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Free-Water Corrected Diffusion MRI Measures Reveal White Matter Disorganization in Parkinson’s Disease Patients with Memory Impairment.1Imaging, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 2Neuropsychology, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 3Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
Synopsis
Diffusion MRI (dMRI) could be used to understand cognitive impairment in Parkinson’s disease (PD). However, single-tensor (ST)-derived fractional anisotropy (FA) measures are biased due to the presence of crossing-fibers and contamination from cerebrospinal fluid (CSF). CSF contamination can be corrected by fitting a bi-tensor model to estimate free water (FW) contamination. Hence, in this study, we compared FW and FW-corrected ST dMRI-derived measures between PD patients (with and without cognitive impairment) and healthy controls (HC) estimated with multishell dMRI data. Our analysis suggests that FW-corrected dMRI analyses are more powerful in understanding WM disorganization in PD patients with cognitive impairment.
Introduction
Almost 80% of Parkinson’s disease (PD) patients progress to dementia (PDD) within twelve years of diagnosis1. Previous studies have suggested that mild cognitive impairment (MCI) in PD (PD-MCI) increases the risk of PDD2–4. A recent study by Jones et al.5, however, suggests that PD-MCI who revert to normal cognition (PD-NC) are also at a greater risk of developing PDD. This discrepancy may be because the diagnosis of PD-MCI is complex and fluctuates over time6. White matter (WM) changes captured using diffusion MRI (dMRI) are shown to be sensitive in PD, especially to capture early disease-related neurodegeneration7. Single-tensor (ST) dMRI-derived measures have also been shown to be affected with cognition in PD7–10 with WM fiber tracts of genu and splenium of corpus callosum (CC), cingulate bundle, and prefrontal and temporal WM tracts shown to have reduced FA9,11. However, ST-derived fractional anisotropy (FA) measures that are estimated in routine clinical investigations are biased not only due to the presence of crossing-fibers12 but also due to the contamination from cerebrospinal fluid (CSF)13. Such CSF-contamination can be corrected by fitting a bi-tensor model14,15 to correct for free water (FW) contamination. Hence, in this study, we compared FW and FW-corrected ST dMRI-derived measures between PD-NC, PD-MCI, and healthy controls (HC) estimated with high spatial and angular resolution dMRI data. We also tested for correlations of both FW and FW-corrected ST dMRI-derived measures with clinical measures in our cohort. We hypothesized that FW corrected dMRI-derived measures may be more sensitive to capture changes in WM disorganization and their correlation with clinical measures as compared to conventional ST dMRI-derived measuresMethods
Participants: Twenty-nine PD participants and 49 HC (26 Females; Age: 70.35±6.33years; Years of Education (YOE): 16.37±2.42years) were recruited at the Center for Neurodegeneration and Translational Neuroscience, Cleveland Clinic Lou Ruvo Center for Brain Health. 14 PD participants were identified as PD-MCI (3 Females; Age: 67.64±6.6years; YOE: 15.43±2.6years) and 15 PD participants were identified as PD-NC (5 Females; Age: 68.07±6.99years; YOE: 15.33±2.19years) according to a consensus diagnosis made by a neurologist and a neuropsychologist based on results of a comprehensive neuropsychological test battery following the Litvan criteria16. dMRI acquisition: dMRI was acquired for all participants on a 3T Siemens Skyra using the following protocol: 213 diffusion encoding directions (DEC) and 25 non-diffusion weighted (b0) images interspersed between the DEC, Multiband factor=3, GRAPPA=2, TR=5218ms, TE=100ms, resolution=1.5mm3, 3 b-values: 500s/mm2, 1000s/mm2, and 2500s/mm2, and phase-encoding directions of P>>A; opposite phase-encoding (A>>P) b0 image. Acquisition time: 23:13 minutes. Preprocessing: All data were corrected for eddy-current distortion using eddy17 tools from FSL and head motion was computed across the session for each participant. Processing: (i) ST dMRI-derived measures were estimated using dtifit tool of FSL and (ii) FW and FW-corrected ST dMRI measures were obtained using DiPY implementation of multi-shell dMRI acquisition18. Of note, since FA and its derivatives are only reliable at b<=1000s/mm2, estimation of ST FA and FW-corrected FA was only done using b-values<=1000s/mm2. Statistical Analysis: PALM toolbox19 in FSL was used to extract significantly different or correlated ST or FW-corrected dMRI-derived measures with clinical variables. Significance was established at pcorr<0.05, and was family-wise error correction was performed across various measures for every technique utilized in PALM. Of note, age, education, sex, years of education, head motion, and intracranial volume were utilized as covariates of no interest in statistical analysis.Results
The average head motion across for each group was less than 1mm, and there was no statistically significant difference between any of the demographical variables. None of the ST dMRI-derived measures were different between any group. However, FW-corrected FA was significantly greater in HC when compared to PD-NC (Fig.1) encompassing tracts of CC, corticospinal tract (CST), and left inferior longitudinal fasciculus (ILF). Both ST dMRI-derived axial diffusivity (AxD) and FW-corrected AxD encompassing CC and CST were positively correlated with freezing of gait questionnaire (FOGQ) in the PD-MCI group (Fig.2). Only FW-corrected FA had a positive correlation with MDS-UPDRS Part-III scores in PD-MCI (Fig.3) encompassing genu of the CC. Levodopa equivalent dose (LEDD) showed a positive correlation with FW-corrected FA in both PD-MCI and PD-NC groups (Fig.4) predominantly encompassing CC. FW-corrected AxD was negatively correlated with LEDD in PD-MCI encompassing CC (Fig.5).Discussion and Conclusion
Our analyses reveal a paradoxical positive correlation between FW-corrected FA measures and UPDRS along with a paradoxical negative correlation of FW-corrected AxD with LEDD suggesting axonal death in CC of PD-MCI. Our analysis suggests that FW-corrected dMRI analyses are more powerful in understanding WM disorganization in PD patients with cognitive impairment. Analysis of correlation of ST and FW-corrected ST dMRI-derived measures with neuropsychological scores will reveal specific neuroanatomical correlates of WM disorganization in PD patients with cognitive impairment. Furthermore advanced dMRI metrics such as those evaluated using diffusion kurtosis imaging (DKI)20 and neurite orientation dispersion and density imaging (NODDI)21 might be better able to characterize if the observed changes in ST dMRI-derived measures are due to less prevalence of crossing-fibers that might be responsible for paradoxical positive correlation of dMRI-derived measures and clinical variables.Acknowledgements
This study is supported by the National Institutes of Health (grant R01NS117547, P20GM109025, R01NS118760, and R01NS118760-S1) and the Keep Memory Alive-Young Investigator Award (Keep Memory Alive Foundation).References
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