• fMRI;
  • Language development;
  • Pediatric;
  • Epilepsy;
  • Lateralization index


  1. Top of page
  2. Abstract
  6. Acknowledgments

Summary: Purpose: The goal of this study was to compare language lateralization between pediatric epilepsy patients and healthy children.

Methods: Two groups of subjects were evaluated with functional magnetic resonance imaging (fMRI) by using a silent verb-generation task. The first group included 18 pediatric epilepsy patients, whereas the control group consisted of 18 age/gender/handedness-matched healthy subjects.

Results: A significant difference in hemispheric lateralization index (LI) was found between children with epilepsy (mean LI =−0.038) and the age/gender/handedness–matched healthy control subjects (mean LI = 0.257; t = 6.490, p < 0.0001). A dramatic difference also was observed in the percentage of children with epilepsy (77.78%) who had atypical LI (right-hemispheric or bilateral, LI < 0.1) when compared with the age/gender/handedness-matched group (11.11%; χ2= 16.02, p < 0.001). A linear regression analysis showed a trend toward increasing language lateralization with age in healthy controls (R2= 0.152; p = 0.108). This association was not observed in pediatric epilepsy subjects (R2= 0.004, p = 0.80). A significant association between language LI and epilepsy duration also was found (R2= 0.234, p < 0.05).

Conclusions: This study shows that epilepsy during childhood is associated with neuroplasticity and reorganization of language function.

The localization and lateralization of language processing have been investigated extensively in electrocortical stimulation experiments and in lesional, neuropsychological, and imaging studies. It has been well established that language functions are primarily distributed in cerebral cortical areas such as inferior frontal gyrus (Broca's area), superior temporal and middle temporal gyri, supramarginal gyrus, and angular gyrus (Wernicke's area), with strong left hemispheric dominance (1–14). This left-dominant pattern is observed not only in adults and children but also, based on recent findings, during infancy (15).

Epilepsy is one of the neurologic disorders (others including stroke, brain tumors, etc.) that can exert profound and prolonged impact on language functions (1,16–18). One manifestation of such impact is the “atypical” language-distribution pattern that has been observed in many adult functional magnetic resonance imaging (fMRI) studies (19–26). In a comprehensive fMRI study (19) that compared language dominance in a large number of adult subjects (100 healthy control subjects and 50 epilepsy subjects), it was found that a significantly larger percentage of epilepsy patients had bilateral or right-hemispheric language dominance than did healthy controls. A later fMRI study showed similar findings: in comparison to healthy subjects; adults with left temporal lobe epilepsy (TLE) tended to have greater language activation in right hemisphere, and their overall language representation was more bilateral (25). The findings of atypical language lateralization in adult epilepsy patients are further supported by a recent fMRI study that showed evidence supporting “a significant intra- and interhemispheric functional reorganization of language-related neuronal networks in left TLE” (26).

In contrast to the wealth of adult epilepsy studies, the differences in the neural substrates of language between pediatric epilepsy patients and healthy children have been well less documented in fMRI studies. The published fMRI literature about the impact of epilepsy on language lateralization in children has either been clinical research that focused on evaluating the accuracy of presurgical and postsurgical fMRI assessment, or studies comparing the language organization patterns among epilepsy patients (27–30). No fMRI study thus far has provided quantitative comparison directly to contrast pediatric epilepsy patients and healthy children in terms of their language dominance patterns during development.

In general, language activation patterns in healthy children, even as early as infancy, are usually distributed in pattern similar to that of their adult counterparts (e.g., 14,15,31–33). However, extrapolating the influence of epilepsy on language function in pediatric population from adult data may be oversimplifying and presumably may result in inaccuracy, especially in light of the mounting evidence from recent studies suggesting that age plays an important role in determining language activation patterns and lesion-related neuroplasticity (14,32,34). The convolution of epilepsy and development makes it extremely difficult to distinguish the actual impact of epilepsy on the neural substrates of language based only on language-distribution patterns observed in pediatric epilepsy patients. To investigate this issue, a quantitative group comparison is needed to contrast fMRI results between pediatric epilepsy patients and healthy control subjects. Based on the extent to which these patterns differ, it might be possible to extrapolate further information about the relation of the abnormal patterns and epilepsy-related neuroplasticity in the pediatric patients. Surprisingly, no such study has been reported to date.

We present fMRI data obtained from children with epilepsy and examine the patterns of language lateralization (lateralization index, LI) among these patients by comparing these data with the patterns obtained in healthy control subjects. We were also interested in studying the influence of epilepsy on the correlation between LI and age in children. Our hypotheses were that significant differences exist in LI between children with epilepsy and healthy controls and that the relation between age and LI is weaker, if still existing, in patients with epilepsy than in healthy counterparts. To test our hypotheses, we took a novel approach by directly contrasting fMRI activation patterns in pediatric epilepsy patients with an equal number of age-gender-handedness-matched healthy subjects. These comparisons should provide new insights into the issue of age dependence of language lateralization and show the extent to which the brain reorganizes to compensate for damage associated with epilepsy during childhood.


  1. Top of page
  2. Abstract
  6. Acknowledgments

This was a retrospective study in which the fMRI data for the patients and healthy subjects were obtained on two different MR scanners at different time periods between 1997 and 2004. Some of the data have been reported previously (32,34–36). Because fMRI data acquisition techniques have evolved and been refined over these years, differences in methods will be clarified whenever necessary. The fMRI data from epilepsy patients and healthy control subjects were analyzed by using identical methods to minimize cross-platform variability. For example, a region-of-interest (ROI)-based objective thresholding method (described later) was used to calculate an LI, providing a data analysis scheme that was robust and portable across scanners and field strength (Table 1).

Table 1. Summary of clinical information for pediatric epilepsy patients
 Age (mo) Patient/controlSexHandednessOnset age (yr)EEG lateralizationMRI structureEpilepsy diagnosisLI Patient/control
  1. LI, lateralization index; AVM, arteriovenous malformation; BRE, benign rolandic epilepsy; ENCEPH, encephalomalacia; MIGR, migration abnormality; N/A, not available.

 1101/98ML1L multifocalL hippocampal atrophyPartial L−0.150/0.136
 2111/111FR3L frontotemporalR frontal atrophyPartial R0.024/0.179
 3133/134MR9BifrontalNormalIdiopathic generalized0.043/0.387
 4136/137FR10R parietalR temporoparietal dysplasiaPartial R−0.022/0.215
 5141/141FR10R & L temporoparietal R temporoparietal slowingNormalPartial R0.193/0.276
 6141/147ML2L posterior spike–waveL occipital ENCEPHPartial L−0.310/0.315
 7149/149MR10L centrotemporal (BRE)NormalBRE, L−0.062/0.098
 8150/150MR12NormalNormalPartial, 2nd generalized−0.140/0.089
11176/179MR14L temporalL temporal gliomaPartial L−0.110/0.178
12177/177FR9R parietotemporalR temporoparietal MIGRPartial R−0.071/0.136
13179/179MR9R frontalNormalPartial R, 2nd generalized0.136/0.489
14183/182FR14Bifrontal slowingNormalIdiopathic generalized−0.168/0.292
15194/198MR15L focalNormalPartial L0.251/0.381
16208/208MR10BicentralNormalPartial R−0.156/0.334
17219/218ML14L temporalL temporal AVMPartial L−0.113/0.376
18228/227FR7R temporalNormalPartial R−0.169/0.240
Pediatric patientsMatching controls
No. of subjects: 18No. of subjects: 18
No. of boys: 11, No. of girls: 7No. of boys: 11, No. of girls: 7
No. of left handed: 3No. of left handed: 3
No. of right handed: 15No. of right handed: 15
Age in mo.: Mean = 165, SD = 35.15Age in mo.: Mean = 164.83, SD = 35.03
LI: Mean =−0.038, SD = 0.152LI: Mean = 0.257, SD = 0.120


Twenty-three pediatric patients (age 8–18 years) diagnosed with epilepsy by a pediatric neurologist (R.H.S.) were recruited for the fMRI language study. fMRI data from a silent verb generation task (described later) was successfully obtained from 18 of these patients (age 8–18 years; 10 males, eight females). All the patients were recruited irrespective of their race, gender, epilepsy syndrome, lesion site, or handedness. After a complete description of the study was given, written consent was obtained from the parents, and the subjects gave either verbal or written consent. This study was approved by the Institutional Review Board of the Cincinnati Children's Hospital Medical Center (CCHMC).

Patients' demographic and clinical information is summarized in Table 1. Among the 18 subjects that finished fMRI scanning, 17 had a complete set of information about seizure onset age, EEG, MRI structure, and epilepsy classification. Fifteen subjects were diagnosed with partial epilepsy (including two with secondarily generalized seizures). Two subjects had the diagnosis of idiopathic generalized seizure with normal MR imaging and bisynchronous epileptiform potentials on EEG. The majority (n = 15) of this patient group had focal or multifocal epileptiform discharges on EEG, with the other two subjects failing to present any EEG abnormality. Preliminary results shows that the language lateralization index (LI, as defined later) in patients with left focal epilepsy (n = 7; mean LI =−0.07; SD, 0.17) is not significantly different (t= 0.40, p = 0.70) from the patients that have right focal epilepsy (n = 4; mean LI =−0.03; SD, 0.13). MR imaging was abnormal in seven (n = 7), demonstrating atrophy, dysplasia, encephalomalacia, glioma, migrational abnormality, and arteriovenous malformation in various locations of the brain. MRI observation showed close agreement (with subject 2 in Table 1 as the only exception) with the abnormality localized by EEG when lesions were present.

The control group in our study consisted of 18 age-gender-handedness-matched healthy children. These children were selected from the cohort (N = 336; age = 5–18 years; 166 males, 170 females) of a large-scale fMRI language development study (NIH-R01-HD38578-05), and they were determined as follows: for each pediatric epilepsy patient, one healthy child with the same gender, handedness, and with the closest age in months was selected from the entire healthy control subjects group. The age-gender-handedness-matched healthy control group (n = 18) was used to demonstrate the contrast between patients and healthy controls, with the presumption that matching the subjects would eliminate the confounding influences of age, gender, and handedness on language development and allow us to focus the analysis on other factors including language lateralization and localization.

The description of the entire healthy cohort has been presented in previous publications (14,32,34–36). Preliminary data analysis showed that our healthy control group was not significantly different from the entire cohort from which it was selected in terms of male/female ratio (χ2= 0.89, p ≈ 1), percentage of left-handers (χ2= 1.64; p = 0.20), mean LI (t = 1.30, p = 0.21), and percentage of atypical lateralization determined by fMRI (χ2= 0.946, p = 1). The findings for the entire healthy cohort will not be discussed in detail in this study. However, they will provide background in which the data from the two studied cohorts are discussed.

Verb generation task and language paradigm in fMRI scanning

The language task used in the study was a child-friendly word-fluency paradigm based on the verb generation task introduced by Peterson (37) and Benson et al. (38). This task involves the presentation of a series of concrete nouns to the subject via an MRI-compatible audio system. The subjects were asked to generate verbs covertly corresponding to the presented noun. For example, when the subjects heard the noun “ball,” they would think of the verbs like “kick,”“play,”“hit,” etc. The subjects were instructed to generate the verbs silently to avoid head-motion artifacts that could result from overt speech. This language task has been used consistently by our group for the past 10 years. Additional details about the task are described in our earlier publications (32,34).

The language paradigm was administered in a periodic block design in which 30 s of verb generation was interleaved with 30 s of control task. The control task for healthy children was bilateral finger tapping cued by a tone at 5-s intervals (FM tones centered on 400 Hz with 25% modulation), whereas patients were instructed to remain silent during this period of the experiment. Functional MRI scanning proceeded during the alternating periods of activation and control at a rate of one EPI (echo-planar imaging) acquisition every 3 s. In healthy children, 110 time points were acquired during five activation periods and six control periods, resulting in a total fMRI acquisition time of 330 s. The first 10 image volumes of control periods were discarded in postprocessing to eliminate nonequilibrium effect. In epilepsy patients, the initial control period was not included, and only 100 EPI images (five activation periods and five control periods) were acquired with a total scanning time of 300 s.

Imaging data acquisition

MRI/fMRI scans for the epilepsy patient group were performed on a 1.5-Tesla, GE Signa Horizon MRI scanner with Echospeed gradients (GE, Milwaukee, WI, U.S.A.). Each fMRI scan consisted of 100 single-shot EPI gradient-echo images acquired with TR/TE = 3,000/40 m/sec, FOV = 220 × 220 mm, matrix = 64 × 64. Six sagittal slices were acquired in each hemisphere for each time point, leading to a total of 1200 slices of fMRI data. Slice thickness was 5 mm with a 1-mm gap. The slices were positioned such that the outermost slice on each hemisphere extended to the most lateral aspect of the temporal lobe, as illustrated in Fig. 1. A T2-weighted image was obtained in the same location for anatomic superposition. T2-weighted 2D axial images and T1-weighted whole brain sagittal images also were acquired to define the parameters necessary for the transformation into Talairach reference frame.


Figure 1. The axial view of slice selection in the MR imaging on 1.5-T GE scanner for pediatric patients with epilepsy. Only the pixels on slices with Talaraich coordinate x > 35 or x < −35, y > −45, z > −10 were included for the calculation of the lateralization index.

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MRI/fMRI scans for healthy control subjects were performed on a 3-T Bruker Biospec 30/60 MRI scanner (Bruker Medizintechnik, Karsruhe, Germany). The whole brain anatomic images were obtained by using a 3D MDEFT (Modified Driven Equilibrium Fourier Transform) in axial plane (39,40) with the parameters as follows: TR/TE/tau, 15.7/4.3/550 ms; FOV, 192 × 256 × 160 mm; matrix, 256 × 192 128. Functional MRI scans were acquired in the transverse plane by using a T*2-weighted, gradient-echo, EPI sequence (TR/TE, 3,000/38 ms; FOV, 25.6 × 25.6 cm; matrix, 64 × 64; slice thickness, 5 mm). Each scan consisted of 24 slices.

The differences in fMRI data acquisition

As noted earlier, one of the limitations in this retrospective study comes from the fact that the fMRI data for epilepsy patients and healthy controls were collected by using different MR scanners with different magnetic field strength. Understanding that this is not an ideal situation, we believe the comparison is still valid because the calculation of LI (as outlined in the following sections) is based on a ratio of t-statistics in the two hemispheres for the same individual and in the same scan. By definition, the LI represents a relative hemispheric difference for an individual that is self-normalizing in terms of relative blood oxygen level dependent (BOLD) activity corresponding to the neuronal recruitment necessary to perform the language task. As noted later, the brain areas included for LI calculation were determined so that they were consistent in the two populations while still conforming to the traditional standard of ROI selection for language lateralization. Every effort has been made to ensure that the methods applied in the LI calculations of two subject groups are compatible.

fMRI postprocessing and data analysis

fMRI image postprocessing was conducted by using Cincinnati Children's Hospital Image Processing Software (CCHIPS) developed in the Imaging Research Center at CCHMC in the IDL environment (Research Systems Inc., Boulder, CO, U.S.A.). A Hamming filter was applied in k-space data before Fourier transformation to reduce truncation artifacts and high frequency noise (41). Then the images were coregistered to reduce the influence of motion artifact based on a pyramid coregistration algorithm developed by Thevenaz and Unser (42). Thereafter, a quadratic baseline correction algorithm was used to correct baseline drift (43,44). Finally, Talairach transformation was performed on each subject's anatomic and functional data to put them in a common reference frame for further data analysis.

On a pixel-by-pixel basis, t-statistics were calculated after grouping the time series data into active and control states. A hemispheric LI was calculated for each subject by first counting the activated pixels within the predefined ROI. For epilepsy patients and healthy control subjects, the ROIs used for the calculation included all pixels with Talairach coordinate x > 35 (in right hemisphere) or x < −35 (in left hemisphere). Additional restrictions applied to ROI selection were Talairach coordinate y > −45 (to include only the anterior two thirds of the brain) and z > −10 (to exclude cerebellar regions). For consistency, identical ROIs were used for data processing and analysis in both epilepsy patients and healthy control subjects. These restrictions applied in our LI calculation conform to the traditional selection of ROIs within cerebral cortex and effectively prevented the influence from occipital lobe and cerebellum activation. A threshold was determined by calculating the mean value of the t-statistics for all pixels within the ROIs. The number of pixels exceeding this threshold was counted for both the left- and right-side ROIs. The lateralization index was calculated as follows:

  • image

where ΣNL and ΣNR represented the sum of the fMRI pixels that exceeded the threshold for the left and right hemispheric ROIs, respectively. Calculating the LI in this manner avoided the biases introduced by arbitrary thresholding and clustering schemes, as well as possible differences in BOLD contrast-to-noise ratio between the two scanners operating at different field strengths. Both features were crucial for the present study to ensure the consistency in our between-group data analysis.

This approach yields LIs that range between −1 (right-sided activation only/maximum right hemispheric dominant) and 1 (left-sided activation only/maximum left dominant). Values close to “0” (i.e., −0.1 ≤ LI ≤ 0.1) define bilateral language distribution (32,40). A subject with LI > 0.1 is categorized as left dominant, whereas a subject with LI < −0.1 is categorized as right-side dominant.


  1. Top of page
  2. Abstract
  6. Acknowledgments

For both pediatric epilepsy patients and age-gender-handedness-matched healthy children, cortical activation during the verb-generation task was observed in all subjects in the classic language areas including the inferior frontal gyrus (BA 44, 45), superior and middle temporal gyri (BA 41, 42, 21, 22), angular gyrus (BA 39), and supramarginal gyrus (BA 40). In general, brain activation in the Broca and Wernicke areas is stronger and more concentrated in healthy children than that in children with epilepsy.

Several notable differences are evident in the quantitative subject group comparison of LI. As shown in Table 1, among 18 children with epilepsy, six (33.3%) patients had bilateral language distributions (−0.1 < LI < 0.1); eight (44.4%) patients had right-side language dominance (LI < −0.1); only four (22.2%) patients demonstrated left-side language dominance (LI > 0.1). In contrast, for the same number of healthy children, only two subjects (11.1%) were bilateral, whereas the majority (n = 16, 88.89%) was categorized as left dominant for language (LI > +0.1). When we combined the bilateral and right-side dominant subjects, they constituted the portion of subjects who had “atypical” language lateralization. The percentages of subjects with “typical” and “atypical” language dominance in the patient group are significantly different (χ2= 16.2, p < 0.001) than those of age/gender/handedness-matched healthy children. The Welch Modified Two-sample t test showed that the mean LI for the epilepsy patients was −0.038 (SD, 0.152; n = 18), a value significantly different (t= 6.490, p < 0.0001) when compared with the age/gender/handedness-controlled healthy children (mean LI = 0.257, SD = 0.120, n = 18).

Figure 2 shows percentage histograms comparing the distribution of LI values between 18 epilepsy patients and 18 age/gender/handedness-matched healthy subjects. The LIs from the two subject groups form two distinct distributions with leftward shift on the histogram for the epilepsy patients by ∼0.2–0.3. This shift suggests relatively more language activation in the right hemisphere for the pediatric epilepsy patients than for the healthy controls.


Figure 2. Comparison of lateralization index for pediatric epilepsy patients (n = 18) versus age/gender/handedness-matched healthy control subjects (n = 18).

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A linear regression model was used to examine the relation between LI and age (Fig. 3). LI was found to increase with age with moderate strength of correlation (R2= 0.15) in the age-gender-handedness-matched healthy children group, which was in line with our previous report that included a larger cohort (14,32,34). However, the correlation in our smaller group of healthy children did not reach statistical significance (p = 0.11). Conversely, no correlation between LI and age was found for the epilepsy patients (R2 < 0.004, p = 0.80). Comparison of residuals from the linear regression found that both groups were unbiased, with a mean value of zero. No increasing or decreasing spread about the regression line was observed as the age increased. The standard deviation of the residual for the children with epilepsy (SD = 0.15) was larger than that of the age/gender/handedness-matched healthy children group (SD, 0.12).


Figure 3. The linear regression of language lateralization index (LI) with age for the pediatric patients with epilepsy (n = 18) and the age/gender/handedness-matched healthy group (n = 18).

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Linear regression analysis also was conducted to study whether the age at seizure onset imposed any influence on the distribution of language function. No statistically significant association was observed between LI and seizure-onset age (n = 17, R2= 0.11, p = 0.19). However, as shown in Fig. 4, LI was closely correlated (n = 17; R2= 0.234, p < 0.05) with the duration between seizure onset and fMRI scanning.


Figure 4. The linear regression of language lateralization index with duration between onset seizure and fMRI scanning for pediatric patients.

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  1. Top of page
  2. Abstract
  6. Acknowledgments

This is the first fMRI study that directly compares the degree of abnormality of language lateralization during development in epilepsy patients and healthy controls. Our results show that the language distribution patterns in epilepsy patients differ significantly from those found in healthy control subjects in terms of the mean value and the variability of the LI. We also find that pediatric epilepsy patients have a higher incidence of atypical (bilateral or right hemispheric) language dominance than do healthy controls, an observation that has been consistently reported in previous adult studies (19,25,26). Unlike the conclusions by Springer et al. (19) and Saltzman et al. (45), our results did not demonstrate any correlation between age at seizure onset as a significant predictor of language lateralization, in agreement with other studies (e.g., 26,46,47). Further analysis of the timing factor in our study showed that the time between seizure onset and fMRI scanning (i.e., duration of epilepsy) seems to be a more reliable predictor for LI, an observation different from the findings by Thivard et al. (47).

Another interesting finding in our study is the possible divergence between healthy controls and patients with epilepsy in the LI-versus-Age relation. Our previous studies showed that LI in healthy children increases with age during development (14,32,34). In other words, younger healthy children are more likely to have atypical (relative to adult findings) language distribution than older healthy children, indicating an increasing specialization of language functions to the left hemisphere as age increases. As a subset of the cohort used in previous studies, the age-gender-handedness-matched healthy control group in this study was expected to demonstrate a similar pattern. Nevertheless, our study showed that this hypothesis was not entirely correct. As shown in Fig. 3, the age-gender-handedness-matched healthy control group did present a moderate linear association between LI and age (R2= 0.15, p = 0.11) as a general trend throughout childhood and adolescence. However, it must be noted at the same time that this correlation did not reach statistical significance as it has in its superset shown in our previous reports (14,32,34). This lack of significance is likely due to the limited number of subjects within the group. Conversely, the pediatric epilepsy patients did not show any association between LI and age (R2= 0.004, p = 0.80) which, again, was possibly due to the limited sample size, as was the case for the age-gender-handedness-matched healthy children group. However, a more likely cause might be related to the effects of epilepsy on brain development, which could be further masked by other effects (e.g., very nonuniform cohort of epilepsy patients with various pathologies and types of epilepsy). From the perspective of neural development, the lack of correlation of LI with age in epilepsy patients suggests that epilepsy may disrupt the specialization and consolidation of functional language areas in the normal developmental process, in which the convergence toward certain preferred areas (such as the Broca and Wernicke areas in the left hemisphere) coincides with age in healthy controls. Another possible explanation is that the damage associated with epilepsy stimulates the brain either to activate preexisting connections to the dormant functional language areas or to develop new areas in the right hemisphere to compensate for the deficits in the left hemisphere language centers.

Our research shows that LI can serve as a simple yet robust method for demonstrating differences in brain-activation patterns between epilepsy patients and healthy control subjects during development. The question remains whether the atypical language lateralization in epilepsy patients is due to the prolonged effect of seizures on the brain, or whether preceding brain pathologies cause both the atypical lateralization and the epilepsy. The fact that the degree of this abnormality, as quantified by LI, shows a strong correlation with the epilepsy duration seems to suggest a possible causal relation in which the seizure activity in epilepsy subjects causes redistribution of language function in the developing brain to compensate for injury to traditionally left dominant language areas or connections to them (i.e., cortical plasticity). Because of the limited sample size in our study, it was not feasible to conduct multivariate analysis to account for the variability of seizure onset age, the inhomogeneous epilepsy pathologies, various lesion sites, intelligence, socioeconomic status, and other possible confounding factors. We were not able to establish a conclusive cause-and-effect relation between the atypical lateralization and epilepsy or to rule out the alternative explanation that preexisting brain abnormalities lead to the development of epilepsy as well as the subsequent aberrant language localization/lateralization. Further longitudinal study, such as a design that involves pediatric subjects with new onset seizures, will be necessary to help clarify the role and consequences of epilepsy on neuroplasticity in language development.


  1. Top of page
  2. Abstract
  6. Acknowledgments

Acknowledgment:  This study is supported in part by NIH grant 1-RO1-HD38578 (S.K.H.).


  1. Top of page
  2. Abstract
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