Skip to main content
Erschienen in: Journal of Neurology 3/2018

Open Access 30.01.2018 | Original Communication

Diffusion tensor imaging in metachromatic leukodystrophy

verfasst von: Diane F. van Rappard, Marsh Königs, Marjan E. Steenweg, Jaap Jan Boelens, Jaap Oosterlaan, Marjo S. van der Knaap, Nicole I. Wolf, Petra J. W. Pouwels

Erschienen in: Journal of Neurology | Ausgabe 3/2018

Abstract

Objective

We aimed to gain more insight into the pathomechanisms of metachromatic leukodystrophy (MLD), by comparing magnitude and direction of diffusion between patients and controls at diagnosis and during follow-up.

Methods

Four late-infantile, 16 juvenile and 8 adult onset MLD patients [of which 13 considered eligible for hematopoietic cell transplantation (HCT)] and 47 controls were examined using diffusion tensor imaging. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were quantified and compared between groups using tract-based spatial statistics (TBSS). Diffusion measures were determined for normal-appearing white matter (NAWM), corpus callosum, thalamus (all based on subject-wise segmentation), and pyramidal tracts, determined with probabilistic tractography. Measures were compared between HCT-eligible patients, non-eligible patients and controls using general linear model and nonparametric permutation analyses (randomise) for TBSS data, considering family-wise error corrected p < 0.05 significant.

Results

Throughout white matter (WM), FA was decreased and MD and RD increased in both patient groups compared to controls, while AD was decreased in NAWM and corpus callosum. In the thalamus, no differences in FA were observed, but all diffusivities were increased in both patient groups. Differences were most pronounced between controls and patients non-eligible for HCT. Longitudinally (median follow-up 3.9 years), diffusion measures remained relatively stable for HCT-treated patients, but were progressively abnormal for non-eligible patients.

Interpretation

The observed diffusion measures confirm that brain microstructure is changed in MLD, reflecting different pathological processes including loss of myelin and sulfatide accumulation. The observation of both increased and decreased AD probably reflects a balance between myelin and axonal loss vs. intracellular sulfatide storage in macrophages, depending on region and disease stage.
Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s00415-018-8765-3) contains supplementary material, which is available to authorized users.
Nicole I. Wolf and Petra J. W. Pouwels contributed equally to this work.

Introduction

Metachromatic leukodystrophy (MLD, OMIM 250100) is an autosomal recessive lysosomal disorder caused by mutations in the ARSA gene. This results in deficiency of the enzyme arylsulfatase A (ASA), essential for sulfatide metabolism [1]. Sulfatides are major myelin lipids; their accumulation, mainly in membranes, leads to demyelination and subsequently storage in macrophages that cannot digest them [1]. MLD is a devastating disease: without treatment, eventually all acquired skills are lost and patients die.
MLD has three clinical subtypes, based on age of onset. The late-infantile form starts before 30 months, usually presenting with motor deterioration. The juvenile form presents with a combination of motor and cognitive decline before 16 years. The adult form begins with cognitive decline and psychiatric symptoms thereafter [2]. When performed early, hematopoietic cell therapy (HCT) has promising results, especially for juvenile and adult patients [3, 4].
Brain magnetic resonance imaging (MRI) in MLD is characterized by bilateral symmetric T2 signal hyperintensities, starting in the corpus callosum and subsequently involving periventricular, central and subcortical white matter (WM), in addition to projection fibers such as the corticospinal tract, and eventually cerebellar WM [5]. Thalamic volume and signal intensity on T2-weighted images in the thalamus are decreased already at diagnosis [6, 7]. Typical for MLD are stripes of low signal intensity throughout the hyperintense signal on T2-weighted images in the cerebral WM, related both to the accumulation of macrophages bursting with undigested lipids and to better preserved perivascular myelin [8].
Brain diffusion tensor imaging (DTI) is based on the motion of water molecules, which is more restricted perpendicular to than along WM fibers, a feature termed diffusion anisotropy [9]. Magnitude and direction of diffusivity are determined by molecules, membranes and microtubules, and provide information about tissue composition and microstructure and its architectural organization [10, 11].
The tensor model is a relatively simple model using diffusion-weighted images (DWI) obtained with one b value. It results in axial diffusivity (AD), radial diffusivity (RD) and the derived fractional anisotropy (FA) and mean diffusivity (MD) [11]. Often, increased RD is thought to be correlated with myelin degradation and changes in AD to axonal degeneration or inflammation and gliosis [1217], but it is difficult to unequivocally associate the interpretation of diffusivity variations with specific biophysical changes [18].
The precise pathomechanisms involved in MLD, such as importance of inflammation or how accumulated sulfatides lead to demyelination, are not completely understood. DTI is, taking into account its recognized limitations, a valuable tool to gain more insight into changes in tissue properties in MLD. We, therefore, compared diffusion measures (FA and the three diffusivities) between patients who were eligible for HCT, patients not eligible at time of diagnosis, and controls. HCT-eligible patients are typically in an early disease stage, while patients not eligible for HCT have more advanced disease with extensive demyelination of the WM. We also studied the longitudinal behavior of diffusion measures of both treated and untreated patients.

Methods

Patients and control subjects

All 28 MLD patients (4 late-infantile, 16 juvenile, and 8 adult onset), visiting the Center for Childhood White Matter Disorders, who underwent a quantitative MRI protocol at time of diagnosis between January 2007 and April 2017 were included in this retrospective study, in addition to 47 control subjects in the same age range (Table 1), after informed consent. The study was approved by the institutional review board. Diagnosis of MLD was established by brain MRI, ASA activity and ARSA mutation analysis [4]. Motor function was scored by the MLD Gross Motor Function (MLD-GMF) at baseline and at latest clinical follow-up [19]. Cognitive function was evaluated through neuropsychological examination using the Wechsler Intelligence scale for Children or Adults, as appropriate for age. Eligibility for HCT was based on patients’ neurological examination (no major abnormalities and able to walk independently) and cognitive function (IQ > 75). Treatment with HCT was performed as described before [4]. Characteristics of individual patients are described in Table 2. Thirteen patients were considered eligible for HCT, and 15 non-eligible for HCT. Fourteen patients received HCT (2 patients initially classified as non-eligible; one eligible patient declined HCT) [4]. Follow-up MRI examinations were available for 17 patients (12 HCT-eligible, 5 non-eligible). Median follow-up time between first and last MRI examination was 3.9 years (range 2.5 months to 11.2 years).
Table 1
Demographic information
 
Controls
All MLD patients
Eligible for HCT
Not eligible for HCT
Contrasts p
Number of subjects
47
28
13
15
ns
Male/female
23/24
9/19
6/7
3/12
Age at first scan (mean, SD, years)
10.5 (5.3)
14.5 (9.5)
16.9 (10.6)
12.4 (8.3)
0.017a
Late-infantile/juvenile/adult MLD
na
4/16/8
2/5/6
2/11/2
ns
ns not significant, na not applicable
aPost hoc Dunnett’s T3 revealed no significant pairwise group differences
Table 2
Characteristics of MLD patients
Patient
MLD type
Age (years)
Baseline scan (T)
Follow-up scans (n)
Eligible for HCT
HCT-treated
045
Late-infantile
2.0
1.5
3b
Yes
Yes
050
Late-infantile
2.1
1.5
1b
Yes
Yes
057
Late-infantile
2.4
3
0
No
No
026
Late-infantile
2.6
1.5
0
No
No
016
Juvenile
6.5
1.5
3d
Yes
Yes
039
Juvenile
7.0
1.5
1c
No
No
029
Juvenile
7.1
1.5
0
No
No
053
Juvenile
7.1
1.5
1b
No
Yes
005
Juvenile
7.2
1.5
1c
No
No
065
Juvenile
7.4
3
2c
Yes
Yes
064
Juvenile
8.6
3
0
No
No
006
Juvenile
8.6
1.5
1c
No
No
054
Juvenile
12.5
1.5
1c
No
No
060
Juvenile
13.1
3
0
No
No
058
Juvenile
13.8
3
3c
Yes
Yes
014
Juvenile
14.1
1.5
4d
Yes
Yes
022
Juvenile
15.1
1.5
0
No
No
067
Juvenile
17.6
3
0
Yes
Yes
068
Juvenile
19.2
3
0
No
No
061
Juvenile
20.1
3
0
No
No
021
Adult
17.8
1.5
3d
Yes
Yes
051
Adult
22.5
1.5
0
No
Yes
063
Adult
23.1
3
1c
Yes
Yes
041
Adult
25.4
1.5
6d
Yes
Yes
032
Adult
26.9
1.5
2d
Yes
No
002
Adult
28.4
1.5
5d
Yes
Yes
056
Adult
32.5
1.5
0
No
No
015
Adult
35.2
1.5
4d
Yes
Yes
aAge at baseline examination
bFollow-up examinations at 1.5 T
cFollow-up examinations at 3 T
dFollow-up examinations at both 1.5 and 3 T
Control subjects at 1.5-T underwent MRI for reasons like mild developmental delay, headache; they had normal MRI and neurological examination. Controls at 3 T had experienced a non-neurological trauma and were included in a previous study [20].

Acquisition

Between January 2007 and April 2013, 19 patients and 20 controls had a baseline examination at 1.5 T (Siemens Sonata, Erlangen, Germany). Between May 2013 and April 2017, 9 patients and 27 controls had a baseline examination at 3 T (GE MR750, Milwaukee, WI, USA).
Conventional imaging included sagittal 3-dimensional (3D)-T1 and axial FLAIR, using the same spatial resolution at both field strengths [20, 21]. FLAIR imaging was not performed for control subjects at 3 T. DTI was obtained with a multi-slice echo planar imaging sequence and isotropic 2.5 × 2.5 × 2.5 mm3 voxels. At 1.5 T, we obtained 1 b0 volume and 12 gradient directions with b value 750 s/mm2, 2 acquisitions, TR/TE 6700/81 ms [21]. At 3 T, we obtained 5 b0 volumes and 30 gradient directions with b value 750 s/mm2, 1 acquisition, TR/TE 5100/75 ms, and parallel imaging factor 2 [20].

Analysis

Diffusion tensor imaging data were analyzed using FMRIB’s software library FSL after correction of eddy current distortion and subject motion. The diffusion tensor was fitted resulting in maps of FA, AD, RD and MD. Tract-based spatial statistics (TBSS) was used to align FA images from all subjects into a common space and to create a mean FA skeleton. The aligned FA images of all participants were projected onto this skeleton and fed into voxel-wise crossparticipant statistics using randomise (see “Statistical analysis”) [22].
Based on the regional differences found in the TBSS analyses, we further analyzed diffusion measures in the following regions of interest (ROIs): normal-appearing white matter (NAWM, corpus callosum and thalamus in all subjects, and abnormal cerebral WM in patients. In addition, we analyzed the pyramidal tracts, which were determined for each subject by tractography between motor cortex and cerebral peduncles (see below).
To determine these ROIs in DTI subject space, we first outlined abnormal WM on 2D FLAIR images of patients using clusterize, a semiautomatic segmentation algorithm involving iterative region growing followed by interactive selection of abnormal WM clusters [23]. The mask of abnormal WM was registered to the corresponding 3DT1, and filled with signal intensity resembling NAWM [24]. This 3DT1 image was then segmented with the FSL tools FAST [25] and FIRST [26] to obtain WM, gray matter (GM) and deep GM (DGM) structures, including the thalamus. DGM and abnormal WM were subtracted from the WM mask to obtain NAWM. ROIs for corpus callosum and cerebral peduncles were identified using the Johns Hopkins University (JHU) WM atlas defined in standard Montreal Neurological Institute (MNI) space [27]. The motor cortex was identified in MNI space using the Automated Anatomical Labeling (AAL) atlas [28]. ROIs in MNI space were warped into 3DT1 subject space after linear and non-linear registration using FSL tools FLIRT and FNIRT. All ROIs were subsequently registered to DTI subject space using nearest neighbor interpolation.
The ROIs of motor cortex and cerebral peduncles were used as seed and target for probabilistic tractography using the FSL tools bedpostx and probtrackx2 to obtain the left and right pyramidal tract [29]. Mean diffusion measures within the pyramidal tracts were determined by weighting the underlying FA and diffusivity maps by the probability of a voxel within the tract.

Statistical analysis

Statistical analyses were performed for HCT-eligible and non-eligible patients at baseline and control subjects. Groups were compared on demographic variables using ANOVA and Chi-square tests, as appropriate. In the TBSS analysis, FA, MD, AD and RD were compared among the three groups with nonparametric permutation analysis (randomise), using age and scanner as covariate. We considered a family-wise error (FWE) corrected p < 0.05 significant.
General linear model analyses (ANOVA) including age and scanner as covariates were performed for a three-group comparison of diffusion measures at baseline within selected ROIs. In case of main group effects, we performed post hoc pairwise comparisons between groups, using Dunnett’s T3.
To evaluate longitudinal evolution of disease, we created scatter plots of ROI-based diffusion measures as function of age, and we qualitatively described changes for patients with follow-up examinations.
The pyramidal tracts primarily regulate motor function. We, therefore, determined Spearman rank correlations between diffusion measures of all 28 patients at baseline and motor function, determined with MLD-GMF, at latest clinical follow-up. p < 0.05 was considered significant.

Results

Baseline

Controls, HCT-eligible and non-eligible patients did not differ on demographic variables. Only for age, a main group effect was detected (p = 0.017), which was not reflected in post hoc testing (Table 1).
In the TBSS analysis of all baseline examinations (see Fig. 1), FA was decreased in HCT-eligible and non-eligible patients compared to controls, and in non-eligible patients compared to eligible patients in almost the entire skeleton. The differences were highly significant, since they remained present at FWE-corrected p < 0.001, as indicated by the yellow color. An increase of MD and RD in both patient groups compared to controls was also observed in almost the whole skeleton, again highly significant (FWE-corrected p < 0.001), as indicated by the light blue color. An increase of MD and RD in non-eligible patients compared to eligible patients was limited to a smaller part of the skeleton. An increase in AD in HCT-eligible patients compared to controls was restricted to part of the periventricular WM and the genu and splenium of the corpus callosum. In HCT-non-eligible patients compared to both controls and eligible patients, AD was increased mainly in the thalamus, but decreased in a large part of the skeleton, including the corpus callosum.
In the selected ROIs (see Fig. 2), FA was decreased for both patient groups compared to controls in NAWM, corpus callosum and pyramidal tracts. Differences were most pronounced between controls and HCT-non-eligible patients. The relative and absolute decrease in FA was largest in corpus callosum. In patients, FA in abnormal WM was smaller than in NAWM, and lower in HCT-non-eligible patients than in eligible patients (supplementary material, Table 1). Although the TBSS analysis showed group differences in FA in the skeletonized thalamus, there were no FA differences in the thalamus based on a ROI analysis. The FA observations in all ROIs were replicated when splitting the groups based on field strength (supplementary material, Fig. 1).
Following the TBSS findings, MD and RD were increased in both patient groups in NAWM, corpus callosum, pyramidal tracts and thalamus, and differences were most pronounced between controls and non-eligible patients. Differences in RD were larger than differences in MD, again with the corpus callosum showing most prominent differences between groups. MD and RD within abnormal WM were higher than in NAWM, but did not differ between patient groups (supplementary material, Table 1).
In the pyramidal tracts, there were no group differences in AD. Following the TBSS findings, in NAWM and corpus callosum, AD was lower in both patient groups than in controls, whereas in the thalamus AD was higher in patients. Again, these differences were most pronounced between HCT-non-eligible patients and controls. AD within abnormal WM was higher than in NAWM, and lower in non-eligible patients than in eligible patients (supplementary material, Table 1).
We observed similar patterns for MD, RD and AD when splitting the groups based on field strength (supplementary material, Fig. 1).
Spearman rank correlations with MLD-GMF were significant for FA (− 0.84), MD (0.78) and RD (0.86), all p < 0.01. Thus, low FA and high MD and RD of the pyramidal tracts at baseline indicate poor motor function at follow-up.

Longitudinal evolvement of diffusion measures

For illustration, a selection of longitudinal diffusion measures in selected ROIs is shown in Fig. 3. For each patient with follow-up measurements, symbols are connected by lines. The longitudinal variation indicates the actual course, but also the reproducibility of the measurement, including the effect of examinations at both field strengths for some patients. The effect of field strength can also be appreciated when comparing control subjects at 3 and 1.5 T. Overall, measures remained relatively stable, especially for HCT-eligible patients after treatment. In non-eligible patients, values showed a progressively abnormal trend.
In NAWM, FA mildly fluctuated for treated eligible patients, while FA further decreased for HCT-non-eligible patients (Fig. 3a). Diffusivities remained relatively stable for all patients. In the corpus callosum, FA tended to decrease in the treated eligible patients, while the reduction in the non-eligible patients was marginal (Fig. 3b). However, MD, AD and RD longitudinally increased especially in non-eligible patients, which meant that AD, which was decreased at baseline, showed a pseudo-normalization (Fig. 3c).
In the pyramidal tracts, FA remained constant or slightly increased over time in most treated eligible patients, whereas FA slightly decreased in HCT-non-eligible patients (Fig. 3d). Diffusivities showed some longitudinal variability, but no clear trend was observed.
In the thalamus, in which FA did not differ between groups at baseline, FA remained stable in treated eligible patients, and showed a slight decrease in non-eligible patients (Fig. 3e). Diffusivities in treated eligible patients remained stable or showed a mild increase, whereas a larger increase was observed in non-eligible patients (as shown for AD in Fig. 3f).

Discussion

Using diffusion-weighted MRI, we compared magnitude and direction of diffusion between MLD patients and controls to gain insight into the microstructure of affected brain tissue. At baseline, FA was decreased and MD and RD were increased in MLD patients compared to controls throughout the WM, not only in the corpus callosum (affected early in the disease), but also in NAWM. FA measures of the thalamus did not differ between groups, but its components AD and RD were both increased in patients compared to controls. Whereas AD was increased in thalamus, it was unchanged in the pyramidal tracts and decreased in the corpus callosum and, to a lesser degree, in NAWM. All differences were most pronounced between controls and HCT-non-eligible patients.
Longitudinally, in treated HCT-eligible patients, diffusion measures remained stable or showed only minor changes. FA remained constant or even tended to increase in NAWM, pyramidal tracts, and thalamus, whereas it slightly decreased in the corpus callosum. HCT-non-eligible patients had less follow-up examinations than eligible patients, but those available showed clear increases of RD and AD, causing a small FA reduction in all investigated regions. The treatment effect of HCT most likely influenced the longitudinal differences between treated eligible patients (approaching control values in NAWM and pyramidal tracts), and untreated non-eligible patients (increasingly abnormal values). This is in line with our observation that metabolite concentrations observed with magnetic resonance spectroscopy partially normalized in successfully transplanted patients, whilst concentrations for non-treated patients further deteriorated [30].
The diffusion tensor model used in this study reflects the underlying structural characteristics in a simplified manner, hampered by partial volume effects and crossing fibers [18]. Advanced multi-compartment diffusion models, such as the composite hindered and restricted model of diffusion (CHARMED), are more sensitive than the conventional ones [3133]. However, since our study, ongoing since 2007, concerns a rare disease, application of these advanced diffusion models was not feasible.
This implies that we can merely hypothesize about the precise mechanism responsible for the observed differences rather than draw general conclusions because different cellular processes may lead to identical changes [911]. Since both animal [13, 34, 35] and human studies [36] have shown an increased RD parallel to myelin loss, our results of increased RD in WM suggest myelin loss in patients, in line with histopathological findings [37]. This is also supported by our observation that high RD and low FA in the pyramidal tracts at baseline indicate poor motor function at follow-up.
With regard to AD, animal and human studies provide discrepant results, correlating axonal damage with either an AD decrease [13, 17] or increase, [38, 39], respectively. This reflects the difficulty in relating AD to underlying pathological processes. Our observation of opposing AD patterns in MLD suggests that different pathological mechanisms can cause either a decrease or an increase in AD, with the overall balance between these effects depending on brain region and disease stage. MLD is characterized by accumulation of sulfatides, major myelin lipids mainly synthesized by oligodendrocytes. Analytical studies of MLD patients’ brain tissue showed that metachromatic deposits are mainly present in the WM, with a sulfatide content up to eight times higher than normal, with relatively minor chemical GM changes [37]. Regarding WM, we assume that the massive intracellular sulfatide accumulation in swollen macrophages, in vain trying to digest these lipids, causes overall diffusion restriction and thereby AD reduction. Using conventional DWI, restricted diffusion in the outermost part of the demyelinated WM has indeed been described for single cases in relatively early disease stage [40, 41]. However, as the disease progresses, axons are increasingly damaged. Based on the previous observations in human studies, we expect that loss of both myelin and axons will lead to an AD increase [38, 39]. Our results suggest that, particularly in the corpus callosum of non-eligible patients at baseline, diffusion restriction due to sulfatide accumulation in macrophages contributes more to the severely reduced AD values than increased diffusion due to myelin and axonal loss. Our longitudinal observation of an increase, and thereby a pseudo-normalization of AD, in the corpus callosum suggests that myelin and axonal loss likely becomes more prominent in progressive disease. The corpus callosum is, of the investigated ROIs, least hindered by limitations of the tensor model, suggesting that the interpretation of AD values is not much influenced by the presence of crossing fibers. Whether inflammation and gliosis also contribute to AD changes in this setting is uncertain.
The thalamus is a DGM structure, in which sulfatide accumulation is much more limited than in WM [37]. In addition, the thalamus has a different microstructure than WM as it largely consists of neurons and contains few axons. In control subjects, this is mirrored by low thalamic FA, as the difference between AD and RD is much smaller than for a WM structure like the corpus callosum. In the thalamus of patients, we observed an increase of AD and RD of similar relative magnitude, which had hardly any effect on FA. This increase of both AD and RD probably implies an increase in extracellular space due to neuronal loss, which apparently dominates reductions in diffusivity due to storage material.
Limitations of this study were its retrospective character, a large age range of patients (inherent to the inclusion of patients with all disease types), and a limited age range of controls at 3 T. The combination of 1.5 and 3 T data also introduced some variability, although these differences were typically smaller than differences between controls and patients. In fact, the ROI-based analysis of baseline data at 1.5 and at 3 T separately (as shown in the supplementary material) showed identical patterns as those observed in the combined analysis. We noticed the same main group effects, although in these smaller groups fewer post hoc pairwise comparisons were significant.
Overall, the observed changes of FA, RD, and especially AD indicate that MLD alters certain aspects of brain microstructure. These changes most likely reflect a multitude of pathological processes such as accumulation of metachromatic material, followed by myelin and axonal loss. The differences between untreated and treated patients indicate that diffusion measures are positively affected by HCT, further emphasizing the beneficial effects of this intervention on WM and supporting the findings of other quantitative MR measures as proton MR spectroscopy [30]. Altogether, quantitative MR measures provide more insight into time-dependent disease mechanisms and might in the future aid in determining the right window for intervention.

Acknowledgements

The authors thank all the patients with MLD and their families for participating in this study.

Compliance with ethical standards

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical standard

The study has been approved by the ethics committee of VU University Medical Center.
Informed consent was obtained from the participants or their guardians.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Unsere Produktempfehlungen

e.Med Interdisziplinär

Kombi-Abonnement

Für Ihren Erfolg in Klinik und Praxis - Die beste Hilfe in Ihrem Arbeitsalltag

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf SpringerMedizin.de.

Neuer Inhalt

e.Med Neurologie & Psychiatrie

Kombi-Abonnement

Mit e.Med Neurologie & Psychiatrie erhalten Sie Zugang zu CME-Fortbildungen der Fachgebiete, den Premium-Inhalten der dazugehörigen Fachzeitschriften, inklusive einer gedruckten Zeitschrift Ihrer Wahl.

Weitere Produktempfehlungen anzeigen
Anhänge

Electronic supplementary material

Below is the link to the electronic supplementary material.
Literatur
1.
Zurück zum Zitat Eckhardt M (2008) The role and metabolism of sulfatide in the nervous system. Mol Neurobiol 37:93–103CrossRefPubMed Eckhardt M (2008) The role and metabolism of sulfatide in the nervous system. Mol Neurobiol 37:93–103CrossRefPubMed
2.
Zurück zum Zitat van Rappard DF, Boelens JJ, Wolf NI (2015) Metachromatic leukodystrophy: disease spectrum and approaches for treatment. Best Pract Res Clin Endocrinol Metab 29:261–273CrossRefPubMed van Rappard DF, Boelens JJ, Wolf NI (2015) Metachromatic leukodystrophy: disease spectrum and approaches for treatment. Best Pract Res Clin Endocrinol Metab 29:261–273CrossRefPubMed
3.
Zurück zum Zitat Groeschel S, Kuhl JS, Bley AE, Kehrer C et al (2016) Long-term outcome of allogeneic hematopoietic stem cell transplantation in patients with juvenile metachromatic leukodystrophy compared with nontransplanted control patients. JAMA Neurol 73:1133–1140CrossRefPubMed Groeschel S, Kuhl JS, Bley AE, Kehrer C et al (2016) Long-term outcome of allogeneic hematopoietic stem cell transplantation in patients with juvenile metachromatic leukodystrophy compared with nontransplanted control patients. JAMA Neurol 73:1133–1140CrossRefPubMed
4.
Zurück zum Zitat van Rappard DF, Boelens JJ, van Egmond ME, Kuball J et al (2016) Efficacy of hematopoietic cell transplantation in metachromatic leukodystrophy: the Dutch experience. Blood 127:3098–3101CrossRefPubMed van Rappard DF, Boelens JJ, van Egmond ME, Kuball J et al (2016) Efficacy of hematopoietic cell transplantation in metachromatic leukodystrophy: the Dutch experience. Blood 127:3098–3101CrossRefPubMed
5.
Zurück zum Zitat van der Knaap MS, Valk J (2015) Magnetic resonance of myelination and myelin disorders, 3rd edn. Springer, Berlin van der Knaap MS, Valk J (2015) Magnetic resonance of myelination and myelin disorders, 3rd edn. Springer, Berlin
6.
Zurück zum Zitat Tillema JM, Derks MG, Pouwels PJ, de Graaf Pim et al (2015) Volumetric MRI data correlate to disease severity in metachromatic leukodystrophy. Ann Clin Transl Neurol 2:932–940CrossRefPubMedPubMedCentral Tillema JM, Derks MG, Pouwels PJ, de Graaf Pim et al (2015) Volumetric MRI data correlate to disease severity in metachromatic leukodystrophy. Ann Clin Transl Neurol 2:932–940CrossRefPubMedPubMedCentral
7.
Zurück zum Zitat Martin A, Sevin C, Lazarus C, Bellesme C et al (2012) Toward a better understanding of brain lesions during metachromatic leukodystrophy evolution. Am J Neuroradiol 33:1731–1739CrossRefPubMed Martin A, Sevin C, Lazarus C, Bellesme C et al (2012) Toward a better understanding of brain lesions during metachromatic leukodystrophy evolution. Am J Neuroradiol 33:1731–1739CrossRefPubMed
8.
Zurück zum Zitat van der Voorn JP, Pouwels PJ, Kamphorst W, Powers JM et al (2005) Histopathologic correlates of radial stripes on MR images in lysosomal storage disorders. Am J Neuroradiol 26:442–446PubMed van der Voorn JP, Pouwels PJ, Kamphorst W, Powers JM et al (2005) Histopathologic correlates of radial stripes on MR images in lysosomal storage disorders. Am J Neuroradiol 26:442–446PubMed
9.
Zurück zum Zitat Assaf Y, Pasternak O (2008) Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J Mol Neurosci 34:51–61CrossRefPubMed Assaf Y, Pasternak O (2008) Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J Mol Neurosci 34:51–61CrossRefPubMed
10.
Zurück zum Zitat Basser PJ, Jones DK (2002) Diffusion-tensor MRI: theory, experimental design and data analysis—a technical review. NMR Biomed 15:456–467CrossRefPubMed Basser PJ, Jones DK (2002) Diffusion-tensor MRI: theory, experimental design and data analysis—a technical review. NMR Biomed 15:456–467CrossRefPubMed
11.
Zurück zum Zitat Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254CrossRefPubMed Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254CrossRefPubMed
12.
Zurück zum Zitat Song SK, Sun SW, Ramsbottom MJ, Chang C et al (2002) Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage 17:1429–1436CrossRefPubMed Song SK, Sun SW, Ramsbottom MJ, Chang C et al (2002) Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage 17:1429–1436CrossRefPubMed
13.
Zurück zum Zitat Song SK, Yoshino J, Le TQ, Lin SJ et al (2005) Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 26:132–140CrossRefPubMed Song SK, Yoshino J, Le TQ, Lin SJ et al (2005) Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 26:132–140CrossRefPubMed
14.
Zurück zum Zitat Klawiter EC, Xu J, Naismith RT, Benzinger TL et al (2012) Increased radial diffusivity in spinal cord lesions in neuromyelitis optica compared with multiple sclerosis. Mult Scler 18:1259–1268CrossRefPubMedPubMedCentral Klawiter EC, Xu J, Naismith RT, Benzinger TL et al (2012) Increased radial diffusivity in spinal cord lesions in neuromyelitis optica compared with multiple sclerosis. Mult Scler 18:1259–1268CrossRefPubMedPubMedCentral
16.
Zurück zum Zitat Boretius S, Escher A, Dallenga T, Wrzos C et al (2012) Assessment of lesion pathology in a new animal model of MS by multiparametric MRI and DTI. Neuroimage 59:2678–2688CrossRefPubMed Boretius S, Escher A, Dallenga T, Wrzos C et al (2012) Assessment of lesion pathology in a new animal model of MS by multiparametric MRI and DTI. Neuroimage 59:2678–2688CrossRefPubMed
17.
Zurück zum Zitat Brennan FH, Cowin GJ, Kurniawan ND, Ruitenberg MJ (2013) Longitudinal assessment of white matter pathology in the injured mouse spinal cord through ultra-high field (16.4 T) in vivo diffusion tensor imaging. Neuroimage 82:574–585CrossRefPubMed Brennan FH, Cowin GJ, Kurniawan ND, Ruitenberg MJ (2013) Longitudinal assessment of white matter pathology in the injured mouse spinal cord through ultra-high field (16.4 T) in vivo diffusion tensor imaging. Neuroimage 82:574–585CrossRefPubMed
18.
Zurück zum Zitat Wheeler-Kingshott CA, Cercignani M (2009) About, “axial” and “radial” diffusivities. Magn Reson Med 61:1255–1260CrossRefPubMed Wheeler-Kingshott CA, Cercignani M (2009) About, “axial” and “radial” diffusivities. Magn Reson Med 61:1255–1260CrossRefPubMed
19.
Zurück zum Zitat Kehrer C, Blumenstock G, Raabe C, Krageloh-Mann I (2011) Development and reliability of a classification system for gross motor function in children with metachromatic leucodystrophy. Dev Med Child Neurol 53:156–160CrossRefPubMed Kehrer C, Blumenstock G, Raabe C, Krageloh-Mann I (2011) Development and reliability of a classification system for gross motor function in children with metachromatic leucodystrophy. Dev Med Child Neurol 53:156–160CrossRefPubMed
21.
Zurück zum Zitat Steenweg ME, Wolf NI, van Wieringen WN, Barkhof F et al (2016) Quantitative MRI in hypomyelinating disorders: correlation with motor handicap. Neurology 87:752–758CrossRefPubMed Steenweg ME, Wolf NI, van Wieringen WN, Barkhof F et al (2016) Quantitative MRI in hypomyelinating disorders: correlation with motor handicap. Neurology 87:752–758CrossRefPubMed
22.
Zurück zum Zitat Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31:1487–1505CrossRefPubMed Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31:1487–1505CrossRefPubMed
23.
Zurück zum Zitat Clas P, Groeschel S, Wilke M (2012) A semi-automatic algorithm for determining the demyelination load in metachromatic leukodystrophy. Acad Radiol 19:26–34CrossRefPubMed Clas P, Groeschel S, Wilke M (2012) A semi-automatic algorithm for determining the demyelination load in metachromatic leukodystrophy. Acad Radiol 19:26–34CrossRefPubMed
24.
Zurück zum Zitat Chard DT, Jackson JS, Miller DH, Wheeler-Kingshott CA (2010) Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J Magn Reson Imaging 32(1):223–228CrossRefPubMed Chard DT, Jackson JS, Miller DH, Wheeler-Kingshott CA (2010) Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J Magn Reson Imaging 32(1):223–228CrossRefPubMed
25.
Zurück zum Zitat Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20:45–57CrossRefPubMed Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20:45–57CrossRefPubMed
26.
Zurück zum Zitat Patenaude B, Smith SM, Kennedy D, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56:907–922CrossRefPubMedPubMedCentral Patenaude B, Smith SM, Kennedy D, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56:907–922CrossRefPubMedPubMedCentral
27.
Zurück zum Zitat Hua K, Zhang J, Wakana S, Jiang H et al (2008) Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39:336–347CrossRefPubMed Hua K, Zhang J, Wakana S, Jiang H et al (2008) Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39:336–347CrossRefPubMed
28.
Zurück zum Zitat Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289CrossRefPubMed Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289CrossRefPubMed
29.
Zurück zum Zitat Behrens TE, Berg HJ, Jbabdi S, Rushworth MF et al (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34:144–155CrossRefPubMed Behrens TE, Berg HJ, Jbabdi S, Rushworth MF et al (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34:144–155CrossRefPubMed
30.
Zurück zum Zitat van Rappard DF, Klauser A, Steenweg ME, Boelens JJ et al (2018) Quantitative MR spectroscopic imaging in metachromatic leukodystrophy: value for prognosis and treatment. J Neurol Neurosurg Psychiatry 89:105–111CrossRefPubMed van Rappard DF, Klauser A, Steenweg ME, Boelens JJ et al (2018) Quantitative MR spectroscopic imaging in metachromatic leukodystrophy: value for prognosis and treatment. J Neurol Neurosurg Psychiatry 89:105–111CrossRefPubMed
31.
Zurück zum Zitat Harms RL, Fritz FJ, Tobisch A, Goebel R et al (2017) Robust and fast nonlinear optimization of diffusion MRI microstructure models. Neuroimage 155:82–96CrossRefPubMedPubMedCentral Harms RL, Fritz FJ, Tobisch A, Goebel R et al (2017) Robust and fast nonlinear optimization of diffusion MRI microstructure models. Neuroimage 155:82–96CrossRefPubMedPubMedCentral
32.
Zurück zum Zitat De Santis S, Drakesmith M, Bells S, Assaf Y et al (2014) Why diffusion tensor MRI does well only some of the time: variance and covariance of white matter tissue microstructure attributes in the living human brain. Neuroimage 89:35–44CrossRefPubMedPubMedCentral De Santis S, Drakesmith M, Bells S, Assaf Y et al (2014) Why diffusion tensor MRI does well only some of the time: variance and covariance of white matter tissue microstructure attributes in the living human brain. Neuroimage 89:35–44CrossRefPubMedPubMedCentral
33.
Zurück zum Zitat Assaf Y, Basser PJ (2005) Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage 27:48–58CrossRefPubMed Assaf Y, Basser PJ (2005) Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage 27:48–58CrossRefPubMed
34.
Zurück zum Zitat Hofling AA, Kim JH, Fantz CR, Sands MS et al (2009) Diffusion tensor imaging detects axonal injury and demyelination in the spinal cord and cranial nerves of a murine model of globoid cell leukodystrophy. NMR Biomed 22:1100–1106PubMedPubMedCentral Hofling AA, Kim JH, Fantz CR, Sands MS et al (2009) Diffusion tensor imaging detects axonal injury and demyelination in the spinal cord and cranial nerves of a murine model of globoid cell leukodystrophy. NMR Biomed 22:1100–1106PubMedPubMedCentral
35.
Zurück zum Zitat Ruest T, Holmes WM, Barrie JA, Griffiths IR et al (2011) High-resolution diffusion tensor imaging of fixed brain in a mouse model of Pelizaeus–Merzbacher disease: comparison with quantitative measures of white matter pathology. NMR Biomed 24:1369–1379CrossRefPubMed Ruest T, Holmes WM, Barrie JA, Griffiths IR et al (2011) High-resolution diffusion tensor imaging of fixed brain in a mouse model of Pelizaeus–Merzbacher disease: comparison with quantitative measures of white matter pathology. NMR Biomed 24:1369–1379CrossRefPubMed
36.
Zurück zum Zitat Schmierer K, Wheeler-Kingshott CA, Tozer DJ, Boulby PA et al (2008) Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation. Magn Reson Med 59:268–277CrossRefPubMedPubMedCentral Schmierer K, Wheeler-Kingshott CA, Tozer DJ, Boulby PA et al (2008) Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation. Magn Reson Med 59:268–277CrossRefPubMedPubMedCentral
37.
38.
Zurück zum Zitat Hulst HE, Steenwijk MD, Versteeg A, Pouwels PJ et al (2013) Cognitive impairment in MS: impact of white matter integrity, gray matter volume, and lesions. Neurology 80:1025–1032CrossRefPubMed Hulst HE, Steenwijk MD, Versteeg A, Pouwels PJ et al (2013) Cognitive impairment in MS: impact of white matter integrity, gray matter volume, and lesions. Neurology 80:1025–1032CrossRefPubMed
39.
Zurück zum Zitat Klistorner A, Vootakuru N, Wang C, Yiannikas C et al (2015) Decoding diffusivity in multiple sclerosis: analysis of optic radiation lesional and non-lesional white matter. PLoS One 10:e0122114CrossRefPubMedPubMedCentral Klistorner A, Vootakuru N, Wang C, Yiannikas C et al (2015) Decoding diffusivity in multiple sclerosis: analysis of optic radiation lesional and non-lesional white matter. PLoS One 10:e0122114CrossRefPubMedPubMedCentral
40.
Zurück zum Zitat Oguz KK, Anlar B, Senbil N, Cila A (2004) Diffusion-weighted imaging findings in juvenile metachromatic leukodystrophy. Neuropediatrics 35:279–282CrossRefPubMed Oguz KK, Anlar B, Senbil N, Cila A (2004) Diffusion-weighted imaging findings in juvenile metachromatic leukodystrophy. Neuropediatrics 35:279–282CrossRefPubMed
41.
Zurück zum Zitat Sener RN (2003) Metachromatic leukodystrophy. Diffusion MR imaging and proton MR spectroscopy. Acta Radiol 44:440–443PubMed Sener RN (2003) Metachromatic leukodystrophy. Diffusion MR imaging and proton MR spectroscopy. Acta Radiol 44:440–443PubMed
Metadaten
Titel
Diffusion tensor imaging in metachromatic leukodystrophy
verfasst von
Diane F. van Rappard
Marsh Königs
Marjan E. Steenweg
Jaap Jan Boelens
Jaap Oosterlaan
Marjo S. van der Knaap
Nicole I. Wolf
Petra J. W. Pouwels
Publikationsdatum
30.01.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Neurology / Ausgabe 3/2018
Print ISSN: 0340-5354
Elektronische ISSN: 1432-1459
DOI
https://doi.org/10.1007/s00415-018-8765-3

Weitere Artikel der Ausgabe 3/2018

Journal of Neurology 3/2018 Zur Ausgabe

Leitlinien kompakt für die Neurologie

Mit medbee Pocketcards sicher entscheiden.

Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag

Sind Frauen die fähigeren Ärzte?

30.04.2024 Gendermedizin Nachrichten

Patienten, die von Ärztinnen behandelt werden, dürfen offenbar auf bessere Therapieergebnisse hoffen als Patienten von Ärzten. Besonders gilt das offenbar für weibliche Kranke, wie eine Studie zeigt.

Akuter Schwindel: Wann lohnt sich eine MRT?

28.04.2024 Schwindel Nachrichten

Akuter Schwindel stellt oft eine diagnostische Herausforderung dar. Wie nützlich dabei eine MRT ist, hat eine Studie aus Finnland untersucht. Immerhin einer von sechs Patienten wurde mit akutem ischämischem Schlaganfall diagnostiziert.

Niedriger diastolischer Blutdruck erhöht Risiko für schwere kardiovaskuläre Komplikationen

25.04.2024 Hypotonie Nachrichten

Wenn unter einer medikamentösen Hochdrucktherapie der diastolische Blutdruck in den Keller geht, steigt das Risiko für schwere kardiovaskuläre Ereignisse: Darauf deutet eine Sekundäranalyse der SPRINT-Studie hin.

Frühe Alzheimertherapie lohnt sich

25.04.2024 AAN-Jahrestagung 2024 Nachrichten

Ist die Tau-Last noch gering, scheint der Vorteil von Lecanemab besonders groß zu sein. Und beginnen Erkrankte verzögert mit der Behandlung, erreichen sie nicht mehr die kognitive Leistung wie bei einem früheren Start. Darauf deuten neue Analysen der Phase-3-Studie Clarity AD.

Update Neurologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.