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

Abstract

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.

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

“…Sivaranjini and Sujatha ( 2019 ) directly introduced the AlexNet model, which was trained by the transfer learned network. Shen et al ( 2019b ) proposed an improved DBN model with an overlapping group lasso sparse penalty to learn useful low-level feature representations. To incorporate multiple brain neuroimaging modalities, Zhang et al ( 2018b ) and McDaniel and Quinn ( 2019 ) both used a GCN model and presented an end-to-end pipeline without extra parameters involved for view pooling and pairwise matching.…”

Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls

“…The results are shown in Table 6. With the same training and test datasets, Shen et al (25) proposed a framework based on GLS-DBN to distinguish between PD and NC subjects. The classification accuracy achieved 90.0% in that study.…”

“…Eckert et al (24) combined visual assessment of individual scans with blinded computer assessment in the differential diagnosis of PD. Shen et al (25) improved a framework based on Group Lasso Sparse Deep Belief Network (GLS-DBN) to distinguish between PD and normal controls (NC) subjects based on FDG-PET imaging, and established the computer-aided classifier for PD and NC.…”

Artificial intelligence for molecular neuroimaging

“…This study found that diagnostic power was lowest for any of these datasets alone, and that the greatest diagnostic power was obtained with combined [ 18 F]DOPA and metabolic datasets (AUC =0.98). One limitation of AI includes overfitting of data, Shen et al aimed to address this issue by adding the Group Lasso Sparse model to a Deep Belief Network (47). They applied their model to [ 18 F] FDG PET scans from 2 cohorts of PD and healthy control subjects, the first cohort was randomly divided into training, validation, and test datasets, while the second cohort was used only to test the model.…”