Published online September 1, 2008
PEDIATRICS Vol. 122 No. 3 September 2008, pp. 500-506 (doi:10.1542/peds.2007-2816)
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow E-mail this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My File Cabinet
Right arrow Download to citation manager
Right arrowRequest Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via CrossRef
Right arrow Citing Articles via Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Murakami, A.
Right arrow Articles by Sugimoto, T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Murakami, A.
Right arrow Articles by Sugimoto, T.
Related Collections
Right arrow Neurology & Psychiatry
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Facebook   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

ARTICLE

Fiber-Tracking Techniques Can Predict the Degree of Neurologic Impairment for Periventricular Leukomalacia

Aki Murakami, MDa, Masafumi Morimoto, MD, PhDa, Kei Yamada, MD, PhDb, Osamu Kizu, MD, PhDb, Akira Nishimura, MD, PhDa, Tsunehiko Nishimura, MD, PhDb and Tohru Sugimoto, MD, PhDa

a Departments of Pediatrics
b Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan


    ABSTRACT
 TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
OBJECTIVE. Preterm or low birth weight infants display a greater propensity for white matter injury caused by hypoxic-ischemic encephalopathy in the perinatal period. Such episodes can result in periventricular leukomalacia, which may substantially influence later brain development. Noninvasive methods of assessing the severity of injury at the earliest stage of life have not yet been established.

METHODS. We used diffusion tensor imaging to evaluate sensorimotor fibers in periventricular leukomalacia. Region-of-interest measurements and tractography-based measurements were performed for 10 patients with periventricular leukomalacia. The mean age of the patients was 19 ± 9.5 months (range: 9–41 months). Motor functions were assessed at a mean age of 28 ± 14.5 months.

RESULTS. Measured fractional anisotropy values of the motor tract were significantly higher in all mild periventricular leukomalacia cases than in severe cases. A fractional anisotropy cutoff value of <0.5 was useful for predicting severe periventricular leukomalacia. Region-of-interest measurements were less sensitive, compared with tractography-based measurements.

CONCLUSIONS. Fiber-tracking techniques can provide information on the pathophysiologic features of motor disability in patients with periventricular leukomalacia. Early screening of patients with a history of asphyxia may facilitate early intervention (eg, rehabilitation), to achieve better motor function.


Key Words: cerebral palsy • developmental outcome • hypoxic-ischemic encephalopathy • neurodevelopmental • neuroimaging

Abbreviations: PVL—periventricular leukomalacia • CST—corticospinal tract • CP—cerebral palsy • ROI—region of interest • FA—fractional anisotropy • DTI—diffusion tensor imaging

Periventricular leukomalacia (PVL) is now recognized as one of the most important causes of cerebral palsy (CP) in preterm infants. The incidence of intraventricular hemorrhage and associated complications has declined recently, and PVL has become the dominant neuropathologic condition in premature infants. PVL is the major neurologic basis of spastic motor deficits and cognitive abnormalities observed later in such infants.1 On MRI scans, findings of PVL are related to injury to developing periventricular white matter during the late second trimester and early third trimester of pregnancy, with resultant T1 and/or T2 prolongation, thinning of the posterior body of the corpus callosum, enlargement of the lateral ventricles, and irregularity of the ventricular walls.2 Conventional imaging methods, including MRI3 and ultrasonography,4 have been largely limited to evaluation of the geographic distribution of brain damage and not functional aspects of the damage. Direct methods allowing the observation of brain damage in relation to vital fiber tracts, including sensory and motor pathways, would clearly be of great clinical benefit.

Diffusion tensor imaging (DTI) is a recently developed imaging technique that characterizes water-diffusion properties in each MRI voxel.5,6 The diffusion tensor describes an ellipsoid in space, and the size, shape, and orientation of the ellipsoid are given by the eigenvalues and eigenvectors of the tensor. On the basis of the diffusion tensor, several quantifiable and absolute measures can be determined and mapped, including fractional anisotropy (FA) and the apparent diffusion coefficient, which are sensitive to microstructural abnormalities that are occult on conventional MRI scans. DTI has already been proven useful for evaluating brain development and white matter injury.79

Through extension of DTI techniques, the trajectory of neuronal fibers can now be observed with fiber-tracking techniques or tractography. This technique enables 3-dimensional segmentation of axonal bundles, allowing measurements of DTI parameters in specific white matter pathways.10 This tract-based measurement technique has potential advantages over standard, region-of-interest (ROI) methods, showing reduced susceptibility to operator variability, and therefore may provide increased reproducibility for tract localization in serial studies and between different subjects.11,12 Fiber-tracking has been used widely for adult diseases, such as stroke13 and tumor,14 but less so for neonates and infants, primarily because of a lower degree of anisotropy. However, several studies successfully reported tractography in this population.9,11,15

The present study sought to investigate whether these newer techniques are able to characterize the brains of patients with PVL at the earliest stage. One of the most important clinical features in PVL is abnormality of tone and movement, notably spasticity, which has been attributed to a loss of descending pyramidal corticospinal tracts (CSTs).16,17 Our study thus aimed to characterize these vital tracts by using the fiber-tracking technique and investigated the possibility of predicting prognoses for patients at the earliest stage.


    METHODS
 TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Patient Population
This study was approved by the ethics committee of our university, and written informed consent was obtained from the parents of each patient. MRI was performed between December 2002 and June 2004 for 60 children with suspected neonatal brain injury. We performed fiber-tracking in these 60 cases before selecting the cases that met our inclusion criteria for PVL. This was performed to minimize bias from operator-dependent processes in the fiber-tracking technique. Only 10 of the 60 patients met the following inclusion criteria: (1) history of hypoxia in the perinatal period and (2) diagnosis of PVL based on both clinical and imaging findings (Table 1). Because clinical symptoms of PVL usually are difficult to detect until the neurologic sequelae become apparent in later infancy, MRI played a crucial role in diagnosis. Imaging diagnosis of PVL was based on focal necrotic lesions in the periventricular white matter and/or more-diffuse white matter damage. The mean age at the time of scanning was 19 ± 9.5 months (range: 9–41 months). Motor functions were assessed at a mean age of 28 ± 14.5 months (range: 15–63 months).


View this table:
[in this window]
[in a new window]

 
TABLE 1 Clinical Features and MRI Findings of Patients

 
Clinical history and present illness findings for this group are summarized in Table 1. Five children (patients 1, 4, 5, 7, and 9) displayed severe complications, characterized by CP (spastic paraplegia or quadriplegia), mental retardation, and/or epilepsy (patients 1 and 4). These patients constituted the CP group. The remaining 5 children had almost-normal development, without paralysis or seizures. Functionally nonimpaired patients with PVL constituted the non-CP group. CP is defined as a nonprogressive disorder of posture and movement, often associated with mental retardation, epilepsy, and abnormalities of speech, vision, and intellect, resulting from a defect or lesion of the developing brain.

Imaging Methods
DTI data for fiber-tracking were obtained in 264 seconds. DTI was performed at the end of the routine child protocol used at our institute. Images were obtained by using a 1.5-T, whole-body scanner (Gyroscan Intera; Philips Medical Systems, Best, Netherlands) with a gradient strength of 30 mT/m. A single-shot echo-planar imaging technique was used for DTI (repetition time: 6000 milliseconds; excitation time: 88 milliseconds), with a motion-probing gradient in 15 orientations, a field of view of 230 mm, b values of 0 and 1000 seconds/mm2, and image averaging over 2 measurements. Recorded data matrix were 128 x 37, with the parallel imaging technique. A total of 36 slices (thickness: 3 mm), without an interslice gap, were obtained.

Data Postprocessing and Fiber-Tracking Method
Anisotropy at each voxel was calculated, and color maps were created. The procedure for mapping neural connections was started through designation of 3 arbitrary ROIs in the 3-dimensional imaging space. We determined ROIs on axial slices of the color vector map for all cases (Fig 1). Slices for all ROI placements were determined on the basis of commonly identifiable anatomic landmarks for consistency, based on previously proposed methods.11,12We first performed fiber-tracking of the motor tract, usually on the left side, and set 3 ROIs for CSTs. The first ROI was set at the ventral part of the pons. The second ROI was set at the internal capsule, at the level of the anterior border of the genu of the corpus callosum. The third ROI was set on the primary motor cortex of the frontal lobe. Tracking was terminated (stop criteria) when a pixel with low FA and/or a predetermined trajectory curvature between 2 contiguous vectors was reached. The stop criterion of FA = 0.18 was used as a default. We did not use the threshold of FA = 0.2 that is generally considered standard in adult studies because this would not allow tracking of the less-mature fiber tracts in infants. Fiber tracts passing through all ROIs were designated as the final tracts of interest. When tracking of bilateral CSTs was complete, we performed sensory fiber-tracking in a similar manner. To perform fiber-tracking of sensory tracts, we set 2 ROIs in all cases. The first ROI was set at the dorsal part of the pons, with the second ROI at the white matter adjacent to the somatosensory cortex of the parietal lobe (Fig 1). Fiber-tracking was performed in a blinded manner in all cases, without knowledge of patient histories. Fiber-tracking was performed by a single operator (Dr Murakami), to maintain consistency in the placement of seed points.


Figure 1
View larger version (15K):
[in this window]
[in a new window]

 
FIGURE 1 Locations of ROIs used for fiber-tracking, superimposed on color vector maps. The color maps show the direction of local fibers, represented by red (left-right), green (anterior-posterior), and blue (superior-inferior). Purple ROIs represent those used for the CSTs. The first ROI was set at the ventral pons, the second ROI at the internal capsule (at the level of the anterior border of the genu of the corpus callosum on a sagittal slice), and the third ROI on the primary motor cortex. Green ROIs represent those used for sensory tracts. The first ROI was set at the dorsal pons and the second on the somatosensory cortex.

 
FA and Apparent Diffusion Coefficient Measurements
We used 2 different methods to record FA and apparent diffusion coefficient values for each anatomic landmark of the brain. In the first method, FA measurements were performed by manually drawing ROIs at the centrum semiovale on each side of the brain (Fig 2). The second method was performed by measuring the FA of all voxels that constituted the depicted tract. This was performed in a semiautomatic manner by using custom-made software ("fiber statistics"). We called this method "tract-specific measurement" (Fig 3). We used the FA values of the genu/splenium of the corpus callosum as references.


Figure 2
View larger version (10K):
[in this window]
[in a new window]

 
FIGURE 2 ROI-specific measurement. FA measurements of a manually drawn ROI at the level of the centrum semiovale were performed.

 

Figure 3
View larger version (27K):
[in this window]
[in a new window]

 
FIGURE 3 Tractography of sensorimotor fibers of a 14-month-old patient (patient 2). Motor (purple) and sensory (green) fibers were reconstructed in 3-dimensional space. Bilateral sensorimotor fiber tracts were assessed successfully in this case, without disruption by the white matter lesion. Lt indicates left; Rt, right.

 

    RESULTS
 TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Fiber-Tracking
CSTs of all 10 PVL cases were depicted successfully. Sensory tracts were depicted successfully except for 1 patient with severe quadriplegia (patient 4). One representative case is provided as an example (patient 10) (Fig 3).

ROI-Specific Measurements
Results of conventional ROI-based measurements are shown in Fig 4. We performed data analysis to compare the 2 groups by using Welch's t test after the F test; almost-normal development was present in 5 cases (non-CP group; FA: 0.428 ± 0.095), and severe complications were present in the remaining 5 cases (CP group; FA: 0.390 ± 0.122). Both groups had 10 values (n = 10) measured from the bilateral centrum semiovale. This conventional ROI method failed to identify any differences (P = .227; t test). The results of pairwise comparisons between the 2 groups at the genu and splenium of the corpus callosum with this method also were not statistically significant (genu: P = .268; splenium: P = .156; t test).


Figure 4
View larger version (15K):
[in this window]
[in a new window]

 
FIGURE 4 Results of ROI-specific measurement. This method failed to identify any differences between the non-CP and CP groups (P = .227; Welch's t test). The gray line shows the average of all values. The breadth of each green rhomboid shows the number of cases in the group, the height shows the 95% confidence interval, the middle line shows the average of the group, and the horizontal lines in the upper and lower parts show the overlap marks. The overlap marks of the 2 groups are not separated in this figure, indicating that there is no significant statistical difference between these 2 groups.

 
Tract-Specific Measurements
Results of direct FA measurements from motor tracts depicted with tractography are shown in Table 2 and Fig 5. Each group had 10 values (n = 10), because bilateral measurements of motor tracts were made for each of the 5 patients. Comparison of the non-CP group (FA: 0.535 ± 0.016) and the CP group (FA: 0.414 ± 0.016) revealed significant differences (P < .001; t test). The results of pairwise comparisons between the 2 groups at the genu and splenium of the corpus callosum with this method were not statistically significant (genu: P = .161; splenium: P = .154; t test).


View this table:
[in this window]
[in a new window]

 
TABLE 2 FA Values of Depicted Motor Tracts in Tract-Specific Measurement

 

Figure 5
View larger version (15K):
[in this window]
[in a new window]

 
FIGURE 5 Results of tract-specific measurement. The comparison of the non-CP and CP groups revealed differences (P < .001; Welch's t test). The overlap marks of the 2 groups are not overlapping in this figure, indicating that there is a statistically significant difference between the 2 groups.

 
Logistic regression analysis found that a FA value of 0.5 was effective in differentiating between the CP and non-CP groups (P < .001; R2 = 1), despite the relatively wide range of patients' gestational ages and timing of MRI. All except 1 patient in the CP group displayed FA values of <0.5, which suggested damage to the motor tract. Conversely, measured FA values for the 5 patients in the non-CP group were >0.5 (Table 2 and Fig 5). One exceptional patient in the CP group (patient 1) displayed a FA value of >0.5 on one side, but this agreed well with the clinical symptoms, because paralysis was unilateral (left side). The depicted tractographic results were symmetrical in 9 cases, with this 1 case being the exception.


    DISCUSSION
 TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
PVL is one of the most common causes of CP for low birth weight infants, causing not only CP in the form of quadriplegia and paraplegia but also various aftereffect injuries, including mental retardation and epilepsy. Early accurate evaluation of these patients would have significant clinical impact, because certain training programs can be initiated as soon as the possibility of a defect is identified.18,19 Evaluation of early-stage PVL has relied traditionally on clinical examinations at follow-up visits, typically at ages when the pediatrician is able to observe certain developmental milestones. Recent advances in cross-sectional imaging techniques, particularly MRI, have enabled earlier evaluation of these patients. For example, the degree of white matter loss,20 lateral ventricular volume,21 and myelination22 have been shown to allow prediction of patient outcomes to a certain degree. In addition, classification of PVL severity on the basis of various imaging modalities (not including DTI) has been undertaken, with limited success.21 Those investigations revealed that conventional imaging methods have substantial limitations. Even when similar degrees of decreases in white matter volume or expansion of a cerebral ventricle are recognized, the severity of aftereffect injuries may vary from case to case. Predicting prognosis by using the various laboratory procedures that have been applied, such as electroencephalography23 and measurements of levels of serum-free oxygen radicals,24 natriuretic peptides,25 and bilirubin,26 also is difficult.

Fiber-tracking techniques represent recent advances in MRI that enable assessment of the major fiber pathways of the brain.5,27,28 This method has been applied clinically for various pathologic conditions of the adult brain.29 This technique is generally difficult to apply to children, however, because of small brain size, greater incidence of motion artifacts, and rapid evolution of myelination. Evaluation is particularly difficult during the neonatal period and infancy, because FA values are lower throughout the white matter.8,30

Some attempts at fiber-tracking have been used successfully to assess patients with PVL, but most such studies were performed with patients >6 years of age. Those studies showed that the degree of degeneration of motor, sensory, and possibly commissural pathways correlated with final clinical outcomes.16,31

We examined 10 cases of PVL during infancy. Measurements of FA were made by using 2 different methods, to identify the method that might offer better biomarker results that predict clinical outcomes. The well-accepted method that has been commonly used is ROI-specific measurement, which is performed by placing ROIs at certain anatomic landmarks, such as the pons, central semiovale, and corpus callosum. ROI analysis has been shown to be sensitive enough to show a significant reduction in FA at the posterior limb of the internal capsule in infants with CP.32 Normal brain maturation can also be studied effectively by using ROI-specific measurement.33

This ROI-based method is, however, somewhat limited in reproducibility and susceptible to operator variability in the placement of ROIs. We placed ROIs on commonly identifiable anatomic landmarks, on the basis of previously proposed methods, and confirmed the location of ROIs in each case by referring to sagittal and coronal slices. Furthermore, we used ROIs of similar sizes for each part of the measurements. To reduce the possibility of bias further, we performed measurements without knowing the patients' disease status or functional outcomes. Despite such efforts, the weakness of this operator-dependent procedure was indicated by the results of our analysis, which was unable to reveal any differences between patients with good and poor outcomes. ROI-specific measurement was thus considered an imperfect tool for examining our series of cases.

The second method we used measures the FA of all voxels constituting the entire fiber bundle. This calculation is a semiautomated process that is built into the software. A similar method was used previously to study the maturity of white matter tracts and myelination in pediatric populations.34,35 Those earlier studies indicated that CSTs can be better assessed with tractography, in an easier manner and with reduced partial-volume effects. Those studies also showed that the FA of white matter bundles correlates well with the known stages of white matter maturation and myelination. FA values of CSTs also were shown to change in relation to age, reaching FA values close to adult levels by 20 weeks of age.3537

Our population consisted of infants >6 months of age (corrected by the gestational age at birth) and thus can be considered to have been in this plateau phase in terms of FA maturation. Tract-specific measurement of the entire fiber bundle of motor tracts yielded a value of 0.54 ± 0.02 in the non-CP group, compared with 0.41 ± 0.02 in the CP group. Pairwise comparisons using the F test and Welch's t test revealed a significant difference among groups in motor tracts (P < .0001). The gestational age at birth was not significantly different ({chi}2 analysis), and neither was the age at the time of MRI (Fig 6).


Figure 6
View larger version (12K):
[in this window]
[in a new window]

 
FIGURE 6 Ages and FA values for the non-CP group (x) and the CP group ({blacksquare}). The ages at the time of MRI were not statistically significantly different, both within each group and between the 2 groups.

 
We analyzed our data by using logistic regression analysis, with the independent variable being values of measurements and the dependent variable being prognosis (CP or non-CP). This analysis proved that a FA value of 0.5 was highly effective in differentiating the 2 groups (P < .001; R2 = 1.00). In fact, all FA measurements for 5 patients in the non-CP group showed FA values of >0.5, whereas 5 patients in the CP group showed FA values of <0.5. A single case (patient 1) in the CP group displayed only right motor FA value of <0.5, but this was actually in good agreement with the clinical symptoms, because the patient displayed left-sided paralysis only. Pyramidal tracts were depicted in a symmetrical fashion in the other 9 cases. It is of note that these FA values were unrelated to gestational age, birth weight, or Apgar scores and were related only to later motor function. These results suggest that clinical outcomes can be reliably predicted from the results of tractography performed in the early stage of development.

This study demonstrated that fiber-tracking techniques can provide more information for understanding the pathophysiologic features of motor disability and associated sensory handicaps with PVL. We may be able to screen patients with a history of asphyxia by using tractography and then start early intervention (eg, rehabilitation), to maximize the salvage of grave motor deficits.


    ACKNOWLEDGMENTS
 
We thank Katsumi Yagi, Kyoto Prefectural University of Medicine, for discussion on the data analysis.


    FOOTNOTES
 
Accepted Dec 17, 2007.

Address correspondence to Aki Murakami, MD, Department of Pediatrics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kawaramachi-Hirokoji, Kamigyo, Kyoto 602-8566, Japan. E-mail: akkylin{at}koto.kpu-m.ac.jp

The authors have indicated they have no financial relationships relevant to this article to disclose.


What's Known on This Subject

Periventricular leukomalacia is recognized as one of the most important causes of cerebral palsy in preterm infants. However, noninvasive methods of assessing the severity of injury at the earliest stage of life have not yet been established.

 

What This Study Adds

The present study suggests that clinical outcomes can be predicted reliably with fiber-tracking techniques in the early stage of development. Early screening of patients with a history of asphyxia may facilitate early intervention (eg, rehabilitation) to achieve better motor function.

 


    REFERENCES
 TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Volpe JJ. Neurobiology of periventricular leukomalacia in the premature infant. Pediatr Res. 2001;50 (5):553 –562[Web of Science][Medline]
  2. Flodmark O, Lupton B, Li D, et al. MR imaging of periventricular leukomalacia in childhood. AJR Am J Roentgenol. 1989;152 (3):583 –590[Abstract/Free Full Text]
  3. Fedrizzi E, Inverno M, Bruzzone MG, Botteon G, Saletti V, Farinotti M. MRI features of cerebral lesions and cognitive functions in preterm spastic diplegic children. Pediatr Neurol. 1996;15 (3):207 –212[CrossRef][Web of Science][Medline]
  4. Maalouf EF, Duggan PJ, Counsell SJ, et al. Comparison of findings on cranial ultrasound and magnetic resonance imaging in preterm infants. Pediatrics. 2001;107 (4):719 –727[Abstract/Free Full Text]
  5. Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol. 1999;45 (2):265 –269[CrossRef][Web of Science][Medline]
  6. Mori S, van Zijl PC. Fiber tracking: principles and strategies: a technical review. NMR Biomed. 2002;15 (7–8):468 –480[CrossRef][Web of Science][Medline]
  7. Zhai G, Lin W, Wilber KP, Gerig G, Gilmore JH. Comparisons of regional white matter diffusion in healthy neonates and adults performed with a 3.0-T head-only MR imaging unit. Radiology. 2003;229 (3):673 –681[Abstract/Free Full Text]
  8. Hüppi PS, Murphy B, Maier SE, et al. Microstructural brain development after perinatal cerebral white matter injury assessed by diffusion tensor magnetic resonance imaging. Pediatrics. 2001;107 (3):455 –460[Abstract/Free Full Text]
  9. Hermoye L, Saint-Martin C, Cosnard G, et al. Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood. Neuroimage. 2006;29 (2):493 –504[Medline]
  10. Xue R, van Zijl PCM, Crain BJ, Solaiyappan M, Mori S. In vivo three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging. Magn Reson Med. 1999;42 (6):1123 –1127[CrossRef][Web of Science][Medline]
  11. Partridge SC, Mukherjee P, Berman JI, et al. Tractography-based quantitation of diffusion tensor imaging parameters in white matter tracts of preterm newborns. J Magn Reson Imaging. 2005;22 (4):467 –474[CrossRef][Web of Science][Medline]
  12. Partridge SC, Vigneron DB, Charlton NN, et al. Pyramidal tract maturation after brain injury in newborns with heart disease. Ann Neurol. 2006;59 (4):640 –651[CrossRef][Web of Science][Medline]
  13. Kunimatsu A, Aoki S, Masutani Y, et al. Three-dimensional white matter tractography by diffusion tensor imaging in ischaemic stroke involving the corticospinal tract. Neuroradiology. 2003;45 (8):532 –535[CrossRef][Web of Science][Medline]
  14. Nimsky C, Ganslandt O, Merhof D, et al. Intraoperative visualization of the pyramidal tract by diffusion-tensor-imaging-based fiber tracking. Neuroimage. 2006;30 (4):1219 –1229[CrossRef][Medline]
  15. Staudt M, Braun C, Gerloff C, Erb M, Grodd W, Krageloh-Mann I. Developing somatosensory projections bypass periventricular brain lesions. Neurology. 2006;67 (3):522 –525[Abstract/Free Full Text]
  16. Thomas B, Eyssen M, Peeters R, et al. Quantitative diffusion tensor imaging in cerebral palsy due to periventricular white matter injury. Brain. 2005;128 (11):2562 –2577[Abstract/Free Full Text]
  17. Fan GG, Yu B, Quan SM, Sun BH, Guo QY. Potential of diffusion tensor MRI in the assessment of periventricular leukomalacia. Clin Radiol. 2006;61 (4):358 –364[CrossRef][Web of Science][Medline]
  18. Kanda T, Pidcock FS, Hayakawa K, Yamori Y, Shikata Y. Motor outcome differences between two groups of children with spastic diplegia who received different intensities of early onset physiotherapy followed for 5 years. Brain Dev. 2004;26 (2):118 –126[CrossRef][Web of Science][Medline]
  19. Ohgi S, Fukuda M, Akiyama T, Gima H. Effect of an early intervention programme on low birthweight infants with cerebral injuries. J Paediatr Child Health. 2004;40 (12):689 –695[CrossRef][Web of Science][Medline]
  20. Staudt M, Pavlova M, Bohm S, Grodd W, Krageloh-Mann I. Pyramidal tract damage correlates with motor dysfunction in bilateral periventricular leukomalacia (PVL). Neuropediatrics. 2003;34 (4):182 –188[CrossRef][Web of Science][Medline]
  21. Melhem ER, Hoon AH Jr, Ferrucci JT Jr, et al. Periventricular leukomalacia: relationship between lateral ventricular volume on brain MR images and severity of cognitive and motor impairment. Radiology. 2000;214 (1):199 –204[Abstract/Free Full Text]
  22. Carmody DP, Dunn SM, Boddie-Willis AS, DeMarco JK, Lewis M. A quantitative measure of myelination development in infants, using MR images. Neuroradiology. 2004;46 (9):781 –786[CrossRef][Web of Science][Medline]
  23. Azzopardi D, Guarino I, Brayshaw C, et al. Prediction of neurological outcome after birth asphyxia from early continuous two-channel electroencephalography. Early Hum Dev. 1999;55 (2):113 –123[CrossRef][Web of Science][Medline]
  24. Nangia S, Saili A, Dutta AK, Batra S, Ray GN. Free oxygen radicals: predictors of neonatal outcome following perinatal asphyxia. Indian J Pediatr. 1998;65 (3):419 –427[Medline]
  25. Okumura A, Kato T, Hayakawa F, Kidokoro H, Kuno K, Watanabe K. A pilot study on umbilical venous level of natriuretic peptides in preterm infants and their relation to periventricular leukomalacia and antenatal complications. Brain Dev. 2002;24 (1):30 –32[CrossRef][Web of Science][Medline]
  26. Oh W, Tyson JE, Fanaroff AA, et al. Association between peak serum bilirubin and neurodevelopmental outcomes in extremely low birth weight infants. Pediatrics. 2003;112 (4):773 –779[Abstract/Free Full Text]
  27. Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci. 2003;4 (6):469 –480[CrossRef][Web of Science][Medline]
  28. Lehéricy S, Ducros M, Van de Moortele PF, et al. Diffusion tensor fiber tracking shows distinct corticostriatal circuits in humans. Ann Neurol. 2004;55 (4):522 –529[CrossRef][Web of Science][Medline]
  29. Jones DK, Lythgoe D, Horsfield MA, Simmons A, Williams SC, Markus HS. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke. 1999;30 (2):393 –397[Abstract/Free Full Text]
  30. Schneider JF, Il'yasov KA, Hennig J, Martin E. Fast quantitative diffusion-tensor imaging of cerebral white matter from the neonatal period to adolescence. Neuroradiology. 2004;46 (4):258 –266[CrossRef][Web of Science][Medline]
  31. Hoon AH Jr, Lawrie WT Jr, Melhem ER, et al. Diffusion tensor imaging of periventricular leukomalacia shows affected sensory cortex white matter pathways. Neurology. 2002;59 (5):752 –756[Abstract/Free Full Text]
  32. Arzoumanian Y, Mirmiran M, Barnes PD, et al. Diffusion tensor brain imaging findings at term-equivalent age may predict neurologic abnormalities in low birth weight preterm infants. AJNR Am J Neuroradiol. 2003;24 (8):1646 –1653[Abstract/Free Full Text]
  33. Mukherjee P, Miller JH, Shimony JS, et al. Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation. AJNR Am J Neuroradiol. 2002;23 (9):1445 –1456[Abstract/Free Full Text]
  34. Partridge SC, Mukherjee P, Henry RG, et al. Diffusion tensor imaging: serial quantitation of white matter tract in premature newborns. Neuroimage. 2004;22 (3):1302 –1314[CrossRef][Web of Science][Medline]
  35. Dubois J, Panier LH, Lambertz GD, Cointepas Y, Le Bihan D. Assessment of the early organization and maturation of infant's cerebral white matter fiber bundles: a feasibility study using quantitative diffusion tensor imaging and tractography. Neuroimage. 2006;30 (4):1121 –1132[CrossRef][Web of Science][Medline]
  36. Wakana S, Caprihan A, Panzenboeck MM, et al. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage. 2007;36 (3):630 –644[CrossRef][Web of Science][Medline]
  37. Cosottini M, Giannelli M, Siciliano G, et al. Diffusion-tensor MR imaging of corticospinal tract in amyotrophic lateral sclerosis and progressive muscular atrophy. Radiology. 2006;237 (1):258 –264[CrossRef][Web of Science]

PEDIATRICS (ISSN 1098-4275). ©2008 by the American Academy of Pediatrics

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Facebook Facebook   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
J Child NeurolHome page
F. Saadani-Makki, S. Kannan, M. Makki, O. Muzik, J. Janisse, R. Romero, and D. Chugani
Intrauterine Endotoxin Administration Leads to White Matter Diffusivity Changes in Newborn Rabbits
J Child Neurol, September 1, 2009; 24(9): 1179 - 1189.
[Abstract] [PDF]


Home page
PediatricsHome page
M. V. Johnston
Diffusion Tensor Imaging of White Matter and Developmental Outcome
Pediatrics, September 1, 2008; 122(3): 656 - 657.
[Full Text] [PDF]


This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow E-mail this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My File Cabinet
Right arrow Download to citation manager
Right arrowRequest Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via CrossRef
Right arrow Citing Articles via Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Murakami, A.
Right arrow Articles by Sugimoto, T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Murakami, A.
Right arrow Articles by Sugimoto, T.
Related Collections
Right arrow Neurology & Psychiatry
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Facebook   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?