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Review
. 2022 Nov 9;8(1):151.
doi: 10.1038/s41531-022-00416-6.

Microstructural but not macrostructural cortical degeneration occurs in Parkinson's disease with mild cognitive impairment

Xueqin Bai #  1 , Tao Guo #  1 , Jingwen Chen  1 , Xiaojun Guan  1 , Cheng Zhou  1 , Jingjing Wu  1 , Xiaocao Liu  1 , Haoting Wu  1 , Jiaqi Wen  1 , Luyan Gu  2 , Ting Gao  2 , Min Xuan  1 , Peiyu Huang  1 , Baorong Zhang  2 , Xiaojun Xu  1 , Minming Zhang  3
Affiliations
Review

Microstructural but not macrostructural cortical degeneration occurs in Parkinson's disease with mild cognitive impairment

Xueqin Bai et al. NPJ Parkinsons Dis. .

Abstract

This study aimed to investigate the cortical microstructural/macrostructural degenerative patterns in Parkinson's disease (PD) patients with mild cognitive impairment (MCI). Overall, 38 PD patients with normal cognition (PD-NC), 38 PD-MCI, and 32 healthy controls (HC) were included. PD-MCI was diagnosed according to the MDS Task Force level II criteria. Cortical microstructural alterations were evaluated with Neurite Orientation Dispersion and Density Imaging. Cortical thickness analyses were derived from T1-weighted imaging using the FreeSurfer software. For cortical microstructural analyses, compared with HC, PD-NC showed lower orientation dispersion index (ODI) in bilateral cingulate and paracingulate gyri, supplementary motor area, right paracentral lobule, and precuneus (PFWE < 0.05); while PD-MCI showed lower ODI in widespread regions covering bilateral frontal, parietal, occipital, and right temporal areas and lower neurite density index in left frontal area, left cingulate, and paracingulate gyri (PFWE < 0.05). Furthermore, compared with PD-NC, PD-MCI showed reduced ODI in right frontal area and bilateral caudate nuclei (voxel P < 0.01 and cluster >100 voxels) and the ODI values were associated with the Montreal Cognitive Assessment scores (r = 0.440, P < 0.001) and the memory performance (r = 0.333, P = 0.004) in the PD patients. However, for cortical thickness analyses, there was no difference in the between-group comparisons. In conclusion, cortical microstructural alterations may precede macrostructural changes in PD-MCI. This study provides insightful evidence for the degenerative patterns in PD-MCI and contributes to our understanding of the latent biological basis of cortical neurite changes for early cognitive impairment in PD.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. ODI comparisons among groups.
Compared with HC, PD-NC patients showed lower ODI in bilateral cingulate and paracingulate gyri, supplementary motor area, right paracentral lobule and precuneus (a); PD-MCI patients showed widespread lower ODI throughout bilateral frontal, parietal, occipital and right temporal areas (b). Compared with PD-NC, PD-MCI patients showed reduced ODI in right frontal area and bilateral caudate nuclei for GBSS analyses with cluster-based thresholding method (voxel P < 0.01 and cluster >100 contiguous voxels) (c). HC healthy control, PD-NC Parkinson’s disease with normal cognition, ODI orientation dispersion index, PD-MCI Parkinson’s disease with mild cognitive impairment.
Fig. 2
Fig. 2. NDI comparisons among groups.
Compared with HC, only PD-MCI patients showed reduced NDI predominantly in left frontal area, left cingulate and paracingulate gyri. HC healthy control, NDI neurite density index, PD-MCI Parkinson’s disease with mild cognitive impairment.
Fig. 3
Fig. 3. Correlation analyses in PD patients.
The ODI values of the right frontal area were positively correlated with MoCA scores (a) and the memory performance (b) in all PD patients. ODI orientation dispersion index, MoCA Montreal Cognitive Assessment.
Fig. 4
Fig. 4. Processing steps of GBSS.
a GM fraction maps were generated by subtracting the WM fraction (estimated by two-tissue class segmentation of resampled FA maps using Atropos segmentation tool) and the CSF fraction (resampled fiso maps from NODDI) from one. The GM, WM, and CSF fraction were used to generate pseudo T1-weighted images. b Pseudo T1-weighted images from all subjects were registered to the OASIS-30_Atropos_template and all resampled NODDI maps and GM fraction maps were aligned to the template. c GM fraction images from all subjects were averaged to generate mean GM images, which were skeletonized using FSL’s tbss_skeleton tool. All NODDI parameters maps and GM fraction maps were projected onto this GM skeleton. GBSS gray matter-based spatial statistics, GM gray matter, WM white matter, FA fractional anisotropy, CSF cerebrospinal fluid, NODDI Neurite Orientation Dispersion and Density Imaging, NDI neurite density index, ODI orientation dispersion index, fiso volume fraction of isotropic diffusion, FSL FMRIB Software Library.
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