Objective: To assess the predictive power of Diffusion Kurtosis Imaging (DKI) parameters obtained from Neurite orientation dispersion and density imaging tool (NODDI) to differentiate between Parkinson’s disease (PD) subjects and Healthy Controls (HC).

Background: Diffusion indices have been utilized by researchers to check for pathological abnormalities in the SNc, but the outcomes of these investigations have been inconsistent. More advanced diffusion models, such as NODDI [1] have recently been proven to better capture diseased tissue changes. Free water has recently emerged as a valuable indicator for the diagnosis and monitoring of disease progression in Parkinsonism [2]. Only a few studies have employed NODDI to examine SNc pathology in PD patients, and the results have been contadictory [3]. In this study, we are investigating the usefulness of NODDI for identifying the pathological changes in the SNc.

Method: 25 PD and 17 age-matched HC were evaluated. Image acquisition was performed on Philips Ingenia 3T scanner with a 32-channel head coil. DKI images were converted to NIFTI format and fed to the image pre-processing pipeline. Denoising, Gibbs artefact removal, eddy current correction, and brain mask synthesis were among the pre-processing processes. NODDI model instance was created and pre-processed DKI images, brain mask, bval files, and bvec files were fed to the instance in order to generate isotropic volume (Viso), Orientation dispersion (ODI) and Intracellular volume (Vic) maps to capture free water, neurite density, and intracellular packing information respectively. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) maps were also generated from pre-processed DKI images using DiPy library. All maps were registered on MNI space using FSL. SNc was located and uniform regions of interest (ROIs) were placed on the B0 image. Mean values from the ROI were obtained from all the maps. These values were statistically compared between the PD and HC groups.

Results: Significant differences were found in FA, Viso, Vic, and ODI between PD and HC, whereas there was no difference in MD between PD and HC (Table1). Mean values of the DKI parameters obtained from SNc were found to be increased in PD subjects as compared to HC (Figure1).

Conclusion: DKI parameters obtained using NODDI toolbox were significantly increased in the SNc of PD patients compared to the HC group.

References: 1. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. Neuroimage. 2012 Jul 16;61(4):1000-16. doi: 10.1016/j.neuroimage.2012.03.072.
2. Neurite orientation dispersion and density imaging (NODDI) and free-water imaging in Parkinsonism. Mitchell T, Archer DB, Chu WT, Coombes SA, Lai S, Wilkes BJ, McFarland NR, Okun MS, Black ML, Herschel E, Simuni T, Comella C, Xie T, Li H, Parrish TB, Kurani AS, Corcos DM, Vaillancourt DE.Hum Brain Mapp. 2019 Dec 1;40(17):5094-5107. doi: 10.1002/hbm.24760.
3. Neurite orientation dispersion and density imaging in the substantia nigra in idiopathic Parkinson disease. Kamagata K, Hatano T, Okuzumi A, Motoi Y, Abe O, Shimoji K, Kamiya K, Suzuki M, Hori M, Kumamaru KK, Hattori N, Aoki S. Eur Radiol. 2016 Aug;26(8):2567-77. doi: 10.1007/s00330-015-4066-8.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/noddi-based-maps-as-potential-marker-for-parkinsons-disease/