Epilepsy Research (2014) 108, 1326—1334
journal homepage: www.elsevier.com/locate/epilepsyres
Brain morphometry of Dravet Syndrome Alejandro Pérez a,∗, Lorna García-Pentón a, Erick J. Canales-Rodríguez b,c, Garikoitz Lerma-Usabiaga a, Yasser Iturria-Medina d, Francisco J. Román e, Doug Davidson a, Yasser Alemán-Gómez f, Joana Acha g, Manuel Carreiras a,g,h a
Basque Center on Cognition Brain and Language, BCBL, Donostia-San Sebastián, Spain Centro de Investigación Biomédica en Red de Salud Mental (CIBERSam), 28007 Madrid, Spain c FIDMAG Germanes Hospitalàries, 08830, Sant Boi de Llobregat, Barcelona, Spain d Neuroimaging Department, Cuban Neuroscience Center, La Habana, Cuba e Facultad de Psicología, Departamento de Psicología Biológica y de la Salud, Universidad Autónoma de Madrid, 28049 Madrid, Spain f Instituto de Investigación Sanitaria Gregorio Mara˜ nón, IiSGM, HGUGM, CIBERSAM, Madrid, Spain g Euskal Herriko Unibertsitatea/Universidad del País Vasco EHU/UPV, Bilbao, Spain h Ikerbasque, Basque Foundation for Science, Bilbao, Spain b
Received 19 September 2013; received in revised form 6 June 2014; accepted 28 June 2014 Available online 7 July 2014
KEYWORDS Dravet Syndrome; Morphometry; VBM; Cortical gyrification; SCN1A
∗
Summary The aim of this study was to identify differential global and local brain structural patterns in Dravet Syndrome (DS) patients as compared with a control subject group, using brain morphometry techniques which provide a quantitative whole-brain structural analysis that allows for specific patterns to be generalized across series of individuals. Nine patients with the diagnosis of DS that tested positive for mutation in the SCN1A gene and nine well-matched healthy controls were investigated using voxel brain morphometry (VBM), cortical thickness and cortical gyrification measurements. Global volume reductions of gray matter (GM) and white matter (WM) were related to DS. Local volume reductions corresponding to several white matter regions in brainstem, cerebellum, corpus callosum, corticospinal tracts and association fibers (left inferior fronto-occipital fasciculus and left uncinate fasciculus) were also found. Furthermore, DS showed a reduced cortical folding in the right precentral gyrus. The present findings describe DS-related brain structure abnormalities probably linked to the expression of the SCN1A mutation. © 2014 Elsevier B.V. All rights reserved.
Corresponding author at: Paseo Mikeletegi 69, 20009 Donostia-San Sebastián, Spain. Tel.: +34 943 309 300; fax: +34 943 309 052. E-mail address: [email protected] (A. Pérez).
http://dx.doi.org/10.1016/j.eplepsyres.2014.06.006 0920-1211/© 2014 Elsevier B.V. All rights reserved.
Brain morphometry of Dravet Syndrome
Introduction Dravet Syndrome (DS), also termed severe myoclonic epilepsy of infancy (SMEI), is a rare form of epilepsy occurring in the first year of life (up to 15 months) in apparently normal infants (Dravet, 1978; Dravet and Guerrini, 2011). It is characterized by the onset of recurrent febrile and/or afebrile hemiclonic or generalized seizures, or status epilepticus, in a previously healthy infant, followed by the appearance of multiple seizure types generally resistant to anti-epileptic drugs, with developmental arrest or regression (Dravet et al., 2005; Jansen et al., 2006; Wolff et al., 2006). Of these cases, 60—80% are caused by SCN1A mutations (Brunklaus et al., 2012; Catarino et al., 2011; Depienne et al., 2009; Marini et al., 2009; Mullen and Scheffer, 2009). Nowadays, the term DS has been proposed to describe the group of severe infantile onset epilepsies associated with mutations in the SCN1A gene (Stenhouse et al., 2013). Evolution is insidious, with a significant mortality of up to 15% by 20 years (Dravet et al., 2005). Neurological declines also occur in adulthood, with cognitive and motor deterioration (Dravet et al., 2005). Correct diagnosis, treatment and monitoring of DS have made an impact at several levels: family, social and economic (Skluzacek et al., 2011), and there has been increased interest in the prevalence of this syndrome (Brunklaus et al., 2012; Verbeek et al., 2013), its cognitive outcome (Chieffo et al., 2011a,b; Ragona et al., 2010, 2011), neuropathology (Catarino et al., 2011) and specially epileptogenesis (Cheah et al., 2012; Higurashi et al., 2013; Jiao et al., 2013; Liu et al., 2013). However, while the perspective of screening for appropriate drugs to be used in therapies is promising, the brain structural and functional counterparts of the common pathogenesis in DS have not been generally described (Jansen et al., 2006; Moehring et al., 2013; Siegler et al., 2005; Striano et al., 2007). Functional and structural traits related to the DS brain could provide extra criteria for diagnosis, as well as biological indicators for monitoring the progression of the condition, especially relevant in the follow-up of novel drug treatments. Despite the fact that a common genetic etiology in DS (i.e. SCN1A mutation) might confer a unique brain profile or convergent brain pattern, findings of brain structural abnormalities across different DS studies (and patients) are not consistent (Dalla Bernardina et al., 1982; Dravet et al., 2005; Ferrie et al., 1996; Gaily et al., 2013; Guerrini et al., 2011; Jansen et al., 2006; Sakakibara et al., 2009; Siegler et al., 2005; Striano et al., 2007). Also, brain functional patterns diverge across subjects (Moehring et al., 2013). However, all the structural reports are from qualitative assessments of computer tomography or magnetic resonance imaging (MRI) data, performed by neuroimaging experts. To our knowledge, no study so far has investigated DS brain abnormalities using a whole-brain quantitative neuroimaging approach, which is highly desirable (Guerrini et al., 2011). Nowadays, neuroimaging analysis techniques quantifying structural brain properties have been developed and more fine-grained brain structural studies on DS can now be conducted. In fact, these approaches could detect the structural brain abnormalities that appear normal on conventional MRI (Kakeda and Korogi, 2010). This is the case of voxel-based and surface-based morphometry techniques
1327 (see Greve, 2011 for an overview) like voxel-based morphometry (VBM; Ashburner and Friston, 2000) and FreeSurfer (Dale et al., 1999; Fischl et al., 1999), respectively. The former allows for investigation of volume differences in brain anatomy, while the later allows, for example, for the automated measurement of cortical thickness (Fischl and Dale, 2000; Fischl et al., 2002) and cortical folding (Schaer et al., 2008). Through the quantitative estimation of gray matter (GM), white matter (WM) and cerebro-spinal fluid (CSF), probable global differences in the brains of DS patients can be assessed. In addition, as the procedures already mentioned imply a transformation of the individual brains to a common brain space, local differences in the measurements/indexes can also be assessed and related to specific brain areas, according to a brain atlas. Here, we took advantage of these morphometric methods to search for common brain structural abnormalities in DS patients. We hypothesized the existence of common brain structural abnormalities in DS as assessed by volumetry, which involves global volume reductions in the DS brain because of underlying mechanisms such as Wallerian degeneration, apoptotic cell death, inflammation and excitotoxicity that take place in the condition (Guerrini et al., 2011) and lead to brain atrophy. Local volume reductions may also be expected at structures linked to the core symptoms of DS, i.e. neurological signs and psychomotor developmental delay, because it has been demonstrated that seizure-induced changes may affect the brain selectively (Liu et al., 2003). Hypotheses about cortical thickness patterns related to the DS brain are not so clear; for typical populations in general, cortical thickness decreases with age during development due to axonal pruning mechanisms (Shaw et al., 2008). It is therefore plausible that some areas would have abnormally greater cortical thickness in DS. With respect to the cortical folding, quantified by the local gyrification index (lGI), abnormal indices have already been associated with MRI-negative epilepsy (Ronan et al., 2011). Therefore, we expect a reduced lGI in the DS patients, suggestive of malformations of cortical development. Summarizing, here we try to identify differential global and local brain structural patterns in a DS patients group as compared with a well-matched healthy control group, using volumetric, voxel-based and surface-based analyses.
Methods Subjects A selected sample of nine patients with DS (age mean: 13.6, SD: 5.2), all members of the Spanish Dravet Foundation took part in the present study. They were diagnosed using the criteria proposed in Dravet et al. (2005): patients with seizure onset in the first year, intractable epileptic seizures triggered by infections and increased temperature, normal development in the first year and no evidence of structural-metabolic etiology at seizure onset. In addition, all had undertaken the genetic study and tested positive for SCN1A mutation. Exclusion criteria included having a history of brain trauma or other neurological disease. All the patients use several medications in their drug treatment (mean: 3 drugs) with topiramate, stiripentol, valproic acid
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Table 1 Age in years and gender of the participants (F for feminine and M for masculine). HC group
DS group
Age
Gender
Age
Gender
20.4 11 9.5 11.4 22.1 10 20.9 13.6 9.2 14.3 6.4
F F F F M M M M M M F
20.7 11 9.4 11.9 22.6 10.5 20.4 13.7 10 16 6.9
F F F F M M M M M F M
and benzodiazepines being the most common. A matched sample for age (age mean: 13.1, SD: 5.2) and sex, of healthy control subjects (HC) (see Table 1) was recruited from the local community via poster and web-based advertisement. They were healthy people with no reported history of neurological/mental illness and/or treatment with psychotropic medication. All participants gave verbal and written informed consent prior to involvement, in accordance with the Declaration of Helsinki, and the research protocol was approved by the BCBL Ethics Committee.
Structural imaging All subjects underwent structural MRI scanning in a single session, using the same 3.0 Tesla Magnetom Trio Tim scanner (Siemens AG, Erlangen, Germany), located at the BCBL in Donostia-San Sebastián. A high-resolution T1-weighted scan was acquired with a 3D ultrafast gradient echo (MPRAGE) pulse sequence. Acquisition parameters used were: matrix size 256 × 256; 160 contiguous axial slices; voxel resolution 1 × 1 × 1 mm3 ; TE/TR/TI = 2.97 ms/2300 ms/1100 ms, respectively; flip angle 9◦ .
Voxel-based morphometry Structural data were analyzed with an optimized voxelbased morphometry (VBM) analysis carried out using the Statistical Parametric Mapping (SPM) software package (SPM8, Wellcome Trust Centre for Neuroimaging, UK), which allows for the detection of potential differences in the local gray and white matter volume between different groups of participants. Data were manually reoriented, segmented into different tissue types GM and WM, and then normalized to the same anatomical space. Segmentation was performed using the New Segmentation tool by estimating the model parameters for a maximum a posteriori solution alternating among classification, bias correction and registration steps in the same generative model (Ashburner and Friston, 2005). The registration was carried out using the DARTEL tool, which involves a high-resolution diffeomorphic anatomical registration (Ashburner, 2007), using the Large Deformation Diffeomorphic Metric Mapping approach (Beg et al., 2005).
The resulting normalized images were averaged to create a study-specific template, to which the native GM and WM images were nonlinearly re-registered. These images were modulated (to correct for local expansion or contraction) by multiplying by the Jacobian of the warp field. Each normalized and modulated volume was smoothed with a Gaussian kernel of 8-mm full-width at half-maximum (FWHM). Group comparison between patients and controls was carried out using a voxel-wise general linear model and permutation-based nonparametric testing. This was carried out via the Statistical nonParametric Mapping (SnPM) toolbox for SPM (Nichols and Holmes, 2002). The number of permutations was set to 5000 and the intracranial volume (ICV) was included as a continuous nuisance regressor. Regional differences were reported as significant at p < 0.05, fully corrected for multiple comparisons across space via the Gaussian Random Field theory, applying topological false discovery rate (FDR) correction (Genovese et al., 2002) with an extent threshold of 50 voxels. Anatomical locations of significant regions were determined by reference to the MNI structural atlas integrated into MRIcron software and the Johns Hopkins University (JHU) white-matter tractography atlas (Mori et al., 2005).
Surface-based and volumetric analyses Cortical reconstruction and volumetric segmentation was performed with the FreeSurfer (version 5.1) image analysis suite (http://surfer.nmr.mgh.harvard.edu/). Briefly, this processing includes motion correction, removal of non-brain tissue, automated Talairach transformation, segmentation of the subcortical WM and deep GM volumetric structures, tessellation of the GM and WM boundaries, automated topology correction, and surface deformation following intensity gradients to optimally place the GM/WM and GM/CSF borders at the location where the greatest shift in intensity defines the transition to the other tissue class (Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 2002; Segonne et al., 2004). A number of deformation procedures were performed in the data analysis pipeline, including surface inflation and registration to a spherical atlas. This method uses both intensity and continuity information from the entire three-dimensional MR images in the segmentation and deformation algorithms to produce representations of cortical thickness, calculated as the closest distance from the GM/WM boundary to the GM/CSF boundary at each vertex on the tessellated surface. Moreover, from the resulting maps local measurements of gyrification (i.e., lGI) were computed as described in Schaer et al. (2008). These maps are not restricted to the voxel resolution of the original data and are thus capable of detecting sub-millimeter differences between groups. Prior to the statistical analysis, the individual cortical thickness and lGI maps were smoothed in cortex using a Gaussian filter with (FWHM) of 10 mm. Finally, a vertex-wise general linear model was applied. Statistical inference was carried out with FreeSurfer tools based on non-parametric Monte Carlo testing, using a cluster-wise correction method for multiple comparisons with initial cluster-forming threshold (p < 0.01). In this analysis, only those clusters with a corrected value of p < 0.05 were considered as significant.
Brain morphometry of Dravet Syndrome
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Results Global volumetry Nonparametric Friedman’s tests were performed to compare global GM, WM and CSF volumes between groups while considering the VBM and FreeSurfer outputs together. This tested for volume effects after adjusting for possible effects of the different morphometry techniques. The tests revealed a significant effect of Group on GM (2 (1) = 16.98, p = 3.77e−5) and WM (2 (1) = 19.1, p = 1.24e−5) but not on CSF (2 (1) = 0.1, p = 0.75), indicating that the DS group have statistically significant reductions of global volumes in GM and WM as compared to the HC group. The DS group’s mean global volume reduction in GM is 8.67% according to VBM and 14.82% according to FreeSurfer. Volume reduction in WM was larger, with 20.71% according to VBM and 20.47% according to FreeSurfer. Correlation of the total intracranial volume with age A general linear model was fitted using robust regression to model the ICV as function of age and group and their interaction (i.e. allowing different slopes and intercepts for each group of subjects). The effects of age were statistically significantly different between groups (positive slope for the control group whilst apparently negative for the patient group, p = 2.6e−3) (see Fig. 1). Specifically, the difference of ICV between groups was not yet statistically significant at the age of 8 years (p = 0.2), while it achieved statistical significance at the age of 9 years (p = 0.046).
Subcortical volumetry Individual t-tests were performed to compare the mean volumes of the subcortical structures provided by the FreeSurfer segmentation (i.e., including thalamus, caudate, putamen, pallidum, brainstem, hippocampus, amygdala and accumbens) between DS and HC groups. This analysis allows for the comparison of both native/raw volumes and normalized volumes corrected for the intracranial volume (ICV). Correction by multiple comparisons was controlled via the Bonferroni method. The tests comparing native volumes revealed statistically significant differences (p < 0.05 corrected) between groups in the brainstem (p = 4e−4) and in three bilateral structures: thalamus (p = 2.4e−3), pallidum (p = 1e−4) and amygdala
Figure 1 Linear correlations (FreeSurfer), for both groups.
between
age
and
ICV
(p = 3.5e−3), all showing volume reduction for the DS group. On the other hand, no significant differences were obtained in the analysis using normalized volumes.
Voxel base morphometry Grey matter At a p < 0.05 corrected, no significant increases or reductions in local GM volume were found in patients. White matter At a p < 0.05 corrected, no significant increases in local WM volume were found in DS patients but significant reductions were observed in four areas. One of these was located in the left inferior fronto-occipital fasciculus, covering the left uncinate fasciculus. The second significant area was centered in the brainstem, bilaterally reaching the corticospinal tracts. A third significant region was located in the cerebellum and includes the right superior cerebellar peduncle. Finally, a further anomalous region was located in the body of the corpus callosum (see Fig. 2 and Table 2 for a more extended report).
Figure 2 WM regions showing significant volume reduction in the DS group. The background brain image is the brain template in MNI space.
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Table 2 Brain areas showing significant reduced WM volume in the patients group at p < 0.05, topological FDR-corrected for multiple comparison across voxels. Cluster
Num. of voxels
T
Peak MNI coordinates x
y
z
Locations Left inferior fronto-occipital fasciculus and left uncinate fasciculus
1
245
6.46
−32
41
−9
2
2242
5.7 5.41 5.28
10 −3 3
−31 −14 −35
−13 −20 −20
Brainstem Left corticospinal tract Right corticospinal tract
3
253
4.65
−3 5
−47 −46
−29 −28
Cerebellum Right superior cerebellar peduncle
4
83
3.62
9
1
28
Surface-based morphometry Cortical thickness At a p < 0.05 corrected, there were no areas where DS patients had significantly thinner or thicker cortex than HS controls. Cortical gyrification At a p < 0.05 corrected, no significant increases in lGI were found in DS patients but significant reductions were observed in a cluster located in the right precentral gyrus (peak in MNI space [24.6, −10.2, 47.4]; p = 1e−4) (see Fig. 3).
Discussion We found significant reductions in the global GM and WM volumes of the DS patients. This result is in agreement with previous studies (Jansen et al., 2006; Sakakibara et al., 2009; Striano et al., 2007). It could have been expected that decreased brain volume is compensated by increased CSF volume, i.e. enlarged ventricles (Striano et al., 2007), but this was not the case. These global volume reductions were more pronounced for WM. Similar findings have been
Figure 3 Brain areas showing significant decreased lGI in the DS group are shown in red. The background brain image is the right hemisphere inflated template.
Body of the corpus callosum
reported for temporal lobe epilepsy (Jing-Jing et al., 2013) and are in line with the WM hyperintensities reported in DS (Dalla Bernardina et al., 1982; Dravet et al., 2005; Siegler et al., 2005) which is an indication of volume reduction. Although it is impossible to know from the present data what underlying mechanism is responsible for such large WM atrophy observed in DS patients, a potential mechanism involved could be dysfunction in the myelination process as a result of seizures occurring during maturation (Mitchell et al., 2003). Involvement of other mechanisms such as neuronal heterotopia (Sankar et al., 2008) or microdysgenesis (Thom et al., 2000) could also be speculated, in addition to the excitotoxic effects of spreading epileptogenic activity. On the other hand, we found a statistically significantly effect of age on total intracranial volume among groups. This result suggests a differential developmental trajectory in patients. Volumetric VBM analysis at the local level revealed no differences in GM volume but WM volume reductions in several regions of DS patients. One of these regions is shared by association fibers of the inferior fronto-occipital fasciculus (IFOF) that connects the frontal and occipital lobes and the uncinate fasciculus, which connects the anterior temporal lobe (include hippocampal formation) to the orbital cortex left. Other regions involve the brainstem and its major white matter track of the superior cerebellar peduncle (right), which is the main efferent pathway from the dentate nucleus of the cerebellum toward the thalamus (Oishi et al., 2011). Finally, there are WM regions of the cerebellum and the body of corpus callosum, which connects bilateral motor regions. Accounting for the possible relationship between these local findings and the core symptoms of the DS we could say that, for example, IFOF has been implicated in topdown modulations of attention in visual and visuomotor tasks (Urbanski et al., 2008) while the uncinate fasciculus with working memory (Charlton et al., 2010). More specifically, in temporal lobe epilepsy, structural compromise in both fasciculus (strongly left-lateralized) have been found associated to disturbances in memory and language performance (McDonald et al., 2008) and task-switching (Kucukboyaci et al., 2012). Importantly, attention deficit is the most constant and precocious neuropsychological trait in DS (Chieffo et al., 2011a; Dravet and Guerrini, 2011) while language
Brain morphometry of Dravet Syndrome is impaired. In the case of the brainstem abnormalities found here, they are congruent with the multiple neurological signs that occur in DS, for example, pyramidal signs and autonomic symptoms (Dravet and Guerrini, 2011). Interestingly, no significant alterations in the brainstem have been found before for DS (Guerrini et al., 2011), even using quantitative analysis (Catarino et al., 2011) suggesting a high sensitivity of the morphometric techniques to detect alterations in this structure. On the other hand, cerebellar atrophy has already been described in DS (Jansen et al., 2006). The cerebellum is involved in fine movements, equilibrium, posture and motor learning (Fine et al., 2002). Also in some cognitive functions such as attention and language (Timmann and Daum, 2007). Thus, the findings here could be related to the ataxia and walking disturbances common in DS patients (Dravet and Guerrini, 2011) and to the fact that most skills: motor, linguistic and visual abilities are deeply affected in DS patients (Cassé-Perrot et al., 2001; Wolff et al., 2006). Finally, the body of the corpus callosum, which connects bilateral motor regions, has been found abnormal in different types of epilepsies (Liu et al., 2011; Scanlon et al., 2013). Volume reductions in this region could be suggestive of a disconnection of the homologous motor areas. It is important to note that the VBM analysis performed here was intended to find local differences that cannot be explained by global differences, because the ICV was included as a covariate into the statistical model. Therefore, the detected altered regions are not necessarily all the regions altered in the illness. Indeed, results of the global volumetric analysis revealed large brain volume reductions in the DS patients. These large reductions may be more likely explained by a ‘uniform’ pattern of atrophy in the whole brain than by a sparse pattern of small local affected areas. This hypothesis is reinforced when considering the results from the subcortical volumetric analysis, which depicted significant reductions in the raw volumes of several structures, including the brainstem, thalamus, pallidum and amygdala, but not in their normalized volumes corrected by ICV. The surface-based analyses revealed an area of abnormal cortical folding (i.e., lGI) in the right precentral gyrus of the DS patients. Interestingly, a study with Dravet mice (i.e. Scn1a+/− mice) showed affected neuronal networks in the prefrontal cortex associated to behavioral problems that include: hyperactivity, stereotyped behaviors and social interaction deficits (Han et al., 2012). These abnormal behaviors in mice parallel behavioral and cognitive impairments present in DS patients (Brunklaus et al., 2011; Chieffo et al., 2011a; Nabbout et al., 2013; Ragona et al., 2011). This result suggests the existence of malformations of cortical development directly linked to comorbidities of DS. On the other hand, the absence of findings using the cortical thickness approach may be explained by a lack of statistical power due to the small sample size. Another possible hypothesis is that the local and global morphometric changes occurring in the illness are not mainly related to cortical thickness changes. In the future, further studies to detect structural abnormalities should be conducted with larger sample sizes and using different image modalities, such as advanced diffusion tensor imaging analyses (Canales-Rodríguez et al., in press) and graph-based connectivity (Iturria-Medina et al.,
1331 2011) approaches to account more specifically for the major axonal disturbances present in DS (Manninen et al., 2013) and their implications at a network level. It remains to be seen whether the differences in severity of seizures and cognitive and motor impairment correlate with the anatomical patterns. A more comprehensive characterization of the disorder requires further studies based on longitudinal designs. Additional analyses could be implemented to probe the medication effects on the GM and WM volumes.
Conclusion Core brain structural patterns associated to DS have remained elusive despite the etiological homogeneity of this condition that makes the existence of such patterns very plausible. Here we applied automatic, voxel- and surface-based morphometry techniques to investigate the existence of these patterns, specifically in terms of global and local volume/cortical thickness/gyrification differences compared to healthy individuals. In general, the global reductions in GM and WM described are gross, especially in WM; while local structural findings may be linked to the core neurological signs/symptoms in DS, which are the best predictors of a mutation in the SCN1A gene (Fountain-Capal et al., 2011). The findings here describe DS-related brain structure abnormalities that maybe linked to the expression of SCN1A mutation.
Funding source The study has been supported by private funding from the Spanish Dravet Foundation, a non-profit organization which had no role in the study design; the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.
Conflicts of interest statement None of the authors has any conflict of interest to disclose.
Acknowledgment The authors thank Margaret Gillon Dowens for her helpful comments and the reviewing of the manuscript.
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1333 Specchio, N., Veggiotti, P., Vigevano, F., Guerrini, R., Guzzetta, F., Dravet, C., 2011. Cognitive development in Dravet syndrome: a retrospective, multicenter study of 26 patients. Epilepsia 52, 386—392. Ronan, L., Scanlon, C., Murphy, K., Maguire, S., Delanty, N., Doherty, C.P., Fitzsimons, M., 2011. Cortical curvature analysis in MRI-negative temporal lobe epilepsy: a surrogate marker for malformations of cortical development. Epilepsia 52, 28—34. Sakakibara, T., Nakagawa, E., Saito, Y., Sakuma, H., Komaki, H., Sugai, K., Sasaki, M., Kurahashi, H., Hirose, S., 2009. Hemiconvulsion-hemiplegia syndrome in a patient with severe myoclonic epilepsy in infancy. Epilepsia 50, 2158—2162. Sankar, T., Bernasconi, N., Kim, H., Bernasconi, A., 2008. Temporal lobe epilepsy: differential pattern of damage in temporopolar cortex and white matter. Hum. Brain Mapp. 29, 931—944. Scanlon, C., Mueller, S.G., Cheong, I., Hartig, M., Weiner, M.W., Laxer, K.D., 2013. Grey and white matter abnormalities in temporal lobe epilepsy with and without mesial temporal sclerosis. J. Neurol. 260, 2320—2329. Schaer, M., Cuadra, M.B., Tamarit, L., Lazeyras, F., Eliez, S., Thiran, J.P., 2008. A surface-based approach to quantify local cortical gyrification. IEEE Trans. Med. Imaging 27, 161—170. Segonne, F., Dale, A.M., Busa, E., Glessner, M., Salat, D., Hahn, H.K., Fischl, B., 2004. A hybrid approach to the skull stripping problem in MRI. Neuroimage 22, 1060—1075. Shaw, P., Kabani, N.J., Lerch, J.P., Eckstrand, K., Lenroot, R., Gogtay, N., Greenstein, D., Clasen, L., Evans, A., Rapoport, J.L., Giedd, J.N., Wise, S.P., 2008. Neurodevelopmental trajectories of the human cerebral cortex. J. Neurosci. 28, 3586—3594. Siegler, Z., Barsi, P., Neuwirth, M., Jerney, J., Kassay, M., Janszky, J., Paraicz, E., Hegyi, M., Fogarasi, A., 2005. Hippocampal sclerosis in severe myoclonic epilepsy in infancy: a retrospective MRI study. Epilepsia 46, 704—708. Skluzacek, J.V., Watts, K.P., Parsy, O., Wical, B., Camfield, P., 2011. Dravet syndrome and parent associations: the IDEA League experience with comorbid conditions, mortality, management, adaptation, and grief. Epilepsia 52 (Suppl. 2), 95—101. Stenhouse, S.A., Ellis, R., Zuberi, S., 2013. SCN1A genetic test for Dravet Syndrome (severe myoclonic epilepsy of infancy and its clinical subtypes) for use in the diagnosis, prognosis, treatment and management of Dravet Syndrome. PLoS Curr. 5. Striano, P., Mancardi, M.M., Biancheri, R., Madia, F., Gennaro, E., Paravidino, R., Beccaria, F., Capovilla, G., Dalla Bernardina, B., Darra, F., Elia, M., Giordano, L., Gobbi, G., Granata, T., Ragona, F., Guerrini, R., Marini, C., Mei, D., Longaretti, F., Romeo, A., Siri, L., Specchio, N., Vigevano, F., Striano, S., Tortora, F., Rossi, A., Minetti, C., Dravet, C., Gaggero, R., Zara, F., 2007. Brain MRI findings in severe myoclonic epilepsy in infancy and genotype-phenotype correlations. Epilepsia 48, 1092—1096. Thom, M., Holton, J.L., D‘Arrigo, C., Griffin, B., Beckett, A., Sisodiya, S., Alexiou, D., Sander, J.W., 2000. Microdysgenesis with abnormal cortical myelinated fibres in temporal lobe epilepsy: a histopathological study with calbindin D-28K immunohistochemistry. Neuropathol. Appl. Neurobiol. 26, 251—257. Timmann, D., Daum, I., 2007. Cerebellar contributions to cognitive functions: a progress report after two decades of research. Cerebellum 6, 159—162. Urbanski, M., Thiebaut de Schotten, M., Rodrigo, S., Catani, M., Oppenheim, C., Touze, E., Chokron, S., Meder, J.F., Levy, R., Dubois, B., Bartolomeo, P., 2008. Brain networks of spatial
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journal homepage: www.elsevier.com/locate/epilepsyres
Brain morphometry of Dravet Syndrome Alejandro Pérez a,∗, Lorna García-Pentón a, Erick J. Canales-Rodríguez b,c, Garikoitz Lerma-Usabiaga a, Yasser Iturria-Medina d, Francisco J. Román e, Doug Davidson a, Yasser Alemán-Gómez f, Joana Acha g, Manuel Carreiras a,g,h a
Basque Center on Cognition Brain and Language, BCBL, Donostia-San Sebastián, Spain Centro de Investigación Biomédica en Red de Salud Mental (CIBERSam), 28007 Madrid, Spain c FIDMAG Germanes Hospitalàries, 08830, Sant Boi de Llobregat, Barcelona, Spain d Neuroimaging Department, Cuban Neuroscience Center, La Habana, Cuba e Facultad de Psicología, Departamento de Psicología Biológica y de la Salud, Universidad Autónoma de Madrid, 28049 Madrid, Spain f Instituto de Investigación Sanitaria Gregorio Mara˜ nón, IiSGM, HGUGM, CIBERSAM, Madrid, Spain g Euskal Herriko Unibertsitatea/Universidad del País Vasco EHU/UPV, Bilbao, Spain h Ikerbasque, Basque Foundation for Science, Bilbao, Spain b
Received 19 September 2013; received in revised form 6 June 2014; accepted 28 June 2014 Available online 7 July 2014
KEYWORDS Dravet Syndrome; Morphometry; VBM; Cortical gyrification; SCN1A
∗
Summary The aim of this study was to identify differential global and local brain structural patterns in Dravet Syndrome (DS) patients as compared with a control subject group, using brain morphometry techniques which provide a quantitative whole-brain structural analysis that allows for specific patterns to be generalized across series of individuals. Nine patients with the diagnosis of DS that tested positive for mutation in the SCN1A gene and nine well-matched healthy controls were investigated using voxel brain morphometry (VBM), cortical thickness and cortical gyrification measurements. Global volume reductions of gray matter (GM) and white matter (WM) were related to DS. Local volume reductions corresponding to several white matter regions in brainstem, cerebellum, corpus callosum, corticospinal tracts and association fibers (left inferior fronto-occipital fasciculus and left uncinate fasciculus) were also found. Furthermore, DS showed a reduced cortical folding in the right precentral gyrus. The present findings describe DS-related brain structure abnormalities probably linked to the expression of the SCN1A mutation. © 2014 Elsevier B.V. All rights reserved.
Corresponding author at: Paseo Mikeletegi 69, 20009 Donostia-San Sebastián, Spain. Tel.: +34 943 309 300; fax: +34 943 309 052. E-mail address: [email protected] (A. Pérez).
http://dx.doi.org/10.1016/j.eplepsyres.2014.06.006 0920-1211/© 2014 Elsevier B.V. All rights reserved.
Brain morphometry of Dravet Syndrome
Introduction Dravet Syndrome (DS), also termed severe myoclonic epilepsy of infancy (SMEI), is a rare form of epilepsy occurring in the first year of life (up to 15 months) in apparently normal infants (Dravet, 1978; Dravet and Guerrini, 2011). It is characterized by the onset of recurrent febrile and/or afebrile hemiclonic or generalized seizures, or status epilepticus, in a previously healthy infant, followed by the appearance of multiple seizure types generally resistant to anti-epileptic drugs, with developmental arrest or regression (Dravet et al., 2005; Jansen et al., 2006; Wolff et al., 2006). Of these cases, 60—80% are caused by SCN1A mutations (Brunklaus et al., 2012; Catarino et al., 2011; Depienne et al., 2009; Marini et al., 2009; Mullen and Scheffer, 2009). Nowadays, the term DS has been proposed to describe the group of severe infantile onset epilepsies associated with mutations in the SCN1A gene (Stenhouse et al., 2013). Evolution is insidious, with a significant mortality of up to 15% by 20 years (Dravet et al., 2005). Neurological declines also occur in adulthood, with cognitive and motor deterioration (Dravet et al., 2005). Correct diagnosis, treatment and monitoring of DS have made an impact at several levels: family, social and economic (Skluzacek et al., 2011), and there has been increased interest in the prevalence of this syndrome (Brunklaus et al., 2012; Verbeek et al., 2013), its cognitive outcome (Chieffo et al., 2011a,b; Ragona et al., 2010, 2011), neuropathology (Catarino et al., 2011) and specially epileptogenesis (Cheah et al., 2012; Higurashi et al., 2013; Jiao et al., 2013; Liu et al., 2013). However, while the perspective of screening for appropriate drugs to be used in therapies is promising, the brain structural and functional counterparts of the common pathogenesis in DS have not been generally described (Jansen et al., 2006; Moehring et al., 2013; Siegler et al., 2005; Striano et al., 2007). Functional and structural traits related to the DS brain could provide extra criteria for diagnosis, as well as biological indicators for monitoring the progression of the condition, especially relevant in the follow-up of novel drug treatments. Despite the fact that a common genetic etiology in DS (i.e. SCN1A mutation) might confer a unique brain profile or convergent brain pattern, findings of brain structural abnormalities across different DS studies (and patients) are not consistent (Dalla Bernardina et al., 1982; Dravet et al., 2005; Ferrie et al., 1996; Gaily et al., 2013; Guerrini et al., 2011; Jansen et al., 2006; Sakakibara et al., 2009; Siegler et al., 2005; Striano et al., 2007). Also, brain functional patterns diverge across subjects (Moehring et al., 2013). However, all the structural reports are from qualitative assessments of computer tomography or magnetic resonance imaging (MRI) data, performed by neuroimaging experts. To our knowledge, no study so far has investigated DS brain abnormalities using a whole-brain quantitative neuroimaging approach, which is highly desirable (Guerrini et al., 2011). Nowadays, neuroimaging analysis techniques quantifying structural brain properties have been developed and more fine-grained brain structural studies on DS can now be conducted. In fact, these approaches could detect the structural brain abnormalities that appear normal on conventional MRI (Kakeda and Korogi, 2010). This is the case of voxel-based and surface-based morphometry techniques
1327 (see Greve, 2011 for an overview) like voxel-based morphometry (VBM; Ashburner and Friston, 2000) and FreeSurfer (Dale et al., 1999; Fischl et al., 1999), respectively. The former allows for investigation of volume differences in brain anatomy, while the later allows, for example, for the automated measurement of cortical thickness (Fischl and Dale, 2000; Fischl et al., 2002) and cortical folding (Schaer et al., 2008). Through the quantitative estimation of gray matter (GM), white matter (WM) and cerebro-spinal fluid (CSF), probable global differences in the brains of DS patients can be assessed. In addition, as the procedures already mentioned imply a transformation of the individual brains to a common brain space, local differences in the measurements/indexes can also be assessed and related to specific brain areas, according to a brain atlas. Here, we took advantage of these morphometric methods to search for common brain structural abnormalities in DS patients. We hypothesized the existence of common brain structural abnormalities in DS as assessed by volumetry, which involves global volume reductions in the DS brain because of underlying mechanisms such as Wallerian degeneration, apoptotic cell death, inflammation and excitotoxicity that take place in the condition (Guerrini et al., 2011) and lead to brain atrophy. Local volume reductions may also be expected at structures linked to the core symptoms of DS, i.e. neurological signs and psychomotor developmental delay, because it has been demonstrated that seizure-induced changes may affect the brain selectively (Liu et al., 2003). Hypotheses about cortical thickness patterns related to the DS brain are not so clear; for typical populations in general, cortical thickness decreases with age during development due to axonal pruning mechanisms (Shaw et al., 2008). It is therefore plausible that some areas would have abnormally greater cortical thickness in DS. With respect to the cortical folding, quantified by the local gyrification index (lGI), abnormal indices have already been associated with MRI-negative epilepsy (Ronan et al., 2011). Therefore, we expect a reduced lGI in the DS patients, suggestive of malformations of cortical development. Summarizing, here we try to identify differential global and local brain structural patterns in a DS patients group as compared with a well-matched healthy control group, using volumetric, voxel-based and surface-based analyses.
Methods Subjects A selected sample of nine patients with DS (age mean: 13.6, SD: 5.2), all members of the Spanish Dravet Foundation took part in the present study. They were diagnosed using the criteria proposed in Dravet et al. (2005): patients with seizure onset in the first year, intractable epileptic seizures triggered by infections and increased temperature, normal development in the first year and no evidence of structural-metabolic etiology at seizure onset. In addition, all had undertaken the genetic study and tested positive for SCN1A mutation. Exclusion criteria included having a history of brain trauma or other neurological disease. All the patients use several medications in their drug treatment (mean: 3 drugs) with topiramate, stiripentol, valproic acid
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Table 1 Age in years and gender of the participants (F for feminine and M for masculine). HC group
DS group
Age
Gender
Age
Gender
20.4 11 9.5 11.4 22.1 10 20.9 13.6 9.2 14.3 6.4
F F F F M M M M M M F
20.7 11 9.4 11.9 22.6 10.5 20.4 13.7 10 16 6.9
F F F F M M M M M F M
and benzodiazepines being the most common. A matched sample for age (age mean: 13.1, SD: 5.2) and sex, of healthy control subjects (HC) (see Table 1) was recruited from the local community via poster and web-based advertisement. They were healthy people with no reported history of neurological/mental illness and/or treatment with psychotropic medication. All participants gave verbal and written informed consent prior to involvement, in accordance with the Declaration of Helsinki, and the research protocol was approved by the BCBL Ethics Committee.
Structural imaging All subjects underwent structural MRI scanning in a single session, using the same 3.0 Tesla Magnetom Trio Tim scanner (Siemens AG, Erlangen, Germany), located at the BCBL in Donostia-San Sebastián. A high-resolution T1-weighted scan was acquired with a 3D ultrafast gradient echo (MPRAGE) pulse sequence. Acquisition parameters used were: matrix size 256 × 256; 160 contiguous axial slices; voxel resolution 1 × 1 × 1 mm3 ; TE/TR/TI = 2.97 ms/2300 ms/1100 ms, respectively; flip angle 9◦ .
Voxel-based morphometry Structural data were analyzed with an optimized voxelbased morphometry (VBM) analysis carried out using the Statistical Parametric Mapping (SPM) software package (SPM8, Wellcome Trust Centre for Neuroimaging, UK), which allows for the detection of potential differences in the local gray and white matter volume between different groups of participants. Data were manually reoriented, segmented into different tissue types GM and WM, and then normalized to the same anatomical space. Segmentation was performed using the New Segmentation tool by estimating the model parameters for a maximum a posteriori solution alternating among classification, bias correction and registration steps in the same generative model (Ashburner and Friston, 2005). The registration was carried out using the DARTEL tool, which involves a high-resolution diffeomorphic anatomical registration (Ashburner, 2007), using the Large Deformation Diffeomorphic Metric Mapping approach (Beg et al., 2005).
The resulting normalized images were averaged to create a study-specific template, to which the native GM and WM images were nonlinearly re-registered. These images were modulated (to correct for local expansion or contraction) by multiplying by the Jacobian of the warp field. Each normalized and modulated volume was smoothed with a Gaussian kernel of 8-mm full-width at half-maximum (FWHM). Group comparison between patients and controls was carried out using a voxel-wise general linear model and permutation-based nonparametric testing. This was carried out via the Statistical nonParametric Mapping (SnPM) toolbox for SPM (Nichols and Holmes, 2002). The number of permutations was set to 5000 and the intracranial volume (ICV) was included as a continuous nuisance regressor. Regional differences were reported as significant at p < 0.05, fully corrected for multiple comparisons across space via the Gaussian Random Field theory, applying topological false discovery rate (FDR) correction (Genovese et al., 2002) with an extent threshold of 50 voxels. Anatomical locations of significant regions were determined by reference to the MNI structural atlas integrated into MRIcron software and the Johns Hopkins University (JHU) white-matter tractography atlas (Mori et al., 2005).
Surface-based and volumetric analyses Cortical reconstruction and volumetric segmentation was performed with the FreeSurfer (version 5.1) image analysis suite (http://surfer.nmr.mgh.harvard.edu/). Briefly, this processing includes motion correction, removal of non-brain tissue, automated Talairach transformation, segmentation of the subcortical WM and deep GM volumetric structures, tessellation of the GM and WM boundaries, automated topology correction, and surface deformation following intensity gradients to optimally place the GM/WM and GM/CSF borders at the location where the greatest shift in intensity defines the transition to the other tissue class (Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 2002; Segonne et al., 2004). A number of deformation procedures were performed in the data analysis pipeline, including surface inflation and registration to a spherical atlas. This method uses both intensity and continuity information from the entire three-dimensional MR images in the segmentation and deformation algorithms to produce representations of cortical thickness, calculated as the closest distance from the GM/WM boundary to the GM/CSF boundary at each vertex on the tessellated surface. Moreover, from the resulting maps local measurements of gyrification (i.e., lGI) were computed as described in Schaer et al. (2008). These maps are not restricted to the voxel resolution of the original data and are thus capable of detecting sub-millimeter differences between groups. Prior to the statistical analysis, the individual cortical thickness and lGI maps were smoothed in cortex using a Gaussian filter with (FWHM) of 10 mm. Finally, a vertex-wise general linear model was applied. Statistical inference was carried out with FreeSurfer tools based on non-parametric Monte Carlo testing, using a cluster-wise correction method for multiple comparisons with initial cluster-forming threshold (p < 0.01). In this analysis, only those clusters with a corrected value of p < 0.05 were considered as significant.
Brain morphometry of Dravet Syndrome
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Results Global volumetry Nonparametric Friedman’s tests were performed to compare global GM, WM and CSF volumes between groups while considering the VBM and FreeSurfer outputs together. This tested for volume effects after adjusting for possible effects of the different morphometry techniques. The tests revealed a significant effect of Group on GM (2 (1) = 16.98, p = 3.77e−5) and WM (2 (1) = 19.1, p = 1.24e−5) but not on CSF (2 (1) = 0.1, p = 0.75), indicating that the DS group have statistically significant reductions of global volumes in GM and WM as compared to the HC group. The DS group’s mean global volume reduction in GM is 8.67% according to VBM and 14.82% according to FreeSurfer. Volume reduction in WM was larger, with 20.71% according to VBM and 20.47% according to FreeSurfer. Correlation of the total intracranial volume with age A general linear model was fitted using robust regression to model the ICV as function of age and group and their interaction (i.e. allowing different slopes and intercepts for each group of subjects). The effects of age were statistically significantly different between groups (positive slope for the control group whilst apparently negative for the patient group, p = 2.6e−3) (see Fig. 1). Specifically, the difference of ICV between groups was not yet statistically significant at the age of 8 years (p = 0.2), while it achieved statistical significance at the age of 9 years (p = 0.046).
Subcortical volumetry Individual t-tests were performed to compare the mean volumes of the subcortical structures provided by the FreeSurfer segmentation (i.e., including thalamus, caudate, putamen, pallidum, brainstem, hippocampus, amygdala and accumbens) between DS and HC groups. This analysis allows for the comparison of both native/raw volumes and normalized volumes corrected for the intracranial volume (ICV). Correction by multiple comparisons was controlled via the Bonferroni method. The tests comparing native volumes revealed statistically significant differences (p < 0.05 corrected) between groups in the brainstem (p = 4e−4) and in three bilateral structures: thalamus (p = 2.4e−3), pallidum (p = 1e−4) and amygdala
Figure 1 Linear correlations (FreeSurfer), for both groups.
between
age
and
ICV
(p = 3.5e−3), all showing volume reduction for the DS group. On the other hand, no significant differences were obtained in the analysis using normalized volumes.
Voxel base morphometry Grey matter At a p < 0.05 corrected, no significant increases or reductions in local GM volume were found in patients. White matter At a p < 0.05 corrected, no significant increases in local WM volume were found in DS patients but significant reductions were observed in four areas. One of these was located in the left inferior fronto-occipital fasciculus, covering the left uncinate fasciculus. The second significant area was centered in the brainstem, bilaterally reaching the corticospinal tracts. A third significant region was located in the cerebellum and includes the right superior cerebellar peduncle. Finally, a further anomalous region was located in the body of the corpus callosum (see Fig. 2 and Table 2 for a more extended report).
Figure 2 WM regions showing significant volume reduction in the DS group. The background brain image is the brain template in MNI space.
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A. Pérez et al.
Table 2 Brain areas showing significant reduced WM volume in the patients group at p < 0.05, topological FDR-corrected for multiple comparison across voxels. Cluster
Num. of voxels
T
Peak MNI coordinates x
y
z
Locations Left inferior fronto-occipital fasciculus and left uncinate fasciculus
1
245
6.46
−32
41
−9
2
2242
5.7 5.41 5.28
10 −3 3
−31 −14 −35
−13 −20 −20
Brainstem Left corticospinal tract Right corticospinal tract
3
253
4.65
−3 5
−47 −46
−29 −28
Cerebellum Right superior cerebellar peduncle
4
83
3.62
9
1
28
Surface-based morphometry Cortical thickness At a p < 0.05 corrected, there were no areas where DS patients had significantly thinner or thicker cortex than HS controls. Cortical gyrification At a p < 0.05 corrected, no significant increases in lGI were found in DS patients but significant reductions were observed in a cluster located in the right precentral gyrus (peak in MNI space [24.6, −10.2, 47.4]; p = 1e−4) (see Fig. 3).
Discussion We found significant reductions in the global GM and WM volumes of the DS patients. This result is in agreement with previous studies (Jansen et al., 2006; Sakakibara et al., 2009; Striano et al., 2007). It could have been expected that decreased brain volume is compensated by increased CSF volume, i.e. enlarged ventricles (Striano et al., 2007), but this was not the case. These global volume reductions were more pronounced for WM. Similar findings have been
Figure 3 Brain areas showing significant decreased lGI in the DS group are shown in red. The background brain image is the right hemisphere inflated template.
Body of the corpus callosum
reported for temporal lobe epilepsy (Jing-Jing et al., 2013) and are in line with the WM hyperintensities reported in DS (Dalla Bernardina et al., 1982; Dravet et al., 2005; Siegler et al., 2005) which is an indication of volume reduction. Although it is impossible to know from the present data what underlying mechanism is responsible for such large WM atrophy observed in DS patients, a potential mechanism involved could be dysfunction in the myelination process as a result of seizures occurring during maturation (Mitchell et al., 2003). Involvement of other mechanisms such as neuronal heterotopia (Sankar et al., 2008) or microdysgenesis (Thom et al., 2000) could also be speculated, in addition to the excitotoxic effects of spreading epileptogenic activity. On the other hand, we found a statistically significantly effect of age on total intracranial volume among groups. This result suggests a differential developmental trajectory in patients. Volumetric VBM analysis at the local level revealed no differences in GM volume but WM volume reductions in several regions of DS patients. One of these regions is shared by association fibers of the inferior fronto-occipital fasciculus (IFOF) that connects the frontal and occipital lobes and the uncinate fasciculus, which connects the anterior temporal lobe (include hippocampal formation) to the orbital cortex left. Other regions involve the brainstem and its major white matter track of the superior cerebellar peduncle (right), which is the main efferent pathway from the dentate nucleus of the cerebellum toward the thalamus (Oishi et al., 2011). Finally, there are WM regions of the cerebellum and the body of corpus callosum, which connects bilateral motor regions. Accounting for the possible relationship between these local findings and the core symptoms of the DS we could say that, for example, IFOF has been implicated in topdown modulations of attention in visual and visuomotor tasks (Urbanski et al., 2008) while the uncinate fasciculus with working memory (Charlton et al., 2010). More specifically, in temporal lobe epilepsy, structural compromise in both fasciculus (strongly left-lateralized) have been found associated to disturbances in memory and language performance (McDonald et al., 2008) and task-switching (Kucukboyaci et al., 2012). Importantly, attention deficit is the most constant and precocious neuropsychological trait in DS (Chieffo et al., 2011a; Dravet and Guerrini, 2011) while language
Brain morphometry of Dravet Syndrome is impaired. In the case of the brainstem abnormalities found here, they are congruent with the multiple neurological signs that occur in DS, for example, pyramidal signs and autonomic symptoms (Dravet and Guerrini, 2011). Interestingly, no significant alterations in the brainstem have been found before for DS (Guerrini et al., 2011), even using quantitative analysis (Catarino et al., 2011) suggesting a high sensitivity of the morphometric techniques to detect alterations in this structure. On the other hand, cerebellar atrophy has already been described in DS (Jansen et al., 2006). The cerebellum is involved in fine movements, equilibrium, posture and motor learning (Fine et al., 2002). Also in some cognitive functions such as attention and language (Timmann and Daum, 2007). Thus, the findings here could be related to the ataxia and walking disturbances common in DS patients (Dravet and Guerrini, 2011) and to the fact that most skills: motor, linguistic and visual abilities are deeply affected in DS patients (Cassé-Perrot et al., 2001; Wolff et al., 2006). Finally, the body of the corpus callosum, which connects bilateral motor regions, has been found abnormal in different types of epilepsies (Liu et al., 2011; Scanlon et al., 2013). Volume reductions in this region could be suggestive of a disconnection of the homologous motor areas. It is important to note that the VBM analysis performed here was intended to find local differences that cannot be explained by global differences, because the ICV was included as a covariate into the statistical model. Therefore, the detected altered regions are not necessarily all the regions altered in the illness. Indeed, results of the global volumetric analysis revealed large brain volume reductions in the DS patients. These large reductions may be more likely explained by a ‘uniform’ pattern of atrophy in the whole brain than by a sparse pattern of small local affected areas. This hypothesis is reinforced when considering the results from the subcortical volumetric analysis, which depicted significant reductions in the raw volumes of several structures, including the brainstem, thalamus, pallidum and amygdala, but not in their normalized volumes corrected by ICV. The surface-based analyses revealed an area of abnormal cortical folding (i.e., lGI) in the right precentral gyrus of the DS patients. Interestingly, a study with Dravet mice (i.e. Scn1a+/− mice) showed affected neuronal networks in the prefrontal cortex associated to behavioral problems that include: hyperactivity, stereotyped behaviors and social interaction deficits (Han et al., 2012). These abnormal behaviors in mice parallel behavioral and cognitive impairments present in DS patients (Brunklaus et al., 2011; Chieffo et al., 2011a; Nabbout et al., 2013; Ragona et al., 2011). This result suggests the existence of malformations of cortical development directly linked to comorbidities of DS. On the other hand, the absence of findings using the cortical thickness approach may be explained by a lack of statistical power due to the small sample size. Another possible hypothesis is that the local and global morphometric changes occurring in the illness are not mainly related to cortical thickness changes. In the future, further studies to detect structural abnormalities should be conducted with larger sample sizes and using different image modalities, such as advanced diffusion tensor imaging analyses (Canales-Rodríguez et al., in press) and graph-based connectivity (Iturria-Medina et al.,
1331 2011) approaches to account more specifically for the major axonal disturbances present in DS (Manninen et al., 2013) and their implications at a network level. It remains to be seen whether the differences in severity of seizures and cognitive and motor impairment correlate with the anatomical patterns. A more comprehensive characterization of the disorder requires further studies based on longitudinal designs. Additional analyses could be implemented to probe the medication effects on the GM and WM volumes.
Conclusion Core brain structural patterns associated to DS have remained elusive despite the etiological homogeneity of this condition that makes the existence of such patterns very plausible. Here we applied automatic, voxel- and surface-based morphometry techniques to investigate the existence of these patterns, specifically in terms of global and local volume/cortical thickness/gyrification differences compared to healthy individuals. In general, the global reductions in GM and WM described are gross, especially in WM; while local structural findings may be linked to the core neurological signs/symptoms in DS, which are the best predictors of a mutation in the SCN1A gene (Fountain-Capal et al., 2011). The findings here describe DS-related brain structure abnormalities that maybe linked to the expression of SCN1A mutation.
Funding source The study has been supported by private funding from the Spanish Dravet Foundation, a non-profit organization which had no role in the study design; the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.
Conflicts of interest statement None of the authors has any conflict of interest to disclose.
Acknowledgment The authors thank Margaret Gillon Dowens for her helpful comments and the reviewing of the manuscript.
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