Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study
arxiv(2024)
摘要
Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly
understood physiopathology and no established biomarkers for the diagnosis of
early stages and for prediction of disease progression. Several neuroimaging
biomarkers have been studied recently, but these are susceptible to several
sources of variability. In this context, an evaluation of the robustness of
such biomarkers is essential. This study is part of a larger project
investigating the replicability of potential neuroimaging biomarkers of PD.
Here, we attempt to reproduce (same data, same method) and replicate (different
data or method) the models described in Nguyen et al., 2021 to predict
individual's PD current state and progression using demographic, clinical and
neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We
use the Parkinson's Progression Markers Initiative dataset (PPMI,
ppmi-info.org), as in Nguyen et al.,2021 and aim to reproduce the original
cohort, imaging features and machine learning models as closely as possible
using the information available in the paper and the code. We also investigated
methodological variations in cohort selection, feature extraction pipelines and
sets of input features. The success of the reproduction was assessed using
different criteria. Notably, we obtained significantly better than chance
performance using the analysis pipeline closest to that in the original study
(R2 > 0), which is consistent with its findings. The challenges encountered
while reproducing and replicating the original work are likely explained by the
complexity of neuroimaging studies, in particular in clinical settings. We
provide recommendations to further facilitate the reproducibility of such
studies in the future.
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