The Parkinson Progression Marker Initiative (PPMI).

By K. Marek, D. Jennings, Shirley Lasch, A. Siderowf, C. Tanner, T. Simuni, Chris Coffey, K. Kieburtz, Emily Flagg, S. Chowdhury and 74 more. Progress in neurobiology, 2011

1,831 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Complementary and alternative medicine, Neurology

View Paper: https://www.semanticscholar.org/paper/c17b1e8b3bfea4cfaa1414a853d31b9d3e50c6fc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Bruce Fischl
2. Axel Thielscher

In Neurology

1. Dinggang Shen
2. Bruce Fischl
3. Axel Thielscher
4. Guha Balakrishnan
5. Heung-Il Suk
6. Seong-Whan Lee
7. Arvid Lundervold
8. Yanqing Zhang

Citations in the corpus

• Transformers in Medical Image Analysis: A Review (2022, background): “[26] Parkinson the Parkinson’s Progression Marker Initiative database [108] Image synthesis (SPECT)”
• Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, methodology): “Healthcare [122] PPMI 2011 1059 The PPMI dataset will include 200 healthy volunteers and 400 recently diagnosed PD patients who will be followed longitudinally for clinical, imaging, and biospecimen biomarker assessment at 21 clinical sites utilizing standardized data gathering techniques.”
• Diffusion models in medical imaging: A comprehensive survey (2022, background): “They elaborated their findings on one private and three public datasets (Sherbrooke 3-Shell [140], Stanford HARDI [141], and Parkinson’s Progression Markers Initiative (PPMI) [142]) and reported superior denoising performances.”
• Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature (2021, methodology): “7%), data used were from public repositories and databases, including University of California at Irvine (UCI) Machine Learning Repository (Dua and Graff, 2018) (n = 44), Parkinson’s Progression Markers Initiative (Marek et al., 2011) (PPMI; n = 33), PhysioNet (Goldberger et al.”
• Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration (2018, methodology): “We use a large-scale, multi-site dataset of 7829 T1-weighted brain MRI scans from eight publicly available datasets: ADNI [25], OASIS [22], ABIDE [12], ADHD200 [24], MCIC [15], PPMI [23], HABS [9], and Harvard GSP [16].”
• SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining (2021, positive): “T1mix: 1,000 T1 scans at 1 mm isotropic resolution from seven datasets: ABIDE [21], ADHD200 [18], GSP [33], HABS [19], MCIC [29], OASIS [49], and PPMI [50].”
• VoxelMorph: A Learning Framework for Deformable Medical Image Registration (2018, methodology): “1) Dataset: We use a large-scale, multi-site, multistudy dataset of 3731 T1–weighted brain MRI scans from eight publicly available datasets: OASIS [69], ABIDE [70], ADHD200 [71], MCIC [72], PPMI [73], HABS [74], Harvard GSP [75], and the FreeSurfer Buckner40 [1].”