2.1 Participants
Twenty-three right-handed patients who were clinically diagnosed as PD-MCI and 23 right-handed patients who were diagnosed as PD-NCI, were recruited from Department of Neurology at the Sunshine Union Hospital, Weifang (from January 12,2018 to December 20, 2019). The diagnosis of PD was clinically determined according to the UK Parkinson's Disease Brain Bank criteria[18]. PD-NCI patients exhibit no impairment on cognitive abilities or do not take cognitive dysfunction on any perception, and PD-MCI patients met the as following criteria: a decrease in cognitive abilities observed by a clinician or reported by patients; cognitive impairments can be distinguished by cognitive rating scales, but related dementia do not reach clinical criteria [19]; the cognitive deficits are not attributable to age or other certain systemic diseases. Exclusion criteria were: brain lesion contraindication on MRI; sever concomitant diseases that might influence brain metabolic alterations; history of current psychiatric illness. All diagnosis was managed by at least two professional neurologists. Twenty-three gender- and age- and education-matched healthy participants from the community served as the controls.
2.2 Neurological and neuropsychological
The Unified Parkinson's Disease Rating Scale (UPDRS_III) was used to measure the severity of motor symptoms in the PD patients in the "ON" state, and Hoehn and Lahr (H&Y) scale was used to evaluate disease stage of PD patients [20]. general cognitive assessments were executed by using the Mini-Mental Status Examination (MMSE) [21] and Montreal Cognitive Assessment (MoCA) [4], which evaluated memory and executive functions in the patient groups. Clinical and demographic details of all participants are showed in Table 1.
2.3 Imaging data acquisition
Data were acquired with a Siemens Magnetom TIM Trio 3.0 T scanner (Siemens, Munich, Germany). The examination was performed in darkness. Earplugs and foam pads were used to reduce noise and head motion. Participants were required to move as little as possible and close their eyes and relax but not fall asleep. Three-dimensional axial ) T1-weighted MPRAG (magnetization-prepared rapid gradient echo) magnetic resonance images were collected with the following parameters: TR=2530 ms; TE =3.42 ms; FOV = 256× 256cm; flip angle = 15°; matrix = 256× 256; 176 single-shot interleaved slices with no gap; thickness = 1.1 mm. resting-state functional MRI data were collected using a gradient echo-planar imaging (EPI) sequence (TR=2000 ms, thickness =3 mm, flip angle = 90°, FOV=24 cm × 24 cm, TE=60 ms, 34 axial slices, 3 mm thickness without gap matrix=64 × 64, containing 160 volumes).
2.4 Anatomic data analysis
The anatomical images were processed and analyzed using the CAT12 toolbox implemented in Statistical Parametric Mapping (SPM12; www.fil.ion.ucl.ac.uk/spm). CAT12 provides processing pipelines for both voxel-based morphometry (VBM) as well as surface-based morphometry (SBM) including cortical thickness, allowing us to perform all analyses with this software package. For the steps of processing and analysis, the parameters used default settings met the standard protocol (http://www.neuro.uni-jena.de/cat12/CAT12-Manual.pdf). This tool has been previously used and validated in morphometric studies in PD [22, 23] and other neurodegenerative diseases [24]. A two-step quality assurance was also included by processing: first, all images were inspected for artifacts (prior to preprocessing) visually; secondly, statistical quality control was performed for overall image quality and inter-subject homogeneity as included in the CAT12 toolbox after segmentation.
The analysis of cortical thickness based on the same for extraction of the cortical surface implemented in CAT 12 was performed. The cortical thickness was estimated using a projection-based distance measure. The vertex-wise cortical thickness measures were resampled and smoothed by a 12 mm full width at half-maximum (FWHM) standard Gaussian kernel.
For VBM analysis, the anatomical images were normalized to a standard template by DARTEL approach and then segmented into three voxel classes: gray matter, white matter, and cerebrospinal fluid using partial volume segmentation with MAP approach. Then the regional gray matter density differences were tested using modulated normalized gray matter maps. The abstracted GM maps were smoothed utilizing a 8 mm FWHM kernel and used for further analysis.
2.5 Functional data analysis
Resting-state functional data was preprocessing by Data Processing Assistant for Resting-state fMRI toolkit[25]. The first 10 volumes of the functional imaging were discarded for the MRI signal equilibrium and the adaptation of the participants to the scanning circumstance. The remaining 150 volumes were then corrected for the intra-volume acquisition time delay between slices for inter-volume geometrical displacement due to head motion. No participants were excluded under a criterion of head displacement of > 2.5 mm or angular rotation of >2.50 in any direction. Afterward, individual T1-weighted images were coregistered to the mean realigned functional images using a linear transformation. Finally, functional images were spatially normalized into the Montreal Neurological Institute (MNI) template and smoothed with a 6 mm FWHM standard Gaussian kernel.
The ALFF calculation was executed by the Resting-State fMRI Data Analysis Toolkit (http://restfmri.net/forum/REST). Briefly, for a given voxel, fast Fourier transform was used to convert the time domain to the frequency domain. The mean square root of the power spectrum was computed and averaged throughout 0.01 Hz to 0.08 Hz at all voxels. The resulting ALFF was converted into z-scores by subtracting the mean and dividing by the global standard deviation for standardization purposes. And reducing the global effects of variability across participants
2.4 Statistical analysis
Statistical analyses of anatomical imaging data were performed in the CAT12 SPM12 statistical module using a two-sample t-test to each of both morphometric measures (gray matter density with VBM and cortical thickness with SBM). Using age, gender, and educational years as covariates (and for VBM analyses, additionally, total intracranial volume, TIV). The group differences of statistical thresholds were set at P < 0.05 with FDR correction for multiple comparisons.
For the ALFF difference of between-group, a two-sample t-test was also performed with age, gender, educational years, head motion, and gray matter volume as variables between each pair of the three groups. Voxel-level intensity threshold of P<0.01 with a minimum cluster size of 50 contiguous voxels was used to correct for multiple comparisons using the Random Gaussian field (GRF) theory.
Demographic and clinical data analysis was performed by SPSS 20 Statistics software package (IBM Corporation, New York, EUA). T-tests were used to test differences between groups, and Pearson correlation was used to calculate the relationship between imaging and clinical data.