Brain structural impairment in spinocerebellar ataxia type 6: not restricted to the cerebellum

Brain structural impairment in spinocerebellar ataxia type 6: not restricted to the cerebellum | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Brain structural impairment in spinocerebellar ataxia type 6: not restricted to the cerebellum Breno Kazuo Massuyama, Thiago Junqueira Ribeiro Rezende, Marcondes Cavalcante França Junior, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6907975/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Spinocerebellar ataxias (SCA) refer to a group of autosomal dominant ataxic disorders that result from the degeneration of the cerebellum and its connections. SCA6 is described as the prototype of pure cerebellar ataxia, with preservation of other brain regions. However, the calcium receptor subunit affected in SCA6 appears to be ubiquitous in neurons throughout the brain. Additionally, there are observations of clinical involvement of non-cerebellar systems, structural cerebral damage and hypometabolism in various brain areas. Objectives To characterize the structural brain signature in SCA6 patients. Methods Eighteen SCA6 patients underwent cross-sectional analyses using multimodal MRI-based techniques, which combined cerebral and cerebellar volumetric analyses with diffusion tensor imaging (DTI). Furthermore, we investigated whether structural abnormalities correlated with clinical findings. Results Individuals with SCA6, compared to non-ataxic controls, exhibited significant volumetric reduction in cerebellar white matter, cortex, and several lobules. There was increased axial diffusivity (AD) in the left inferior cerebellar peduncle, left middle cerebellar peduncle, left superior cerebellar peduncle, left superior corona radiata, left fornix-stria terminalis, left sagittal stratum, left genu of the corpus callosum, left midbrain, right cerebral peduncle, right fornix-stria terminalis, right middle cerebellar peduncle, right body of the corpus callosum, right midbrain, and left optic tract. Significant correlations were found between AD and the Inventory of Non-ataxia Symptoms Count and the Epworth Sleepiness Scale. Conclusions We provided valuable insights into the extracerebellar structural abnormalities associated with SCA6. DTI-based analyses of cerebellar connections and supratentorial structures emerge as potential sources of biomarkers for SCA6. Ataxia Spinocerebellar Ataxia Diffusion Tensor Imaging Magnetic Resonance Imaging Figures Figure 1 Figure 2 Figure 3 Introduction Spinocerebellar ataxias (SCA) refer to a group of autosomal dominant ataxic disorders that result from the degeneration of the cerebellum and its afferent and efferent connections ( 1 ). SCA6 (MIM: 183086) is the third most common subtype of SCA ( 2 ). It is caused by a CAG expansion on chromosome 19, exon 47, in the gene CACNA1A, which codes for the α1A (Cav2.1) subunit. This subunit is the primary component of the voltage-dependent P/Q-type neuronal calcium channel, found in Purkinje cells (PC) ( 3 ). SCA6 is described as the prototype of pure cerebellar ataxia, with preservation of other brain regions ( 1 , 4 ). It is characterized by cortical cerebellar degeneration involving PC ( 4 ). However, the calcium receptor subunit affected in SCA6 appears to be ubiquitous in neurons throughout the brain ( 5 ). Structural magnetic resonance imaging (MRI) of SCA6 patients reveals cerebellar atrophy, primarily axial, affecting the cerebellar vermis ( 6 – 9 ). Cortical and cerebello-olivary atrophy can also be observed, but they are attributed to transsynaptic degeneration following the loss of PC ( 10 ). Accordingly, SCA6 exhibits hypometabolism, in the anterior lobe of the cerebellar cortex and cerebellar vermis ( 11 ). It is worth noting that, compared to healthy controls, SCA6 patients show hypometabolism not only in the cerebellar hemispheres but also in the brainstem, basal ganglia, and frontal, temporal, and occipital cerebral cortices ( 10 ). In this sense, mild widespread cortical atrophy has been reported in SCA6 ( 12 ). Additionally, SCA6 patients may present with mild external ophthalmoplegia, spasticity, or peripheral neuropathy ( 13 ). Taken together, the observation of clinical involvement of non-cerebellar systems, structural cerebral damage and hypometabolism in these diverse brain areas suggests that SCA6 might not be a purely cerebellar syndrome. To assess this hypothesis, we designed the current study to characterize the structural signature of the brain in a representative cohort of SCA6 patients through cross-sectional analyses using multimodal MRI-based techniques. These techniques combined cerebral and cerebellar volumetric analyses with diffusivity-based analyses. Additionally, we investigated whether the structural abnormalities correlated with the clinical findings. Materials and methods Subjects’ Selection Eighteen clinically and molecularly proven SCA6 patients who were evaluated at the Ataxia Unit, Federal University of Sao Paulo, agreed to participate in this study between 2020 and 2022. This study was approved by our Institutional Review Board under the number CAAE 36018820.8.0000.5505. All patients provided written informed consent. Patients with concomitant neurological disorders, those unable to undergo an MRI scan, or those who did not provide consent were excluded from the study. A control group of eighteen age- and sex-matched healthy individuals underwent MRI scans, and the results were compared with those of SCA6 patients. Individuals with any prior medical or family history of psychiatric or other neurological disorders were excluded. Clinical Protocol For each patient, we gathered information on the age at onset, time from ataxia onset, the length of the (CAG) repeats in the longer allele, and the presence of any additional neurological symptoms. Disease severity was quantified using the “Scale for the Assessment and Rate of Ataxia” (SARA) and the “International Cooperative Ataxia Rating Scale” (ICARS) ( 14 , 15 ). For evaluation of non-motor and extracerebellar features, we applied “Inventory of Non-ataxia symptoms” (INAS), “REM Sleep Behavior Screening Questionnaire” (RBDSQ), “Epworth Sleepiness Scale” (ESS), and “Fatigue Severity Scale” (FSS) ( 16 – 19 ). The INAS Count serves as a semiquantitative measure of extracerebellar involvement in SCA. RBDSQ is a numerical scale in which scores of five or higher suggest the presence of REM sleep behavior disorder (RBD). The ESS is an n-point scale where a threshold of ten or higher is used to diagnose excessive daytime sleepiness. FSS is an n-point scale where values greater than twenty-eight are indicators of the presence of fatigue. The clinical protocol was conducted on the same day as the imaging acquisition. MRI Acquisition All subjects underwent high-resolution MRI acquisition on a 3T Phillips Achieva Scanner (Philips, Best, The Netherlands). The MRI scans were performed exclusively at the University of Campinas. Routine T2-weighted sequences were performed in all subjects to exclude unrelated abnormalities. A standard eight-channel head coil was used in all acquisition. For gray matter analyses (FastSurfer and CerebNet), we used high-resolution T1 volumetric images of the brain with sagittal orientation, voxel matrix 240 × 240 ×180, voxel size 1 × 1 × 1mm 3 , repetition time (TR)/echo time (TE) 7/3.201 ms, and flip angle 8 o . For diffusion tensor imaging (DTI) Multi-Atlas analyses, we used a spin echo DTI sequence: 2 × 2 × 2mm³ acquiring voxel size, interpolated to 1 × 1 × 2 mm3, reconstructed matrix 256 × 256, 70 slices, TE/TR 61/8,500 ms, flip angle 90 o , 32 gradient directions, no averages, max b-factor = 1,000 s/mm 2 , and 6-minute scan. MRI Analysis FastSurfer GM structures were evaluated using the FastSurfer software v.2.0.6, a deep learning-based tool for cerebral cortex and deep GM analysis ( 20 ). FastSurfer shows improved performance when compared to the FreeSurfer pipeline v. 7.4.1 ( 21 ). To accomplish that, the brain is parcellated using a convolutional neural network with U-Net architecture, followed by the standard FreeSurfer pipeline ( 20 , 22 ). In this step, two surfaces are created, the pial and white surfaces, using Gaussian filter with 10-mm full-width half-maximum to smooth both surfaces and enabling the fitting of a triangular mesh over them ( 23 ). Cortical thickness is calculated as the shortest distance between the pial and white surface at each vertex across the cortical layer ( 22 ). To better assess brainstem regions, we employed the FreeSurfer brainstem substructure segmentation pipeline, which allows us the identification of brainstem subunites such as, medulla oblongata, pons, midbrain, and superior cerebellar peduncle ( 24 ). Such metrics were estimated using the cross-sectional subregion segmentation module within FreeSurfer (v.7.4.1) ( 15 ) that relied on a probabilistic Bayesian atlas of the brainstem ( 24 ). CerebNet For cerebellar volumetry, we used the CerebNet software( 25 ), v1.0, a secondary tool under the FastSurfer framework ( 20 , 25 ). CerebNet is also a deep learning-based solution dedicated to cerebellar segmentation. For better performance and generalizability, Cerebnet was trained using both controls and ataxic individuals ( 25 ). WHITE MATTER ANALYSIS To assess white matter microstructural changes, we employed the DTI multiatlas feature from “MRICloud” (MRICloud.org), a public web-based service for multicontrast imaging segmentation and quantification. Raw DTI-weighted images were first corrected for eddy currents and co-registered to remove subject motion using a 12-parameter affine transform ( 26 , 27 ). To quantify the DTI-parameters, we used a multivariate linear fitting. After that, the skull-stripping was performed by intensity threshold using the b = 0 image, a tool of RoiEditor software (Li X, Jiang H, Yue Li, and Mori S; Johns Hopkins University, www.MriStudio.org or www.kennedykrieger.org ). Next, we used the multicontrast LDDMM algorithm to register the atlas to the images and then the parcellation, which uses a DLFA algorithm ( 28 , 29 ). Eight atlases (JHU adult atlas version 1) were used to generate 168 structures. All analyses were performed in native space. Computations were processed on the Gordon cluster of XSEDE ( 30 ). Statistical Analysis Patients were compared to age- and sex-matched controls for all analyses. To assess group differences, we used the ANCOVA test with age, sex, and estimated total intracranial volume as covariates to remove their effects from all variables. We employed the Pearson correlation coefficient to evaluate the correlations between MRI-based parameters and clinical metrics. All tests underwent multiple comparisons correction (Bonferroni-adjusted p < 0.05). Additionally, we calculated effect sizes (ES) for all statistically significant results using Cohen’s d formula. According to established conventions, we classified ES values as follows: 0.2 as small, 0.5 as moderate, 0.8 as large, and > 1.2 as very large (Cohen J. Statistical power analysis for the behavioral sciences. Second edition ed. Hillsdale, N.J: L. Erlbaum Associates, 1988). For all imaging techniques, statistical analyses were based on regions of interest (ROI), and we evaluated all anatomical labels provided for each tool. Statistical analysis was done using the Matlab R2017b software ( https://www.mathworks.com/products/matlab.html ). Results Comprehensive statistics, including coefficients and adjusted p-values for the MRI metrics, are provided in Table S1 of the Supplementary Appendix. Demographics and Clinical Characteristics The demographic, genetic, and clinical data of the study population are summarized in Table 1 . In short, this is a cohort of 18 SCA6 patients with late disease onset (mean = 53.3 years; SD = 9.1), long disease duration (mean = 13.0 years; SD = 7.3), and short CAG repeat length (mean = 22.1; SD = 1.2). Of note, the CAG repeat length was unavailable for six patients, and one patient had both expanded alleles (20 and 24), with only the allele with the larger expansion being considered for the statistics. Table 1 Demographic, clinical and genetic data of the ataxic study participants SAC6 patients (N = 18) Control group (N = 18) Age (years); mean ± SD 67.7 ± 9.2 67.4 ± 9.0 Sex, n (male/female) 6/12 6/12 Disease duration; mean ± SD 13.0 ± 7.3 N/A Age at the onset of symptoms 53.3 ± 9.1 N/A CAG repeat length, long allele*✝ 22.1 ± 1.2 N/A SARA; mean ± SD 13.3 ± 6.7 N/A ICARS; mean ± SD 37.0 ± 17.1 N/A INAS Count; mean ± SD 1.7 ± 1.0 N/A RBDSQ; mean ± SD 4.0 ± 26.6 N/A ESS; mean ± SD 5.9 ± 4.0 N/A FSS; mean ± SD 34.0 ± 13.0 N/A Legend of Table 1 : ESS: Epworth Sleepiness Scale; FSS: Fatigue Severity Scale; ICARS: International Cooperative Ataxia Rating Scale; INAS: Inventory of Non-ataxia symptoms; RBDSQ: REM Sleep Behavior Screening Questionnaire; SARA: Scale for the Assessment and Rating of Ataxia; SD: standard deviation. *CAG repeat length unavailable for six patients. ✝One patient had both expanded alleles (20 and 24): only the allele with the higher repeat number was considered for the statistics. Regarding ataxia severity, the SARA range was 2.5 to 25 (mean = 13.30 and the ICARS range was 8 to 66 (mean = 37). Moreover, we observed a high frequency of non-motor features in SCA6 patients beyond cerebellar signs: INAS Count (mean = 1.77), RBDSQ (mean = 4), ESS (mean = 5.94), and FSS (mean = 34). MRI Analysis The cerebellar analyses revealed significant volumetric reduction in bilateral WM (left and right: P < 0.001), in bilateral cerebellar cortex (left and right: P < 0.001) and in several cerebellar lobules: bilateral lobules I-IV (left and right: P < 0.001), bilateral lobule V (left and right: P < 0.001), bilateral lobule VI (left and right: P < 0.001), vermis VI (P < 0.001), bilateral crus I (left and right: P < 0.001), left crus II (P < 0.001), bilateral lobule VIIb (left and right: P < 0.001), bilateral lobule VIIIa (left and right: P < 0.001), bilateral lobule VIIIb (left and right: P < 0.001), bilateral lobule X (left and right: P < 0.001), vermis VII (P < 0.001), and vermis VIII (P 1.5). Following Bonferroni correction for multiple comparisons, there were no significant alterations in cerebral cortical thickness, brainstem, and deep GM. It is worth noting that there were relevant changes in the CC (P 1.1) and in the bilateral accumbens area (P 0.9), and an important volumetric reduction in the superior cerebral peduncles (P 1.9). DTI Analysis There were increased axial diffusivity (AD) in the left inferior cerebellar peduncle (P < 0.001), left middle cerebellar peduncle (P < 0.001), left superior cerebellar peduncle (P = 0.001), left superior corona radiata (P = 0.001), left fornix-stria terminalis (P = 0.001), left sagittal stratum (P < 0,001), left genu of the corpus callosum (P < 0.001), left midbrain (P < 0.001), right cerebral peduncle (P < 0.001), right fornix-stria terminalis (P < 0.001), right middle cerebellar peduncle (P < 0.001), right body of the corpus callosum (P < 0.001), right midbrain (P < 0.001), and left optic tract (P 1.0). Increased mean diffusivity (MD) values were found in left inferior cerebellar peduncle (P < 0.030), left superior cerebellar peduncle (P < 0.038), left superior corona radiata (P < 0.002), left fornix stria terminalis (P < 0.042), left sagittal stratum (P < 0.011), left middle cerebellar peduncle (P < 0.016), left midbrain (P < 0.001), right midbrain (P < 0.020), and left optic tract (P < 0.003). After Bonferroni adjustment for multiple comparisons, there were no significant alterations in MD, fractional anisotropy (FA), and radial diffusivity (RD). Correlations Between MRI Metrics and Clinical Scores Significant correlations (P < 0.05) were found between MRI metrics and clinical scores for AD with INAS Count (left superior corona radiata, left middle cerebellar peduncle, bilateral midbrain, and right body of corpus callosum) and ESS (left inferior cerebellar peduncle)(Fig. 2). After applying the Bonferroni adjustment, the cerebellar volumetric reduction was significant with ICARS in right lobule X, and with ESS in vermis VII (Fig. 3). Discussion In this study, we present the clinical and radiological findings from a cohort of eighteen patients with SCA6. Overall, we identified extracerebellar structural abnormalities, with some showing clinical correlations. As expected, we found significant volumetric reduction in bilateral cerebellar WM, bilateral cerebellar cortex, and several cerebellar lobules, all of which exhibited very large ES. Of particular interest, in line with our hypothesis of extracerebellar involvement in SCA6, the DTI study demonstrated increased AD in cerebellar connections and novel extracerebellar findings, particularly highlighting alterations in various supratentorial structures. All these structures exhibited at least large ES. In addition, we found significant correlations between MRI metrics and clinical scores for AD with the INAS Count in extracerebellar structures, and for AD with ESS in the left inferior cerebellar peduncle. Cerebellar ataxias encompass a heterogenous group of conditions that include both sporadic and genetic etiologies ( 31 ). Among the autosomal dominant cerebellar ataxias, the most common group is SCA, with over fifty-one distinct types clinically described (SCA1–51) ( 32 ). In our Ataxia Unit in Brazil, among 1,332 patients with several types of ataxias, 326 are related to SCA, with SCA6 being the fifth most frequent (5.21%) ( 33 ). SCA6 is referred to as pure cerebellar ataxia, with preservation of other brain regions ( 1 , 4 ). However, the calcium receptor subunit affected in SCA6 appears to be widespread in neurons throughout the brain ( 5 ). Recent studies suggest that cerebellar degeneration becomes widespread, potentially leading to transneuronal degeneration in other brain regions ( 4 , 34 – 37 ). Moreover, PET-FDG demonstrated multiple-regional brain hypometabolism, including cerebellar hemispheres, brainstem, cortical regions, and basal ganglia ( 38 ). Nevertheless, finding hypometabolism in various brain regions does not necessarily mean there is neuronal degeneration. Instead, it could simply indicate metabolic dysfunction in neurons that are still structurally intact or subclinical neuropathological changes ( 38 ). Falcon et al. demonstrated that, in SCA6, functional connectivity between regions of the cerebral cortex and cerebellum is accompanied by DTI metrics reflecting structural changes in the cerebral and cerebellar peduncles ( 39 ). To illustrate the possible extracerebellar and non-motor changes in SCA6, within our cohort of SCA6 patients, one patient exhibited parkinsonism and dopaminergic dysfunction on a DAT (dopamine active transporter) scan ( 40 ). Additionally, we reported that patients with SCA6 experience more frequent respiratory events and sleep apnea compared to a control group ( 41 ). In this context, one might speculate that SCA6 would have alterations in the spinal cord. In fact, spinal cord abnormalities appear early and worsen progressively in SCA1, SCA2, and SCA3. However, SCA6 does not exhibit any morphometric abnormalities in the spinal cord ( 42 , 43 ). AD refers to diffusivity along a WM fiber tract, making it most relevant in regions where axons are coherently oriented without fiber crossings ( 44 ). Increased isotropic diffusion appears to enhance both radial and axial diffusivity in chronic diseases marked by significant axonal damage ( 45 ). Our patient group exhibited increased AD levels compared to the control group, observed not only in the cerebellar connecting structures (including the inferior cerebellar peduncle, middle cerebellar peduncle, superior cerebellar peduncle, cerebral peduncle, and midbrain) but also in the corona radiata, fornix-stria terminalis, sagittal stratum, genu of the corpus callosum, body of the corpus callosum, and optic tract. Despite that, AD maps may not consistently align with the direction of the underlying fiber tract ( 44 ). On the other hand, MD is a more reliable DTI metric for assessing multiple fiber populations or complex fiber geometries ( 46 ). Additionally, MD shows a closer relationship to neurite density compared to FA, AD, or RD measures ( 47 ). It is also a valuable tool for identifying and quantifying WM damage in neurodegenerative conditions, particularly in SCA ( 48 ). From his perspective, although no significant alterations in MD were identified following Bonferroni correction, several structures exhibited increased MD values, with findings showing asymmetrical distribution toward the left hemisphere of supratentorial regions and the left cerebellar connections. This aligns with the asymmetric alterations found in SYNE1-ataxia ( 49 ), another genetic hereditary ataxia. Neurodegenerative diseases may have lateralized abnormalities, which can be explained by the concept of selective vulnerability ( 50 ). Collectively, our results reinforce the hypothesis of extracerebellar structural changes in SCA6. Cerebellar activity plays a role in numerous aspects of sensorimotor control and motor learning ( 51 ). Nevertheless, the involvement of the cerebellum in cognitive functions is still debated. There are cerebellar pathways that connect to the frontal cortex through the cerebello-ponto-thalamo-cortical connections. Consequently, the loss of cerebellar efferents to the frontal cortex could lead to cognitive changes ( 52 ). Individuals with SCA6 exhibit notable impairments in attention and executive functions ( 53 ). There is evidence that a network composed of the amygdala, various cortical regions, the hippocampus, basal forebrain, the nucleus accumbens, the stria terminalis, and the striatum participates in the modulation of memory consolidation ( 54 ). Moreover, a previous study demonstrated a correlation between cognitive deficits and the MD of the stria terminalis in SCA2 ( 55 ). Given that we also observed increased MD values in the stria terminalis and the striatum, it is plausible that a similar pathophysiological correlation between cognitive changes and these structures may occur in SCA6. Conducting motor-independent cognitive tests in patients with purely cerebellar ataxias is crucial to elucidate the cerebellum's role in cognition. Identifying non-motor and extracerebellar signs in SCA patients is key to the preclinical characterization of these diseases ( 56 ). This study uncovers new aspects of SCA6, though the findings are preliminary and should be considered withing the context of the following limitations. First, we did not establish a correlation between the radiological changes and the CACNA1A gene expression in the brain. Therefore, it remains unclear whether the extracerebellar findings result from local alterations directly induced by the mutated gene or if they indicate selective transneuronal degeneration. Second, considering that this is a cross-sectional study, it is not possible to determine if the non-motor and extra-cerebellar features developed prior to the ataxia. Further longitudinal studies are necessary to confirm these findings. Third, the Bonferroni correction adjusts p-values to reduce the risk of Type I errors in multiple statistical tests. However, it has been criticized for potentially undermining sound statistical judgment and increasing the risk of Type II errors due to its conservative nature, especially as the number of comparisons increases. Conclusion In conclusion, we provided valuable insights into the extracerebellar structural abnormalities associated with SCA6, fundamentally challenging the conventional understanding of SCA6 as a purely cerebellar ataxia. It remains unclear whether the findings, restricted to white matter, indicate discrete extracerebellar features of SCA6 or rather reflect engagement of the complex cerebro-cerebellar networks involved in motor, cognitive, and affective processing. From a clinical standpoint, identifying biomarkers is essential for drug development aimed at treating or slowing the disease's progression. Considering this, DTI-based analyses of cerebellar connections and supratentorial structures emerge as potential sources of biomarkers for SCA6. This underscores the need for further longitudinal studies to unravel the wider implications of SCA6 on cognitive and non-motor functions. Declarations Authhors' Roles Conceptualization: Breno Kazuo Massuyama; Thiago Junqueira Ribeiro Rezende; Marcondes Cavalcante França Junior; Orlando Graziani Povoas Barsottini; José Luiz Pedroso. Methodology: Breno Kazuo Massuyama; Thiago Junqueira Ribeiro Rezende; Formal analysis and investigation: Breno Kazuo Massuyama; Thiago Junqueira Ribeiro Rezende. Writing - original draft preparation: Breno Kazuo Massuyama Writing - review and editing: Thiago Junqueira Ribeiro Rezende; Marcondes Cavalcante França Junior; Orlando Graziani Povoas Barsottini; José Luiz Pedroso. Financial Disclosure/Conflict of Interest: The authors declare that there are no conflicts of interest relevant to this work. Funding Sources for study: No specific funding was received for this work. Financial interests: T.J.R.R. has consulted for Biogen and PTC and received research grants from FAPESP, Friedreich Ataxia Research Alliance (FARA), NIH and Biogen. J.L.P. receiveed research grants from FAPESP. Ethical Approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the local Research Ethics Committee at the Federal University of Sao Paulo (No. 4.358.994). Consent to Participate: All the participants signed the Informed Consent to participate in the study. References Schöls L, Bauer P, Schmidt T, Schulte T, Riess O. Autosomal dominant cerebellar ataxias: clinical features, genetics, and pathogenesis. Lancet Neurol. 2004;3(5):291–304. Hersheson J, Haworth A, Houlden H. The inherited ataxias: genetic heterogeneity, mutation databases, and future directions in research and clinical diagnostics. Hum Mutat. 2012;33(9):1324–32. Zhuchenko O, Bailey J, Bonnen P, Ashizawa T, Stockton DW, Amos C, et al. 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Gierga K, Schelhaas HJ, Brunt ER, Seidel K, Scherzed W, Egensperger R, et al. Spinocerebellar ataxia type 6 (SCA6): neurodegeneration goes beyond the known brain predilection sites. Neuropathol Appl Neurobiol. 2009;35(5):515–27. Reetz K, Costa AS, Mirzazade S, Lehmann A, Juzek A, Rakowicz M, et al. Genotype-specific patterns of atrophy progression are more sensitive than clinical decline in SCA1, SCA3 and SCA6. Brain. 2013;136(Pt 3):905–17. Rüb U, Brunt ER, Petrasch-Parwez E, Schöls L, Theegarten D, Auburger G, et al. Degeneration of ingestion-related brainstem nuclei in spinocerebellar ataxia type 2, 3, 6 and 7. Neuropathol Appl Neurobiol. 2006;32(6):635–49. Soong B, Liu R, Wu L, Lu Y, Lee H. Metabolic characterization of spinocerebellar ataxia type 6. Arch Neurol. 2001;58(2):300–4. Falcon MI, Gomez CM, Chen EE, Shereen A, Solodkin A. Early Cerebellar Network Shifting in Spinocerebellar Ataxia Type 6. Cereb Cortex. 2016;26(7):3205–18. Pedroso JL, de Carvalho Campos-Neto G, Speciali DS, Barsottini OG, Bor-Seng-Shu E, Felicio AC. Spinocerebellar ataxia type 6 presenting with parkinsonism, pre-synaptic dopaminergic dysfunction and hyperechogenicity of the substantia nigra. J Neurol Sci. 2017;376:60–2. Rueda AD, Pedroso JL, Truksinas E, Do Prado GF, Coelho FM, Barsottini OG. Polysomnography findings in spinocerebellar ataxia type 6. J Sleep Res. 2016;25(6):720–3. Rezende TJR, Adanyaguh I, Barsottini OGP, Bender B, Cendes F, Coutinho L, et al. Genotype-specific spinal cord damage in spinocerebellar ataxias: an ENIGMA-Ataxia study. J Neurol Neurosurg Psychiatry. 2024;95(7):682–90. de Borba FC, Fernandes JMS, de Rezende TJR, González-Salazar C, de Melo Teixeira Branco L, Wolmer PS, et al. Tract-specific spinal damage in SCA2, SCA3 and SCA6. J Neurol. 2024;272(1):6. O’Donnell LJ, Westin CF. An introduction to diffusion tensor image analysis. Neurosurg Clin N Am. 2011;22(2):185–96. viii. Winklewski PJ, Sabisz A, Naumczyk P, Jodzio K, Szurowska E, Szarmach A. Understanding the Physiopathology Behind Axial and Radial Diffusivity Changes-What Do We Know? Front Neurol. 2018;9:92. Figley CR, Uddin MN, Wong K, Kornelsen J, Puig J, Figley TD. Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure. Front Neurosci. 2021;15:799576. Genc S, Malpas CB, Holland SK, Beare R, Silk TJ. Neurite density index is sensitive to age related differences in the developing brain. NeuroImage. 2017;148:373–80. Guerrini L, Lolli F, Ginestroni A, Belli G, Della Nave R, Tessa C, et al. Brainstem neurodegeneration correlates with clinical dysfunction in SCA1 but not in SCA2. A quantitative volumetric, diffusion and proton spectroscopy MR study. Brain. 2004;127(Pt 8):1785–95. Gama MTD, Piccinin CC, Rezende TJR, Dion PA, Rouleau GA, França Junior MC, et al. Multimodal neuroimaging analysis in patients with SYNE1 Ataxia. J Neurol Sci. 2018;390:227–30. Mattsson N, Schott JM, Hardy J, Turner MR, Zetterberg H. Selective vulnerability in neurodegeneration: insights from clinical variants of Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2016;87(9):1000–4. Manto M, Bower JM, Conforto AB, Delgado-García JM, da Guarda SNF, Gerwig M, et al. Consensus paper: roles of the cerebellum in motor control–the diversity of ideas on cerebellar involvement in movement. Cerebellum. 2012;11(2):457–87. Dum RP, Strick PL. An unfolded map of the cerebellar dentate nucleus and its projections to the cerebral cortex. J Neurophysiol. 2003;89(1):634–9. Klinke I, Minnerop M, Schmitz-Hübsch T, Hendriks M, Klockgether T, Wüllner U, et al. Neuropsychological features of patients with spinocerebellar ataxia (SCA) types 1, 2, 3, and 6. Cerebellum. 2010;9(3):433–42. McGaugh JL. Memory consolidation and the amygdala: a systems perspective. Trends Neurosci. 2002;25(9):456. Hernandez-Castillo CR, Vaca-Palomares I, Galvez V, Campos-Romo A, Diaz R, Fernandez-Ruiz J. Cognitive Deficits Correlate with White Matter Deterioration in Spinocerebellar Ataxia Type 2. J Int Neuropsychol Soc. 2016;22(4):486–91. Soong B, Liu R, Wu L, Lu Y, Lee H. Metabolic characterization of spinocerebellar ataxia type 6. Arch Neurol. 2001;58(2):300–4. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviews received at journal 05 Sep, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers invited by journal 26 Jun, 2025 Editor assigned by journal 20 Jun, 2025 Submission checks completed at journal 20 Jun, 2025 First submitted to journal 16 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6907975","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477110149,"identity":"a61a816c-9e1d-4dea-a954-e0438cc022b4","order_by":0,"name":"Breno Kazuo Massuyama","email":"","orcid":"","institution":"Federal University of Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Breno","middleName":"Kazuo","lastName":"Massuyama","suffix":""},{"id":477110150,"identity":"6e3527db-49ce-4b65-b27d-d9c3ad5c3ca8","order_by":1,"name":"Thiago Junqueira Ribeiro Rezende","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYDACCcYGEMUM4bExyIGoAw/w6OBB12IM1pKAVwsKl40hEWwCPi320s2NH79UMLCbt7c/fFxQZpM+P+zwQ6AtdnK6DThskTnYLC1zhoFZ5syBZOMZ59JyN95OMwBqSTY2O4DLYYkN0pJtDMwSEgnHpHnbDudunJ0A0nIgcRtuLc2/wVrkH7b/5m37n244O/0DIS1tkh/BtjCzMfO2HUiQl84hYMuNxDZrhjMSzBI8aczSPOeSDTdI5xQcSDDA7Rf2GemPb/6osEmWYD/+8DNPmZ28/Oz0zR8+VNjJ4dICAsw8DBLJcJ4BWKUBbuUgwPiDgcEOzpNvwK96FIyCUTAKRh4AAN9xWj2TkOM8AAAAAElFTkSuQmCC","orcid":"","institution":"State University of Campinas","correspondingAuthor":true,"prefix":"","firstName":"Thiago","middleName":"Junqueira Ribeiro","lastName":"Rezende","suffix":""},{"id":477110153,"identity":"44d97120-573b-4296-8d8e-77d3699fb3a0","order_by":2,"name":"Marcondes Cavalcante França Junior","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Marcondes","middleName":"Cavalcante França","lastName":"Junior","suffix":""},{"id":477110155,"identity":"16ee35ca-5054-4aea-9611-d4e3f7416699","order_by":3,"name":"Orlando Graziani Povoas Barsottini","email":"","orcid":"","institution":"Federal University of Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Orlando","middleName":"Graziani Povoas","lastName":"Barsottini","suffix":""},{"id":477110157,"identity":"22322b4a-a5a9-4c48-a8e3-6eeef11c5034","order_by":4,"name":"José Luiz Pedroso","email":"","orcid":"","institution":"Federal University of Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Luiz","lastName":"Pedroso","suffix":""}],"badges":[],"createdAt":"2025-06-16 18:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6907975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6907975/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85836250,"identity":"b509d257-bf56-48f8-b3fa-ba29b8c0a425","added_by":"auto","created_at":"2025-07-02 08:22:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":317305,"visible":true,"origin":"","legend":"\u003cp\u003eCerebral and cerebellar structural damage in patients with SCA6, compared to age- and gender-matched controls, after Bonferroni adjustment for multiple comparisons. Cerebral WM damage refers to the AD results from the DTI Multi-Atlas obtained using MRICloud. Cerebellar GM and WM volumetry obtained using CerebNet. AD: axial diffusivity; DTI: diffusion tensor imaging; GM: gray matter; WM: white matter.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6907975/v1/19447fe751c361186c89e9d3.jpg"},{"id":85836254,"identity":"1228d470-f293-4f14-b19b-168fe86bfc01","added_by":"auto","created_at":"2025-07-02 08:22:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83900,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between AD and INAS Count in left superior corona radiata (A), left middle cerebellar peduncle (B), left midbrain (C), right midbrain (D), and right body of corpus callosum (E). Correlations between AD and ESS in left inferior cerebellar peduncle (F). AD: axial diffusivity; ESS: Epworth Sleepiness Scale; INAS: Inventory of Non-ataxia symptoms.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6907975/v1/eb20f1dd046b87b5ebb11178.jpg"},{"id":85835053,"identity":"51ad2cec-1ee8-484e-bae6-4fdfa6024976","added_by":"auto","created_at":"2025-07-02 08:14:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46756,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between cerebellar volumetric reduction and ESS in vermis VII (A), and ICARS in right lobule X (B). ESS: Epworth Sleepiness Scale; INAS: Inventory of Non-ataxia symptoms.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6907975/v1/72709533f13f95c6bdcd0d54.jpg"},{"id":85837711,"identity":"a852c9e2-8845-48a2-89e7-558cd472edc3","added_by":"auto","created_at":"2025-07-02 08:30:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1092169,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6907975/v1/8843f5f2-8096-45bf-9582-19a939dd61d7.pdf"},{"id":85835055,"identity":"3eac7c52-b4ca-4814-9656-a1a13c1c7d9f","added_by":"auto","created_at":"2025-07-02 08:14:23","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":61349,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6907975/v1/e3b577699c911bd3d8d38735.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain structural impairment in spinocerebellar ataxia type 6: not restricted to the cerebellum","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpinocerebellar ataxias (SCA) refer to a group of autosomal dominant ataxic disorders that result from the degeneration of the cerebellum and its afferent and efferent connections (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). SCA6 (MIM: 183086) is the third most common subtype of SCA (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It is caused by a CAG expansion on chromosome 19, exon 47, in the gene CACNA1A, which codes for the α1A (Cav2.1) subunit. This subunit is the primary component of the voltage-dependent P/Q-type neuronal calcium channel, found in Purkinje cells (PC) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). SCA6 is described as the prototype of pure cerebellar ataxia, with preservation of other brain regions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). It is characterized by cortical cerebellar degeneration involving PC (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, the calcium receptor subunit affected in SCA6 appears to be ubiquitous in neurons throughout the brain (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStructural magnetic resonance imaging (MRI) of SCA6 patients reveals cerebellar atrophy, primarily axial, affecting the cerebellar vermis (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Cortical and cerebello-olivary atrophy can also be observed, but they are attributed to transsynaptic degeneration following the loss of PC (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Accordingly, SCA6 exhibits hypometabolism, in the anterior lobe of the cerebellar cortex and cerebellar vermis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). It is worth noting that, compared to healthy controls, SCA6 patients show hypometabolism not only in the cerebellar hemispheres but also in the brainstem, basal ganglia, and frontal, temporal, and occipital cerebral cortices (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In this sense, mild widespread cortical atrophy has been reported in SCA6 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, SCA6 patients may present with mild external ophthalmoplegia, spasticity, or peripheral neuropathy (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Taken together, the observation of clinical involvement of non-cerebellar systems, structural cerebral damage and hypometabolism in these diverse brain areas suggests that SCA6 might not be a purely cerebellar syndrome.\u003c/p\u003e \u003cp\u003eTo assess this hypothesis, we designed the current study to characterize the structural signature of the brain in a representative cohort of SCA6 patients through cross-sectional analyses using multimodal MRI-based techniques. These techniques combined cerebral and cerebellar volumetric analyses with diffusivity-based analyses. Additionally, we investigated whether the structural abnormalities correlated with the clinical findings.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u0026rsquo; Selection\u003c/h2\u003e \u003cp\u003e Eighteen clinically and molecularly proven SCA6 patients who were evaluated at the Ataxia Unit, Federal University of Sao Paulo, agreed to participate in this study between 2020 and 2022. This study was approved by our Institutional Review Board under the number CAAE 36018820.8.0000.5505. All patients provided written informed consent. Patients with concomitant neurological disorders, those unable to undergo an MRI scan, or those who did not provide consent were excluded from the study. A control group of eighteen age- and sex-matched healthy individuals underwent MRI scans, and the results were compared with those of SCA6 patients. Individuals with any prior medical or family history of psychiatric or other neurological disorders were excluded.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Protocol\u003c/h3\u003e\n\u003cp\u003eFor each patient, we gathered information on the age at onset, time from ataxia onset, the length of the (CAG) repeats in the longer allele, and the presence of any additional neurological symptoms. Disease severity was quantified using the \u0026ldquo;Scale for the Assessment and Rate of Ataxia\u0026rdquo; (SARA) and the \u0026ldquo;International Cooperative Ataxia Rating Scale\u0026rdquo; (ICARS) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). For evaluation of non-motor and extracerebellar features, we applied \u0026ldquo;Inventory of Non-ataxia symptoms\u0026rdquo; (INAS), \u0026ldquo;REM Sleep Behavior Screening Questionnaire\u0026rdquo; (RBDSQ), \u0026ldquo;Epworth Sleepiness Scale\u0026rdquo; (ESS), and \u0026ldquo;Fatigue Severity Scale\u0026rdquo; (FSS) (\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The INAS Count serves as a semiquantitative measure of extracerebellar involvement in SCA. RBDSQ is a numerical scale in which scores of five or higher suggest the presence of REM sleep behavior disorder (RBD). The ESS is an n-point scale where a threshold of ten or higher is used to diagnose excessive daytime sleepiness. FSS is an n-point scale where values greater than twenty-eight are indicators of the presence of fatigue. The clinical protocol was conducted on the same day as the imaging acquisition.\u003c/p\u003e\n\u003ch3\u003eMRI Acquisition\u003c/h3\u003e\n\u003cp\u003eAll subjects underwent high-resolution MRI acquisition on a 3T Phillips Achieva Scanner (Philips, Best, The Netherlands). The MRI scans were performed exclusively at the University of Campinas. Routine T2-weighted sequences were performed in all subjects to exclude unrelated abnormalities. A standard eight-channel head coil was used in all acquisition.\u003c/p\u003e \u003cp\u003eFor gray matter analyses (FastSurfer and CerebNet), we used high-resolution T1 volumetric images of the brain with sagittal orientation, voxel matrix 240 \u0026times; 240 \u0026times;180, voxel size 1 \u0026times; 1 \u0026times; 1mm\u003csup\u003e3\u003c/sup\u003e, repetition time (TR)/echo time (TE) 7/3.201 ms, and flip angle 8\u003csup\u003eo\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor diffusion tensor imaging (DTI) Multi-Atlas analyses, we used a spin echo DTI sequence: 2 \u0026times; 2 \u0026times; 2mm\u0026sup3; acquiring voxel size, interpolated to 1 \u0026times; 1 \u0026times; 2 mm3, reconstructed matrix 256 \u0026times; 256, 70 slices, TE/TR 61/8,500 ms, flip angle 90\u003csup\u003eo\u003c/sup\u003e, 32 gradient directions, no averages, max b-factor\u0026thinsp;=\u0026thinsp;1,000 s/mm\u003csup\u003e2\u003c/sup\u003e, and 6-minute scan.\u003c/p\u003e\n\u003ch3\u003eMRI Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFastSurfer\u003c/h2\u003e \u003cp\u003eGM structures were evaluated using the FastSurfer software v.2.0.6, a deep learning-based tool for cerebral cortex and deep GM analysis (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). FastSurfer shows improved performance when compared to the FreeSurfer pipeline v. 7.4.1 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). To accomplish that, the brain is parcellated using a convolutional neural network with U-Net architecture, followed by the standard FreeSurfer pipeline (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In this step, two surfaces are created, the pial and white surfaces, using Gaussian filter with 10-mm full-width half-maximum to smooth both surfaces and enabling the fitting of a triangular mesh over them (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Cortical thickness is calculated as the shortest distance between the pial and white surface at each vertex across the cortical layer (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo better assess brainstem regions, we employed the FreeSurfer brainstem substructure segmentation pipeline, which allows us the identification of brainstem subunites such as, medulla oblongata, pons, midbrain, and superior cerebellar peduncle (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Such metrics were estimated using the cross-sectional subregion segmentation module within FreeSurfer (v.7.4.1) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) that relied on a probabilistic Bayesian atlas of the brainstem (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCerebNet\u003c/h2\u003e \u003cp\u003eFor cerebellar volumetry, we used the CerebNet software(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), v1.0, a secondary tool under the FastSurfer framework (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). CerebNet is also a deep learning-based solution dedicated to cerebellar segmentation. For better performance and generalizability, Cerebnet was trained using both controls and ataxic individuals (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWHITE MATTER ANALYSIS\u003c/h3\u003e\n\u003cp\u003eTo assess white matter microstructural changes, we employed the DTI multiatlas feature from \u0026ldquo;MRICloud\u0026rdquo; (MRICloud.org), a public web-based service for multicontrast imaging segmentation and quantification. Raw DTI-weighted images were first corrected for eddy currents and co-registered to remove subject motion using a 12-parameter affine transform (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). To quantify the DTI-parameters, we used a multivariate linear fitting. After that, the skull-stripping was performed by intensity threshold using the b\u0026thinsp;=\u0026thinsp;0 image, a tool of RoiEditor software (Li X, Jiang H, Yue Li, and Mori S; Johns Hopkins University, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.MriStudio.org\u003c/span\u003e\u003cspan address=\"http://www.MriStudio.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.kennedykrieger.org\u003c/span\u003e\u003cspan address=\"http://www.kennedykrieger.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Next, we used the multicontrast LDDMM algorithm to register the atlas to the images and then the parcellation, which uses a DLFA algorithm (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Eight atlases (JHU adult atlas version 1) were used to generate 168 structures. All analyses were performed in native space. Computations were processed on the Gordon cluster of XSEDE (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003ePatients were compared to age- and sex-matched controls for all analyses. To assess group differences, we used the ANCOVA test with age, sex, and estimated total intracranial volume as covariates to remove their effects from all variables. We employed the Pearson correlation coefficient to evaluate the correlations between MRI-based parameters and clinical metrics. All tests underwent multiple comparisons correction (Bonferroni-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, we calculated effect sizes (ES) for all statistically significant results using Cohen\u0026rsquo;s d formula. According to established conventions, we classified ES values as follows: 0.2 as small, 0.5 as moderate, 0.8 as large, and \u0026gt;\u0026thinsp;1.2 as very large (Cohen J. Statistical power analysis for the behavioral sciences. Second edition ed. Hillsdale, N.J: L. Erlbaum Associates, 1988). For all imaging techniques, statistical analyses were based on regions of interest (ROI), and we evaluated all anatomical labels provided for each tool. Statistical analysis was done using the Matlab R2017b software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mathworks.com/products/matlab.html\u003c/span\u003e\u003cspan address=\"https://www.mathworks.com/products/matlab.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eComprehensive statistics, including coefficients and adjusted p-values for the MRI metrics, are provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e of the Supplementary Appendix.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eThe demographic, genetic, and clinical data of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In short, this is a cohort of 18 SCA6 patients with late disease onset (mean\u0026thinsp;=\u0026thinsp;53.3 years; SD\u0026thinsp;=\u0026thinsp;9.1), long disease duration (mean\u0026thinsp;=\u0026thinsp;13.0 years; SD\u0026thinsp;=\u0026thinsp;7.3), and short CAG repeat length (mean\u0026thinsp;=\u0026thinsp;22.1; SD\u0026thinsp;=\u0026thinsp;1.2). Of note, the CAG repeat length was unavailable for six patients, and one patient had both expanded alleles (20 and 24), with only the allele with the larger expansion being considered for the statistics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, clinical and genetic data of the ataxic study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAC6 patients (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl group (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years); mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6/12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at the onset of symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAG repeat length, long allele*✝\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARA; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICARS; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINAS Count; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBDSQ; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESS; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSS; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eLegend of\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: ESS: Epworth Sleepiness Scale; FSS: Fatigue Severity Scale; ICARS: International Cooperative Ataxia Rating Scale; INAS: Inventory of Non-ataxia symptoms; RBDSQ: REM Sleep Behavior Screening Questionnaire; SARA: Scale for the Assessment and Rating of Ataxia; SD: standard deviation. *CAG repeat length unavailable for six patients. ✝One patient had both expanded alleles (20 and 24): only the allele with the higher repeat number was considered for the statistics.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding ataxia severity, the SARA range was 2.5 to 25 (mean\u0026thinsp;=\u0026thinsp;13.30 and the ICARS range was 8 to 66 (mean\u0026thinsp;=\u0026thinsp;37). Moreover, we observed a high frequency of non-motor features in SCA6 patients beyond cerebellar signs: INAS Count (mean\u0026thinsp;=\u0026thinsp;1.77), RBDSQ (mean\u0026thinsp;=\u0026thinsp;4), ESS (mean\u0026thinsp;=\u0026thinsp;5.94), and FSS (mean\u0026thinsp;=\u0026thinsp;34).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMRI Analysis\u003c/h2\u003e \u003cp\u003eThe cerebellar analyses revealed significant volumetric reduction in bilateral WM (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), in bilateral cerebellar cortex (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in several cerebellar lobules: bilateral lobules I-IV (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bilateral lobule V (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bilateral lobule VI (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), vermis VI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bilateral crus I (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), left crus II (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bilateral lobule VIIb (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bilateral lobule VIIIa (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bilateral lobule VIIIb (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bilateral lobule X (left and right: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), vermis VII (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and vermis VIII (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;1). All these structures exhibited very large ES (\u0026gt;\u0026thinsp;1.5).\u003c/p\u003e \u003cp\u003eFollowing Bonferroni correction for multiple comparisons, there were no significant alterations in cerebral cortical thickness, brainstem, and deep GM. It is worth noting that there were relevant changes in the CC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ES\u0026thinsp;\u0026gt;\u0026thinsp;1.1) and in the bilateral accumbens area (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ES\u0026thinsp;\u0026gt;\u0026thinsp;0.9), and an important volumetric reduction in the superior cerebral peduncles (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with a very large ES (\u0026gt;\u0026thinsp;1.9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDTI Analysis\u003c/h2\u003e \u003cp\u003eThere were increased axial diffusivity (AD) in the left inferior cerebellar peduncle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), left middle cerebellar peduncle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), left superior cerebellar peduncle (P\u0026thinsp;=\u0026thinsp;0.001), left superior corona radiata (P\u0026thinsp;=\u0026thinsp;0.001), left fornix-stria terminalis (P\u0026thinsp;=\u0026thinsp;0.001), left sagittal stratum (P\u0026thinsp;\u0026lt;\u0026thinsp;0,001), left genu of the corpus callosum (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), left midbrain (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), right cerebral peduncle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), right fornix-stria terminalis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), right middle cerebellar peduncle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), right body of the corpus callosum (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), right midbrain (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and left optic tract (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;1). All these structures exhibited at least large ES (\u0026gt;\u0026thinsp;1.0).\u003c/p\u003e \u003cp\u003eIncreased mean diffusivity (MD) values were found in left inferior cerebellar peduncle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.030), left superior cerebellar peduncle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.038), left superior corona radiata (P\u0026thinsp;\u0026lt;\u0026thinsp;0.002), left fornix stria terminalis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.042), left sagittal stratum (P\u0026thinsp;\u0026lt;\u0026thinsp;0.011), left middle cerebellar peduncle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.016), left midbrain (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), right midbrain (P\u0026thinsp;\u0026lt;\u0026thinsp;0.020), and left optic tract (P\u0026thinsp;\u0026lt;\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eAfter Bonferroni adjustment for multiple comparisons, there were no significant alterations in MD, fractional anisotropy (FA), and radial diffusivity (RD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations Between MRI Metrics and Clinical Scores\u003c/h2\u003e \u003cp\u003eSignificant correlations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were found between MRI metrics and clinical scores for AD with INAS Count (left superior corona radiata, left middle cerebellar peduncle, bilateral midbrain, and right body of corpus callosum) and ESS (left inferior cerebellar peduncle)(Fig.\u0026nbsp;2). After applying the Bonferroni adjustment, the cerebellar volumetric reduction was significant with ICARS in right lobule X, and with ESS in vermis VII (Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we present the clinical and radiological findings from a cohort of eighteen patients with SCA6. Overall, we identified extracerebellar structural abnormalities, with some showing clinical correlations. As expected, we found significant volumetric reduction in bilateral cerebellar WM, bilateral cerebellar cortex, and several cerebellar lobules, all of which exhibited very large ES. Of particular interest, in line with our hypothesis of extracerebellar involvement in SCA6, the DTI study demonstrated increased AD in cerebellar connections and novel extracerebellar findings, particularly highlighting alterations in various supratentorial structures. All these structures exhibited at least large ES. In addition, we found significant correlations between MRI metrics and clinical scores for AD with the INAS Count in extracerebellar structures, and for AD with ESS in the left inferior cerebellar peduncle.\u003c/p\u003e \u003cp\u003eCerebellar ataxias encompass a heterogenous group of conditions that include both sporadic and genetic etiologies (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Among the autosomal dominant cerebellar ataxias, the most common group is SCA, with over fifty-one distinct types clinically described (SCA1\u0026ndash;51) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In our Ataxia Unit in Brazil, among 1,332 patients with several types of ataxias, 326 are related to SCA, with SCA6 being the fifth most frequent (5.21%) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). SCA6 is referred to as pure cerebellar ataxia, with preservation of other brain regions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, the calcium receptor subunit affected in SCA6 appears to be widespread in neurons throughout the brain (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Recent studies suggest that cerebellar degeneration becomes widespread, potentially leading to transneuronal degeneration in other brain regions (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Moreover, PET-FDG demonstrated multiple-regional brain hypometabolism, including cerebellar hemispheres, brainstem, cortical regions, and basal ganglia (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Nevertheless, finding hypometabolism in various brain regions does not necessarily mean there is neuronal degeneration. Instead, it could simply indicate metabolic dysfunction in neurons that are still structurally intact or subclinical neuropathological changes (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Falcon et al. demonstrated that, in SCA6, functional connectivity between regions of the cerebral cortex and cerebellum is accompanied by DTI metrics reflecting structural changes in the cerebral and cerebellar peduncles (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). To illustrate the possible extracerebellar and non-motor changes in SCA6, within our cohort of SCA6 patients, one patient exhibited parkinsonism and dopaminergic dysfunction on a DAT (dopamine active transporter) scan (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Additionally, we reported that patients with SCA6 experience more frequent respiratory events and sleep apnea compared to a control group (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In this context, one might speculate that SCA6 would have alterations in the spinal cord. In fact, spinal cord abnormalities appear early and worsen progressively in SCA1, SCA2, and SCA3. However, SCA6 does not exhibit any morphometric abnormalities in the spinal cord (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAD refers to diffusivity along a WM fiber tract, making it most relevant in regions where axons are coherently oriented without fiber crossings (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Increased isotropic diffusion appears to enhance both radial and axial diffusivity in chronic diseases marked by significant axonal damage (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Our patient group exhibited increased AD levels compared to the control group, observed not only in the cerebellar connecting structures (including the inferior cerebellar peduncle, middle cerebellar peduncle, superior cerebellar peduncle, cerebral peduncle, and midbrain) but also in the corona radiata, fornix-stria terminalis, sagittal stratum, genu of the corpus callosum, body of the corpus callosum, and optic tract.\u003c/p\u003e \u003cp\u003eDespite that, AD maps may not consistently align with the direction of the underlying fiber tract (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). On the other hand, MD is a more reliable DTI metric for assessing multiple fiber populations or complex fiber geometries (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Additionally, MD shows a closer relationship to neurite density compared to FA, AD, or RD measures (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). It is also a valuable tool for identifying and quantifying WM damage in neurodegenerative conditions, particularly in SCA (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). From his perspective, although no significant alterations in MD were identified following Bonferroni correction, several structures exhibited increased MD values, with findings showing asymmetrical distribution toward the left hemisphere of supratentorial regions and the left cerebellar connections. This aligns with the asymmetric alterations found in SYNE1-ataxia (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), another genetic hereditary ataxia. Neurodegenerative diseases may have lateralized abnormalities, which can be explained by the concept of selective vulnerability (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Collectively, our results reinforce the hypothesis of extracerebellar structural changes in SCA6.\u003c/p\u003e \u003cp\u003eCerebellar activity plays a role in numerous aspects of sensorimotor control and motor learning (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Nevertheless, the involvement of the cerebellum in cognitive functions is still debated. There are cerebellar pathways that connect to the frontal cortex through the cerebello-ponto-thalamo-cortical connections. Consequently, the loss of cerebellar efferents to the frontal cortex could lead to cognitive changes (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Individuals with SCA6 exhibit notable impairments in attention and executive functions (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). There is evidence that a network composed of the amygdala, various cortical regions, the hippocampus, basal forebrain, the nucleus accumbens, the stria terminalis, and the striatum participates in the modulation of memory consolidation (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Moreover, a previous study demonstrated a correlation between cognitive deficits and the MD of the stria terminalis in SCA2 (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Given that we also observed increased MD values in the stria terminalis and the striatum, it is plausible that a similar pathophysiological correlation between cognitive changes and these structures may occur in SCA6. Conducting motor-independent cognitive tests in patients with purely cerebellar ataxias is crucial to elucidate the cerebellum's role in cognition. Identifying non-motor and extracerebellar signs in SCA patients is key to the preclinical characterization of these diseases (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study uncovers new aspects of SCA6, though the findings are preliminary and should be considered withing the context of the following limitations. First, we did not establish a correlation between the radiological changes and the CACNA1A gene expression in the brain. Therefore, it remains unclear whether the extracerebellar findings result from local alterations directly induced by the mutated gene or if they indicate selective transneuronal degeneration. Second, considering that this is a cross-sectional study, it is not possible to determine if the non-motor and extra-cerebellar features developed prior to the ataxia. Further longitudinal studies are necessary to confirm these findings. Third, the Bonferroni correction adjusts p-values to reduce the risk of Type I errors in multiple statistical tests. However, it has been criticized for potentially undermining sound statistical judgment and increasing the risk of Type II errors due to its conservative nature, especially as the number of comparisons increases.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we provided valuable insights into the extracerebellar structural abnormalities associated with SCA6, fundamentally challenging the conventional understanding of SCA6 as a purely cerebellar ataxia. It remains unclear whether the findings, restricted to white matter, indicate discrete extracerebellar features of SCA6 or rather reflect engagement of the complex cerebro-cerebellar networks involved in motor, cognitive, and affective processing. From a clinical standpoint, identifying biomarkers is essential for drug development aimed at treating or slowing the disease's progression. Considering this, DTI-based analyses of cerebellar connections and supratentorial structures emerge as potential sources of biomarkers for SCA6. This underscores the need for further longitudinal studies to unravel the wider implications of SCA6 on cognitive and non-motor functions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthhors\u0026apos; Roles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Breno Kazuo Massuyama; Thiago Junqueira Ribeiro Rezende; Marcondes Cavalcante Fran\u0026ccedil;a Junior; Orlando Graziani Povoas Barsottini; Jos\u0026eacute; Luiz Pedroso.\u003c/p\u003e\n\u003cp\u003eMethodology: Breno Kazuo Massuyama; Thiago Junqueira Ribeiro Rezende;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFormal analysis and investigation: Breno Kazuo Massuyama; Thiago Junqueira Ribeiro Rezende.\u003c/p\u003e\n\u003cp\u003eWriting - original draft preparation: Breno Kazuo Massuyama\u003c/p\u003e\n\u003cp\u003eWriting - review and editing: Thiago Junqueira Ribeiro Rezende; Marcondes Cavalcante Fran\u0026ccedil;a Junior; Orlando Graziani Povoas Barsottini; Jos\u0026eacute; Luiz Pedroso.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Disclosure/Conflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that there are no conflicts of interest relevant to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources for study:\u003c/strong\u003e No specific funding was received for this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial interests:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eT.J.R.R. has consulted for Biogen and PTC and received research grants from FAPESP, Friedreich Ataxia Research Alliance (FARA),\u0026nbsp;NIH\u0026nbsp;and\u0026nbsp;Biogen.\u003cem\u003e\u0026nbsp;\u003c/em\u003eJ.L.P. receiveed research grants from FAPESP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the local Research Ethics Committee at the Federal University of Sao Paulo (No. 4.358.994).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e All the participants signed the Informed Consent to participate in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSch\u0026ouml;ls L, Bauer P, Schmidt T, Schulte T, Riess O. Autosomal dominant cerebellar ataxias: clinical features, genetics, and pathogenesis. Lancet Neurol. 2004;3(5):291\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHersheson J, Haworth A, Houlden H. The inherited ataxias: genetic heterogeneity, mutation databases, and future directions in research and clinical diagnostics. Hum Mutat. 2012;33(9):1324\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuchenko O, Bailey J, Bonnen P, Ashizawa T, Stockton DW, Amos C, et al. 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Memory consolidation and the amygdala: a systems perspective. Trends Neurosci. 2002;25(9):456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernandez-Castillo CR, Vaca-Palomares I, Galvez V, Campos-Romo A, Diaz R, Fernandez-Ruiz J. Cognitive Deficits Correlate with White Matter Deterioration in Spinocerebellar Ataxia Type 2. J Int Neuropsychol Soc. 2016;22(4):486\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoong B, Liu R, Wu L, Lu Y, Lee H. Metabolic characterization of spinocerebellar ataxia type 6. Arch Neurol. 2001;58(2):300\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-cerebellum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cere","sideBox":"Learn more about [The Cerebellum](http://link.springer.com/journal/12311)","snPcode":"12311","submissionUrl":"https://submission.nature.com/new-submission/12311/3","title":"The Cerebellum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ataxia, Spinocerebellar Ataxia, Diffusion Tensor Imaging, Magnetic Resonance Imaging","lastPublishedDoi":"10.21203/rs.3.rs-6907975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6907975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSpinocerebellar ataxias (SCA) refer to a group of autosomal dominant ataxic disorders that result from the degeneration of the cerebellum and its connections. SCA6 is described as the prototype of pure cerebellar ataxia, with preservation of other brain regions. However, the calcium receptor subunit affected in SCA6 appears to be ubiquitous in neurons throughout the brain. Additionally, there are observations of clinical involvement of non-cerebellar systems, structural cerebral damage and hypometabolism in various brain areas.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo characterize the structural brain signature in SCA6 patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eEighteen SCA6 patients underwent cross-sectional analyses using multimodal MRI-based techniques, which combined cerebral and cerebellar volumetric analyses with diffusion tensor imaging (DTI). Furthermore, we investigated whether structural abnormalities correlated with clinical findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIndividuals with SCA6, compared to non-ataxic controls, exhibited significant volumetric reduction in cerebellar white matter, cortex, and several lobules. There was increased axial diffusivity (AD) in the left inferior cerebellar peduncle, left middle cerebellar peduncle, left superior cerebellar peduncle, left superior corona radiata, left fornix-stria terminalis, left sagittal stratum, left genu of the corpus callosum, left midbrain, right cerebral peduncle, right fornix-stria terminalis, right middle cerebellar peduncle, right body of the corpus callosum, right midbrain, and left optic tract. Significant correlations were found between AD and the Inventory of Non-ataxia Symptoms Count and the Epworth Sleepiness Scale.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe provided valuable insights into the extracerebellar structural abnormalities associated with SCA6. DTI-based analyses of cerebellar connections and supratentorial structures emerge as potential sources of biomarkers for SCA6.\u003c/p\u003e","manuscriptTitle":"Brain structural impairment in spinocerebellar ataxia type 6: not restricted to the cerebellum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 08:14:18","doi":"10.21203/rs.3.rs-6907975/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-14T02:51:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T11:50:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T22:15:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13561313557422729738814072186638932822","date":"2025-10-13T21:08:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46643526830957950052128689538355962653","date":"2025-10-06T07:43:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-05T22:52:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297689027811816307379676714487791861829","date":"2025-08-17T20:11:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124875357253101714232583126940788726737","date":"2025-07-31T19:29:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-26T16:14:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-20T04:21:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-20T04:18:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Cerebellum","date":"2025-06-16T18:10:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-cerebellum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cere","sideBox":"Learn more about [The Cerebellum](http://link.springer.com/journal/12311)","snPcode":"12311","submissionUrl":"https://submission.nature.com/new-submission/12311/3","title":"The Cerebellum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"88a793a5-9e63-4a83-b34a-9dc581ac3908","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T02:39:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 08:14:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6907975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6907975","identity":"rs-6907975","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}