Temporal Progression Patterns of White-Matter Degeneration in CBS and PSP Identified with Subtype & Stage Inference (SuStaIn) [Proceedings of the 2021 Young Investigator Award]

  • SAITO Yuya
    Department of Radiology, Juntendo University Graduate School of Medicine
  • WIJERATNE Peter A.
    Centre for Medical Image Computing, Department of Computer Science, University College London
  • KAMAGATA Koji
    Department of Radiology, Juntendo University Graduate School of Medicine
  • ANDICA Christina
    Department of Radiology, Juntendo University Graduate School of Medicine
  • UCHIDA Wataru
    Department of Radiology, Juntendo University Graduate School of Medicine Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University
  • AKASHI Toshiaki
    Department of Radiology, Juntendo University Graduate School of Medicine
  • WADA Akihiko
    Department of Radiology, Juntendo University Graduate School of Medicine
  • HORI Masaaki
    Department of Radiology, Toho University Omori Medical Center
  • AOKI Shigeki
    Department of Radiology, Juntendo University Graduate School of Medicine

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Other Title
  • SuStaInを用いた大脳皮質基底核症候群及び進行性核上性麻痺における白質変性の時間的進行パターンの推定[国際飛躍賞記録]

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Abstract

<p> Corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP) are sporadic atypical parkinsonian disorders associated with 4-repeat tauopathies. These neurodegenerative conditions closely overlap in their clinical information, pathology, and genetic risk factors ; therefore, it is difficult to accurately diagnose CBS and PSP. Recently, an unsupervised machine-learning technique, called Subtype and Stage Inference (SuStaIn), has been proposed to reveal the data-driven disease phenotypes with distinct temporal progression patterns from widely available cross-sectional data. To clarify the differences in the temporal white matter (WM) degeneration patterns between CBS and PSP, this study applied SuStaIn for fractional anisotropy (FA) in regional WM, which was sensitive to WM degeneration, based on cross-sectional brain diffusion MRI (dMRI) data.</p><p> We obtained dMRI data from 15 healthy controls, 26 patients with CBS, and 25 patients with PSP. FA was calculated after fitting the diffusion tensor model to the corrected dMRI data for susceptibility and eddy-current induced geometric distortions and inter-volume subject motion. SuStaIn was applied to the cross-sectional regional WM tract FAs to identify both the disease subtypes and their trajectories with distinct WM degeneration patterns. To assess the performance of SuStaIn, the classification accuracy and sensitivity for CBS and PSP were calculated.</p><p> SuStaIn revealed that the CBS degeneration started from the fornix and stria terminalis (FSTs) and corpus callosum (CC), followed by the posterior corona radiata (PCR), posterior thalamic radiation (PTR), and cerebral peduncle (CP), and subsequently extended to the cingulum. Finally, it reached the superior cerebral peduncle (SCP) and corticospinal tract (CST). In contrast, the PSP degeneration started from the SCP and cingulum, followed by the CST, and subsequently extended to the FST and CC. Eventually, it reached the PCR, PTR, and CP. Accordingly, SuStaIn classified CBS and PSP with 0.863 accuracy (sensitivity : CBS, 0.885 ; PSP, 0.840). The results suggested the utility of SuStaIn for classifying patients with CBS and PSP and identifying temporal WM degeneration patterns in patients with CBS and PSP.</p>

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