Abstract
Parkinson’s disease is the second most prevalent neurodegenerative disorder after Alzheimer’s disease. The brainstem, despite its early and crucial involvement in Parkinson’s disease, is largely unexplored in the domain of functional medical imaging. Here we propose a data-driven, connectivity-pattern based framework to extract functional sub-regions within the brainstem and devise a machine learning based tool that can discriminate Parkinson’s disease from healthy participants. We first propose a novel framework to generate a group model of brainstem functional sub-regions by optimizing a community quality function, and generate a brainstem regional network. We then extract graph theoretic features from this brainstem regional network and, after employing an SVM classifier, achieve a sensitivity of disease detection of 94% – comparable to approaches that normally require whole-brain analysis. To the best of our knowledge, this is the first study that employs brainstem functional sub-regions for Parkinson’s disease detection.
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Haq, N.F., Cai, J., Yu, T., McKeown, M.J., Wang, Z.J. (2020). Parkinson’s Disease Detection from fMRI-Derived Brainstem Regional Functional Connectivity Networks. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_4
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