Parkinson’s disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation
Open Access
- 22 January 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Neuroradiology
- Vol. 63 (9), 1451-1462
- https://doi.org/10.1007/s00234-021-02648-4
Abstract
Purpose To investigate whether Parkinson’s disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)–based structural connectome matrices calculated from diffusion-weighted MRI. Methods In this prospective study, 115 PD patients and 115 healthy controls were enrolled. NOS-based and parameter-weighted connectome matrices were calculated from MRI images obtained with a 3-T MRI unit. With 5-fold cross-validation, diagnostic performance of convolutional neural network (CNN) models using those connectome matrices in differentiating patients with PD from healthy controls was evaluated. To identify the important brain connections for diagnosing PD, gradient-weighted class activation mapping (Grad-CAM) was applied to the trained CNN models. Results CNN models based on some parameter-weighted structural matrices (diffusion kurtosis imaging (DKI)–weighted, neurite orientation dispersion and density imaging (NODDI)–weighted, and g-ratio-weighted connectome matrices) showed moderate performance (areas under the receiver operating characteristic curve (AUCs) = 0.895, 0.801, and 0.836, respectively) in discriminating PD patients from healthy controls. The DKI-weighted connectome matrix performed significantly better than the conventional NOS-based matrix (AUC = 0.761) (DeLong’s test, p < 0.0001). Alterations of neural connections between the basal ganglia and cerebellum were indicated by applying Grad-CAM to the NODDI- and g-ratio-weighted matrices. Conclusion Patients with PD can be differentiated from healthy controls by applying the deep learning technique to the parameter-weighted connectome matrices, and neural circuit disorders including those between the basal ganglia on one side and the cerebellum on the contralateral side were visualized.Funding Information
- Japan Agency for Medical Research and Development (JP19dm0307024, JP19dm0307101)
- Japan Society for the Promotion of Science (19K17244)
This publication has 42 references indexed in Scilit:
- A preliminary diffusional kurtosis imaging study of Parkinson disease: comparison with conventional diffusion tensor imagingNeuroradiology, 2014
- Schizophrenia, neuroimaging and connectomicsNeuroImage, 2012
- NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brainNeuroImage, 2012
- Functional Imaging of the Cerebellum and Basal Ganglia During Predictive Motor Timing in Early Parkinson's DiseaseJournal of Neuroimaging, 2011
- Estimation of tensors and tensor‐derived measures in diffusional kurtosis imagingMagnetic Resonance in Medicine, 2010
- Accurate and robust brain image alignment using boundary-based registrationNeuroImage, 2009
- An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interestNeuroImage, 2006
- Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imagingMagnetic Resonance in Medicine, 2005
- Stages in the development of Parkinson’s disease-related pathologyCell and tissue research, 2004
- Estimation of the Effective Self-Diffusion Tensor from the NMR Spin EchoJournal of Magnetic Resonance, Series B, 1994