Jesus E Fajardo1, Vivian B Truong1, Yang Xuan1, Sara E Benitez2, Mary L Vo2, Bo Hu2, Richard Dortch 3, Jun Li2, Yongsheng Chen 1
1Department of Neurology, Wayne State University School of Medicine, Detroit, United States of America
2Department of Neurology, Houston Methodist Research Institute, Houston, United States of America
3Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, United States of America
Presenting Author: Yongsheng Chen
Synopsis
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1. Li, J., et al., Stoichiometric alteration of PMP22 protein determines the phenotype of hereditary neuropathy with liability to pressure palsies. Archives of neurology, 2007. 64(7): p. 974-978. doi:10.1001/archneur.64.7.974 [doi]
2. Svačina, M.K. and H.C. Lehmann, Chronic inflammatory demyelinating polyneuropathy (CIDP): current therapies and future approaches. Current pharmaceutical design, 2022. 28(11): p. 854-862. https://doi.org/10.2174/1381612828666220325102840 [doi]
3. Rajabally, Y.A., et al., CIDP and other inflammatory neuropathies in diabetes—diagnosis and management. Nature reviews neurology, 2017. 13(10): p. 599-611. https://doi.org/10.1038/nrneurol.2017.123 [doi]
4. Pareyson, D. and C. Marchesi, Diagnosis, natural history, and management of Charcot–Marie–Tooth disease. The Lancet Neurology, 2009. 8(7): p. 654-667. doi:10.1016/S1474-4422(09)70110-3 [doi]
5. Chen, Y., et al., Multiparametric quantitative MRI of peripheral nerves to differentiate axonal from demyelinating neuropathies (P11-8.002). Neurology, 2023. 100(17_supplement_2): p. 2196. https://doi.org/10.1212/WNL.0000000000202382 [doi]
6. Campbell, G.J., et al., Quantitative double echo steady state T2 mapping of upper extremity peripheral nerves and muscles. Frontiers in Neurology, 2024. 15: p. 1359033. https://doi.org/10.3389/fneur.2024.1359033 [doi]
7. Nwawka, O.K., et al., Imaging of peripheral nerves: AJR expert panel narrative review. American Journal of Roentgenology, 2025. 224(4): p. e2431064. https://doi.org/10.2214/AJR.24.31064 [doi]
8. Dortch, R.D., et al., Proximal nerve magnetization transfer MRI relates to disability in Charcot-Marie-Tooth diseases. Neurology, 2014. 83(17): p. 1545-1553. https://doi.org/10.1212/WNL.0000000000000919 [doi]
9. Chhabra, A., et al., Whole‐body MR neurography: Prospective feasibility study in polyneuropathy and Charcot‐Marie‐Tooth disease. Journal of Magnetic Resonance Imaging, 2016. 44(6): p. 1513-1521. https://doi.org/10.1002/jmri.25293 [doi]
10. Vaeggemose, M., et al., Magnetic resonance neurography and diffusion tensor imaging of the peripheral nerves in patients with C harcot‐M arie‐T ooth Type 1A. Muscle & nerve, 2017. 56(6): p. E78-E84. https://doi.org/10.1002/mus.25691 [doi]
11. Chen, Y., E.M. Haacke, and J. Li, Peripheral nerve magnetic resonance imaging. F1000Research, 2019. 8: p. F1000 Faculty Rev-1803. doi:10.12688/f1000research.19695.1 [doi]
12. Chen, Y., et al., Multiparametric quantitative MRI of peripheral nerves in the leg: A reliability study. Journal of Magnetic Resonance Imaging, 2024. 59(2): p. 563-574. https://doi.org/10.1002/jmri.28778 [doi]
13. Pedregosa, F., et al., Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 2011. 12: p. 2825-2830.
14. Harris, C.R., et al., Array programming with NumPy. Nature, 2020. 585(7825): p. 357-362. https://doi.org/10.1038/s41586-020-2649-2 [doi]
15. Gommers, R., et al., scipy/scipy: SciPy 1.15. 0. Zenodo, 2024.