BROWSE

Related Scientist

CNIR's photo.

CNIR
뇌과학 이미징 연구단
more info

ITEM VIEW & DOWNLOAD

GraphNet-based imaging biomarker model to explain levodopa-induced dyskinesia in Parkinson's disease

Cited 0 time in webofscience Cited 0 time in scopus
10 Viewed 0 Downloaded
Title
GraphNet-based imaging biomarker model to explain levodopa-induced dyskinesia in Parkinson's disease
Author(s)
Mansu Kim; Ji Sun Kim; Jinyoung Youn; Hyunjin Park; Jin Whan Cho
Subject
GraphNet regularization Parkinson’s disease Levodopa-induced dyskinesia Surface-based biomarker
Publication Date
2020-11
Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v.196, pp.105713
Publisher
ELSEVIER IRELAND LTD
Abstract
© 2020 Elsevier B.V. Background and objective: Levodopa-induced dyskinesia (LID) is a disabling complication of Parkinson’s disease (PD). Imaging-based measurements, especially those related to the surface shape of the basal ganglia, have shown potential for explaining the severity of LID in PD. Here, we aimed to explore a novel application of the methodology to find biomarkers of LID severity in PD using regularization. Methods: We proposed an application of graph-constrained elastic net (GraphNet) regularization to detect surface-based shape biomarkers explaining the severity of LID and compared the approach with other conventional regularization methods. To examine the methods, we used two independent datasets, one as a training dataset to build the model, and the other dataset was used to validate the constructed model. Results: We found that the left striatum (putamen was the greatest and the caudate was second) was the most significant surface-based biomarker related to the severity of LID. Our results improved the interpretability of identified surface-based biomarkers compared to competing methods. We also found that GraphNet regularization improved prediction of the severity of LID better than the conventional reg- ularization methods. Our model performed better in terms of root-mean-squared error and correlation coefficient between predicted and actual clinical scores. Conclusion: The proposed algorithm offers an advantage of interpretable anatomical variations related to the deformation of the cortical surface. The experimental results showed that GraphNet regulariza- tion was robust to identify surface-based shape biomarkers related to both hypokinetic and hyperkinetic movement disorders.
URI
https://pr.ibs.re.kr/handle/8788114/8443
DOI
10.1016/j.cmpb.2020.105713
ISSN
0169-2607
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
Files in This Item:
There are no files associated with this item.

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse