5. Yanbing Hou and Huifang Shang. Magnetic Resonance Imaging Markers for Cognitive Impairment in Parkinson's Disease: Current View. *Frontiers in Aging Neuroscience*, 14, 2022. ISSN 1663-4365. doi: 10.3389/fnagi.2022.788846.

6. Jason D. Warren, Jonathan D. Rohrer, Jonathan M. Schott, Nick C. Fox, John Hardy, and Martin N. Rossor. Molecular nexopathies: a new paradigm of neurodegenerative disease. *Trends in Neurosciences*, 36(10):561-569, 2013. ISSN 0166-2236. doi: 10.1016/j.tins.2013.06.007.

7. YanBing Hou, ChunYan Luo, Jing Yang, RuWei Ou, Wei Song, QianQian Wei, Bei Cao, Bi Zhao, Ying Wu, Hui-Fang Shang, and QiYong Gong. Prediction of individual clinical scores in patients with Parkinson's disease using resting-state functional magnetic resonance imaging. *Journal of the Neurological Sciences*, 366: 27-32, 2016. ISSN 0022-510X. doi: 10.1016/j.jns.2016.04.030.

8. Xiao-Fei Hu, Jiu-Quan Zhang, Xiao-Mei Jiang, Chao-Yang Zhou, Lu-Qing Wei, Xun-Tao Yin, Jing Li, Yan-Ling Zhang, and Jian Wang. Amplitude of Low-frequency Oscillations in Parkinson's Disease: A 2-year Longitudinal Resting-state Functional Magnetic Resonance Imaging Study. *Chinese Medical Journal*, 128 (05):593-601, 2015. doi: 10.4103/0366-6999.151652.

9. HuiZe Pang, ZiYang Yu, HongMei Yu, JiBin Cao, YingMei Li, MiaoRan Guo, ChengHao Cao, and GuoGuang Fan. Use of machine learning method on automatic classification of motor subtype of Parkinson's disease based on multilevel indices of rs-fMRI. *Parkinsonism & Related Disorders*, 90:65-72, 2021. ISSN 1353-8020. doi: 10.1016/j.parkreldis.2021.08.003.

10. Tao Wu, Xiangyu Long, Yufeng Zang, Liang Wang, Mark Hallett, Kuncheng Li, and Piu Chan. Regional homogeneity changes in patients with Parkinson's disease. *Human Brain Mapping*, 30(5):1502-1510, 2009. ISSN 1097-0193. doi: 10.1002/hbm.20622.

11. Yumei Yue, Yasi Jiang, Ting Shen, Jiali Pu, Hsin-Yi Lai, and Baorong Zhang. ALFF and ReHo Mapping Reveals Different Functional Patterns in Early- and Late-Onset Parkinson's Disease. *Frontiers in Neuroscience*, 14, 2020. ISSN 1662-453X. doi: doi.org/10.3389/fnins.2020.00141.

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13. Qi-Hong Zou, Chao-Zhe Zhu, Yihong Yang, Xi-Nian Zuo, Xiang-Yu Long, Qing-Jiu Cao, Yu-Feng Wang, and Yu-Feng Zang. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. *Journal of Neuroscience Methods*, 172(1):137-141, 2008. ISSN 0165-0270. doi: 10.1016/j.jneumeth.2008.04.012.

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using the information available in the paper and the code publicly available at the start
of the reproduction (i.e. without contacting the authors).. This step was unsuccessful
since we could not get the same sample size at Baseline (102 instead of 82 in [1]), and
we decided to contact the authors who provided us the exact subject and visit list used
in the original study. With this list, we were able to build a cohort with the same
participants at Baseline. A potential solution to avoid similar difficulties in future
reproducibility studies would be to register cohorts obtained from public databases
under the same data usage agreements as the original data. In the case of PPMI, a
specific section of the online portal could be created to store cohort definitions and
associate them with published manuscripts.

Even with the original participant identifiers and visit list at Baseline, we could not
retrieve the same Baseline cohort in the PPMI database. Our closest-to-original cohort
included the 82 original participants, but for 4 of them, a different visit than the original
one was used. For 2 of these visits, we intentionally chose to keep the visits selected by
our first query to better fit with the description of the cohort in the paper. For the 2
other visits, the functional images corresponding to these participants and visits were
not available anymore in the PPMI database. Since the PPMI database continuously
adds new participant visits, we chose to keep only the visits that were added more than
a year before the original study publication, since the original authors did not report
the date at which they queried the database. With this filter, the Baseline participants
list and the exact same code used to search for follow-up visits, the cohorts obtained for
follow-up visits were still dissimilar to the original ones, with more participants and
several noteworthy differences in clinical and demographic variables. A first step to
solve this particular issue would be to systematically report the date when databases
are queried. However, the issues faced when attempting to reproduce the original cohort
in fact highlight the need for version control in public databases, using tools such as
DataLad [52] that is for instance adopted in the OpenNeuro database [53]. With version
control, we would be able to retrieve the data from the database as it existed on the
date of the original query. In addition, authors would be able to cite the exact version
of the database used, which would importantly facilitate cohort reproductions.

Reproducing the fMRI pre-processing and feature computation pipelines described
in [1] also raised challenges. First, although authors provided a description of the
different pre-processing steps performed and tools used, exact reproductions of
neuroimaging pipelines require more detailed information — including specific
parameters values, name and version of the standard template used, software versions —
given the overall complexity and flexibility of image analysis methods [54]. To build the
closest possible pipeline to the one used in [1] without contacting the authors, we had to
make informed guesses about important parameters of the analysis. Some of these
choices were conditioned by the nature of the neuroimaging pipelines (e.g., the choice of
standard template to register functional images was constrained by the use of
ICA-AROMA) while other decisions were more arbitrary and led to multiple valid
variations (e.g., the computation of WM and CSF mean time-series for which we
applied three different variations with different software packages and methods).
Reporting guidelines, such as COBIDAS [55], were developed to help document analyses
and facilitate reproduction studies. However, to reproduce complete analyses, sharing
the entirety of the code used in the original experiment remains the most valuable
information, as it contains a both human and machine-readable description of the exact
method employed. In our case the authors did provide all code and their custom atlas
when asked. Code-sharing platforms such as GitHub and GitLab are now widely
available for this purpose and long-term preservation of these code is supported by
archive systems such as Software Heritage [56, 57] or Zenodo. We also note that
different journals have different requirements regarding what is to be submitted beyond
16. Rotem Botvinik-Nezer, Felix Holzmeister, Colin F Camerer, Anna Dreber, Juergen Huber, Magnus Johannesson, Michael Kirchler, Roni Iwanir, Jeanette A Mumford, R Alison Adcock, Paolo Avesani, Blazej M Baczkowski, Aahana Bajracharya, Leah Bakst, Sheryl Ball, and Marco Barilari. Variability in the analysis of a single neuroimaging dataset by many teams. *Nature*, 582(7810):84–88, 2020. doi: https://doi.org/10.1038/s41586-020-2314-9.

17. Tristan Glatard, Lindsay B. Lewis, Rafael Ferreira da Silva, Reza Adalat, Nat- acha Beck, Claude Lepage, Pierre Rioux, Marc-Etienne Rousseau, Tarek Sherif, Ewa Deelman, Najmeh Khalili-Mahani, and Alan C. Evans. Reproducibility of neuroimaging analyses across operating systems. *Frontiers in Neuroinformatics*, 9, 2015. ISSN 1662-5196. doi: 10.3389/fninf.2015.00012.

18. Ed HBM Gronenschild, Petra Habets, Heidi IL Jacobs, Ron Mengelers, Nico Rozendaal, Jim Van Os, and Machteld Marcelis. The effects of freesurfer version, workstation type, and macintosh operating system version on anatomical volume and cortical thickness measurements. *PloS one*, 7(6):e38234, 2012.

19. Sayash Kapoor and Arvind Narayanan. Leakage and the Reproducibility Crisis in ML-based Science, 2022.

20. Gael Varoquaux and Olivier Colliot. Evaluating Machine Learning Models and Their Diagnostic Value. In *Machine Learning for Brain Disorders*, Neuromethods, pages 601–630. Springer US, 2023. ISBN 978-1-07-163195-9. doi: 10.1007/978-1-0716-3195-9_20. URL https://doi.org/10.1007/978-1-0716-3195-9_20.

21. Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz-Melo, Jorge Samper-González, Alexandre Routier, Simona Bottani, Didier Dormont, Stanley Durrleman, Ninon Burgos, and Olivier Colliot. Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. *Medical Image Analysis*, 63:101694, 2020. ISSN 1361-8415. doi: 10.1016/j.media.2020.101694. URL https://www.sciencedirect.com/science/article/pii/S1361841520300591.

22. Lorena A. Barba. Terminologies for reproducible research, 2018.

23. Ullrich Wüllner, Per Borghammer, Chi-un Choe, Ilona Csoti, Björn Falkenburger, Thomas Gasser, Paul Lingor, and Peter Riederer. The heterogeneity of Parkinson's disease. *Journal of Neural Transmission*, 130(6):827–838, 2023. ISSN 0300-9564. doi: 10.1007/s00702-023-02635-4.

24. Simon Klau, Chirag J Patel, John PA Ioannidis, Anne-Laure Boulesteix, Sabine Hoffmann, et al. Comparing the vibration of effects due to model, data pre-processing and sampling uncertainty on a large data set in personality psychology. *Meta-Psychology*, 7, 2023.

25. Russell A. Poldrack, Chris I. Baker, Joke Durnez, Krzysztof J. Gorgolewski, Paul M. Matthews, Marcus R. Munafò, Thomas E. Nichols, Jean-Baptiste Poline, Edward Vul, and Tal Yarkoni. Scanning the horizon: towards transparent and reproducible neuroimaging research. *Nature Reviews Neuroscience*, 18(2):115–126, 2017. ISSN 1471-003X, 1471-0048. doi: 10.1038/nrn.2016.167. URL http://www.nature.com/articles/nrn.2016.167. Number: 2.

26. Kenneth Marek, Sohini Chowdhury, Andrew Siderowf, Shirley Lasch, Christopher S Coffey, Chelsea Caspell-Garcia, Tanya Simuni, Danna Jennings, Caroline M Tanner, John Q Trojanowski, Leslie M Shaw, John Seibyl, Norbert Schuff, Andrew
the manuscript. The original paper [1] was published in P&RD which at the time of
publication of [1] had minimal expectations beyond the manuscript. The authors met
these requirements and beyond, providing a public code repository. Harmonization of
such practice across journal would be highly beneficial to help reproduction of studies.

The use of a custom-based atlas to parcellate the brain in the original study also
created challenges. Future reproducibility studies would benefit from comprehensive
descriptions of the methods used to create such custom data, access to the code to
create the data, and sharing of the data itself through platforms such as Zenodo, the
Open-Science Framework, Figshare, or NeuroVault [58]. Such platforms could also be
used for sharing derived data, for instance whole-brain fALFF and ReHo maps.
However, Data Usage Agreements often requires that derived data have to be shared
under the same conditions. We emphasize again the need for specific platforms in public
databases to host data associated with a published manuscript, including cohort
descriptions and derived imaging data.

The authors of [1] shared code used in the original study, in particular for feature
computation (fALFF and ReHo after pre-processing and clinical/demographic features
search in PPMI study files) and machine-learning models training. The availability of
this code was extremely useful for our reproducibility study, and we warmly
acknowledge the authors for taking the time to share reusable code with their analysis.
Despite the availability of the code, we still faced some difficulties to reproduce the
 workflow presented in the original study, due to discrepancies between the methods
reported in the paper and the code shared, especially for the imaging feature
computation, the cross-validation procedure and the results reports. For instance, we
were not able to retrieve the Z-scoring of whole-brain fALFF and ReHo maps mentioned
in the paper. This discrepancy was likely due to the update of the C-PAC pipeline used
by the authors for pre-processing, in which the documentation still mentioned the
possibility to output Z-scored maps even if this option was not implemented anymore in
the pipeline. This reiterate the importance of code versioning and reporting software
versions. The use of software container engines such as Docker and Singularity in
combination with frameworks such as Boutiques [37] or BIDS-Apps [59] facilitates
reproduction and reduces the technical work required to find and install the software
versions used in the original study. The authors in [1] report that they have begun using
both Singularity/Apptainer and Podman for this exact purpose. For more details on the
benefits of such software containerization, we refer the readers to [60] in which authors
explains how using these particular frameworks can help reproducibility.

Regarding model selection and optimization, we highlight the complexity of nested
cross-validation schemes and the on-going debate on the choice of rigorous
cross-validation procedures [21,61]. Here again, code sharing is required to describe the
exact evaluation method used in the original study. At this level in the analysis, Jupyter
notebooks [62] are an interesting option to document code and mix it with data, natural
text and figures. Initiatives were recently launched to share reproducible Jupyter
notebooks, such as NeuroLibre [63], a platform for sharing re-executable preprints. We
created a Jupyter notebook for our study, that we made publicly available at
https://github.com/elodiegermani/nguyen-etal-2021.

The following box highlights the main recommendations that we propose to facilitate
the reproduction of such studies in the future.
Singleton, Karl Kieburtz, Arthur W Toga, Brit Mollenhauer, Doug Galasko, Lana M Chahine, Daniel Weintraub, Tatiana Foroud, Duygu Tosun-Turgut, Kathleen Poston, Vanessa Arnedo, Mark Frasier, Todd Sherer, and Parkinson's Progression Markers Initiative. The Parkinson's progression markers initiative (PPMI) - establishing a PD biomarker cohort. *Annals of clinical and translational neurology*, 5(12):1460-1477, 2018. ISSN 2328-9503. doi: 10.1002/acn3.644.

27. Krzysztof J. Gorgolewski, Tibor Auer, Vince D. Calhoun, R. Cameron Craddock, Samir Das, Eugene P. Duff, Guillaume Flandin, Satrajit S. Ghosh, Tristan Glatard, Yaroslav O. Halchenko, Daniel A. Handwerker, Michael Hanke, David Keator, Xiangrui Li, Zachary Michael, Camille Maumet, B. Nolan Nichols, Thomas E. Nichols, John Pellman, Jean-Baptiste Poline, Ariel Rokem, Gunnar Schaefer, Vanessa Sochat, William Triplett, Jessica A. Turner, Gaël Varoquaux, and Russell A. Poldrack. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. *Scientific Data*, 3(1):160044, 2016. ISSN 2052-4463. doi: 10.1038/sdata.2016.44.

28. Yaroslav Halchenko, Mathias Goncalves, Pablo Velasco, Visconti di Oleggio Castello Matteo, and Satrajit Ghosh. nipy/heudiconv: v0.13.1, 2023. URL https://zenodo.org/records/7963413.

29. Mark Jenkinson, Christian F. Beckmann, Timothy E. J. Behrens, Mark W. Woolrich, and Stephen M. Smith. FSL. *NeuroImage*, 62(2):782-790, 2012. ISSN 1095-9572(Electronic),1053-8119(Print). doi: 10.1016/j.neuroimage.2011.09.015.

30. Robert W. Cox. AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. *Computers and Biomedical Research*, 29(3): 162-173, 1996. ISSN 0010-4809. doi: 10.1006/cbmr.1996.0014.

31. Brian B. Avants, Nicholas J. Tustison, Gang Song, Philip A. Cook, Arno Klein, and James C. Gee. A reproducible evaluation of ANTs similarity metric performance in brain image registration. *NeuroImage*, 54(3):2033-2044, 2011. ISSN 1053-8119. doi: 10.1016/j.neuroimage.2010.09.025.

32. Raimon H.R. Pruim, Maarten Mennes, Daan van Rooij, Alberto Llera, Jan K. Buitelaar, and Christian F. Beckmann. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. *NeuroImage*, 112:267-277, 2015. ISSN 1053-8119. doi: 10.1016/j.neuroimage.2015.02.064.

33. Krzysztof Gorgolewski. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. *Frontiers in Neuroinformatics*, page 15, 2017. doi: 10.5281/zenodo.581704.

34. Neurodocker, 2023. URL https://github.com/ReproNim/neurodocker/releases/tag/0.9.5.

35. Anaconda software distribution, 2020. https://docs.anaconda.com/.

36. Tristan Glatard, Gregory Kiar, Tristan Aumentado-Armstrong, Natacha Beck, Pierre Bellec, Rémi Bernard, Axel Bonnet, Sorina Camarasu-Pop, Frédéric Cervenansky, Samir Das, Rafael Ferreira da Silva, Guillaume Flandin, Pascal Girard, Krzysztof J. Gorgolewski, Charles R. G. Guttmann, Valérie Hayot-Sasson, Pierre-Olivier Quirion, Pierre Rioux, Marc-Eienne Rousseau, and Alan C. Evans. Bouquets: a flexible framework for automated application integration in computing platforms, 2017.
# Recommendations for more reproducible studies

## Cohort & Data

* Creation of specific tools to create and store cohort definitions (participant lists, image IDs, etc.)

* Version control of public databases to be able to access the database as it was at a specific date, and cite the exact version of the database used in the paper

* Share the derived data used in your experiments (using adapted platforms in compliance with regulations such as OSF, Figshare, etc.).

## Pipeline & Code

* Improve and respect reporting guidelines such as COBIDAS [55]

* Share the entirety of the code on platforms such as GitHub and on platforms for long-term preservation such as Software Heritage [57] or Zenodo

* Harmonization of code and data sharing practice across journals

* Use of containerization tools or containerized software packages to facilitate the retrieval of the exact version used in the study

* For more complex code, use Jupyter Notebooks [62] to facilitate the understandability of the code.

Beyond the limitations related to the challenges of reproducibility, all limitations identified by the authors of [1], including bias of the PPMI cohort towards Caucasian, the use of small sample sizes in particular for prediction at Year 4 and the impact of medication on MDS-UPDRS scores, are also applicable for our study and are further discussed in the original paper [1].

To conclude, we highlighted the challenges associated with the reproduction of neuroimaging studies. We discussed some of the specific difficulties encountered in our study, as well as numerous success in reproduction, and provided some potential solutions to further facilitate this process in the future, in terms of time cost and adequacy of the reproduction. Nevertheless, given the complexity of the data, software and analyses required in current neuroimaging studies, reproducing the experiments made in existing papers remains extremely challenging.

## Code availability

All the experiments were run using Python 3.10, under a NeuroDocker image available on Dockerhub at https:

//hub.docker.com/repository/docker/elodiegermani/nguyen-etal-2021. This Docker image contains all necessary package and software used to perform the analysis:

The code used to run the experiments is available in a public notebook on GitHub, and archived in the Software Heritage platform:

swh:1:dir:2823c6f1cabae5865aa5ab4d8724e219d5bf2661.

To comply with PPMI's Data Usage Agreements that prevent users to re-publish data, the notebook queries and downloads data directly from PPMI. Since PPMI does not have a data access API, we developed our own Python interface to PPMI using Selenium, a widely-supported Python library to automate web browser navigation.
37. Elodie Germani. Image processing, 2023. URL https://doi.org/10.5281/zenodo.10298335.

38. Elodie Germani. Trainer, 2023. URL https://doi.org/10.5281/zenodo.10298359.

39. ICA-AROMA & fmriprep using child template - fmriprep - Neurostars, 2019. URL https://neurostars.org/t/ica-aroma-fmriprep-using-child-template/5139.

40. Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. Machine learning for neuroimaging with scikit-learn. *Frontiers in Neuroinformatics*, 8:14, 2014. ISSN 1662-5196. doi: 10.3389/fninf.2014.00014.

41. Oscar Esteban, Christopher J. Markiewicz, Ross W. Blair, Craig A. Moodie, A. Ilkay Isik, Asier Erramuzpe, James D. Kent, Mathias Goncalves, Elizabeth DuPre, Madeleine Snyder, Hiroyuki Oya, Satrajit S. Ghosh, Jessey Wright, Joke Durnez, Russell A. Poldrack, and Krzysztof J. Gorgolewski. fMRIPrep: a robust preprocessing pipeline for functional MRI. *Nature Methods*, 16(1):111–116, 2019. ISSN 1548-7105. doi: 10.1038/s41592-018-0235-4.

42. Jonathan D. Power, Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. Methods to detect, characterize, and remove motion artifact in resting state fMRI. *NeuroImage*, 84:320–341, 2014. ISSN 1053-8119. doi: 10.1016/j.neuroimage.2013.08.048. URL https://www.sciencedirect.com/science/article/pii/S1053811913009117.

43. Linden Parkes, Ben Fulcher, Murat Yücel, and Alex Fornito. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. *NeuroImage*, 171:415–436, 2018. ISSN 1053-8119. doi: 10.1016/j.neuroimage.2017.12.073. URL https://www.sciencedirect.com/science/article/pii/S1053811917310972.

44. Craddock Cameron, Sikka Sharad, Cheung Brian, Khanuja Ranjeet, Ghosh Satrajit, Yan Chaogan, Li Qingyang, Lurie Daniel, Vogelstein Joshua, Burns Randal, Colcombe Stanley, Mennes Maarten, Kelly Clare, Di Martino Adriana, Castellanos Francisco, and Milham Michael. Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC). *Frontiers in Neuroinformatics*, 7, 2013. ISSN 1662-5196. doi: 10.3389/conf.fninf.2013.09.00042.

45. Alexander Schaefer, Ru Kong, Evan M Gordon, Timothy O Laumann, Xi-Nian Zuo, Avram J Holmes, Simon B Eickhoff, and B T Thomas Yeo. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. *Cerebral Cortex*, 28(9):3095–3114, September 2018. ISSN 1047-3211. doi: 10.1093/cercor/bhx179.

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47. Jörn Diedrichsen, Joshua H. Balsters, Jonathan Flavell, Emma Cussans, and Narender Ramnani. A probabilistic MR atlas of the human cerebellum. *NeuroImage*, 46(1):39–46, 2009. ISSN 1053-8119. doi: 10.1016/j.neuroimage.2009.01.045.
Using this interface, the notebook downloads PPMI study and imaging files to build the cohorts and train the ML models. The utility functions to download and manipulate PPMI data are implemented in LivingPark utils, a Python package available on GitHub (https://github.com/LivingPark-MRI/livingpark-utils).

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<figure><img src="image_8.png" /><figcaption>**Supplementary Figure 1.** Comparison of original Schaefer atlas used in [1] and reproduced atlas obtained from three separate atlas available in FSL.</figcaption></figure>
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<table><caption>**Supplementary Table 1.** Demographics and clinical features set as input for the machine learning models. For baseline MDS-UPDRS scores included for prediction at 1 year, 2 years and 4 years, see section Outcome measurement.</caption><thead><tr><th>Study file</th><th>Feature</th><th>Columns</th><th>Encoding</th></tr></thead><tbody><tr><td rowspan="3">Demographics</td><td>Race</td><td>"RAWHITE", "HISPLAT", "RAINDALS", "RABLACK", "RAASIAN", "RAHAWOPI", "RANOS"</td><td>7 features, encoded as 1 if the participant was considered from this ethnic origin, 0 if not.</td></tr><tr><td>Sex</td><td>"SEX"</td><td>-encoded as 0 if the participant was a woman, 1 if it was a man.</td></tr><tr><td>Handedness</td><td>"HANDED"</td><td>3 features: "RIGHT_HANDED", "LEFT_HANDED" and "AMBIDEXTROUS" with 0 or 1 depending on the handedness of the participant.</td></tr><tr><td>Social</td><td>Years of education</td><td>"EDUCYRS"</td><td>Float corresponding to the n. of years of education of the participant.</td></tr><tr><td>Age</td><td>Age</td><td>"AGE"</td><td>Float corresponding to the age of the participant.</td></tr><tr><td rowspan="3">Parkinson's features</td><td>Presence of tremor, rigidity, or postural instability at baseline</td><td>"DXTREMOR", "DXRIGID", "DXBRADY", "DXPOSINS"</td><td>4 features encoded as 0 if the participants didn't have this symptom, 1 if so. Bradykinesia was not mentioned in the paper but was used in the code, so we added this feature in our model.</td></tr><tr><td>Dominant disease side</td><td>"DOMSIDE"</td><td>"DOMSIDE" feature column was used and split into 3 features: "DOMSIDE_LEFT", "DOMSIDE RIGHT" and "DOMSIDE BOTH" with 0 or 1 depending on the dominant side of disease for the participant.</td></tr><tr><td>Symptom duration and disease duration</td><td>"INFODT", "PDDXDT", "SXDT"</td><td>Symptom duration and disease duration: we respectively computed the number of days between the columns "INFODT" and "SXDT" and between the columns "INFODT" and "PDDXDT". Dates were first converted to "Month-Year" before computing the number of days between the two dates.</td></tr><tr><td>MoCA scores file</td><td>Baseline MoCA score</td><td>"MCATOT"</td><td>Integer value corresponding to the score of the participant. Missing values were replaced by the mean value across other participants. This information was not mentioned in code or paper.</td></tr><tr><td>GDS score file</td><td>Baseline GDS total score</td><td>"GDSSATIS", "GDSDROPD", "GDSEMPTY", "GDSBORED", "GDSGSPIR", "GDSAFRAD", "GDSHAPPY", "GDSHLPLS", "GDSHOME", "GDSMEMRY", "GDSALIVE", "GDSWRTLS", "GDSENRGY", "GDSHOPLS", "GDSBETER"</td><td>We computed the total score by summing all the columns containing "GDS" in the GDS short version study file. Missing values were replaced by the mean value across other participants.</td></tr></tbody></table>
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<table><caption>**Supplementary Table 2.** Terminology of the experiments in the current paper. Blue text denotes cohort or analysis variations that are closest to the original study, green text indicates variations due to unknown information, while red text indicates variations that are less similar to the original study.</caption><thead><tr><th>Experiments</th><th>Cohort</th><th>Image preprocessing</th><th>ALFF/ReHo computation</th><th>Selection of input features</th><th>Workflow name</th></tr></thead><tbody><tr><td>Reproduction</td><td>Closest-to-paper: n=82</td><td>Reproduction with AFNI segmentation</td><td>Z-score</td><td>All</td><td>Reproduction pipeline closest-to-original cohort</td></tr><tr><td>Variations of cohorts</td><td>Ours: n=102</td><td>Reproduction with AFNI segmentation</td><td>Z-score</td><td>All</td><td>Reproduction pipeline replication cohort</td></tr><tr><td rowspan="3">Variations of image preprocessing</td><td>Closest-to-paper: n=82</td><td>Reproduction with FSL segmentation</td><td>Z-score</td><td>All</td><td>Reproduction pipeline FSL segmentation</td></tr><tr><td>Closest-to-paper: n=82</td><td>Reproduction without anatomic priors</td><td>Z-score</td><td>All</td><td>Reproduction pipeline no anatomic priors</td></tr><tr><td>Closest-to-paper: n=82</td><td>fMRIprep pipeline</td><td>Z-score</td><td>All</td><td>fMRIprep pipeline</td></tr><tr><td rowspan="2">Variations of image feature processing</td><td>Closest-to-paper: n=82</td><td>Reproduction with AFNI segmentation</td><td>Not z-scored images</td><td>All</td><td>Reproduction pipeline no z-score</td></tr><tr><td>Closest-to-paper: n=82</td><td>Reproduction with AFNI segmentation</td><td>ALFF instead of fALFF, Z-score</td><td>All</td><td>Reproduction pipeline ALFF</td></tr><tr><td rowspan="4">Variations of sets of input features</td><td>Closest-to-paper: n=82</td><td>Reproduction with AFNI segmentation</td><td>Z-score</td><td>No dominant disease side</td><td>Reproduction pipeline no domside</td></tr><tr><td>Closest-to-paper: n=82</td><td>Reproduction with AFNI segmentation</td><td>Z-score</td><td>No baseline UPDRS</td><td>Reproduction pipeline no UPDRS</td></tr><tr><td>Closest-to-paper: n=82</td><td>Reproduction with AFNI segmentation</td><td>Z-score</td><td>Only imaging features</td><td>Reproduction pipeline only imaging features</td></tr><tr><td>Closest-to-paper: n=82</td><td>/</td><td>Z-score</td><td>No imaging features</td><td>No imaging features</td></tr></tbody></table>
## Supporting information

* **S1 Fig.** Comparison of original Schaefer atlas used in [1] and reproduced atlas obtained from three separate atlases available in FSL.

* **S1 Table.** Demographics and clinical features set as input for the machine learning models.

* **S2 Table.** Terminology of the experiments in the current paper.