Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning

• Published in: Frontiers in computational neuroscience 2021-10, Vol.15, p.735991-735991
• Main Authors: Song, Chenggang, Zhao, Weidong, Jiang, Hong, Liu, Xiaoju, Duan, Yumei, Yu, Xiaodong, Yu, Xi, Zhang, Jian, Kui, Jingyue, Liu, Chang, Tang, Yiqian
• Format: Article
• Language: English
• Subjects: Accuracy; Biomarkers; Brain research; Classification; Datasets; Dopamine; Feature selection; Frontal gyrus; graph theory; Learning algorithms; Machine learning; Magnetic resonance imaging; Medical imaging; Movement disorders; Neurodegenerative diseases; Neuroimaging; Neuroscience; Neurosciences; Parkinson's disease; ReliefF; RFE; stability selection; Statistical analysis; Statistics; Substantia alba; Substantia grisea; Support vector machines
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2021.735991

Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified. It is difficult to identify which brain regions are the true biomarkers of PD. To overcome these two issues, we employed four different feature selection methods [ReliefF, graph-theory, recursive feature elimination (RFE), and stability selection] to obtain a minimal set of relevant features and nonredundant features from gray matter (GM) and white matter (WM). Then, a support vector machine (SVM) was utilized to learn decision models from selected features. Based on machine learning technique, this study has not only extended group level statistical analysis with identifying group difference to individual level with predicting patients with PD from healthy controls (HCs), but also identified most informative brain regions with feature selection methods. Furthermore, we conducted horizontal and vertical analyses to investigate the stability of the identified brain regions. On the one hand, we compared the brain changes found by different feature selection methods and considered these brain regions found by feature selection methods commonly as the potential biomarkers related to PD. On the other hand, we compared these brain changes with previous findings reported by conventional statistical analysis to evaluate their stability. Our experiments have demonstrated that the proposed machine learning techniques achieve satisfactory and robust classification performance. The highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Moreover, most brain regions identified by machine learning were consistent with previous findings, which means that these brain regions are related to the pathological brain changes characteristic of PD and can be regarded as potential biomarkers of PD. Besides, we also found the brain abnormality of superior frontal gyrus (dorsolateral, SFGdor) and lingual gyrus (LING), which have been confirmed in other studies of PD. This further demonstrates that machine learning models are beneficial for clinicians as a decision support system in diagnosing PD.