Abstract #4674

Using Neurite Orientation Dispersion and Density Imaging to Diagnose Data-Driven Parkinson’s Disease Subtypes in Brainstem Nuclei

Wei-Chih Yang^1,2, Ru-Jen Lin³, Ming-Chih Kuo^2,4, Kai-Hsiang Stanley Chen³, Yuh-Fen Wei⁵, Hui-Yu Yang³, Joshua Oon Soon Goh^6,7,8,9, and Yao-Chia Shih²

¹Department of Medical Imaging, National Taiwan University Hospital, Taipei City, Taiwan, ²Graduate Institute of Medicine, Yuan Ze University, Taoyuan City, Taiwan, ³Department of Neurology, National Taiwan University Hospital Hsinchu Branch, Hsinchu City, Taiwan, ⁴Department of Medical Imaging, National Taiwan University Cancer Center, Taipei City, Taiwan, ⁵Department of Medical Imaging, National Taiwan University Hospital Hsinchu Branch, Hsinchu City, Taiwan, ⁶Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan, ⁷Department of Psychology, National Taiwan University, Taipei, Taiwan, ⁸Neuobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan, ⁹Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan

Synopsis

Keywords: DWI/DTI/DKI, Microstructure

Motivation: Little is known about whether microstructural changes in the brainstem nuclei can differentiate three novel data-driven PD subtypes defined by a retrospective multidiscipline dataset.

Goal(s): To compare various diffusion MRI metrics between patients with three novel data-driven PD subtypes.

Approach: Neurite orientation dispersion and density Imaging (NODDI) and diffusion tensor imaging (DTI) metrics were reconstructed to compare three subtypes.

Results: Only a NODDI index, free water fraction in the right substantia nigra can distinguish the symptomatically severest subtype from other two subtypes (AUC=0.798).

Impact: NODDI assists in diagnosing the data-driven PD subtype with the most severe motor/non-motor comorbidities.

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