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Background and Objective <p>Degenerative cerebellar ataxia, a group of progressive neurodegenerative disorders, is characterised by cerebellar atrophy and impaired motor learning. Using CerebNet, a deep learning algorithm for cerebellar segmentation, this study investigated the relationship between cerebellar subregion volumes and motor learning ability.</p> Methods <p>We analysed data from 37 patients with degenerative cerebellar ataxia and 18 healthy controls. Using CerebNet, we segmented four cerebellar subregions: the anterior lobe, superior posterior lobe, inferior posterior lobe, and vermis. Regression analyses examined the associations between cerebellar volumes and motor learning performance (adaptation index [<i>AI</i>]) and ataxia severity (Scale for Assessment and Rating of Ataxia [SARA]).</p> Results <p>The inferior posterior lobe volume showed a 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, 0.20]), an association that was particularly evident in the pure cerebellar ataxia subgroup. 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 analyses. This selective association suggests a specialised role for the inferior posterior lobe in motor learning processes.</p> Conclusion <p>This study reveals the inferior posterior lobe’s distinct role in motor learning in patients with degenerative cerebellar ataxia, 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.</p>

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Specific Contribution of the Cerebellar Inferior Posterior Lobe to Motor Learning in Degenerative Cerebellar Ataxia

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Kyota Bando,
  • Takeru Honda,
  • Kinya Ishikawa,
  • Shinichi Shirai,
  • Ichiro Yabe,
  • Tomohiko Ishihara,
  • Osamu Onodera,
  • Yuichi Higashiyama,
  • Fumiaki Tanaka,
  • Yoshiyuki Kishimoto,
  • Masahisa Katsuno,
  • Takahiro Shimizu,
  • Ritsuko Hanajima,
  • Takumi Kanata,
  • Yuji Takahashi,
  • Hidehiro MizusawaMD

摘要

Background and Objective

Degenerative cerebellar ataxia, a group of progressive neurodegenerative disorders, is characterised by cerebellar atrophy and impaired motor learning. Using CerebNet, a deep learning algorithm for cerebellar segmentation, this study investigated the relationship between cerebellar subregion volumes and motor learning ability.

Methods

We analysed data from 37 patients with degenerative cerebellar ataxia and 18 healthy controls. Using CerebNet, we segmented four cerebellar subregions: the anterior lobe, superior posterior lobe, inferior posterior lobe, and vermis. Regression analyses examined the 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 a 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, 0.20]), an association that was particularly evident in the pure cerebellar ataxia subgroup. 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 analyses. This selective association suggests a specialised role for the inferior posterior lobe in motor learning processes.

Conclusion

This study reveals the inferior posterior lobe’s distinct role in motor learning in patients with degenerative cerebellar ataxia, 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.