Background Mutations in SPG11 are the most frequent known cause of autosomal recessive hereditary spastic paraplegia. Corpus callosum thinning is a hallmark of the condition but little is known about damage to other structures in the CNS.
Objective To evaluate in vivo cerebral damage in patients with SPG11 mutations.
Methods 5 patients and 15 age and sex matched healthy controls underwent high resolution diffusion tensor imaging (32 directions) and a T1 volumetric (1 mm slices) acquisition protocol in a 3 T scanner (Philips Achieva). These sequences were then analysed through voxel based morphometry (VBM) and tract based spatial statistics (TBSS).
Results Mean age of the patients was 23.6±4.5 years (range 14–45) and mean duration of disease was 12 years (range 5–15). All patients presented with progressive spastic paraplegia and three were already wheelchair bound when first evaluated. Mutations found were: c.529_533delATATT, c.704_705delAT, c.733_734delAT, c.118C>T and c.7256A>G. VBM identified significant grey matter atrophy in both the thalamus and lentiform nuclei. TBSS analyses revealed reduced fractional anisotropy involving symmetrically subcortical white matter of the temporal and frontal lobes, the cingulated gyrus, cuneus, striatum, corpus callosum and brainstem.
Conclusions Widespread white matter damage in patients with SPG11 mutations has been demonstrated. Grey matter atrophy was prominent in both the thalamus and basal ganglia but not in the cerebral cortex. These findings suggest that neuronal damage/dysfunction is more widespread than previously recognised in this condition.
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Hereditary spastic paraplegia (HSP) is a heterogeneous group of neurodegenerative disorders characterised by progressive lower limb weakness and spasticity.1 In some patients, these are the sole clinical findings and these patients are classified as pure HSP. In contrast, some additional systemic (cataracts) or neurological (dementia, ataxia, epilepsy) features may be found in other patients; these in turn are classified as complicated HSP. At present, there are at least 48 loci associated with HSP and 17 identified genes.1 ,2 HSP may segregate as an autosomal dominant, autosomal recessive or X linked trait.
Mutations in the SPG11 gene located on chromosome 15q13-15 are now recognised as the most frequent cause of autosomal recessive HSP.3 Patients typically present with gait complaints in the first or second decades but ultimately develop cognitive impairment and peripheral manifestations. Ataxia, parkinsonism and motor neuron disease have lately been described as frequent findings in individuals bearing SPG11 mutations.4–6
MRI usually shows severe corpus callosum thinning and sparse white matter hyperintense foci in this disease.4 The clinical variability that is otherwise characteristic of HSP due to SPG11 mutations suggests that neuronal damage is not restricted to these structures but this has not yet been proved. Modern neuroimaging techniques, including voxel based morphometry (VBM) and diffusion tensor imaging (DTI), make it possible to perform automated and unbiased whole brain analyses and to determine in vivo the distribution of damage to neural structures.7 ,8 These have been successfully used to study similar neurodegenerative disorders, such as Niemman–Pick type C and inherited ataxias.9 ,10 Therefore, we designed an MRI based study to characterise white matter and grey matter abnormalities in a cohort of patients with confirmed SPG11 mutations.
We recruited 11 consecutive adult patients with autosomal recessive HSP, thin corpus callosum and cognitive decline that were regularly followed at the Neurogenetics Outpatient Clinic at UNICAMP hospital between 2007 and 2010. From this group, we found five patients with mutations in the SPG11 gene that underwent detailed clinical and MRI analyses. Severity of disease was quantified with the Spastic Paraplegia Rating Scale.11
Imaging findings were compared with a control group of 15 age and sex matched individuals with no neurological abnormalities (men/women ratio 7/8 and mean age 24.0±3.8 years). These were mostly relatives of patients and volunteers from the local community and were recruited during the study period. None of the patients or controls had significant motion artefacts on MRI scans.
This study was approved by our institutional ethics committee and written informed consent was obtained from all participants.
Genomic DNA from each subject was isolated from lymphocytes of fresh blood by standard methods using phenol–chloroform extraction. We used previously designed forward and reverse primers to perform PCR analyses, as described elsewhere.3 ,4 Purified PCR products were then sequenced on an automatic sequencer MegaBACE 1000 (Amersham Bioscience, Piscataway, New Jersey). All 40 exons and exon–intron boundaries of the SPG11 gene were investigated for each individual.
MRI acquisition protocol
All patients and controls underwent MRI scans on a Phillips Achieva-Intera 3 T scanner at UNICAMP hospital. T1 and T2 weighted images were acquired in axial, coronal and sagittal planes with thin cuts. We also obtained two specific sequences that were later employed for VBM and DTI analyses, respectively.
Volumetric (three-dimensional) T1 gradient echo images—acquired in the sagittal plane with 1 mm slice thickness (flip angle=35°, TR=7.1 ms, TE=3.2 ms, matrix=240 × 240, field of view=24 × 24 cm).
DTI—undertaken via a 32 direction non-collinear echoplanar sequence (flip angle=90°, voxel size=2×2×2 mm3, TR=8500 ms, TE=61 ms, matrix=128 × 128, field of view=256 × 256 mm, 70 slices with 3 mm thickness, b value =1000).
VBM protocol and analysis
MRI scans produce images in DICOM format. These images were converted into ANALYSE format using the MRIcro software (http://www.mricro.com). We used the SPM8 package (Wellcome Department of Imaging Neuroscience, London, UK, http://www.fil.ion.ucl.ac.uk) running on MaTLab 8.0 to perform the preprocessing steps that are required before statistical analyses are performed. These include spatial normalisation of all image data to the same stereotaxic space; segmentation and tissue extraction; spatial smoothing; and correction for volume changes induced by spatial normalisation (modulation). The SPM8 package has improved some of the algorithms needed to perform these initial steps. Regarding spatial normalisation, it now includes a more sophisticated registration model (the DARTEL algorithm) that substantially reduces the imprecision of intersubject registration.12
Processed images of patients and controls were compared using a voxelwise statistical analysis. We looked for differences in white and grey matter volumes between the two groups. We defined the contrast searching for areas of reduced and increased volumes both in white and grey matter. The results were corrected for multiple comparisons using a false discovery rate of 5% and significant differences were set at α<0.05. We used xjView8 (http://www.alivelearn.net/xjview8/), a MatLab toolbox to display our results.
Tract based spatial statistics
We obtained maps of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) using the FMRIB diffusion toolbox which is part of the FSL software V.126.96.36.199 Comparison of groups was then carried out using tract based spatial statistics (TBSS) on the FSL software V.188.8.131.52 TBSS involves several preprocessing steps before final analyses. All FA images are first aligned to a standard space using the non-linear registration. The next step involves the creation of a mean FA template which then enables the creation of the mean FA skeleton. Thereafter, each patient's aligned FA map is then projected over this skeleton; this is an essential step in the processing algorithm because it removes the effect of cross subject spatial variability. These final data are then used to perform the voxelwise statistics. A similar procedure was performed for the other DTI parameters (MD, RD and AD). We performed a two sample t test to compare group of patients and controls, searching for areas with FA, MD, RD and AD differences between the two groups. In order to control for multiple comparisons, we applied a family wise error correction, accepting p values <0.05. We used the Johns Hopkins white matter DTI based atlas within the FSL toolbox to identify the abnormal white matter fibre tracts.
We observed that four patients were homozygous and one was compound heterozygous for mutations in SPG11 (table 1). All mutations found had been previously reported in patients with autosomal recessive HSP and thinning of the corpus callosum.3 ,4 ,15 ,16 They were of the non-sense type, leading to premature protein truncation. Mean age of the patients was 23.6±4.5 years (range 19–31) and mean duration of disease was 12 years (range 5–15). All patients presented with progressive spastic paraplegia and three were already wheelchair bound when first evaluated. Cognitive decline was evident in all patients according to the Mini-Mental State Examination scores (adjusted for age and educational level). Detailed information for each patient is depicted in table 1.
Grey matter results
VBM analysis identified symmetrical and significant grey matter volumetric reduction in both the thalamus, caudate and lentiform nuclei of patients with SPG11 compared with controls (figure 1). We found small areas of cortical volumetric reduction, essentially restricted to the precentral and postcentral gyri (table 2). There were no regions with grey matter volumetric increases.
White matter results
Cerebral white matter was assessed both with VBM and TBSS. These are essentially complementary tools as VBM provides a macroscopic map of atrophy whereas DTI based analyses enable the identification of microstructural damage.
VBM identified severe white matter volumetric reduction around the corpus callosum (figure 2). TBSS, however, revealed reduced FA involving symmetrically subcortical white matter of the temporal and frontal lobes, the cingulated gyrus, cuneus, striatum, corpus callosum, cerebellum and brainstem (figure 3). There was no region of significantly increased FA. MD maps also revealed diffuse white matter abnormalities (figure 4). There were also widespread areas of increased RD, especially involving subcortical white matter of the posterior temporal and occipital lobes (figure 4). We found areas of significantly increased AD around the thalamus and frontal deep white matter, but not in the posterior regions (figure 4).
In this study, we investigated SPG11 mutations in all patients with typical HSP with the thin corpus callosum phenotype17 from our centre, and found that 45% (5/11) of the families tested positive. This confirms that SPG11 is a rather common cause of HSP but that there is also locus heterogeneity within the limits of this phenotype. All mutations identified in this study had been previously reported in a large survey of patients from different ethnic origins.3 ,4 ,15 ,16 This indicates that the genotypes of Brazilian patients with SPG11 related HSP are not different from other populations.18
Damage to grey and white matter structures other than the corpus callosum in a cohort of genetically proven SPG11 patients has not been systematically investigated. We thus used two validated and unbiased methods—VBM and TBSS—to perform whole brain analysis in patients with SPG11 without a priori hypotheses. We were also able to scan these patients on a high field MRI scanner (3 T), which is more sensitive in detecting subtle abnormalities.19 In addition, processing of images to run VBM was accomplished by SPM8 running on MaLab 8.0. This version of the software improved several of the required preprocessing steps but especially the spatial normalisation algorithm.20 There is a single previous DTI based study in SPG11 but the authors included only two patients.21 They performed a voxelwise analysis but without an algorithm to adjust for the alignment of images from multiple subjects. These are major limitations of the study and preclude the extrapolation of the findings. Our study, in turn, enrolled a larger sample of patients and used TBSS analysis, which improves the sensitivity, objectivity and interpretability of DTI data.14
We have found restricted areas of grey matter atrophy in patients with mutations in SPG11. These were symmetrical and included the thalamus and basal ganglia. It is noteworthy that significant volumetric reduction was only found over small regions of the cortical mantle. These findings suggest that neurons in different regions of the CNS may present distinct vulnerability to SPG11 related damage. This possibility is further supported by gene expression data which show much higher spatacsin mRNA levels in subcortical structures than in the cortex.3 This suggests that neuronal populations of deep nuclei require higher spatacsin expression for proper functioning and integrity. Although not widespread, grey matter damage involves key subcortical structures that take part in motor and cognitive networks. Thalamic damage in combination with corpus callosum atrophy might result in dementia and behavioural disturbances in these patients. In addition, we found significant volumetric reduction of the lentiform nuclei but not of the substantia nigra. This suggests that parkinsonism, which has been increasingly recognised in SPG11,5 may be due to dysfunction in the dopaminergic receptors located in the putamen and not in the dopamine producing neurons of the midbrain. This helps to explain why levodopa and dopaminergic agonists do not improve parkinsonian features in many patients with SPG11.
In striking contrast to the grey matter results, we found remarkable and diffuse white matter abnormalities. As predicted, gross structural data provided by VBM confirmed severe corpus callosum atrophy in these individuals. However, we should emphasise the microstructural damage identified by DTI on the cerebellum, corpus callosum, cingulated gyrus and subcortical white matter of the temporal and occipital lobes. These results are similar to those reported by Chen et al in two patients with SPG11 mutations.21 In addition, we found abnormal FA in areas not described previously, such as the brainstem, and internal and external capsulae. Reduced FA indicates disruption of nerve fibre bundles, either due to axonal or myelin compromise. Experimental evidence indicates that axonal damage takes part in this process. In a zebrafish model, Southgate et al used morfolino antisense nucleotides to knockdown SPG11 in embryos, and found severe abnormalities of neuronal differentiation, especially in motor pathways.22 Cranial and spinal motor nerves appeared thinner and with reduced numbers of axons. Oligodendrocyte and myelin abnormalities probably also contribute to white matter microstructural alterations. Proton magnetic resonance spectroscopy of frontal white matter showed mild reduction of N-acetylaspartate (NAA) levels but a prominent increase in choline (Cho) levels in a patient bearing the c.1951C>T mutation at SPG11.4
Analysis of AD and RD maps also provides some insight into the substrate of white matter damage in SPG11 related HSP. AD measures diffusivity along the main direction of diffusion and is considered a marker of axonal damage.23 By contrast, RD evaluates diffusion that is orthogonal to the principal diffusion direction. Experimental data indicate that RD abnormalities are associated with myelin and/or oligodendrocyte damage.24 In our patients, we found increased AD and RD, which indicates that both axonal damage and demyelination indeed take place in SPG11. RD abnormalities were, however, much more widespread than those related to AD, suggesting that CNS myelin is severely affected in the condition and this is especially prominent in posterior white matter.
An unsettled issue about SPG11 is the nature of corpus callosum thinning as it is not clear whether it is due to true atrophy or hypoplasia. Although we are not able to definitively answer this question with the present data, we had the opportunity of studying two patients who had been part of a previous prospective study.25 Indeed, we could document that both patients presented with progressive volumetric reduction of the corpus callosum over a 1 year period (data not shown). This suggests that progressive corpus callosum atrophy occurs at least in some patients with SPG11 mutations.
HSP due to SPG11 mutations is a relatively new and rare condition, thus enabling us to enrol only a small number of patients for MRI scans. Regarding group comparisons, we tried to mitigate this with a large number of age and sex matched controls and a conservative statistical approach. Despite these limitations, our results provide insight into the neurobiology of the disease and help to understand many of the symptoms presented by these patients. Further studies are needed to evaluate the usefulness of MRI as a progression biomarker in SPG11 related HSP.
Funding MCF received a post-doctoral fellowship from Fundação de Amparo à Pesquisa do Estado de São Paulo–FAPESP, São Paulo, Brazil (2008/58605-7). IL-C and FC are supported by FAPESP and Conselho Nacional de Pesquisa (CNPq, Brazil). The funding agencies did not interfere with the design of the study, collection of the data or drafting of the manuscript.
Competing interests None.
Ethics approval Ethics approval was provided by Comitê de Ética da Faculdade de Ciências Médicas (UNICAMP).
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement There are no additional unpublished data from this research. All information is included in the manuscript, tables and figures.
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