Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2016-11, Vol.141, p.206-219
• Main Authors: Adeli, Ehsan, Shi, Feng, An, Le, Wee, Chong-Yaw, Wu, Guorong, Wang, Tao, Shen, Dinggang
• Format: Article
• Language: English
• Subjects: Algorithms; Alzheimer's disease; Artificial intelligence; Biomarkers; Brain - diagnostic imaging; Brain - pathology; Brain research; Classification; Diagnosis; Dopamine; Female; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Joint feature-sample selection; Magnetic resonance imaging; Male; Matrix completion; Medical imaging; Medical research; Middle Aged; Movement disorders; Neurodegenerative diseases; Neuroimaging; Parkinson Disease - diagnostic imaging; Parkinson Disease - pathology; Parkinson's disease; Pattern Recognition, Automated - methods; Principal components analysis; Robust linear discriminant analysis; Sensitivity and Specificity; Sparse regression; Unsupervised Machine Learning
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2016.05.054

Parkinson's disease (PD) is an overwhelming neurodegenerative disorder caused by deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger impairs several brain regions and yields various motor and non-motor symptoms. Incidence of PD is predicted to double in the next two decades, which urges more research to focus on its early diagnosis and treatment. In this paper, we propose an approach to diagnose PD using magnetic resonance imaging (MRI) data. Specifically, we first introduce a joint feature-sample selection (JFSS) method for selecting an optimal subset of samples and features, to learn a reliable diagnosis model. The proposed JFSS model effectively discards poor samples and irrelevant features. As a result, the selected features play an important role in PD characterization, which will help identify the most relevant and critical imaging biomarkers for PD. Then, a robust classification framework is proposed to simultaneously de-noise the selected subset of features and samples, and learn a classification model. Our model can also de-noise testing samples based on the cleaned training data. Unlike many previous works that perform de-noising in an unsupervised manner, we perform supervised de-noising for both training and testing data, thus boosting the diagnostic accuracy. Experimental results on both synthetic and publicly available PD datasets show promising results. To evaluate the proposed method, we use the popular Parkinson's progression markers initiative (PPMI) database. Our results indicate that the proposed method can differentiate between PD and normal control (NC), and outperforms the competing methods by a relatively large margin. It is noteworthy to mention that our proposed framework can also be used for diagnosis of other brain disorders. To show this, we have also conducted experiments on the widely-used ADNI database. The obtained results indicate that our proposed method can identify the imaging biomarkers and diagnose the disease with favorable accuracies compared to the baseline methods. •A novel joint feature‐sample selection (JFSS) algorithm is proposed.•The selected subset best builds a classification model;•A robust classification framework is proposed that de‐noises the training data, while learning the classification model;•In addition, the test data are also de-noised based on supervised cleaned training samples;•The method is applied for Parkinson’s disease (PD) diagnosis, as PD‐data driven me