Early Disease Stage Characterization in Parkinson's Disease from
Resting-state fMRI Data Using a Long Short-term Memory Network
Abstract
Parkinson’s disease (PD) is a common and complex neurodegenerative
disorder with 5 stages in the Hoehn and Yahr scaling. Given the
heterogeneity of PD, it is challenging to classify early stages 1 and 2
and detect brain function alterations. Functional magnetic resonance
imaging (fMRI) is a promising tool in revealing functional connectivity
(FC) differences and developing biomarkers in PD. Some machine learning
approaches like support vector machine and logistic regression have been
successfully applied in the early diagnosis of PD using fMRI data, which
outperform classifiers based on manually selected morphological
features. However, the early-stage characterization in FC changes has
not been fully investigated. Given the complexity and non-linearity of
fMRI data, we propose the use of a long short-term memory (LSTM) network
to characterize the early stages of PD. The study included 84 subjects
(56 in stage 2 and 28 in stage 1) from the Parkinson’s Progression
Markers Initiative (PPMI), the largest-available public PD dataset.
Under a repeated 10-fold stratified cross-validation, the LSTM model
reached an accuracy of 71.63%, 13.52% higher than the best traditional
machine learning method, indicating significantly better robustness and
accuracy compared with other machine learning classifiers. We used the
learned LSTM model weights to select the top brain regions that
contributed to model prediction and performed FC analyses to
characterize functional changes with disease stage and motor impairment
to gain better insight into the brain mechanisms of PD.