This repository contains supporting code for the following manuscript
Óscar Peña-Nogales, Timothy M. Ellmore, Rodrigo de Luis-García, Jessika Suescun, Mya C. Schiess and Luca Giancardo. Longitudinal Connectomes as a Candidate Progression Marker for Prodromal Parkinson's Disease. Frontiers in Neuroscience 12, 1–13 (2019).
https://doi.org/10.3389/fnins.2018.00967
Background and Goal
Parkinson's disease is the second most prevalent neurodegenerative disorder in the Western world. It is estimated that the neuronal loss related to Parkinson’s disease precedes the clinical diagnosis by more than 10 years (prodromal phase) which leads to a subtle decline that translates into non-specific clinical signs and symptoms. By leveraging diffusion magnetic resonance imaging brain (MRI) data evaluated longitudinally, at least at two different time points, we have the opportunity of detecting and measuring brain changes early on in the neurodegenerative process, thereby allowing early detection and monitoring that can enable development and testing of disease modifying therapies.
PD Longitudinal Connectome
In this work, we aim to identify and quantify a longitudinal degenerative Parkinson's disease pattern from the diffusion magnetic resonance imaging connectivity information that can be found in a de novo early Parkinson's disease cohort (n=21) and in a cohort at high risk of being in the Parkinson's disease prodromal phase (n=16) that was not present in cohort matched Controls (n=30). This progression pattern was numerically quantified with a longitudinal brain connectome progression score. This score is generated by an interpretable machine learning algorithm trained, with cross-validation, on the longitudinal connectivity information of Parkinson's disease and Control groups computed on a nigrostriatal pathway-specific parcellation atlas.
Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data); for up to date information on the study, visit www.ppmi-info.org.
Results Experiments indicated that the longitudinal brain connectome progression score was able to discriminate between the progression of Parkinson’s disease and Control groups with an area under the receiver operating curve of 0.89 [confidence interval (CI): 0.81–0.96] and discriminate the progression of the High Risk Prodromal and Control groups with an area under the curve of 0.76 [CI: 0.66–0.92].
Conclusions Results suggest that it is possible to quantify neurodegenerative patterns of progression in the prodromal phase with longitudinal diffusion magnetic resonance imaging connectivity data and use these image-based patterns as progression markers for neurodegeneration.
You can visualize the analysis directly by clicking on longitudinal-connectomes-analysis.ipynb
This code has been tested with Python 2.7 and conda/pip
git clone https://github.com/lgiancaUTH/PD-Longitudinal-Connectome.git
cd PD-Longitudinal-Connectome
Optionally, create a (conda) virtual environment and activate it
conda create -n pd-long-env python=2.7
source activate pd-long-env
and finally, install the required dependencies
pip install -r requirements.txt
if rpy2 fails to compile, you can install the precompiled packages with conda
conda install rpy2==2.8.5
Run the jupyter notebook server
jupyter notebook
and open longitudinal-connectomes-analysis.ipynb
In order to run the full analysis, you will have to request the PPMI datasets at www.ppmi-info.org/data and change the [data/configuration.json] file accordingly.
Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data); for up to date information on the study, visit www.ppmi-info.org.
The Parkinson’s Progression Markers Initiative—a public-private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners including AbbVie, Avid, Biogen, BioLegend Bristol-Myers Squibb, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, and Golub Capital ~
This code is free to use for any non-commercial purposes, provided that the original publication is cited.
We refer to the original publication for additional infomation and acknowledgements.