Distinct Contribution of Cerebellar Inferior Posterior Lobe to Motor Learning in Spinocerebellar Degeneration: A Deep Learning-Based Analysis

Distinct Contribution of Cerebellar Inferior Posterior Lobe to Motor Learning in Spinocerebellar Degeneration: A Deep Learning-Based Analysis | 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 Distinct Contribution of Cerebellar Inferior Posterior Lobe to Motor Learning in Spinocerebellar Degeneration: A Deep Learning-Based Analysis Kyota Bando, Takeru Honda, Kinya Ishikawa, Shinichi Shirai, Ichiro Yabe, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6246488/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jul, 2025 Read the published version in The Cerebellum → Version 1 posted 9 You are reading this latest preprint version Abstract Background and Objective: Spinocerebellar degeneration (SCD) is characterized by cerebellar atrophy and motor learning impairment. Using CerebNet, a deep-learning algorithm for cerebellar segmentation, this study investigated the relationship between cerebellar subregion volumes and motor learning ability. Methods : We analyzed data from 37 patients with SCD and 18 healthy controls. Using CerebNet, we segmented four cerebellar subregions: anterior lobe, superior posterior lobe, inferior posterior lobe, and vermis. Regression analyses examined associations between cerebellar volumes and motor learning performance (Adaptation Index; AI ) and ataxia severity (Scale for Assessment and Rating of Ataxia; SARA). Results : The inferior posterior lobe volume showed significant positive association with AI in both single (B = 0.09; 95% CI: [0.03, 0.16]) and multiple linear regression analyses (B = 0.11; 95% CI: [0.008, 0.2]). SARA scores correlated with anterior lobe, superior posterior lobe, and vermis volumes in single linear regression analyses, but these associations were not maintained in multiple linear regression analysis. This selective association suggests the inferior posterior lobe's specialized role in motor learning processes. Conclusion : This study reveals the inferior posterior lobe's distinct role in motor learning in SCD patients, advancing our understanding of cerebellar function and potentially informing targeted rehabilitation approaches. Our findings highlight the value of advanced imaging technologies in understanding structure-function relationships in cerebellar disorders. Spinocerebellar Degenerations Magnetic Resonance Imaging Cerebellar Subregions Deep Learning Atrophy Figures Figure 1 Introduction Spinocerebellar degeneration (SCD) is a progressive neurodegenerative disorder characterised by cerebellar atrophy. Beyond motor ataxia, patients with SCD exhibit impaired motor learning ability, which significantly impacts their rehabilitation potential [ 1 , 2 ]. Motor learning assessment is a crucial indicator for designing rehabilitation strategies, with prism adaptation tasks emerging as a validated assessment tool [ 3 – 5 ]. The cerebellum comprises anatomically and functionally distinct regions, each of which plays a specialised role [ 6 ]. The anterior lobe mediates sensorimotor control, the superior posterior lobe processes language and spatial cognition, the inferior posterior lobe integrates visual information with motor control, and the vermis regulates posture and gait [ 7 , 8 ]. Understanding the specific cerebellar regions associated with the different symptoms of SCD is crucial for developing targeted interventions. Previous imaging studies have primarily employed voxel-based morphometry (VBM) analysis to investigate the relationship between cerebellar symptoms and volume [ 9 , 10 ]. However, VBM analysis has limitations, including potential false positives due to multiple comparisons and challenges in accurately normalising complex cerebellar structures [ 11 – 13 ]. The intricate folding pattern of the cerebellum, which contains 80% of the brain’s neurons in a relatively small area, poses challenges for conventional imaging analyses [ 14 ]. Surface-based analysis (SBA) has emerged as an alternative approach for analysing complex structures without requiring spatial normalisation [ 15 ]. Studies have demonstrated that SBA has superior sensitivity in detecting structural changes compared with VBM, particularly in conditions affecting intricate brain structures [ 16 ]. However, SBA has limitations in terms of accurate segmentation of complex cerebellar structures [ 17 , 18 ]. The recent development of CerebNet, a deep learning-based cerebellar segmentation method, represents a significant advancement in this field [ 19 ]. This technology enables the precise volumetric analysis of cerebellar subregions from individual magnetic resonance imaging (MRI) data with demonstrated accuracy in patients [ 20 ]. This technological breakthrough allows the precise identification of the cerebellar regions associated with motor learning. This study aimed to leverage the capabilities of CerebNet to investigate the relationship between cerebellar subregion volumes and motor learning ability, as measured using the adaptation index ( AI ), in patients with SCD. We hypothesised that specific cerebellar subregions would show distinct associations with motor learning performance, potentially providing insights into targeted rehabilitation strategies. Patients and Methods Study population This multicentre, cross-sectional study collected data from five research institutions between 2019 and 2021. The study cohort comprised 37 patients with SCD, including spinocerebellar ataxia type 6 (SCA6, n = 10), spinocerebellar ataxia type 31 (SCA31, n = 15), Machado–Joseph disease/spinocerebellar ataxia type 3 (MJD/SCA3, n = 5), and multiple system atrophy–cerebellar type (MSA-C, n = 7), and 18 healthy controls (HC). All participants provided written informed consent after receiving a detailed explanation of the study protocol. Genetic confirmation was available for SCA6, SCA31, and MJD/SCA3 cases, whereas neurologists established MSA-C diagnoses according to the Movement Disorder Society criteria. Motor ataxia severity was assessed using the Scale for Assessment and Rating of Ataxia (SARA) [21]. This study was approved by the local ethics committee of the National Centre of Neurology and Psychiatry) Approval No. A2019-106. All participants were briefed about the experiment and signed a consent form before participation. Prism adaptation assessment The experimental setup was based on the protocol described by Hashimoto et al. [3]. The participants wore goggles equipped with a Fresnel prism plate that induced a 25° rightward visual field shift. An electrically controlled shutter screen in the goggles provides visual feedback during reaching movements. Each trial was initiated by touching the index finger to an ear-mounted sensor switch and then reaching a target (15-mm radius white circle) displayed on a touchscreen. Shutter activation during reaching prevents visual feedback until touch completion. Movement correction in the subsequent trials relied on error feedback from previous studies. Task control was managed using the Katano Tool Software. The assessment protocol consisted of three sequential sessions: 50 baseline trials without a prism (BASELINE), 100 trials with a prism (PRISM), and 50 trials after prism removal (REMOVAL). Following the established criteria, successful trials were defined as those with touch errors of < 25 mm. Analysis of the prism experiment data Motor learning performance was quantified using the AI [3], calculated as follows: AI = a × b × c Where: a: Adaptation index (probability of correct touches in the final 10 PRISM trials) b: Retention index (probability of incorrect touches in the first five REMOVAL trials) c: Extinction index (probability of correct touches in the final 10 REMOVAL trials). AI is a comprehensive measure of motor adaptation capability, encompassing initial adaptation, retention, and readaptation abilities. MRI acquisition Structural MRI data were acquired using two different 3-Tesla MRI systems. The first system was a Siemens scanner equipped with a 32-channel phased-array head coil. Three-dimensional T1-weighted images were obtained using a magnetisation-prepared rapid gradient echo sequence with the following parameters: repetition time (TR) = 1,900 ms, echo time (TE) = 2.52 ms, inversion time (TI) = 900 ms, flip angle = 9°, field of view (FOV) = 256 × 256 mm², and voxel size = 1 × 1 × 1 mm. The second system was a GE scanner with a 24-channel phased array head coil, using a three-dimensional T1-weighted spoiled gradient echo sequence with parameters: TR = 6.76 ms, TE = 2.62 ms, TI = 1,400 ms, flip angle = 14°, FOV = 256 × 256 mm², and voxel size = 1 × 1 × 1 mm³. Previous research has demonstrated that interscanner volumetric differences can be effectively corrected using statistical postprocessing [22]. Image analysis Cerebellar segmentation was performed using CerebNet, implemented using the FastSurfer pipeline [19]. Following established protocols, we analysed four primary cerebellar segments: the anterior lobe (lobules I–V), superior posterior lobe (lobules VI–VII), inferior posterior lobe (lobules VIII–IX), and midline vermis [20]. The segmentation quality was verified by visual inspection of the overlay images (Fig. 1). Volumetric measurements were normalised using the estimated total intracranial volume (eTIV) from FastSurfer’s aseg.stats data. Scanner-related measurement bias was corrected using the ComBat method with age and sex as covariates [23, 24]. Statistical analysis Statistical analyses were performed using the R Software (version 4.3.3) [25]. Following previous methodologies, we normalised the volumes using eTIV and calculated Z-scores relative to HC to account for individual variation [20]. Regression analyses were used to examine the relationship between the AI and cerebellar volume using two approaches: initial single linear regression analyses for each cerebellar segment, followed by multiple linear regression analyses incorporating all segments. Statistical significance was set at p < 0.05. Results Demographic and clinical characteristics The SCD cohort demonstrated moderate ataxia severity, with a mean SARA score of 13.2 (SD: 5.1) points. The handedness distribution revealed only one left-handed individual in the SCD group. All participants performed the prism adaptation task with their right hand to minimise potential handedness effects. The mean AI score in the SCD group was 0.1 (SD: 0.2), with no participants exceeding the HC threshold of 0.6 (Table 1). Relationship between AI and cerebellar volumes Single linear regression analysis revealed a significant positive association between the AI and inferior posterior lobe volume (B = 0.09; 95% confidence interval [CI]: [0.03, 0.16]; p = .008; Table 2). Other regions showed no significant correlations, including the anterior lobe (B = 0.009; 95% CI: [-0.06, 0.08]; p = .79), superior posterior lobe (B = 0.04; 95% CI: [-0.02, 0.11]; p = .16), and vermis (B = 0.02; 95% CI: [-0.04, 0.09]; p = .47). Multiple linear regression analysis demonstrated that only the inferior posterior lobe volume was significantly associated with AI (B = 0.11; 95% CI: [0.008, 0.2]; p = .03). In contrast, other regions showed no significant associations: anterior lobe (B = -0.05; 95% CI: [-0.15, 0.05]; p = .3), superior posterior lobe (B = 0.02; 95% CI: [-0.12, 0.16]; p = .74), and vermis (B = -0.007; 95% CI: [-0.13, 0.11]; p = .9). Relationship between SARA scores and cerebellar volumes Single linear regression analyses demonstrated significant negative correlations between SARA scores and volumes of the anterior lobe (B = -2.29; 95% CI: [-4.21, -0.38]; p = .02; Table 3), superior posterior lobe (B = -2.6; 95% CI: [-4.27, -0.92]; p = .003), and vermis (B = -3.08; 95% CI: [-4.78, -1.39]; p = .001). The inferior posterior lobe showed a trend (B = -1.9; 95% CI: [-4.01, 0.21]; p = .07). In multiple linear regression analysis, no individual region maintained significant associations with SARA scores: anterior lobe (B = 0.25; 95% CI: [-2.62, 3.13]; p = .86), superior posterior lobe (B = -0.49; 95% CI: [-4.45, 3.47]; p = .8), inferior posterior lobe (B = -0.12; 95% CI: [-2.87, 2.63]; p = .92), and vermis (B = -2.77; 95% CI: [-6.22, 0.67]; p = .11). Discussion This study used CerebNet, a deep-learning-based cerebellar segmentation algorithm, to investigate the relationship between cerebellar subregion volumes and motor learning ability in patients with SCD. This is the first comprehensive analysis of structure-function relationships in cerebellar subregions using advanced segmentation technology in patients with SCD. Association between motor learning and cerebellar subregions Regression analyses revealed a specific association between the inferior posterior lobe volume and motor learning performance, as quantified by AI . Notably, this association remained significant in the single linear regression (B = 0.09, 95% CI [0.03, 0.16], p = .008) and multiple linear regression analyses (B = 0.11, 95% CI [0.008, 0.2], p = .03). The persistence of this association in the multiple linear regression analysis, in which the effects of other cerebellar regions were controlled, suggests the importance of the inferior posterior lobe in motor learning processes. The association between inferior posterior lobe volume and motor learning is supported by converging evidence from multiple methodological approaches, including functional neuroimaging, clinical observations, and animal studies [2, 26]. Functional MRI studies have documented lobule VIII activation during prism adaptation tasks [27]. Non-human primate studies have demonstrated that cerebellar hemisphere lobules VII, VIII, and IX and the dentate nucleus are essential for prism adaptation during reaching tasks [28, 29]. Additionally, neurophysiological studies have shown that hand-reaching error signals are encoded in lobules V, VI, and VIII [30]. Several functional MRI studies have reported activation of the anterior and superior posterior lobes during prism adaptation tasks [31–33]. However, these discrepancies may be attributed to methodological differences in cerebellar image processing. Previous studies reporting anterior and superior posterior lobe activation have generally employed standard normalisation procedures without cerebellum-specific optimisation. In contrast, studies using specialised cerebellar templates, such as the Spatially Unbiased Infratentorial Template, have consistently identified lobule VIII activation [27]. The current study’s use of CerebNet enabled a more precise volumetric quantification of cerebellar subregions than conventional approaches. Relationship between cerebellar volumes and SARA scores Single linear regression analyses revealed significant negative correlations between SARA scores and volumes of three cerebellar regions: anterior lobe (B = -2.29, 95% CI [-4.21, -0.38], p = .02), superior posterior lobe (B = -2.6; 95% CI: [-4.27, -0.92]; p = .003), and vermis (B = -3.08; 95% CI: [-4.78, -1.39]; p = .001). However, these associations were not maintained in multiple linear regression analysis. The SARA is a comprehensive assessment tool that evaluates multiple aspects of motor function, including gait, standing balance, sitting balance, and limb ataxia [21]. Previous studies have demonstrated that distinct cerebellar regions are responsible for various motor functions, such as gait control, balance regulation, limb coordination, and speech articulation [6, 34, 35]. In multiple regression analysis, the distinct cerebellar localisation of these motor functions may explain why no significant associations were detected between the individual cerebellar regions and SARA scores. In contrast, the inferior posterior lobe was the only region that did not show significant association with SARA scores even in single linear regression analysis (B = -1.9; 95% CI: [-4.01, 0.21]; p = .07). This finding provides important evidence that the inferior posterior lobe may be involved in information processing beyond motor function. Clinical studies have reported that patients with posterior inferior cerebellar artery infarctions affecting lobule VIII typically present with minimal classical cerebellar motor symptoms such as gait ataxia, limb/truncal ataxia, and dysarthria [36, 37]. Recent functional anatomical studies have suggested that lobule VIII is involved in higher-order motor control rather than in simple motor execution [38]. Specifically, lobule VIII plays a crucial role in motor-cognitive integration and is more intensely engaged in motor processing, which requires task focus [39]. These characteristics align with the requirements of the prism adaptation task used in this study. These findings support our observation that the inferior posterior lobe volume is strongly correlated with AI but not SARA scores. Limitations This study used CerebNet, a cerebellar segmentation algorithm that divides the cerebellum into 30 segments [19]. However, due to sample size constraints, we limited our analysis to four major cerebellar segments: the anterior lobe (consisting of lobules I–V), superior posterior lobe (consisting of lobules VI–VII), inferior posterior lobe (consisting of lobules VIII–IX), and the midline vermis. This segmentation allowed us to identify important structure-function relationships; however, a more detailed analysis of individual lobules could provide additional insights into the specific roles of cerebellar subregions in motor learning and ataxia. Our findings established correlations between cerebellar volumes and the AI and SARA scores; however, the current analysis did not constitute a predictive model. Future studies with larger sample sizes should aim to develop and validate regression prediction models that can potentially inform clinical prognoses and rehabilitation strategies [40]. Additionally, research incorporating a more detailed cerebellar segmentation analysis could help elucidate the specific contributions of individual lobules to motor learning and ataxic symptoms in SCD. Conclusion This study provides the first detailed analysis of cerebellar subregion volumes in patients with SCD using the CerebNet deep learning algorithm, revealing a specific association between motor learning ability and the inferior posterior lobe. The SARA scores correlated with multiple cerebellar regions, reflecting the distributed nature of motor function; however, the inferior posterior lobe showed a unique relationship with motor learning capacity. These findings suggest a specialised role in integrating sensory and motor information in adaptive learning processes. Declarations Acknowledgements We thank the patients and their families who participated in this study, as well as Taro Kato, Yosuke Ariake, Kyoko Todoroki, Wakana Oba, and Yu Ogasawara, for their contributions to the organisation and data collection. Ethical approval. This study was approved by the local ethics committee of the National Centre of Neurology and Psychiatry) Approval No. A2019-106. All participants were briefed about the experiment and signed a consent form before participation. Competing interests. The authors declare no competing interests. Authors' contributions. K.B., T.H., Y.T., and H.M. contributed to the conception and design of the study, statistical analyses, drafting of the text, and preparation of the figures. K.B., T. H., K.I., S.S., I.Y., T.I., O.O., Y.H., F.T., Y.K., M.K., T.S., R.H., and T.K. performed data acquisition and analysis. Funding. This work was supported by Grants-in-Aid for the Practical Research Project for Rare and Intractable Diseases from AMED of Japan 21ek0109420h0003 and Grants-in-Aid for JSPS KAKENHI Grant Number JP21K17485 and Intramural Research Grants (Grant Numbers 3−4 and 6–5) for Neurological and Psychiatric Disorders of National Center of Neurology and Psychiatry. References Donchin O, Timmann D. Helping patients with cerebellar disorders make most of their remaining learning capacity [Internet]. 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Supplementary Files Tables123.docx Cite Share Download PDF Status: Published Journal Publication published 16 Jul, 2025 Read the published version in The Cerebellum → Version 1 posted Editorial decision: Revision requested 26 May, 2025 Reviews received at journal 26 May, 2025 Reviews received at journal 04 Apr, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers invited by journal 24 Mar, 2025 Editor assigned by journal 18 Mar, 2025 Submission checks completed at journal 18 Mar, 2025 First submitted to journal 17 Mar, 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-6246488","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433170613,"identity":"f9519023-47bf-4d0c-8993-af44af180246","order_by":0,"name":"Kyota Bando","email":"","orcid":"","institution":"National Center of Neurology and Psychiatry","correspondingAuthor":false,"prefix":"","firstName":"Kyota","middleName":"","lastName":"Bando","suffix":""},{"id":433170614,"identity":"cd86015e-27a0-4ee1-8323-70e326acb9c6","order_by":1,"name":"Takeru Honda","email":"","orcid":"","institution":"Institute of Science Tokyo (Tokyo Medical and Dental University)","correspondingAuthor":false,"prefix":"","firstName":"Takeru","middleName":"","lastName":"Honda","suffix":""},{"id":433170615,"identity":"27ef22c9-d4b2-4efe-a5b5-4f98dd129508","order_by":2,"name":"Kinya Ishikawa","email":"","orcid":"","institution":"Graduate School, Institute of Science Tokyo (Tokyo Medical and Dental University)","correspondingAuthor":false,"prefix":"","firstName":"Kinya","middleName":"","lastName":"Ishikawa","suffix":""},{"id":433170616,"identity":"57dcc27a-4f76-4495-b8a7-8ddf063bda54","order_by":3,"name":"Shinichi Shirai","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Shinichi","middleName":"","lastName":"Shirai","suffix":""},{"id":433170617,"identity":"27201d9a-380a-409e-bc85-16bf83c6a276","order_by":4,"name":"Ichiro Yabe","email":"","orcid":"","institution":"Hokkaido 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University","correspondingAuthor":false,"prefix":"","firstName":"Yuichi","middleName":"","lastName":"Higashiyama","suffix":""},{"id":433170621,"identity":"40410f46-d243-4a19-93fe-8aeb0661b902","order_by":8,"name":"Fumiaki Tanaka","email":"","orcid":"","institution":"Yokohama City University","correspondingAuthor":false,"prefix":"","firstName":"Fumiaki","middleName":"","lastName":"Tanaka","suffix":""},{"id":433170622,"identity":"431f02d2-60dd-4dcd-b167-fffc172af125","order_by":9,"name":"Yoshiyuki Kishimoto","email":"","orcid":"","institution":"Nagoya University","correspondingAuthor":false,"prefix":"","firstName":"Yoshiyuki","middleName":"","lastName":"Kishimoto","suffix":""},{"id":433170623,"identity":"b55705ff-076f-4bbe-95a5-19cd5407cb73","order_by":10,"name":"Masahisa Katsuno","email":"","orcid":"","institution":"Nagoya University","correspondingAuthor":false,"prefix":"","firstName":"Masahisa","middleName":"","lastName":"Katsuno","suffix":""},{"id":433170624,"identity":"1a9c6de1-4659-40fd-ac83-87c383af2750","order_by":11,"name":"Takahiro Shimizu","email":"","orcid":"","institution":"Tottori University","correspondingAuthor":false,"prefix":"","firstName":"Takahiro","middleName":"","lastName":"Shimizu","suffix":""},{"id":433170625,"identity":"cc053e9e-29e0-4845-9aaf-51efc563ccee","order_by":12,"name":"Ritsuko Hanajima","email":"","orcid":"","institution":"Tottori University","correspondingAuthor":false,"prefix":"","firstName":"Ritsuko","middleName":"","lastName":"Hanajima","suffix":""},{"id":433170626,"identity":"d0b15dfc-4db1-40a7-8980-e90fb36708ec","order_by":13,"name":"Takumi Kanata","email":"","orcid":"","institution":"National Center of Neurology and Psychiatry","correspondingAuthor":false,"prefix":"","firstName":"Takumi","middleName":"","lastName":"Kanata","suffix":""},{"id":433170627,"identity":"eee2c1b7-3051-4617-8f59-966ca1e6a8e6","order_by":14,"name":"Yuji Takahashi","email":"","orcid":"","institution":"National Center of Neurology and Psychiatry","correspondingAuthor":false,"prefix":"","firstName":"Yuji","middleName":"","lastName":"Takahashi","suffix":""},{"id":433170628,"identity":"e66551bf-ac9b-457f-9388-53cc26b58f26","order_by":15,"name":"Hidehiro MizusawaMD","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACZijNjxA6QKQWyQaitcCAAbEKGXTb2Z9J/qg5LG98u/nZ4wKGWjkGxrP4dZsdZkiT5jl22HDbnWPmxjMYjhszMJxLIKTlmDQD223GbTdy2KR5GI4lNjCcMSCghbFN8se/2/abZxCvhZlNgrftduIGCbCWGmK0sDFb8/b9T55xI81MeobBAWM2gn45f/zhzR/f0mz7ZyQ/ky6oqJPjlyAQYiiAmcHgMAObxBnidYBSQh0w6fSQoGUUjIJRMApGAgAAzpJEYyDhpHwAAAAASUVORK5CYII=","orcid":"","institution":"National Center of Neurology and Psychiatry","correspondingAuthor":true,"prefix":"","firstName":"Hidehiro","middleName":"","lastName":"MizusawaMD","suffix":""}],"badges":[],"createdAt":"2025-03-17 16:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6246488/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6246488/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12311-025-01887-y","type":"published","date":"2025-07-16T16:05:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79263059,"identity":"39fd404b-ae70-49a3-b590-8ba1fb2f5dee","added_by":"auto","created_at":"2025-03-26 09:48:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":149808,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative example of cerebellar segmentation\u003c/p\u003e\n\u003cp\u003eNote: Representative overlays of CerebNet segmentation results on individual T1-weighted images. Red regions indicate grey matter, and blue regions indicate white matter. The upper panels show the segmentation results from a healthy control participant, whereas the lower panels show the results from a patient with SCD (coronal and sagittal slices). HC, healthy control; SCD, spinocerebellar degeneration\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6246488/v1/d89ec7cc1c6615d1049d999d.jpg"},{"id":87220357,"identity":"10ce8e4d-66e8-4954-86ad-6c9070bec11f","added_by":"auto","created_at":"2025-07-21 16:11:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":831183,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6246488/v1/04a55ac6-dfb0-41d6-989f-af218db92d28.pdf"},{"id":79263061,"identity":"034a9645-028a-4760-8ea3-c3f727fc4cf5","added_by":"auto","created_at":"2025-03-26 09:48:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":115621,"visible":true,"origin":"","legend":"","description":"","filename":"Tables123.docx","url":"https://assets-eu.researchsquare.com/files/rs-6246488/v1/41c2fd28baf1f28419ec9e70.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinct Contribution of Cerebellar Inferior Posterior Lobe to Motor Learning in Spinocerebellar Degeneration: A Deep Learning-Based Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpinocerebellar degeneration (SCD) is a progressive neurodegenerative disorder characterised by cerebellar atrophy. Beyond motor ataxia, patients with SCD exhibit impaired motor learning ability, which significantly impacts their rehabilitation potential [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Motor learning assessment is a crucial indicator for designing rehabilitation strategies, with prism adaptation tasks emerging as a validated assessment tool [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe cerebellum comprises anatomically and functionally distinct regions, each of which plays a specialised role [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The anterior lobe mediates sensorimotor control, the superior posterior lobe processes language and spatial cognition, the inferior posterior lobe integrates visual information with motor control, and the vermis regulates posture and gait [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Understanding the specific cerebellar regions associated with the different symptoms of SCD is crucial for developing targeted interventions.\u003c/p\u003e \u003cp\u003ePrevious imaging studies have primarily employed voxel-based morphometry (VBM) analysis to investigate the relationship between cerebellar symptoms and volume [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, VBM analysis has limitations, including potential false positives due to multiple comparisons and challenges in accurately normalising complex cerebellar structures [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The intricate folding pattern of the cerebellum, which contains 80% of the brain\u0026rsquo;s neurons in a relatively small area, poses challenges for conventional imaging analyses [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSurface-based analysis (SBA) has emerged as an alternative approach for analysing complex structures without requiring spatial normalisation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies have demonstrated that SBA has superior sensitivity in detecting structural changes compared with VBM, particularly in conditions affecting intricate brain structures [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, SBA has limitations in terms of accurate segmentation of complex cerebellar structures [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe recent development of CerebNet, a deep learning-based cerebellar segmentation method, represents a significant advancement in this field [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This technology enables the precise volumetric analysis of cerebellar subregions from individual magnetic resonance imaging (MRI) data with demonstrated accuracy in patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This technological breakthrough allows the precise identification of the cerebellar regions associated with motor learning.\u003c/p\u003e \u003cp\u003eThis study aimed to leverage the capabilities of CerebNet to investigate the relationship between cerebellar subregion volumes and motor learning ability, as measured using the adaptation index (\u003cem\u003eAI\u003c/em\u003e), in patients with SCD. We hypothesised that specific cerebellar subregions would show distinct associations with motor learning performance, potentially providing insights into targeted rehabilitation strategies.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis multicentre, cross-sectional study collected data from five research institutions between 2019 and 2021. The study cohort comprised 37 patients with SCD, including spinocerebellar ataxia type 6 (SCA6, n = 10), spinocerebellar ataxia type 31 (SCA31, n = 15), Machado\u0026ndash;Joseph disease/spinocerebellar ataxia type 3 (MJD/SCA3, n = 5), and multiple system atrophy\u0026ndash;cerebellar type (MSA-C, n = 7), and 18 healthy controls (HC). All participants provided written informed consent after receiving a detailed explanation of the study protocol. Genetic confirmation was available for SCA6, SCA31, and MJD/SCA3 cases, whereas neurologists established MSA-C diagnoses according to the Movement Disorder Society criteria. Motor ataxia severity was assessed using the Scale for Assessment and Rating of Ataxia (SARA) [21]. This study was approved by the local ethics committee of the National Centre of Neurology and Psychiatry) Approval No. A2019-106. All participants were briefed about the experiment and signed a consent form before participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrism adaptation assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental setup was based on the protocol described by Hashimoto et al. [3]. The participants wore goggles equipped with a Fresnel prism plate that induced a 25\u0026deg; rightward visual field shift. An electrically controlled shutter screen in the goggles provides visual feedback during reaching movements. Each trial was initiated by touching the index finger to an ear-mounted sensor switch and then reaching a target (15-mm radius white circle) displayed on a touchscreen. Shutter activation during reaching prevents visual feedback until touch completion. Movement correction in the subsequent trials relied on error feedback from previous studies. Task control was managed using the Katano Tool Software.\u003c/p\u003e\n\u003cp\u003eThe assessment protocol consisted of three sequential sessions: 50 baseline trials without a prism (BASELINE), 100 trials with a prism (PRISM), and 50 trials after prism removal (REMOVAL). Following the established criteria, successful trials were defined as those with touch errors of \u0026lt; 25 mm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of the prism experiment data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMotor learning performance was quantified using the \u003cem\u003eAI\u003c/em\u003e [3], calculated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAI\u003c/em\u003e = a \u0026times; b \u0026times; c\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003ea: Adaptation index (probability of correct touches in the final 10 PRISM trials)\u003c/p\u003e\n\u003cp\u003eb: Retention index (probability of incorrect touches in the first five REMOVAL trials)\u003c/p\u003e\n\u003cp\u003ec: Extinction index (probability of correct touches in the final 10 REMOVAL trials).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAI\u0026nbsp;\u003c/em\u003eis a comprehensive measure of motor adaptation capability, encompassing initial adaptation, retention, and readaptation abilities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStructural MRI data were acquired using two different 3-Tesla MRI systems. The first system was a Siemens scanner equipped with a 32-channel phased-array head coil. Three-dimensional T1-weighted images were obtained using a magnetisation-prepared rapid gradient echo sequence with the following parameters: repetition time (TR) = 1,900 ms, echo time (TE) = 2.52 ms, inversion time (TI) = 900 ms, flip angle = 9\u0026deg;, field of view (FOV) = 256 \u0026times; 256 mm\u0026sup2;, and voxel size = 1 \u0026times; 1 \u0026times; 1 mm. The second system was a GE scanner with a 24-channel phased array head coil, using a three-dimensional T1-weighted spoiled gradient echo sequence with parameters: TR = 6.76 ms, TE = 2.62 ms, TI = 1,400 ms, flip angle = 14\u0026deg;, FOV = 256 \u0026times; 256 mm\u0026sup2;, and voxel size = 1 \u0026times; 1 \u0026times; 1 mm\u0026sup3;. Previous research has demonstrated that interscanner volumetric differences can be effectively corrected using statistical postprocessing [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCerebellar segmentation was performed using CerebNet, implemented using the FastSurfer pipeline [19]. Following established protocols, we analysed four primary cerebellar segments: the anterior lobe (lobules I\u0026ndash;V), superior posterior lobe (lobules VI\u0026ndash;VII), inferior posterior lobe (lobules VIII\u0026ndash;IX), and midline vermis [20]. The segmentation quality was verified by visual inspection of the overlay images (Fig. 1). Volumetric measurements were normalised using the estimated total intracranial volume (eTIV) from FastSurfer\u0026rsquo;s aseg.stats data. Scanner-related measurement bias was corrected using the ComBat method with age and sex as covariates [23, 24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using the R Software (version 4.3.3) [25]. Following previous methodologies, we normalised the volumes using eTIV and calculated Z-scores relative to HC to account for individual variation [20]. Regression analyses were used to examine the relationship between the \u003cem\u003eAI\u003c/em\u003e and cerebellar volume using two approaches: initial single linear regression analyses for each cerebellar segment, followed by multiple linear regression analyses incorporating all segments. Statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SCD cohort demonstrated moderate ataxia severity, with a mean SARA score of 13.2 (SD: 5.1) points. The handedness distribution revealed only one left-handed individual in the SCD group. All participants performed the prism adaptation task with their right hand to minimise potential handedness effects. The mean \u003cem\u003eAI\u0026nbsp;\u003c/em\u003escore in the SCD group was 0.1 (SD: 0.2), with no participants exceeding the HC threshold of 0.6 (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between \u003cem\u003eAI\u0026nbsp;\u003c/em\u003eand cerebellar volumes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle linear regression analysis revealed a significant positive association between the \u003cem\u003eAI\u0026nbsp;\u003c/em\u003eand inferior posterior lobe volume (B = 0.09; 95% confidence interval [CI]: [0.03, 0.16]; p = .008; Table 2). Other regions showed no significant correlations, including the anterior lobe (B = 0.009; 95% CI: [-0.06, 0.08]; p = .79), superior posterior lobe (B = 0.04; 95% CI: [-0.02, 0.11]; p = .16), and vermis (B = 0.02; 95% CI: [-0.04, 0.09]; p = .47).\u003c/p\u003e\n\u003cp\u003eMultiple linear regression analysis demonstrated that only the inferior posterior lobe volume was significantly associated with \u003cem\u003eAI\u0026nbsp;\u003c/em\u003e(B = 0.11; 95% CI: [0.008, 0.2]; p = .03). In contrast, other regions showed no significant associations: anterior lobe (B = -0.05; 95% CI: [-0.15, 0.05]; p = .3), superior posterior lobe (B = 0.02; 95% CI: [-0.12, 0.16]; p = .74), and vermis (B = -0.007; 95% CI: [-0.13, 0.11]; p = .9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between SARA scores and cerebellar volumes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle linear regression analyses demonstrated significant negative correlations between SARA scores and volumes of the anterior lobe (B = -2.29; 95% CI: [-4.21, -0.38]; p = .02; Table 3), superior posterior lobe (B = -2.6; 95% CI: [-4.27, -0.92]; p = .003), and vermis (B = -3.08; 95% CI: [-4.78, -1.39]; p = .001). The inferior posterior lobe showed a trend (B = -1.9; 95% CI: [-4.01, 0.21]; p = .07).\u003c/p\u003e\n\u003cp\u003eIn multiple linear regression analysis, no individual region maintained significant associations with SARA scores: anterior lobe (B = 0.25; 95% CI: [-2.62, 3.13]; p = .86), superior posterior lobe (B = -0.49; 95% CI: [-4.45, 3.47]; p = .8), inferior posterior lobe (B = -0.12; 95% CI: [-2.87, 2.63]; p = .92), and vermis (B = -2.77; 95% CI: [-6.22, 0.67]; p = .11).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study used CerebNet, a deep-learning-based cerebellar segmentation algorithm, to investigate the relationship between cerebellar subregion volumes and motor learning ability in patients with SCD. This is the first comprehensive analysis of structure-function relationships in cerebellar subregions using advanced segmentation technology in patients with SCD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between motor learning and cerebellar subregions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegression analyses revealed a specific association between the inferior posterior lobe volume and motor learning performance, as quantified by \u003cem\u003eAI\u003c/em\u003e. Notably, this association remained significant in the single linear regression (B = 0.09, 95% CI [0.03, 0.16], p = .008) and multiple linear regression analyses (B = 0.11, 95% CI [0.008, 0.2], p = .03). The persistence of this association in the multiple linear regression analysis, in which the effects of other cerebellar regions were controlled, suggests the importance of the inferior posterior lobe in motor learning processes.\u003c/p\u003e\n\u003cp\u003eThe association between inferior posterior lobe volume and motor learning is supported by converging evidence from multiple methodological approaches, including functional neuroimaging, clinical observations, and animal studies [2, 26]. Functional MRI studies have documented lobule VIII activation during prism adaptation tasks [27]. Non-human primate studies have demonstrated that cerebellar hemisphere lobules VII, VIII, and IX and the dentate nucleus are essential for prism adaptation during reaching tasks [28, 29]. Additionally, neurophysiological studies have shown that hand-reaching error signals are encoded in lobules V, VI, and VIII [30].\u003c/p\u003e\n\u003cp\u003eSeveral functional MRI studies have reported activation of the anterior and superior posterior lobes during prism adaptation tasks [31\u0026ndash;33]. However, these discrepancies may be attributed to methodological differences in cerebellar image processing. Previous studies reporting anterior and superior posterior lobe activation have generally employed standard normalisation procedures without cerebellum-specific optimisation. In contrast, studies using specialised cerebellar templates, such as the Spatially Unbiased Infratentorial Template, have consistently identified lobule VIII activation [27]. The current study\u0026rsquo;s use of CerebNet enabled a more precise volumetric quantification of cerebellar subregions than conventional approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between cerebellar volumes and SARA scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle linear regression analyses revealed significant negative correlations between SARA scores and volumes of three cerebellar regions: anterior lobe (B = -2.29, 95% CI [-4.21, -0.38], p = .02), superior posterior lobe (B = -2.6;\u0026nbsp;95% CI: [-4.27, -0.92]; p = .003), and vermis (B = -3.08; 95% CI: [-4.78, -1.39]; p = .001). However, these associations were not maintained in multiple linear\u0026nbsp;regression analysis. The SARA is a comprehensive assessment tool that evaluates multiple aspects of motor function, including gait, standing balance, sitting balance, and limb ataxia [21]. Previous studies have demonstrated that distinct cerebellar regions are responsible for various motor functions, such as gait control, balance regulation, limb coordination, and speech articulation [6, 34, 35]. In multiple regression analysis, the distinct cerebellar localisation of these motor functions may explain why no significant associations were detected between the individual cerebellar regions and SARA scores.\u003c/p\u003e\n\u003cp\u003eIn contrast, the inferior posterior lobe was the only region that did not show significant association with SARA scores even in single linear regression analysis (B = -1.9; 95% CI: [-4.01, 0.21]; p = .07). This finding provides important evidence that the inferior posterior lobe may be involved in information processing beyond motor function. Clinical studies have reported that patients with posterior inferior cerebellar artery infarctions affecting lobule VIII typically present with minimal classical cerebellar motor symptoms such as gait ataxia, limb/truncal ataxia, and dysarthria [36, 37]. Recent functional anatomical studies have suggested that lobule VIII is involved in higher-order motor control rather than in simple motor execution [38]. Specifically, lobule VIII plays a crucial role in motor-cognitive integration and is more intensely engaged in motor processing, which requires task focus [39]. These characteristics align with the requirements of the prism adaptation task used in this study. These findings support our observation that the inferior posterior lobe volume is strongly correlated with \u003cem\u003eAI\u003c/em\u003e but not SARA scores.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used CerebNet, a cerebellar segmentation algorithm that divides the cerebellum into 30 segments [19]. However, due to sample size constraints, we limited our analysis to four major cerebellar\u0026nbsp;segments: the anterior lobe (consisting of lobules I\u0026ndash;V), superior posterior lobe (consisting of lobules VI\u0026ndash;VII), inferior posterior lobe (consisting of lobules VIII\u0026ndash;IX), and the midline vermis. This segmentation allowed us to identify important structure-function relationships; however, a more detailed analysis of individual lobules could provide additional insights into the specific roles of cerebellar subregions in motor learning and ataxia.\u003c/p\u003e\n\u003cp\u003eOur findings established correlations between cerebellar volumes and the \u003cem\u003eAI\u0026nbsp;\u003c/em\u003eand SARA scores; however, the current analysis did not constitute a predictive model. Future studies with larger sample sizes should aim to develop and validate regression prediction models that can potentially inform clinical prognoses and rehabilitation strategies [40]. Additionally, research incorporating a more detailed cerebellar segmentation analysis could help elucidate the specific contributions of individual lobules to motor learning and ataxic symptoms in SCD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first detailed analysis of cerebellar subregion volumes in patients with SCD using the CerebNet deep learning algorithm, revealing a specific association between motor learning ability and the inferior posterior lobe. The SARA scores correlated with multiple cerebellar regions, reflecting the distributed nature of motor function; however, the inferior posterior lobe showed a unique relationship with motor learning capacity. These findings suggest a specialised role in integrating sensory and motor information in adaptive learning processes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the patients and their families who participated in this study, as well as Taro Kato, Yosuke Ariake, Kyoko Todoroki, Wakana Oba, and Yu Ogasawara, for their contributions to the organisation and data collection.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEthical approval.\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis study was approved by the local ethics committee of the National Centre of Neurology and Psychiatry) Approval No. A2019-106. All participants were briefed about the experiment and signed a consent form before participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCompeting interests.\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthors\u0026apos; contributions.\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eK.B., T.H., Y.T., and H.M. contributed to the conception and design of the study, statistical analyses, drafting of the text, and preparation of the figures. K.B., T. H., K.I., S.S., I.Y., T.I., O.O., Y.H., F.T., Y.K., M.K., T.S., R.H., and T.K. performed data acquisition and analysis.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e\u003c/em\u003e This work was supported by Grants-in-Aid for the Practical Research Project for Rare and Intractable Diseases from AMED of Japan 21ek0109420h0003 and Grants-in-Aid for JSPS KAKENHI Grant Number JP21K17485 and Intramural Research Grants (Grant Numbers 3\u0026minus;4 and 6\u0026ndash;5) for Neurological and Psychiatric Disorders of National Center of Neurology and Psychiatry.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDonchin O, Timmann D. Helping patients with cerebellar disorders make most of their remaining learning capacity [Internet]. 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Curr Bioinform [Internet]. 2023;18:830\u0026ndash;41. https://doi.org/10.2174/1574893618666230710140505\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Spinocerebellar Degenerations, Magnetic Resonance Imaging, Cerebellar Subregions, Deep Learning, Atrophy","lastPublishedDoi":"10.21203/rs.3.rs-6246488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6246488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Objective:\u003c/strong\u003e Spinocerebellar degeneration (SCD) is characterized by cerebellar atrophy and motor learning impairment. Using CerebNet, a deep-learning algorithm for cerebellar segmentation, this study investigated the relationship between cerebellar subregion volumes and motor learning ability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We analyzed data from 37 patients with SCD and 18 healthy controls. Using CerebNet, we segmented four cerebellar subregions: anterior lobe, superior posterior lobe, inferior posterior lobe, and vermis. Regression analyses examined associations between cerebellar volumes and motor learning performance (Adaptation Index; \u003cem\u003eAI\u003c/em\u003e) and ataxia severity (Scale for Assessment and Rating of Ataxia; SARA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The inferior posterior lobe volume showed significant positive association with \u003cem\u003eAI\u003c/em\u003e in both single (B = 0.09; 95% CI: [0.03, 0.16]) and multiple linear regression analyses (B = 0.11; 95% CI: [0.008, 0.2]). SARA scores correlated with anterior lobe, superior posterior lobe, and vermis volumes in single linear regression analyses, but these associations were not maintained in multiple linear regression analysis. This selective association suggests the inferior posterior lobe's specialized role in motor learning processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study reveals the inferior posterior lobe's distinct role in motor learning in SCD patients, advancing our understanding of cerebellar function and potentially informing targeted rehabilitation approaches. Our findings highlight the value of advanced imaging technologies in understanding structure-function relationships in cerebellar disorders.\u003c/p\u003e","manuscriptTitle":"Distinct Contribution of Cerebellar Inferior Posterior Lobe to Motor Learning in Spinocerebellar Degeneration: A Deep Learning-Based Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 09:48:31","doi":"10.21203/rs.3.rs-6246488/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-26T06:29:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T06:04:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T12:15:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92025406089153168444903786860910040671","date":"2025-03-26T07:42:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228081819010186205114237853433696450729","date":"2025-03-24T12:27:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-24T06:36:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T04:14:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T04:11:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Cerebellum","date":"2025-03-17T16:28:42+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":"d1622a37-6c95-470c-8e86-c82018b66371","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T16:09:08+00:00","versionOfRecord":{"articleIdentity":"rs-6246488","link":"https://doi.org/10.1007/s12311-025-01887-y","journal":{"identity":"the-cerebellum","isVorOnly":false,"title":"The Cerebellum"},"publishedOn":"2025-07-16 16:05:40","publishedOnDateReadable":"July 16th, 2025"},"versionCreatedAt":"2025-03-26 09:48:31","video":"","vorDoi":"10.1007/s12311-025-01887-y","vorDoiUrl":"https://doi.org/10.1007/s12311-025-01887-y","workflowStages":[]},"version":"v1","identity":"rs-6246488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6246488","identity":"rs-6246488","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}