Use of Overlapping Group LASSO Sparse Deep Belief Network to Discriminate Parkinson's Disease and Normal Control

Shen, Ting; Jiang, Jiehui; Lin, Wei; Ge, Jingjie; Wu, Ping; Zhou, Yongjin; Zuo, Chuantao; Wang, Jian; Yan, Zhuangzhi; Shi, Kuangyu (2019). Use of Overlapping Group LASSO Sparse Deep Belief Network to Discriminate Parkinson's Disease and Normal Control. Frontiers in neuroscience, 13(396), p. 396. Frontiers Research Foundation 10.3389/fnins.2019.00396

[img]
Preview
Text
Shi_Use of Overlapping Group LASSO Sparse Deep Belief Network to Discriminate Parkinson's Disease and Normal Control..pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (3MB) | Preview

As a medical imaging technology which can show the metabolism of the brain, 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is of great value for the diagnosis of Parkinson's Disease (PD). With the development of pattern recognition technology, analysis of brain images using deep learning are becoming more and more popular. However, existing computer-aided-diagnosis technologies often over fit and have poor generalizability. Therefore, we aimed to improve a framework based on Group Lasso Sparse Deep Belief Network (GLS-DBN) for discriminating PD and normal control (NC) subjects based on FDG-PET imaging. In this study, 225 NC and 125 PD cohorts from Huashan and Wuxi 904 hospitals were selected. They were divided into the training & validation dataset and 2 test datasets. First, in the training & validation set, subjects were randomly partitioned 80:20, with multiple training iterations for the deep learning model. Next, Locally Linear Embedding was used as a dimension reduction algorithm. Then, GLS-DBN was used for feature learning and classification. Different sparse DBN models were used to compare datasets to evaluate the effectiveness of our framework. Accuracy, sensitivity, and specificity were examined to validate the results. Output variables of the network were also correlated with longitudinal changes of rating scales about movement disorders (UPDRS, H&Y). As a result, accuracy of prediction (90% in Test 1, 86% in Test 2) for classification of PD and NC patients outperformed conventional approaches. Output scores of the network were strongly correlated with UPDRS and H&Y (R = 0.705, p < 0.001; R = 0.697, p < 0.001 in Test 1; R = 0.592, p = 0.0018, R = 0.528, p = 0.0067 in Test 2). These results show the GLS-DBN is feasible method for early diagnosis of PD.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine

UniBE Contributor:

Shi, Kuangyu

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1662-4548

Publisher:

Frontiers Research Foundation

Language:

English

Submitter:

Sabine Lanz

Date Deposited:

18 Dec 2019 10:36

Last Modified:

10 May 2020 02:40

Publisher DOI:

10.3389/fnins.2019.00396

PubMed ID:

31110472

BORIS DOI:

10.7892/boris.136859

URI:

https://boris.unibe.ch/id/eprint/136859

Actions (login required)

Edit item Edit item
Provide Feedback