Automatic classification of early Parkinson's disease with multi-modal MR imaging

Abstract

Background: In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals.

Materials and methods: Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels: ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method.

Results: Using the proposed methods to classify the data set, good results (accuracy = 86.96%, sensitivity = 78.95%, specificity = 92.59%) were obtained.

Conclusions: This method demonstrates a promising diagnosis performance by the integration of information from a variety of imaging modalities, and it shows potential for improving the clinical diagnosis and treatment of PD.

Figure 1. A flowchart of the multi-model method for classification.

Figure 2. Classification performance of the multi-model method.

The ROC curve of the classifier. The area under the ROC curve was 0.951.

Figure 3. Scatter plot of the abnormality index-scores of all subjects.

Positive scores represent subjects classified in the PD group and negative scores represent subjects classified in the NC group.

Figure 4. The brain regions which have been selected as features more than 23 times.

(A) Selected regions with ReHo. (B) Selected regions with ALFF. (C) Selected regions with GM. (D) Selected regions with RFCS. (E) Selected regions with WM. (F) Selected regions with CSF.