Parkinson's Disease Diagnosis beyond Clinical Features: A Bio-marker using Topological Machine Learning of Resting-state Functional Magnetic Resonance Imaging.

• Spatial–temporal dimension reduction of rs-fMRI is developed for automatic PD diagnosis. • Both region-specific and patient-specific diagnoses can be achieved with nonlinear manifold learning. • Temporal lobe, partial Parietal lobe, partial Occipital lobe, and motor region have high classifiability for PD diagnosis using rs-fMRI and the proposed method. • Multi-region classification can significantly improve the PD diagnosis accuracy compared with the single region classification (from 87.8% to 96.4%). Parkinson's disease (PD) is one of the leading causes of neurological disability, and its prevalence is expected to increase rapidly in the following few decades. PD diagnosis heavily depends on clinical features using the patient's symptoms. Therefore, an accurate, robust, and non-invasive bio-marker is of critical clinical importance for PD. This study proposes to develop a new bio-marker for PD diagnosis using resting-state functional Magnetic Resonance Imaging (rs-fMRI). Unlike most existing rs-fMRI data analytics using correlational analysis, a Topological Machine Learning approach is proposed to construct the bio-marker. The default functional network is identified first using rs-fMRI. Next, rs-fMRI's high dimensional spatial–temporal data structure is mapped on a Riemann Manifold using topological dimensional reduction. Following the topological dimensional reduction, machine learning is used for classification and sensitivity analysis. The proposed methodology is applied to three open fMRI databases for demonstration and validation. The PD diagnosis accuracy can reach 96.4 % when the proposed methodology is used. Thus, rs-fMRI and topological machine learning provide a quantifiable and verifiable bio-marker for future PD early detection and treatment evaluation. [ABSTRACT FROM AUTHOR]