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Preprints, Working Papers, ... Year : 2024

Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study

Abstract

Parkinson’s disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology. In clinical practice, challenges are encountered in the diagnosis of early stages and in the prediction of the disease progression due to the absence of established biomarkers. Several biomarkers obtained using neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI) have been studied recently. However, the reliability and generalizability of neuroimaging-based measurements are susceptible to several different sources of variability, including those introduced by different analysis methods or population sampling. 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 used the Parkinson’s Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in Nguyen et al. 2021 and tried 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. Using the reproduction workflow, we managed to obtain better than chance performance for all our models (R2>0), but this performance remained very different from the ones reported in the original study. 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 facilitate the reproducibility of such studies in the future, for instance with the use of version control tools, standardization of pipelines and publication of analysis code and derived data.
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inserm-04465765 , version 1 (20-02-2024)

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  • HAL Id : inserm-04465765 , version 1

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Elodie Germani, Nikhil Baghwat, Mathieu Dugré, Rémi Gau, Albert Montillo, et al.. Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study. 2024. ⟨inserm-04465765⟩
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