurology, London, UK) on the high-resolution anatomical images, were retained for further analysis, in order to minimize any possible partial volume effects. Specifically, the voxels in the higher-resolution white matter images generated from SPM were averaged into the geometry of the lower-resolution DTI images and only voxels with a probability greater than 80% of being in white matter were analyzed. Using routines written in IDL (Research Systems Inc., Boulder, CO), the components of the diffusion tensor were computed and transformed into stereotaxic coordinates (Talairach and Tournoux 1988), with normalization parameters calculated from the high-resolution T1-whole brain image.
The fractional anisotropy (FA) (Papadakis, et al. 1999) and mean diffusivity (MD) values were computed for each subject. FA is a marker for diffusion anisotropy and has a range of zero, in the case of completely isotropic diffusion, to one, in the case of completely anisotropic diffusion, while MD is a directionally-averaged measure of water diffusion, reflecting tissue density. As a further safeguard against partial-volume effects, only voxels with a MD less than 1e-5 cm2/s were retained for the subsequent correlational analyses, which were only performed on the subset of voxels judged to be in white matter in 25 or more subjects. Across all subjects where the given voxel was retained for analysis, the FA and MD values were tested for significant correlation with the results from the WISC-IQ test on a voxel-by-voxel basis with subject age being used as a covariate, to account for any age-related effects due to gross morphological differences. The results were “Gaussianized” into Z-score maps, which were then filtered with a Gaussian filter of width 3 mm, in order to increase contrast-to-noise. After filtering the results were masked to the original set of voxels used in the analysis, to prevent “bleeding” of correlated regions into areas containing gray matter or CSF. The clustering method (Xiong, et al. 1995) was used to increase sensitivity. Regions with statistically significant correlations were determined using the criterion of at least 10 contiguous pixels with a Z > 4.0. A Monte Carlo simulation revealed that the chosen filter width, Z-threshold, and cluster size corresponded to a corrected double-tailed p < 0.001. The voxels determined to be significantly correlated with the WISC-IQ scores were then overlaid on the averaged anatomical whole-brain dataset for visualization purposes.
Regions of statistically significant negative correlations of FA (Figure 1) and positive correlations of MD (Figure 2) with the WISC-IQ scores were found, and are listed in Tables 2 and and3,3, respectively. Significant positive correlations of FA with IQ were found mainly in association fibers, including the left arcuate fasciculus, frontal areas bilaterally, the junction of frontal areas with the genu of the corpus callosum bilaterally, and right parietal and temporo-parietal areas. Significant negative correlations of MD were found in the junction of the internal and external capsules in the left hemisphere, the junction of the splenium of the corpus callosum and the surrounding white matter bilaterally, and in frontal white matter in the right hemisphere.
No regions were found with statistically significant negative correlations of FA or positive correlations of MD with WISC-IQ scores except for an area in the left globus pallidus displaying a positive correlation of MD with WISC-IQ. As the contrast between the globus pallidus and the surrounding white matter (internal capsule) is quite poor on the T1-weighted anatomical images, this result may be due to partial-volume effects with the adjoining internal capsule, as the boundary between the internal capsule and globus pallidus would not be clearly delineated using the SPM segmentation technique. However, the other correlated areas (Figures 1 and and2)2) are not adjacent to low-contrast gray matter areas, making such a partial volume effect for these findings much less likely. As additional verification that the analysis was restricted to white matter, the subset of correlated voxels analyzed for each subject was superimposed on the T1-weighted whole-brain images and visually inspected as being within the white matter for each subject.
Since all of the subjects were specifically recruited for the investigation of normal brain development and their neurological, psychological, and structural measures were all within the normal range, we consider our data representative of a healthy population and are able to avoid any potential problem of transferability of results (Rivkin 2000), within the limitations of our sample being skewed toward the higher end of the IQ range and containing significantly more girls than boys.
It has been well documented (Clark and Le Bihan 2000; Mulkern, et al. 1999) that the diffusion properties of brain tissue follow a multiexponential, rather than a monoexponential, model. The range of diffusion coefficients in vivo may approach a continuum and not reflect only two components (Pfeuffer, et al. 1999). The exact origins of the diffusion components continue to remain a topic of debate. For example, results from a recent spectroscopic study (Inglis, et al. 2001) performed on an excised rat brain are consistent with the hypothesis that the faster-diffusing component corresponds predominantly to the extracellular water. However, studies performed on rat brains in vivo (Duong, et al. 1998; Duong, et al. 2001) have challenged the assumption that intracellular and extracellular water have different intrinsic diffusion coefficients.
Our observed changes in FA and MD are due almost completely to a faster-diffusing component, as the relatively low b-value used in this experiment of 710 s/mm2 will minimize any significant contribution from a slower-diffusing component (Clark and Le Bihan 2000). The fraction of the fast diffusion component has been shown to be larger in newborns than in adults (Mulkern, et al. 2001), a result attributed to cellular development and myelination. However, since the lower age limit of our study is five years old, at which time gross myelination is virtually complete (Nakagawa, et al. 1998), we do not expect there to be significant differences in the fast diffusion fraction. Still, further research will be necessary in order to confirm this hypothesis, especially with regard to evidence of ongoing white matter tract development in older children (Schmithorst, Radiology 2002).
With regard to development, it has previously been proposed (Schmithorst, et al. 2002) that there are at least two processes contributing to the maturation of white matter in the normal pediatric population: 1) increasingly dense and ordered packing of the fiber tracts, resulting in more directionally restricted extracellular space; and 2) changes in the intracellular compartment including a greater concentration of membranes and a greater membrane surface-to-cell volume ratio. Process 1) should lead to a marked increase in diffusion anisotropy as well as a possible decrease in MD, due to the increasingly restricted diffusion perpendicular to the axon direction; process 2) should lead only to a decrease in MD, as suggested by Baratti et al. (Baratti, et al. 1999) in a detailed longitudinal study investigating maturation of the cat brain.
Based on the diffusion properties of white matter laid out above, the areas exhibiting increases in FA with increasing cognitive abilities reflect an overall correlation of fiber organization and/or density with IQ. The areas which display negative correlations of MD with IQ should either display a greater concentration of macromolecules and a greater membrane surface-to-cell ratio due to a decrease in processes and organelles (Caley 1971), but might also be compatible with a more dense extracellular space (corresponding to process 1 above). To examine this question further we investigated whether there was a significant correlation between the average MD values and the average FA values in the regions displaying correlations with MD (Table 4). As all regions display a significant negative correlation of the MD values with the FA values, we think it quite likely that the changes in MD do not represent a separate developmental process than the changes in FA, but are representative of the same mechanism (greater fiber organization), with the failure of some of these regions to reach significance on the FA correlational analyses due to insufficient sensitivity and/or sample size. Thus our results are in agreement with an earlier study (Schmithorst, et al. 2002) investigating normal development of white matter in the pediatric population displaying regionally-specific increases in FA, with more widespread decreases in MD, and indicate that efficient organization of white matter association fibers is essential for optimal cognitive performance.
Our findings also show a striking spatial overlap with earlier findings of Klingberg et al (Klingberg, et al. 2000) on a small group of reading impaired and normal adults. They found a positive correlation of FA in a left temporo-parietal area with higher reading skills, and the spatial location of that region is extremely close to the region exhibiting changes in FA with general cognitive ability (Figure 1, middle slice, middle row). Reading is a task that heavily relies on highly specialized brain areas in frontal, temporo-parietal, and occipito-temporal regions and their fast and efficient connection (Habib 2000; Pugh, et al. 2000). In fact, it has been hypothesized that in reading-impaired individuals, “temporo-parietal difficulties disrupt this developmental trajectory” (Pugh, et al. 2001), which is consistent with neuroimaging studies demonstrating pathological patterns of posterior activation in reading-impaired subjects, with a compensatory shift involving stronger activation in frontal areas (Pugh, et al. 2001). The DTI results in the reading-impaired (adult) individuals thus reflect the disruption of a normal (anatomical and functional) connection between cooperating brain regions. Our results, however, reflect normal variations in healthy children that correlate with their individual, overall cognitive abilities as measured by a broad range of tasks in a test of “general” intelligence (which is normal in most dyslexic individuals (Habib 2000)). To further investigate we looked at the average FA from the ROI of correlated voxels in the slice at Z = +25 in left temporo-parietal white matter (detailed in Figure 1) and computed the partial correlation coefficients for the average FA versus Full-scale IQ (R = 0.58), Verbal IQ (R = 0.6), and Performance IQ (R=0.43), with subject age as the covariate, indicating that the majority of the FA differences in this area may be traced to verbal proficiency. Incorporating Full-scale IQ as well as age as a covariate, while neither correlation with Verbal IQ (R = 0.21) or Performance IQ (R = −0.19) reached significance the signs of the correlation coefficients provide a further indication of the relation of FA in this area to verbal proficiency.
Our results also match well with a recent voxel-based morphometry study investigating correlations of brain structure with intelligence in the normal pediatric population (Wilke, et al. 2003). Gray matter volume positively correlated with cognitive function in anterior brain regions and in posterior temporal/inferior parietal regions. Our findings of increased FA in adjoining white matter areas are compatible with a shift of cognitive functions to frontal and temporal regions during normal development (Schlaggar, et al. 2002; Wilke, et al. 2003). Therefore, our findings could be related and secondary to gray matter developmental processes. However, more research addressing this issue will be necessary, especially with regard to questions relating to causality.
In order to investigate changes in white matter structure, we chose to use a well-known statistical software package (SPM99) (Friston, et al. 1995) to remove gray matter and CSF voxels from further analysis. Since we desired to investigate changes in white matter microstructure rather than gross morphological differences related to intelligence, we chose to use a rather strict threshold in order to safeguard against partial-volume effects, despite the fact that that might result in the discarding of some voxels actually in white matter and a subsequent loss of sensitivity. This was necessary due to the rather low acquired spatial resolution (2 × 3 × 5 mm), which limits detectability of smaller fiber tracts, and due to the affine spatial normalization, which, while shown to be fairly reliable for subjects in our age range (Muzik, et al. 2000; Wilke, et al. 2002), is nevertheless not as robust as it is for adult subjects. In addition, since our analysis was, of its nature, exploratory, we chose to use a voxelwise approach than a region-of-interest approach for the analysis. Hence it is likely that some white matter areas actually related to intelligence and cognitive function were not found in our analysis.
DTI was performed on 47 normal children ages 5–18. Statistically significant correlations with IQ were seen for both FA and MD in specific white matter association areas, indicating that cognitive function correlates with greater fiber organization in the pediatric population.
This work was supported in part by a grant from the U.S. National Institute of Child Health and Human Development, R01-HD38578-30.
The authors acknowledge the assistance of Dr. Anna Byars, Ph.D., in the administration of the Wechsler Full-Scale IQ tests, of Dr. Richard Strawsburg for performing the neurological examinations, and of Dr. William Ball for reading the structural scans.