Skip to main content
Advertisement

Main menu

  • Home
  • Content
    • Current Issue
    • Accepted Manuscripts
    • Article Preview
    • Past Issue Archive
    • Video Articles
    • AJNR Case Collection
    • Case of the Week Archive
    • Case of the Month Archive
    • Classic Case Archive
  • Special Collections
    • Low-Field MRI
    • Alzheimer Disease
    • ASNR Foundation Special Collection
    • Photon-Counting CT
    • AJNR Awards
    • View All
  • Multimedia
    • AJNR Podcasts
    • AJNR SCANtastic
    • Trainee Corner
    • MRI Safety Corner
    • Imaging Protocols
  • For Authors
    • Submit a Manuscript
    • Submit a Video Article
    • Submit an eLetter to the Editor/Response
    • Manuscript Submission Guidelines
    • Statistical Tips
    • Fast Publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Imaging Protocol Submission
    • Author Policies
  • About Us
    • About AJNR
    • Editorial Board
    • Editorial Board Alumni
  • More
    • Become a Reviewer/Academy of Reviewers
    • Subscribers
    • Permissions
    • Alerts
    • Feedback
    • Advertisers
    • ASNR Home

User menu

  • Alerts
  • Log in

Search

  • Advanced search
American Journal of Neuroradiology
American Journal of Neuroradiology

American Journal of Neuroradiology

ASHNR American Society of Functional Neuroradiology ASHNR American Society of Pediatric Neuroradiology ASSR
  • Alerts
  • Log in

Advanced Search

  • Home
  • Content
    • Current Issue
    • Accepted Manuscripts
    • Article Preview
    • Past Issue Archive
    • Video Articles
    • AJNR Case Collection
    • Case of the Week Archive
    • Case of the Month Archive
    • Classic Case Archive
  • Special Collections
    • Low-Field MRI
    • Alzheimer Disease
    • ASNR Foundation Special Collection
    • Photon-Counting CT
    • AJNR Awards
    • View All
  • Multimedia
    • AJNR Podcasts
    • AJNR SCANtastic
    • Trainee Corner
    • MRI Safety Corner
    • Imaging Protocols
  • For Authors
    • Submit a Manuscript
    • Submit a Video Article
    • Submit an eLetter to the Editor/Response
    • Manuscript Submission Guidelines
    • Statistical Tips
    • Fast Publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Imaging Protocol Submission
    • Author Policies
  • About Us
    • About AJNR
    • Editorial Board
    • Editorial Board Alumni
  • More
    • Become a Reviewer/Academy of Reviewers
    • Subscribers
    • Permissions
    • Alerts
    • Feedback
    • Advertisers
    • ASNR Home
  • Follow AJNR on Twitter
  • Visit AJNR on Facebook
  • Follow AJNR on Instagram
  • Join AJNR on LinkedIn
  • RSS Feeds

AJNR at ASNR25 | Join us at BOOTH 312 and more. Check out our schedule

Research ArticlePediatrics
Open Access

Diffusion Tensor Imaging Properties and Neurobehavioral Outcomes in Children with Hydrocephalus

W. Yuan, R.C. McKinstry, J.S. Shimony, M. Altaye, S.K. Powell, J.M. Phillips, D.D. Limbrick, S.K. Holland, B.V. Jones, A. Rajagopal, S. Simpson, D. Mercer and F.T. Mangano
American Journal of Neuroradiology February 2013, 34 (2) 439-445; DOI: https://doi.org/10.3174/ajnr.A3218
W. Yuan
eUniversity of Cincinnati College of Medicine (W.Y., M.A., J.M.P., S.K.H., B.V.J., F.T.M.), Cincinnati, Ohio
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
R.C. McKinstry
fMallinckrodt Institute of Radiology (R.C.M., J.S.S.)
iWashington University School of Medicine (R.C.M., J.S.S., S.K.P., D.D.L., D.M.), Washington University, St. Louis, Missouri
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
J.S. Shimony
fMallinckrodt Institute of Radiology (R.C.M., J.S.S.)
iWashington University School of Medicine (R.C.M., J.S.S., S.K.P., D.D.L., D.M.), Washington University, St. Louis, Missouri
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
M. Altaye
bDivision of Biostatistics and Epidemiology (M.A.)
eUniversity of Cincinnati College of Medicine (W.Y., M.A., J.M.P., S.K.H., B.V.J., F.T.M.), Cincinnati, Ohio
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
S.K. Powell
gDepartment of Neurology (S.K.P.)
iWashington University School of Medicine (R.C.M., J.S.S., S.K.P., D.D.L., D.M.), Washington University, St. Louis, Missouri
jDepartment of Psychology (S.K.P.), St. Louis Children's Hospital, St. Louis, Missouri.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
J.M. Phillips
cDivision of Developmental and Behavioral Pediatrics-Psychology (J.M.P.)
eUniversity of Cincinnati College of Medicine (W.Y., M.A., J.M.P., S.K.H., B.V.J., F.T.M.), Cincinnati, Ohio
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
D.D. Limbrick Jr
hDepartment of Neurological Surgery (D.D.L., D.M.)
iWashington University School of Medicine (R.C.M., J.S.S., S.K.P., D.D.L., D.M.), Washington University, St. Louis, Missouri
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
S.K. Holland
aFrom the Department of Radiology (W.Y., S.K.H., B.V.J., A.R., S.S.)
eUniversity of Cincinnati College of Medicine (W.Y., M.A., J.M.P., S.K.H., B.V.J., F.T.M.), Cincinnati, Ohio
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
B.V. Jones
aFrom the Department of Radiology (W.Y., S.K.H., B.V.J., A.R., S.S.)
eUniversity of Cincinnati College of Medicine (W.Y., M.A., J.M.P., S.K.H., B.V.J., F.T.M.), Cincinnati, Ohio
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
A. Rajagopal
aFrom the Department of Radiology (W.Y., S.K.H., B.V.J., A.R., S.S.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
S. Simpson
aFrom the Department of Radiology (W.Y., S.K.H., B.V.J., A.R., S.S.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
D. Mercer
hDepartment of Neurological Surgery (D.D.L., D.M.)
iWashington University School of Medicine (R.C.M., J.S.S., S.K.P., D.D.L., D.M.), Washington University, St. Louis, Missouri
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
F.T. Mangano
dDivision of Pediatric Neurosurgery (F.T.M.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
eUniversity of Cincinnati College of Medicine (W.Y., M.A., J.M.P., S.K.H., B.V.J., F.T.M.), Cincinnati, Ohio
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • Responses
  • References
  • PDF
Loading

Abstract

BACKGROUND AND PURPOSE: White matter structural alterations and the correlation with neuropsychological deficits in children with hydrocephalus have not been well investigated. In this prospective study, the objectives were the following: 1) to apply DTI to detect in vivo white matter alterations based on diffusion properties in children with acute hydrocephalus, 2) to quantify early neuropsychological deficits, and 3) to explore the correlation between potential neuropsychological deficits and abnormalities in functionally related white matter.

MATERIALS AND METHODS: A total of 44 children, 24 with hydrocephalus and 20 controls, were enrolled in the study. DTI indices, FA, MD, AD, and RD, were evaluated in the gCC, sCC, PLIC, and ALIC. The ABAS-II was used as a broad screener of development, including conceptual, social, practical, and motor skills. The correlation between the Motor Scale and DTI indices in the PLIC was analyzed.

RESULTS: DTI analyses showed that the gCC and sCC in children with hydrocephalus had lower FA and higher MD, driven by the increased RD with statistical significance (P < .05) or trend-level significance (P = .06). The PLIC and ALIC had significantly higher AD in children with hydrocephalus (P < .05). On the ABAS-II, parent ratings of general adaptive skills, conceptual skills, and motor skills were significantly lower in children with hydrocephalus (all at P < .05). The MD and RD values in the PLIC were found to have trend-level or significant correlation with the Motor Scale (P = .057, .041, respectively).

CONCLUSIONS: DTI reveals alterations in the white matter structure in children with hydrocephalus with preliminary findings suggesting correlation with clinical motor deficits.

ABBREVIATIONS:

ABAS-II
Adaptive Behavior Assessment System, Second Edition
AD
axial diffusivity
ALIC
anterior limb of internal capsule
FA
fractional anisotropy
gCC
genu of the corpus callosum
IVH
intraventricular hemorrhage
MD
mean diffusivity
PLIC
posterior limb of the internal capsule
RD
radial diffusivity
sCC
splenium of the corpus callosum

Hydrocephalus is a pathologic imbalance in the production and absorption of CSF. The resulting increased intracranial pressure can cause extensive structural displacement, mechanical distortion, and eventually injury throughout the brain.1 The resulting damage to the corpus callosum, periventricular white matter, fornix, internal capsule, and other white matter structures has been consistently reported to be a major factor associated with poor neurologic outcomes.1⇓⇓⇓⇓–6 However, despite increasing recognition of the role of white matter injury in hydrocephalus-related neurologic challenges, the pathophysiology remains poorly understood. Conventional MR imaging techniques, such as T1WI or T2WI, offer the capability to detect major structural abnormalities but lack the sensitivity and/or specificity to quantify white matter abnormalities in patients with hydrocephalus.7,8

DTI is an advanced MR imaging technique that quantifies the microscopic diffusion properties of water molecules in biologic tissues. These diffusion parameters can be used to characterize the degree of organization of the neural tissue in the brain. In pathologic conditions causing structural abnormalities or tissue degeneration, DTI measurements can quantify these changes and can be used as a noninvasive imaging biomarker to monitor disease progression or therapeutic efficacy; furthermore, DTI may have a unique role in determining neurologic prognosis. In recent studies of hydrocephalus, DTI has shown white matter anisotropic changes in hydrocephalus as well as postoperative normalization in successfully treated children.9⇓⇓⇓–13 However, these studies were often small in size and retrospective in nature or included a heterogeneous population of children with hydrocephalus.

The purpose of this study was to prospectively quantify the diffusion properties in white matter structures of a group of children presenting with the age range and clinical profiles typical of pediatric hydrocephalus. We hypothesized that children with hydrocephalus would demonstrate abnormal diffusion properties that may be characteristically different in the corpus callosum and internal capsule and that neuropsychological outcome measures would be abnormal and correlated with the DTI measurements.

Materials and Methods

All participants were selected from an ongoing multicenter, prospective, longitudinal imaging study of pediatric hydrocephalus. The present study reports initial results derived from imaging and neurobehavioral tests at baseline before surgical management.

Participants.

A total of 44 participants, 24 children with hydrocephalus (age range, 0–51 months) and 20 healthy control children (age range, 3–68 months) were recruited. Among the 24 patients with hydrocephalus, 3 had communicating hydrocephalus, 1 had unknown etiology, and the remaining 20 had obstructive hydrocephalus including different etiologies: aqueduct stenosis (n = 7), IVH (n = 4), posterior fossa or arachnoid cyst (n = 4), and other etiologies (n = 5). DTI and ABAS-II14 data were acquired from 15 and 17 patients, respectively. Two of the 15 patients with hydrocephalus on DTI had severe hydrocephalus and ventriculomegaly, which did not allow reliable delineation of any region of interest. Among the 20 healthy participants, 2 children were found to have standard scores below 80 across all 4 ABAS-II domains, as well as the motor subdomain, and thus were disqualified and excluded from further analyses. DTI data were acquired from 17 of the remaining 18 healthy participants; ABAS-II data were acquired from 16. The final region of interest–based group comparisons of DTI indices involved 13 patients with hydrocephalus (mean age, 10 months; range, 0–39 months) and 17 controls (mean age, 34 months; range, 3–68 months). The group comparisons of ABAS-II scores involved 17 patients with hydrocephalus (mean age, 17 months; range, 0–51 months) and 16 healthy controls (mean age, 35 months; range, 3–68 months). The correlation analysis included 8 children with hydrocephalus (5 of the 13 patients with hydrocephalus described above did not have an ABAS-II test). Detailed demographic information for the control and patient groups are listed in On-line Tables 1 and 2, respectively.

Two hospitals, Cincinnati Children's Hospital Medical Center and St. Louis Children's Hospital, were the sites of recruitment. Parents gave consent when participants were enrolled into the study according to institutional review board guidelines at both institutions. Inclusion criteria for the hydrocephalus group were the following: 1) age, 0–15 years, 2) referral to radiology for brain MR imaging for evaluation of hydrocephalus and later referral for CSF diversion surgery, 3) hydrocephalus with ventriculomegaly, and 4) no evidence of other medical diagnoses that would predispose the child to adverse neurologic outcomes (eg, preterm birth with severe intraventricular hemorrhage, Dandy-Walker syndrome, meningitis, stroke, or spina bifida). Inclusion criteria for the control group were the following: 1) age, 0–16 years, 2) referral to radiology for nonspecific symptoms not clearly related to a neurologic disorder (eg, headaches), 3) diagnosis of normal MR imaging findings, 4) no clinical or radiographic history of neurologic or psychological disorders before the scan, and 5) no evidence of any neurologic disorders (eg, epilepsy, stroke) or suggestion of white matter–related brain pathology within 3 months after the initial MR imaging per medical records.

MR Imaging/DTI Data Acquisition and Processing.

DTI data were acquired as part of a clinical scan on 1.5T scanners at either Cincinnati Children's Hospital Medical Center (Signa; GE Healthcare, Milwaukee, Wisconsin) or St. Louis Children's Hospital (Avanto; Siemens, Erlangen, Germany). A diffusion-weighted spin-echo sequence with single-shot EPI was used with the following specifications: FOV = 240 × 240 mm, matrix = 96 × 96, in-plane resolution = 2.5 × 2.5 mm, section thickness = 2.5 mm, number of sections = 76, TR/TE = 9400/93.2 ms, array spatial sensitivity encoding technique or integrated parallel acquisition technique factor = 2. Diffusion weighting was applied with b = 1000 s/mm2 along 15 independent noncollinear orientations. One additional image with no diffusion weighting (b = 0) was also acquired. Two averages were used to increase the SNR. High-resolution sagittal T1-weighted images were also acquired (1 × 1 × 1 mm3) for assessing ventricle size on the basis of the fronto-occipital horn ratio.

Image processing and analysis were performed by using the DTIStudio 3.02 software (Johns Hopkins University, Baltimore, Maryland),15 in which the automated image-registration method was used to correct for head motion and eddy current artifacts.16 An automatic bad-sector detection method and additional visual inspection were used to identify signal drop-out on DWI. DTI metrics, including FA, MD, AD, and RD, were calculated by using the standard technique.17 The ROIs included the gCC, sCC, ALIC, and PLIC. Delineation of ROIs was based on the approach of Hermoye et al,18 which was also used in our previous work.12,13 DTI measurements for ALIC and PLIC were initially calculated bilaterally and were averaged, because no significant difference was found between the 2 hemispheres.

Quality Assurance in Multicenter and Multiscanner Neuroimaging Research.

To establish the MR imaging compatibility across the 2 sites (two 1.5T Signa scanners from Cincinnati Children's Hospital Medical Center and one 1.5T Avanto from St. Louis Children's Hospital), we tested the complete MR imaging protocol, including DTI, T1WI, and T2WI sequences, at both sites by using the same MR imaging phantom (Functional Bioinformatics Research Network, http://www.na-mic.org/Wiki/index.php/FBIRN:Main_Page) and the same human subject before the start of the study; this process has been repeated annually since. The protocol also requires both sites to collect quality assurance data by using American College of Radiology phantoms within a week of each subject's scan to assess the stability of the scanner performance and to detect system-performance change relative to the established baseline. As reported elsewhere,19 the scanners from the 2 sites showed high compatibility during the initial setup; the routine quality assurance scans also showed that scanner performance remained highly stable throughout the period in geometric measurement, SNR, and DTI measurement. It has been shown in several multicenter imaging studies that it is possible to obtain consistent and compatible image quality across sites and scanners if one appropriately attends to details of scanner performance and protocols.20,21 In the present study, all the quality assurance procedures described above were followed rigorously and results were up-to-date at the time of data analysis for the study. In addition, the different scanners are also controlled as a source of variability in the statistical analysis model we used.22

Neuropsychological Evaluation.

A battery of neuropsychological tests was included in the ongoing parent imaging study. The present report highlights composite scores from the ABAS-II14 and their correlation with imaging results. The ABAS-II is a comprehensive age-normed caregiver-report measure of a child's independence skills, covering specific functional domains, including communication, daily living, and social and motor skills. The results comprise an overall score (General Adaptive Composite) and 3 functional domain scores (Conceptual, Social, and Practical). In addition, there is a Motor Scale independent of the functional domain scales. Parents were asked to complete the ABAS-II shortly after initial diagnosis but before surgical intervention.

Statistical Analysis.

Statistical analyses were performed by using SAS 9.2 (SAS Institute, Cary, North Carolina). We initially examined the distribution of data for normality and understanding of the relationship of age with DTI outcomes. Because age and DTI outcomes exhibit an exponential relationship, we transformed the age variable to a log scale to make the relationship linear. Subsequently, we fitted a general linear model to compare the difference between control and hydrocephalus groups after controlling for log-transformed age and potential scanner effect. We also looked at the correlation between DTI and behavioral outcomes by using Spearman correlation coefficients. Before running the correlation analysis, we removed the inherent age effect by fitting a regression model relating DTI outcome with log-transformed age. Examining the distribution of neuropsychological measures plotted against age, a linear relationship was observed for some of the measures. Hence, we removed the age effect from the neuropsychological data by fitting a linear regression model relating ABAS-II scores to age. We then extracted the residuals (difference between fitted and original DTI values) that were free of age effect and correlated these with the residual behavioral outcome by using the Spearman correlation for each group separately.

Results

The values of the 4 DTI indices of individual participants are displayed in the scatterplots in Figs 1⇓⇓–4. As expected, there was a general trend of increase in FA with age and in MD, AD, and RD with age in the control group. A similar trend was also found in patients with hydrocephalus. The scanner was not found to be a significant factor in any of the comparisons. The significance of age and scanner factors and the group DTI differences with adjustment for age and scanner are listed in Table 1 and described in detail below. Ventricle size (fronto-occipital horn ratio) was found to be significantly different between the control and patient groups (mean, 0.31 ± 0.03 versus 0.59 ± 0.14; P < .0001). No significant correlation was found between fronto-occipital horn ratio and any of the DTI indices.

Fig 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig 1.

Scatterplot showing the group difference of (A) FA; (B) MD; (C) AD; and (D) RD in the gCC.

Fig 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig 2.

Scatterplot showing the group difference of (A) FA; (B) MD; (C) AD; and (D) RD in the sCC.

Fig 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig 3.

Scatterplot showing the group difference of (A) FA; (B) MD; (C) AD; and (D) RD in the PLIC.

Fig 4.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig 4.

Scatterplot showing the group difference of (A) FA; (B) MD; (C) AD; and (D) RD in the ALIC.

View this table:
  • View inline
  • View popup
Table 1:

Group difference in the DTI measurement between the patients with HCP and the controls

Abnormal DTI Indices in the Corpus Callosum

Age was found to influence DTI values with statistical significance in both the gCC and sCC (Table 1). In the gCC (Fig 1), FA values increased with age (P < .0001, corrected), while MD and RD values decreased with age (P < .0001, P < .005, respectively; both corrected). After adjustment for age and scanner, we found the children with hydrocephalus to have significantly lower FA (P < .005, corrected), higher MD (P < .005, corrected), and higher RD (P < .005, corrected) in the gCC compared with controls.

In the sCC, a similar influence of age, as well as age- and scanner-adjusted group differences, was found in DTI indices (Fig 2 and Table 1).

Abnormal DTI Indices in the Internal Capsule

Similar to the corpus callosum, age was found to be a significant factor affecting DTI indices for both the ALIC and PLIC (all P < .005, corrected; Table 1). Compared with controls, the patients with hydrocephalus had significantly higher AD values in the PLIC (P < .01, corrected; Table 1 and Fig 3). They also had higher AD with statistical significance (P < .005, corrected) and higher FA with marginal significance (P = .058, corrected) in the ALIC (Table 1 and Fig 4).

Abnormal Neuropsychological Outcomes

As shown in Table 2 and Fig 5, parent response on the ABAS-II yielded significantly lower General Adaptive Composite scores in the hydrocephalus group compared with controls, with significant deficits noted in all the subcategories of the results, which included Conceptual, Practical, Social, and Motor and Motor Scales (all P < .05).

View this table:
  • View inline
  • View popup
Table 2:

Group comparison of ABAS-II scores

Fig 5.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig 5.

Comparison of neuropsychological results between control subjects and the children with hydrocephalus. The asterisk indicates a statistically significant group difference at P < .05; double asterisks, a statistically significant group difference at P < .01.

In the hydrocephalus group, the ABAS-II Motor score was found to have a negative correlation with both MD and RD residuals in the PLIC after adjusting for age at trend-level significance or with statistical significance (Spearman correlation coefficient = −0.71, −0.74, respectively; P = .057, .046, respectively; Fig 6). No significant or trend-level correlations were found between the Motor score and other DTI indices in patients with hydrocephalus or between the Motor score and any DTI indices in the control group.

Fig 6.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig 6.

Correlation between DTI parameters in PLIC and the Motor scores based on ABAS-II parent report form.

Discussion

Long-term, prospective, longitudinal DTI studies of hydrocephalus in children are lacking. The ongoing parent project of this study is the first of its kind, to our knowledge. Its overall goal is to use DTI to evaluate white matter integrity as a noninvasive biomarker to quantify in vivo injury and posttreatment recovery in children with hydrocephalus. The data presented in the current report are the initial results derived from baseline data collected before CSF diversion surgery. Our main findings in this initial study include the following: First, the diffusion properties in children with hydrocephalus are significantly altered in several important white matter regions in the corpus callosum and internal capsule. Second, the patterns of abnormality in DTI measurements vary in different white matter regions. Patients with hydrocephalus were found to have lower FA and higher MD in the gCC and sCC, driven by the increase of RD and the relatively unchanged AD. In contrast, in the PLIC and ALIC, many patients with hydrocephalus were found to have abnormally high FA values, while the remaining patients had abnormally low FA values. Finally, moderate-to-strong correlations were found between DTI indices and parent-reported motor skills.

DTI has been used to study various diseases and neurologic disorders in both clinical and basic experimental research. The characteristics of diffusion properties and the patterns of abnormality vary in different situations. Similar results have been found in some previous studies.9,11,12,23,24 In DWI studies, hydrocephalus was often found to be associated with abnormally high ADC values in periventricular white matter.23⇓–25 In DTI studies, lower FA values have been consistently found in the corpus callosum in hydrocephalus, while mixed FA changes have been reported in some other white matter structures, such as the internal capsule.9⇓⇓–12

There have been extensive discussions on the interpretation of different abnormality patterns and the ability of DTI to derive information about underlying cellular changes. The observations reported in the present study fit well with some of these scenarios. In general, impairment in myelination and axonal damage are often associated with decreased FA and increased MD but are differentiated by an increase of RD (eg, in retinal ischemia) or AD (eg, in multiple sclerosis).26⇓–28 Increased FA accompanied by decreased RD or increased AD or both is often regarded as the result of a compression of white matter tracts, such as results from space-occupying lesions. According to these general themes, the abnormalities seen in hydrocephalus may be attributed to various different underlying injury mechanisms. The decreased FA and increased MD and RD seen in the corpus callosum in our patient population are likely due to delayed or abnormal myelination.26,27 Similar patterns of abnormality have sometimes been regarded as a reflection of white matter damage that is difficult to recover.29 On the other hand, the increased FA in the internal capsule in many of our patients with hydrocephalus may suggest that in these areas, hydrocephalus causes increased homogeneity in fiber orientation. These changes can be attributed to mechanical compression, often regarded as benign and likely reversible after ventriculomegaly has been reduced by CSF drainage.

Although the group difference in RD was consistent in all 4 ROIs when younger participants were examined, the difference seemed to persist throughout the age range studied in gCC and sCC (Figs 1 and 2) but not in PLIC and ALIC. Attributing the changes to delay in myelination may be confounded by the development stage, at least for some brain regions, which should remain a topic for further investigation. On the other hand, in both the PLIC and ALIC, while many older children with hydrocephalus had higher FA, some very young patients had lower FA and higher MD, AD, and/or RD compared with the control group (Figs 3 and 4). There appears to be a potential bifurcation in the pattern of diffusion abnormality in patients with hydrocephalus. However, it is difficult to subcategorize the patient group with an objective criterion to further study the 2 patterns. We cannot rule out the possibility that bifurcation in the pattern of diffusion abnormality represents a limitation of our ability to draw accurate ROIs in the youngest age group, which contributes to the larger spread in values of the FA in that plot.

Neuropsychological Deficits in Hydrocephalus and the Correlation with DTI

In this report, we present a prospective DTI study examining the correlation of DTI parameters with neuropsychological deficits in surgical patients with pediatric hydrocephalus. The lack of such data in the literature is perhaps logistic; most notably, the narrow window between the time of diagnosis and surgical treatment often does not allow a comprehensive preoperative neuropsychological evaluation. It has been well documented that the most prevalent functional deficits in children with hydrocephalus are in visuospatial and motor domains.30 However, given the young age of the patients recruited for this project (between birth and approximately 51 months), the measures selected for this study were limited to broader developmental tests. Our ongoing long-term project includes formal infant testing by using the Bayley Scale of Infant Development-III, third edition. However, the current study looked solely at initial parent ratings on the ABAS-II, a test selected as an age-appropriate caregiver-report measure that can be carried out consistently across all ages throughout the study. It was chosen to provide insight into potential neurodevelopmental and functional deficits accompanying hydrocephalus and to assess the predictive power of DTI for outcome measures at various stages of hydrocephalus. Because of the small sample size and the non-normal distribution of the data, we used a nonparametric estimate of the correlation coefficient to represent this relationship. The correlation analysis has demonstrated moderate-to-strong associations between parent estimates of motor skill development and DTI indices of white matter integrity. Specifically, lower motor scores in the hydrocephalus group were negatively correlated with higher MD and RD in the PLIC. This can be interpreted as initial evidence for an association between functional deficits and underlying white matter alteration in our patient population. A larger sample size will be needed for more comprehensive modeling of the relation between the 2 domains. In addition, longitudinal data are needed to assess the correlation between DTI and cognitive and adaptive outcome data at various points in time, from immediately postoperative to preschool and school-age so that we can better meet the needs of children as they become more independent.

Limitation of the Study

The participants in the control group were recruited from patients who were initially referred for clinical MR imaging but whose scan findings were negative. This was mainly due to the difficulty expected in acquiring good-quality DTI data in nonsedated, age-appropriate, healthy children. Although stringent criteria were adopted in the recruitment, it is still possible that these criteria will pose a potential confounding factor for the interpretation of our results. Another limitation arises from the region of interest–based DTI data analysis method used in the study. The severity and variability of ventriculomegaly seen in our patient population combined with developmental changes exclude the possibility of registering brain images to a common template for voxelwise group comparison. The major white matter structures, such as the corpus callosum and internal capsule, can still be delineated reliably in hydrocephalic brains. However, it would pose significant technical difficulty if used in other structures that might also be vulnerable to hydrocephalus (eg, frontal lobe white matter regions). An additional limitation comes from the age and scanner differences in the 2 groups. Although effort has been made in the data analysis to remove the age and scanner effect, it is possible that our initial results were influenced by the heterogeneity in these 2 factors.

Conclusions

We demonstrated that the DTI indices derived from the prospectively acquired data are significantly affected in white matter structures in children with hydrocephalus at the acute stage, before surgical treatment. The various patterns of DTI abnormalities in the corpus callosum and internal capsule suggest region-specific multifactorial variation in underlying white matter injury mechanisms. In neuropsychological tests, parents of children with hydrocephalus endorsed significant developmental deficits, in which the motor scores were found to correlate with DTI measures in functionally relevant white matter structures. As further data are collected longitudinally, we will quantify the progression of white matter structural changes and assess their clinical relevance to both short- and long-term surgical outcomes.

Footnotes

  • Disclosures: Robert C. McKinstry—UNRELATED: Consultancy: Siemens, Comments: I was paid as an actor in a Siemens TV commercial “Somewhere in America,” Payment for Lectures (including Speakers Bureaus): Siemens, Comments: Siemens will be flying me to a meeting to present unrelated research using the new PET/MRI scanner, Other: Siemens, Comments: Siemens will be flying me to Germany to tour the factory and talk with engineers and product managers about future product innovations. Joshua Shimony—RELATED: Grant: NIH. Jannel Phillips—RELATED: Grant: NIH,* Comments: The case was part of a larger multicenter study looking at outcomes of hydrocephalus. David Limbrick—RELATED: Grant: NIH/NINDS,* Comments: Grant: longitudinal DTI study in children treated for congenital hydrocephalus (R01 NS066932). Scott Holland—RELATED: Grant: NIH,* Comments: This work was supported by a grant from NINDS R01 NS066932 to the principal investigator, W. Yuan, Support for Travel to Meetings for the Study or Other Purposes: NIH,* Comments: This work was supported by a grant from NINDS R01 NS066932 to the principal investigators, W. Yuan and F.T. Mangano. Mekibib Altaye—RELATED: Grant: NIH,* Comments: I have a percentage time effort on this grant that is paid to me as part of my salary from the institution. Sarah Simpson—RELATED: NIH, Comments: My salary comes from the grant from NIH that supports this research. Deanna Mercer—RELATED: Grant: NIH/NINDS grant R01NS066932–03. *Money paid to the institution.

  • This work was supported by grant National Institutes of Health/National Institute of Neurological Disorders and Stroke 1R01 NS066932.

  • The data reported in this manuscript have not been presented or published elsewhere.

Indicates open access to non-subscribers at www.ajnr.org

References

  1. 1.↵
    1. Del Bigio MR,
    2. Wilson MJ,
    3. Enno T
    . Chronic hydrocephalus in rats and humans: white matter loss and behavior changes. Ann Neurol 2003;53:337–46
    CrossRefPubMed
  2. 2.↵
    1. Del Bigio MR,
    2. Zhang YW
    . Cell death, axonal damage, and cell birth in the immature rat brain following induction of hydrocephalus. Exp Neurol 1998;154:57–69
    CrossRefPubMed
  3. 3.↵
    1. Fletcher JM,
    2. Bohan TP,
    3. Brandt ME,
    4. et al
    . Cerebral white matter and cognition in hydrocephalic children. Arch Neurol 1992;49:818–24
    CrossRefPubMed
  4. 4.↵
    1. Fletcher JM,
    2. McCauley SR,
    3. Brandt ME,
    4. et al
    . Regional brain tissue composition in children with hydrocephalus: relationships with cognitive development. Arch Neurol 1996;53:549–57
    CrossRefPubMed
  5. 5.↵
    1. Hannay HJ
    . Functioning of the corpus callosum in children with early hydrocephalus. J Int Neuropsychol Soc 2000;6:351–61
    CrossRefPubMed
  6. 6.↵
    1. Khan OH,
    2. Enno TL,
    3. Del Bigio MR
    . Brain damage in neonatal rats following kaolin induction of hydrocephalus. Exp Neurol 2006;200:311–20
    CrossRefPubMed
  7. 7.↵
    1. Zimmerman RD,
    2. Fleming CA,
    3. Lee BC,
    4. et al
    . Periventricular hyperintensity as seen by magnetic resonance: prevalence and significance. AJR Am J Roentgenol 1986;146:443–50
    CrossRefPubMed
  8. 8.↵
    1. Bradley WG Jr.
    . MR prediction of shunt response in NPH: CSF morphology versus physiology. AJNR Am J Neuroradiol 1998;19:1285–86
    PubMed
  9. 9.↵
    1. Assaf Y,
    2. Ben-Sira L,
    3. Constantini S,
    4. et al
    . Diffusion tensor imaging in hydrocephalus: initial experience. AJNR Am J Neuroradiol 2006;27:1717–24
    Abstract/FREE Full Text
  10. 10.↵
    1. Hasan KM,
    2. Eluvathingal TJ,
    3. Kramer LA,
    4. et al
    . White matter microstructural abnormalities in children with spina bifida myelomeningocele and hydrocephalus: a diffusion tensor tractography study of the association pathways. J Magn Reson Imaging 2008;27:700–09
    CrossRefPubMed
  11. 11.↵
    1. Herweh C,
    2. Akbar M,
    3. Wengenroth M,
    4. et al
    . DTI of commissural fibers in patients with Chiari II-malformation. Neuroimage 2009;44:306–11
    CrossRefPubMed
  12. 12.↵
    1. Yuan W,
    2. Mangano FT,
    3. Air EL,
    4. et al
    . Anisotropic diffusion properties in infants with hydrocephalus: a diffusion tensor imaging study. AJNR Am J Neuroradiol 2009;30:1792–98
    Abstract/FREE Full Text
  13. 13.↵
    1. Air EL,
    2. Yuan W,
    3. Holland SK,
    4. et al
    . Longitudinal comparison of pre- and postoperative diffusion tensor imaging parameters in young children with hydrocephalus. J Neurosurg Pediatr 2010;5:385–91
    CrossRefPubMed
  14. 14.↵
    1. Harrison PL,
    2. Oakland T
    . Adaptive Behavior Assessment System. 2nd ed, San Antonio, Texas: Harcourt Assessment; 2003
  15. 15.↵
    1. Jiang H,
    2. van Zijl PC,
    3. Kim J,
    4. et al
    . DTIStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed 2006;81:106–16
    CrossRefPubMed
  16. 16.↵
    1. Woods RP,
    2. Grafton ST,
    3. Watson JD,
    4. et al
    . Automated image registration. II. Intersubject validation of linear and nonlinear models. J Comput Assist Tomogr 1998;22:153–65
    CrossRefPubMed
  17. 17.↵
    1. Basser PJ,
    2. Pierpaoli C
    . A simplified method to measure the diffusion tensor from seven MR images. Magn Reson Med 1998;39:928–34
    CrossRefPubMed
  18. 18.↵
    1. Hermoye L,
    2. Saint-Martin C,
    3. Cosnard G,
    4. et al
    . Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood. Neuroimage 2006;15 29:493–504
  19. 19.↵
    1. Yuan W,
    2. HS,
    3. Shimony JS,
    4. Jones BV,
    5. et al
    . Quality assurance in multi-institution and multi-scanner MRI neuroimaging research. In: Proceedings of the 6th Congress and Exhibition of the Joint Societies of Paediatric Radiology, London, UK. May 27–31, 2011
  20. 20.↵
    1. Kruggel F,
    2. Turner J,
    3. Muftuler LT
    . Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort. Neuroimage 2010; 49:2123–33
    CrossRefPubMed
  21. 21.↵
    1. Colombo P,
    2. Baldassarri A,
    3. Del Corona M,
    4. et al
    . Multicenter trial for the set-up of a MRI quality assurance programme. Magn Reson Imaging 2004;22:93–101
    CrossRefPubMed
  22. 22.↵
    1. Van Horn JD,
    2. Toga AW
    . Multisite neuroimaging trials. Current Opin Neurol 2009;22:370–78
    CrossRefPubMed
  23. 23.↵
    1. Uluğ AM,
    2. Truong TN,
    3. Filippi CG,
    4. et al
    . Diffusion imaging in obstructive hydrocephalus. AJNR Am J Neuroradiol 2003;24:1171–76
    Abstract/FREE Full Text
  24. 24.↵
    1. Leliefeld PH,
    2. Gooskens RH,
    3. Tulleken CA,
    4. et al
    . Noninvasive detection of the distinction between progressive and compensated hydrocephalus in infants: is it possible? J Neurosurg Pediatr 2010;5:562–68
    CrossRefPubMed
  25. 25.↵
    1. Del Bigio MR,
    2. Slobodian I,
    3. Schellenberg AE,
    4. et al
    . Magnetic resonance imaging indicators of blood-brain barrier and brain water changes in young rats with kaolin-induced hydrocephalus. Fluids Barriers CNS 2011;8:22
    CrossRefPubMed
  26. 26.↵
    1. Song SK,
    2. Sun SW,
    3. Ramsbottom MJ,
    4. et al
    . Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage 2002;17:1429–36
    CrossRefPubMed
  27. 27.↵
    1. Song SK,
    2. Yoshino J,
    3. Le TQ,
    4. et al
    . Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 2005;26:132–40
    CrossRefPubMed
  28. 28.↵
    1. Budde MD,
    2. Kim JH,
    3. Liang HF,
    4. et al
    . Axonal injury detected by in vivo diffusion tensor imaging correlates with neurological disability in a mouse model of multiple sclerosis. NMR Biomed 2008;21:589–97
    CrossRefPubMed
  29. 29.↵
    1. Goebell E,
    2. Paustenbach S,
    3. Vaeterlein O,
    4. et al
    . Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor MR imaging. Radiology 2006;239:217–22
    CrossRefPubMed
  30. 30.↵
    1. Mataró M,
    2. Junque C,
    3. Poca MA,
    4. et al
    . Neuropsychological findings in congenital and acquired childhood hydrocephalus. Neuropsychol Rev 2001;11:169–78
    CrossRefPubMed
  • Received March 22, 2012.
  • Accepted after revision April 9, 2012.
  • © 2013 by American Journal of Neuroradiology
View Abstract
PreviousNext
Back to top

In this issue

American Journal of Neuroradiology: 34 (2)
American Journal of Neuroradiology
Vol. 34, Issue 2
1 Feb 2013
  • Table of Contents
  • Index by author
Advertisement
Print
Download PDF
Email Article

Thank you for your interest in spreading the word on American Journal of Neuroradiology.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Diffusion Tensor Imaging Properties and Neurobehavioral Outcomes in Children with Hydrocephalus
(Your Name) has sent you a message from American Journal of Neuroradiology
(Your Name) thought you would like to see the American Journal of Neuroradiology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Cite this article
W. Yuan, R.C. McKinstry, J.S. Shimony, M. Altaye, S.K. Powell, J.M. Phillips, D.D. Limbrick, S.K. Holland, B.V. Jones, A. Rajagopal, S. Simpson, D. Mercer, F.T. Mangano
Diffusion Tensor Imaging Properties and Neurobehavioral Outcomes in Children with Hydrocephalus
American Journal of Neuroradiology Feb 2013, 34 (2) 439-445; DOI: 10.3174/ajnr.A3218

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
0 Responses
Respond to this article
Share
Bookmark this article
Diffusion Tensor Imaging Properties and Neurobehavioral Outcomes in Children with Hydrocephalus
W. Yuan, R.C. McKinstry, J.S. Shimony, M. Altaye, S.K. Powell, J.M. Phillips, D.D. Limbrick, S.K. Holland, B.V. Jones, A. Rajagopal, S. Simpson, D. Mercer, F.T. Mangano
American Journal of Neuroradiology Feb 2013, 34 (2) 439-445; DOI: 10.3174/ajnr.A3218
del.icio.us logo Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Purchase

Jump to section

  • Article
    • Abstract
    • ABBREVIATIONS:
    • Materials and Methods
    • Results
    • Discussion
    • Conclusions
    • Footnotes
    • References
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • Responses
  • References
  • PDF

Related Articles

  • PubMed
  • Google Scholar

Cited By...

  • The Construction of a Predictive Composite Index for Decision-Making of CSF Diversion Surgery in Pediatric Patients following Prenatal Myelomeningocele Repair
  • White Matter Microstructural Abnormality in Children with Hydrocephalus Detected by Probabilistic Diffusion Tractography
  • Crossref (42)
  • Google Scholar

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

  • Hydrocephalus in children
    Kristopher T Kahle, Abhaya V Kulkarni, David D Limbrick, Benjamin C Warf
    The Lancet 2016 387 10020
  • Preterm neuroimaging and neurodevelopmental outcome: a focus on intraventricular hemorrhage, post-hemorrhagic hydrocephalus, and associated brain injury
    Rebecca A. Dorner, Vera Joanna Burton, Marilee C. Allen, Shenandoah Robinson, Bruno P. Soares
    Journal of Perinatology 2018 38 11
  • An update on research priorities in hydrocephalus: overview of the third National Institutes of Health-sponsored symposium “Opportunities for Hydrocephalus Research: Pathways to Better Outcomes”
    James P. McAllister, Michael A. Williams, Marion L. Walker, John R. W. Kestle, Norman R. Relkin, Amy M. Anderson, Paul H. Gross, Samuel R. Browd
    Journal of Neurosurgery 2015 123 6
  • Altered neonatal white and gray matter microstructure is associated with neurodevelopmental impairments in very preterm infants with high-grade brain injury
    Rachel E. Lean, Rowland H. Han, Tara A. Smyser, Jeanette K. Kenley, Joshua S. Shimony, Cynthia E. Rogers, David D. Limbrick, Christopher D. Smyser
    Pediatric Research 2019 86 3
  • Long-term supratentorial brain structure and cognitive function following cerebellar tumour resections in childhood
    T. Moberget, S. Andersson, T. Lundar, B.J. Due-Tønnessen, A. Heldal, T. Endestad, L.T. Westlye
    Neuropsychologia 2015 69
  • Opportunities in posthemorrhagic hydrocephalus research: outcomes of the Hydrocephalus Association Posthemorrhagic Hydrocephalus Workshop
    Jenna E. Koschnitzky, Richard F. Keep, David D. Limbrick, James P. McAllister, Jill A. Morris, Jennifer Strahle, Yun C. Yung
    Fluids and Barriers of the CNS 2018 15 1
  • Relationship between ventricular size, white matter injury, and neurocognition in children with stable, treated hydrocephalus
    Abhaya V. Kulkarni, Ruth Donnelly, Donald J. Mabbott, Elysa Widjaja
    Journal of Neurosurgery: Pediatrics 2015 16 3
  • Diffusion tensor imaging study of pediatric patients with congenital hydrocephalus: 1-year postsurgical outcomes
    Francesco T. Mangano, Mekibib Altaye, Robert C. McKinstry, Joshua S. Shimony, Stephanie K. Powell, Jannel M. Phillips, Holly Barnard, David D. Limbrick, Scott K. Holland, Blaise V. Jones, Jonathan Dodd, Sarah Simpson, Deanna Mercer, Akila Rajagopal, Sarah Bidwell, Weihong Yuan
    Journal of Neurosurgery: Pediatrics 2016 18 3
  • Diffusion tensor imaging with direct cytopathological validation: characterisation of decorin treatment in experimental juvenile communicating hydrocephalus
    Anuriti Aojula, Hannah Botfield, James Patterson McAllister, Ana Maria Gonzalez, Osama Abdullah, Ann Logan, Alexandra Sinclair
    Fluids and Barriers of the CNS 2016 13 1
  • Differential vulnerability of white matter structures to experimental infantile hydrocephalus detected by diffusion tensor imaging
    Ramin Eskandari, Osama Abdullah, Cameron Mason, Kelley E. Lloyd, Amanda N. Oeschle, James P. McAllister
    Child's Nervous System 2014 30 10

More in this TOC Section

  • SyMRI & MR Fingerprinting in Brainstem Myelination
  • Comparison of Image Quality and Radiation Dose in Pediatric Temporal Bone CT Using Photon-Counting Detector CT and Energy-Integrating Detector CT
  • Venous Sinus Stenosis in Mucopolysaccharidosis I
Show more Pediatrics

Similar Articles

Advertisement

Indexed Content

  • Current Issue
  • Accepted Manuscripts
  • Article Preview
  • Past Issues
  • Editorials
  • Editor's Choice
  • Fellows' Journal Club
  • Letters to the Editor
  • Video Articles

Cases

  • Case Collection
  • Archive - Case of the Week
  • Archive - Case of the Month
  • Archive - Classic Case

More from AJNR

  • Trainee Corner
  • Imaging Protocols
  • MRI Safety Corner

Multimedia

  • AJNR Podcasts
  • AJNR Scantastics

Resources

  • Turnaround Time
  • Submit a Manuscript
  • Submit a Video Article
  • Submit an eLetter to the Editor/Response
  • Manuscript Submission Guidelines
  • Statistical Tips
  • Fast Publishing of Accepted Manuscripts
  • Graphical Abstract Preparation
  • Imaging Protocol Submission
  • Evidence-Based Medicine Level Guide
  • Publishing Checklists
  • Author Policies
  • Become a Reviewer/Academy of Reviewers
  • News and Updates

About Us

  • About AJNR
  • Editorial Board
  • Editorial Board Alumni
  • Alerts
  • Permissions
  • Not an AJNR Subscriber? Join Now
  • Advertise with Us
  • Librarian Resources
  • Feedback
  • Terms and Conditions
  • AJNR Editorial Board Alumni

American Society of Neuroradiology

  • Not an ASNR Member? Join Now

© 2025 by the American Society of Neuroradiology All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Print ISSN: 0195-6108 Online ISSN: 1936-959X

Powered by HighWire