Keywords: Parkinson's Disease, fMRI Analysis
Motivation: Employ machine learning approach based on multi-level indices of rs-fMRI to discriminate Parkinson's disease (PD) from progressive supranuclear palsy (PSP).
Goal(s): To investigate the optimal machine learning model and corresponding rs-fMRI based multi-level indices combination.
Approach: 58 PD and 52 PSP patients were involved. Multi-level indices of rs-fMRI was applied to discriminate PD from PSP using machine learning approaches, including K-nearest neighbor (KNN), logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM).
Results: LR and SVM based on multi-level indices of rs-fMRI exhibited significantly superior classification performance than KNN and MLP for distinguishing PD from PSP.
Impact: PD and PSP have similar clinical syndrome but were treated differently. Our finding suggested LR and SVM based on multi-level indices of rs-fMRI can effectively differentiate PD from PSP. It would help the treatment selection for PD and PSP patients.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords