BACKGROUND Tauopathy in Progressive Supranuclear Palsy

Progressive supranuclear palsy (PSP) is a rare form of tauopathy that is composed predominantly of the 4-repeat form of tau.1 Core clinical features include ocular motor dysfunction, postural instability, akinesia, and cognitive dysfunction.2 Typical and diverse clinical phenotypes, such as PSP–Richardson syndrome, PSP–progressive gait freezing (PSP-PGF), PSP–cerebellar ataxia, and PSP–speech/language disorder, have been reported in the literature.2 Although there are many clinical phenotypes, the diagnosis of PSP is based on the presence of neurofibrillary tangles in subcortical nuclei under neuropathologic examination, and these tangles can be visualized by the recently developed technique, in vivo tau PET imaging.3,4 Several studies have shown that second-generation tau PET tracers could improve diagnostic accuracy and allow tracking of tau accumulation in PSP.5,6 Patients with PSP can be identified through the use of MRI, which reveals typical midbrain atrophy with a so-called hummingbird sign in the sagittal plane, as well as various regional brain atrophy.7 MRI has identified different spatiotemporal patterns of cortical and subcortical atrophic profiles in PSP.8 In addition, a recent study using tau PET identified 2 distinct progression patterns of tau trajectories in PSP.9

The glymphatic system is a waste clearance system, and glymphatic dysfunction has been proposed as a final common pathway for the accumulation of pathological proteins leading to clinical symptoms in primary neurodegenerative diseases.10 Several studies have demonstrated impairment of the human glymphatic system in subcortical diseases, such as PSP, Parkinson disease (PD), and corticobasal syndrome.11–13 In the past, an animal study revealed impaired glymphatic function and clearance of tau in an Alzheimer disease (AD) model.14 Recently, our study showed that glymphatic activity mediates the associations between the cortical deposition of tau and cognitive dysfunction in patients with AD.15 In primarily subcortical neurodegenerative disorders, one animal study has shown a close interaction between the aquaporin-4 (AQP4)–mediated glymphatic system and parenchymal alpha-synuclein deposition in a model of PD.16 The role of the glymphatic system in tau deposition in other subcortical diseases remains largely unknown. Because tau deposition in PSP may occur in subcortical nuclei as well as in different cortical regions, it provides an opportunity to investigate the relationships between tau deposition in different brain regions, glymphatic dysfunction, and clinical symptoms in vivo. The aim of the present study was to investigate the associations of topographic distributions of tau burden with diffusion tensor imaging (DTI)–derived glymphatic activity, cortical atrophy, and clinical parameters in patients with PSP.

PATIENTS AND METHODS

This was a cross-sectional study conducted at Linkou Chang Gung Memorial Hospital. The study protocol was approved by the Institutional Review Board (CGMHIRB No. 202002292A0 and 202102215A0). Written informed consent was obtained from each participant before the study procedure. Each participant completed a cognitive evaluation, a brain MRI, and 18F-Florzolotau PET scans.

SUBJECTS

A total of 55 participants, including 23 normal controls (NCs) and 32 patients with PSP, were recruited for this study. The diagnosis of PSP and its clinical phenotypes were based on the International Parkinson and Movement Disorder Society criteria for the diagnosis of PSP.2 Neuropsychological assessments were performed for all participants using the Mini-Mental State Examination (MMSE).17 Disease severity was measured by the Unified Parkinson’s Disease Rating Scale (UPDRS) (range, 0–199) and the PSP rating scale (PSPRS) (range, 0–100).18,19 Patients were asked to discontinue medication before the clinical rating and imaging. The NCs in the study had to be 20 to 80 years old with normal cognitive function (MMSE: 26–30; normal 18F-Florzolotau PET result).

Image Acquisition

18F-Florzolotau was prepared and synthesized at the cyclotron facility of Chang Gung Memorial Hospital.20 All participants were studied with a Biograph mCT PET/CT system (Siemens Medical Solutions, Malvern, PA) and an integrated PET/MR system (Siemens Biograph mMR scanner). The scanning times for all participants were between 15 and 16 minutes. For the 18F-Florzolotau PET study, a 10-minute scan was acquired 90 minutes after an injection of 185 ± 74 MBq of 18F-Florzolotau. PET images were reconstructed based on the software version VB20 provided by the manufacturer. The reconstructed images had a matrix size of 400 × 400 × 148 and a voxel size of 0.68 × 0.68 × 1.5 mm3. The MRI protocol has been reported previously.15 Briefly, it included a sagittal fluid attenuation inversion recovery sequence, whole-brain axial 3-dimensional T1-weighted magnetization-prepared rapid acquisition gradient echo sequence, and whole-head diffusion study. Participants with significant MRI abnormalities were excluded from the study. The diffusion tensor image sequence was acquired along 64 gradient directions for b = 1000 s/mm2 with an echo planar imaging sequence, and one b = 0 s/mm2 image was acquired with the diffusion-weighted imaging (DWI) sequence.

Image Analysis

For each participant, we registered 18F-Florzolotau images to individual T1-weighted MRI scans using the SPM12 toolbox.21 This procedure ensured that each PET image was in alignment with the native MRI scans. The Muller-Gartner method was used for partial volume correction.22 Then, the high-resolution T1-weighted MRI scans in native space were normalized to the Montreal Neurological Institute (MNI) standard space using the Computational Anatomy Toolbox.23 This transform matrix was applied to the PET images. For all 18F-Florzolotau images, a traditional cerebellar gray matter (GM) as the reference region and a technique known as parametric estimation of reference signal intensity (PERSI) (Supplementary Fig. S1, https://links.lww.com/CNM/A462), which is used to perform count normalization by using the cerebrum white matter as the reference region, were implemented to compare the diagnostic discriminations between PSP patients and NCs.5,24 The cortical regions of interest (ROIs) included the bilateral frontal, parietal, temporal, occipital lobes, anterior and posterior cingulate gyri, precuneus, hippocampus, parahippocampus, and sensorimotor cortex, and the subcortical ROIs included the bilateral caudate nucleus, anterior and posterior putamen, globus pallidum, thalamus, midbrain, red nucleus, raphe nucleus, and dentate nucleus. These regions were selected based on the Harvard-Oxford cortical-subcortical structural atlas.25,26 The average values from both sides were used for subsequent analysis, and 2 meta-ROIs (cortical and subcortical meta-ROIs) were created from all cortical and subcortical ROIs. Finally, the regional SUV ratios (SUVRs) from 18F-Florzolotau PET images were calculated by using the mean intensity in the target ROIs divided by the averaged intensity of the reference regions.

Diffusion MRI data analysis was performed by using ExploreDTI to study the glymphatic activity.27 The DWI datasets were coregistered to native T1-weighted images. Then, the resulting DWI data were fitted to the DTI model.28 A fractional anisotropy map of each participant was coregistered to the fractional anisotropy map template of the International Consortium of Brain Mapping (ICBM) DTI-81 Atlas in MNI space, and the accuracy of coregistration was visually confirmed. The ICBM DTI-81 Atlas had labels of the projection (superior and posterior corona radiata) and association (superior longitudinal fasciculus) fibers in the periventricular area. We extracted the periventricular projection and association fibers within the 25 mm to 33 mm range above the anterior-posterior commissure line in MNI space, where the x axis line corresponded to the passing direction of the vessels in the deep white matter. Diffusion tensor image analysis along the perivascular space (DTI-ALPS) index and diffusivity from projection and association fibers derived from the ICBM DTI-81 Atlas were calculated as Taoka et al29 described. A higher DTI-ALPS index represented better glymphatic activity.30 Brainstem volumes were calculated using FreeSurfer v7.4.0, and the midbrain and pons volumes were calculated using T1-weighted images.31 Voxel-based morphometry (VBM) analyses were performed using each participant’s modulated normalized GM tissue images smoothed with an 8 × 8 × 8-mm isotropic Gaussian kernel as input. The SUVRs of the cortical and subcortical meta-ROIs from 18F-Florzolotau PET images were used as covariates to explore the significantly correlated GM volume in whole-brain regions; the estimated total intracranial volumes were used as covariates to correct for different brain sizes by the multiple regression module in the Computational Anatomy Toolbox.23

Statistical Analyses

All statistical analyses were performed using SPSS (version 21.0, Chicago, IL). Continuous variables are expressed as the median ± interquartile range (IQR). Nonparametric Mann-Whitney U tests and χ2/Fisher exact tests were performed to compare data for PSP patients and NCs whenever appropriate. The between-group differences in the regional SUVR values from the 18F-Florzolotau PET images were corrected for multiple comparisons using the Benjamini-Hochberg procedure, and the significance level was defined as a P value less than 0.05. Regression analyses of the associations between the DTI-ALPS indexes and mean regional SUVR values in the PET images and cognition were performed. In the VBM analyses, the GM volumes that were significantly correlated with SUVRs from the cortical and subcortical meta-ROIs were identified based on a voxel-level height threshold of false discovery rate P < 0.05 (false discovery rate corrected) and a cluster-extent threshold of k > 64 voxels (64 mm3).

We used mediation analysis to explore the significance of the regional SUVRs in PET images as a mediator between the DTI-ALPS indexes and PSPRS total scores. Mediation analysis is a statistical model used to quantify a mediating variable in the causal sequence by which an antecedent variable affects a dependent variable32 and was performed using the PROCESS macro for SPSS (model 4) with a level of confidence at 95% and 5000 bootstrap samples.33 The mediation analysis was composed of total, direct, and indirect effects. The percent of mediation (Pm) calculated by the indirect effect divided by the total effect was calculated to study the weight of the DTI-ALPS index in the total effect. Statistical significance was defined as a P value less than 0.05.

RESULTS Demographic Data

Table 1 shows the demographic data of 32 patients with PSP and 23 NCs. PSP phenotypes included PSP–Richardson syndrome (n = 5), PSP-PGF (n = 15), and other phenotypes (n = 12). The mean age of the patients with PSP was not significantly different from that of the NCs (median age of patients with PSP: 68.5 years, IQR: 62.3–72; median age of NCs: 65 years, IQR: 57–71; P = 0.10). No significant group differences were found for sex and years of education (P = 0.30 and P = 0.58, respectively). However, significantly lower MMSE scores, UPDRS total scores, and PSPRS total scores were found in patients with PSP than in NCs (all P’s < 0.01). The SUVRs of the cortical and subcortical meta-ROIs from 18F-Florzolotau using PERSI as a reference showed significant differences between patients with PSP and NCs (P < 0.01 and P < 0.01, respectively). In contrast, using cerebellar GM as a reference, only the SUVRs of the subcortical meta-ROIs significantly differed (P < 0.01). In the MRI analysis, the GM volume and midbrain volume but not the pons volume showed significantly lower values in patients with PSP than in NCs (P = 0.01, P = 0.02, and P = 0.21, respectively). The glymphatic activity that was measured by the DTI-ALPS index significantly differed between patients with PSP and NCs (P = 0.03).

TABLE 1 - Demographic Data of Patients With PSP and NCs NC (n = 23) PSP (n = 32) P* Demographics  Age, median (IQR), y 65 (57–71) 68.5 (62.3–72) 0.10  Gender (M:F) 9:14 17:15 0.30  Education, median (IQR), y 12 (9–15) 12 (9–16) 0.58 Clinical history and subtypes  PSPRS-history, median (IQR) 0 (0–0) 7 (4–9.75) <0.01  PSPRS-mentation, median (IQR) 0 (0–0) 0 (0–3) <0.01  PSPRS-bulbar, median (IQR) 0 (0–0) 3 (1.25–4) <0.01  PSPRS-ocular motor, median (IQR) 0 (0–0) 3 (1–6) <0.01  PSPRS-limb motor, median (IQR) 0 (0–0) 2 (1–4) <0.01  PSPRS-gait midline, median (IQR) 0 (0–0) 11.5 (8–13.75) <0.01 Clinical assessments  PSPRS total scores, median (IQR) 0 (0–0) 42.5 (20–55.5) <0.01  UPDRS total scores, median (IQR) 0 (0–0) 40 (27.75–68.25) <0.01  UPDRS-III scores, median (IQR) 0 (0–0) 21 (14–40) <0.01  MMSE, median (IQR) 29 (27–30) 27 (21–28.75) <0.01 PET parameters  SUVRs in cortical meta-ROI, median (IQR) 0.89 (0.81–0.97) 0.98 (0.89–1.09) <0.01  SUVRs in subcortical meta-ROI, median (IQR) 1.08 (1.04–1.19) 1.31 (1.18–1.43) <0.01 MRI parameters  GM volume, median (IQR), mL 550.46 (519.55–584.06) 520.42 (476.82–570.18) 0.01  Midbrain volume, median (IQR), mL 5.28 (5.09–5.76) 4.99 (4.48–5.59) 0.02  Pons volume, median (IQR), mL 13.62 (12.16–14.23) 12.67 (11.78–14.09) 0.21  DTI-ALPS index, median (IQR) 1.46 (1.27–1.58) 1.29 (1.24–1.39) 0.03

PET parameters were used as a PERSI reference.

*P value: nonparametric Mann-Whitney U tests were used to estimate between-group differences.

Comparison of Different Referential Methods in Distinguishing PSP From NCs

We compared the between-group differences in the SUVR values from the cortical and subcortical regions using the cerebellar GM as a reference and the PERSI as a reference. Table 2 shows that no significant between-group differences were found in any of the cortical regions when cerebellar GM was used as the reference. However, the frontal, parietal, occipital, posterior cingulate, precuneus, and sensorimotor cortices showed significant group differences in PERSI as a reference, even after adjusting for multiple comparisons (all P’s < 0.05). In the subcortical region analyses, both reference methods showed significant between-group differences in the anterior and posterior putamen, globus pallidum, midbrain, red nucleus, and raphe nucleus regions after adjusting for multiple comparisons (all P’s < 0.05). In addition, the dentate nucleus showed borderline significance in the between-group differences with PERSI as a reference (P = 0.047). Using PERSI as a reference, the SUVRs from the cortical and subcortical meta-ROIs showed significantly positive correlations with the PSPRS total scores, UPDRS total scores, and UPDRS-III scores (all P’s < 0.01) (Figs. 1A, B). For MMSE scores, significantly negative correlations were found between the SUVRs from the cortical meta-ROIs and subcortical meta-ROIs (P < 0.01 and P = 0.04, respectively). In contrast, using cerebellar GM as a reference, neither the subcortical nor cortical meta-ROIs showed significant associations with the PSPRS total scores, UPDRS total scores, UPDRS-III scores, or MMSE scores (all P’s > 0.05). Based on these findings, the associations between the SUVR values from the regional 18F-Florzolotau PET images and clinical parameters were analyzed using PERSI as a reference.

TABLE 2 - Comparisons of SUVR From 18F-Florzolotau Tau PET Images Between Patients With PSP and NCs in Cortical and Subcortical Regions Using Cerebellar Gray Matter as a Reference and PERSI as a Reference Cerebellar Gray Matter as Reference PERSI as Reference NC (n = 23) PSP (n = 32) P* NC (n = 23) PSP (n = 32) P* Cortical regions  Frontal lobe 0.81 (0.75–0.94) 0.85 (0.74–0.93) 0.81 0.84 (0.76–0.91) 0.89 (0.84–0.99) <0.05  Parietal lobe 0.81 (0.79–0.94) 0.90 (0.79–0.99) 0.48 0.85 (0.77–0.94) 0.96 (0.88–1.08) <0.05  Temporal lobe 0.96 (0.91–1.05) 0.98 (0.85–1.09) 0.81 0.98 (0.89–1.04) 1.07 (0.95–1.14) 0.07  Occipital lobe 0.87 (0.83–0.98) 0.96 (0.88–1.01) 0.13 0.89 (0.81–0.98) 1.04 (0.94–1.11) <0.05  Hippocampus gyrus 1.19 (0.99–1.87) 1.19 (0.86–1.65) 0.46 1.28 (0.96–1.77) 1.17 (0.90–1.82) 0.40  Parahippocampus gyrus 0.92 (0.87–0.99) 0.93 (0.86–1.04) 0.81 0.92 (0.87–0.99) 0.93 (0.86–1.04) 0.66  Anterior cingulate gyrus 0.88 (0.82–0.95) 0.88 (0.86–1.01) 0.81 0.89 (0.82–0.94) 0.94 (0.87–1.01) 0.13  Posterior cingulate gyrus 0.95 (0.86–0.99) 0.92 (0.91–1.34) 1.00 0.92 (0.87–1.01) 1.02 (0.89–1.19) <0.05  Precuneus 0.91 (0.88–0.97) 0.91 (0.86–1.04) 0.81 0.91 (0.84–1.04) 1.01 (0.91–1.20) <0.05  Sensorimotor cortex 0.81 (0.75–0.92) 0.88 (0.82–0.97) 0.10 0.83 (0.75–0.89) 0.93 (0.89–1.02) <0.05 Subcortical regions  Caudate nucleus 0.65 (0.58–0.74) 0.60 (0.55–0.74) 0.62 0.66 (0.55–0.75) 0.68 (0.61–0.75) 0.32  Anterior putamen 1.03 (0.96–1.11) 1.24 (1.05–1.39) <0.05 1.00 (0.95–1.10) 1.26 (1.21–1.35) <0.05  Posterior putamen 1.05 (0.94–1.13) 1.20 (1.06–1.37) <0.05 1.01 (0.94–1.11) 1.30 (1.19–1.41) <0.05  Globus pallidum 1.24 (1.11–1.29) 1.42 (1.29–1.64) <0.05 1.19 (1.13–1.24) 1.55 (1.43–1.78) <0.05  Thalamus 1.32 (1.09–1.61) 1.37 (1.21–1.52) 0.81 1.33 (1.04–1.55) 1.45 (1.22–1.68) 0.32  Midbrain 0.97 (0.90–1.02) 1.21 (0.98–1.32) <0.05 0.98 (0.89–1.04) 1.19 (1.08–1.40) <0.05  Red nucleus 1.27 (1.18–1.35) 1.45 (1.32–1.74) <0.05 1.20 (1.15–1.35) 1.57 (1.43–1.91) <0.05  Raphe nucleus 1.11 (1.00–1.16) 1.54 (1.15–1.70) <0.05 1.11 (0.97–1.18) 1.49 (1.29–1.86) <0.05  Dentate nucleus 1.36 (1.27–1.43) 1.39 (1.19–1.52) 0.81 1.29 (1.24–1.48) 1.41 (1.31–1.65) 0.05

*P value: nonparametric Mann-Whitney U tests were used to estimate between-group differences related to semiquantitative PET parameters in different regions; in all analyses, the Benjamini-Hochberg procedure was applied to correct for multiple comparisons.

FIGURE 1:

Tau deposition in the cortical and subcortical meta-ROIs is associated with the PSPRS total scores, DTI-ALPS indexes, GM atrophy, and midbrain volumes using the PERSI reference. A, Tau deposition in the cortical meta-ROIs showed a significantly positive correlation with the PSPRS total scores. B, Tau deposition in the subcortical meta-ROIs showed a significantly positive correlation with the PSPRS total scores.

Associations of Cortical and Subcortical Tau Burden With Clinical, GM Volume, and Glymphatic Activity

We used the upper 1.5 standard deviation as a cutoff value (SUVR = 1.04) in the cortical meta-ROIs from NCs to separate patients with PSP into a cortical involvement group (n = 11) and a noncortical involvement group (n = 21). Figure 2A shows the significantly higher SUVRs in the parietal, temporal, and occipital lobes in the cortical involvement group of PSP patients. Table 3 shows the comparison of the clinical measures, DTI-ALPS indexes, and brain volumes in patients with PSP according to the cortical and noncortical involvement groups. Significantly higher PSPRS total scores, UPDRS total scores, and UPDRS-III scores were found in the cortical involvement group than in the noncortical involvement group (P = 0.04, 0.02, and <0.01, respectively). However, there was no significant between-group difference in terms of MMSE scores. The cortical involvement group had significantly higher SUVRs of 18F-Florzolotau images from the cortical meta-ROIs than the noncortical involvement group (P < 0.01). However, the SUVRs from the subcortical meta-ROIs did not show a significant difference between the groups (P = 0.39).

FIGURE 2:

Schematic showing the averaged images from the cortical involvement group (A, n = 11) and the noncortical involvement group (B, n = 21) of patients with PSP after partial volume correction; the group assignment was based on the cutoff value (SUVR = 1.04) in the cortical meta-ROIs. A, In the cortical involvement group, significantly higher tau deposition was observed in the parietal, temporal, and occipital lobes as well as in the basal ganglia. B, In the noncortical involvement group, only the putamen, globus pallidum, midbrain, red nucleus, and raphe nucleus regions showed tau deposition. The color bar represents the corresponding SUVR values.

TABLE 3 - Comparisons of Clinical Parameters, DTI-ALPS Indexes, and Brain Volumes in Patients With PSP According to the Cortical and Noncortical Involvement of Tau Deposition Using the PERSI Reference Cortical Involvement Group (n = 11) Noncortical Involvement Group (n = 21) P* Demographics  Age, median (IQR), y 72 (67–73) 67 (61–71) 0.07  Gender (M:F) 5:6 12:9 0.53  Education, median (IQR), y 12 (6–16) 12 (9–16) 0.70 Clinical assessments  PSPRS total scores, median (IQR) 47 (23–51) 29 (15–31.5) 0.04  UPDRS total scores, median (IQR) 71 (38–87) 39 (25–46) 0.02  UPDRS-III scores, median (IQR) 41 (18–54) 15 (12–25) <0.01  MMSE, median (IQR) 21 (10–29) 27 (22.5–28.5) 0.19 PET parameters  SUVRs in cortical meta-ROI, median (IQR) 1.12 (1.09–1.20) 0.93 (0.85–0.98) <0.01  SUVRs in subcortical meta-ROI, median (IQR) 1.29 (1.01–1.38) 1.33 (1.19–1.46) 0.39 MRI parameters  GM volumes, median (IQR), mL 514.07 (447.27–572.11) 520.59 (494.21–550.37) 0.54  Midbrain volume, median (IQR), mL 5.24 (4.33–5.62) 4.79 (4.49–5.57) 0.77  Pons volume, median (IQR), mL 13.35 (10.28–14.45) 12.28 (11.78–13.68) 0.54  DTI-ALPS index, median (IQR) 1.31 (1.22–1.39) 1.29 (1.24–1.37) 0.79

*P value: nonparametric Mann-Whitney U tests were used to estimate between-group differences.

The Associations Among Regional Tau Depositions, Glymphatic Activity, and GM Volumes

Pearson’s correlation coefficients were used to evaluate associations of the SUVRs from the cortical and subcortical meta-ROIs with the clinical parameters (PSPRS total scores, UPDRS total scores, and MMSE scores) (Fig. 3A). The SUVRs from the cortical and subcortical meta-ROIs showed significant associations with clinical parameters (PSPRS total scores, UPDRS total scores, and MMSE scores). In the regional analysis, the SUVRs from the cortical regions (eg, frontal, parietal, occipital lobes, anterior and posterior cingulate gyri, precuneus, and sensorimotor cortex) and subcortical regions (eg, anterior and posterior putamen, globus pallidum, midbrain, red nucleus, and raphe nucleus) showed significantly positive correlations with the PSPRS total scores even after adjusting for age, sex, and years of education as covariates (all P’s < 0.05). With regard to the MMSE scores, the SUVRs from the cortical regions (eg, frontal, parietal, temporal lobes, anterior and posterior cingulate gyri, precuneus, and sensorimotor cortex) and subcortical regions (eg, caudate nucleus, anterior and posterior putamen, and raphe nucleus) showed significantly negative correlations even after adjustment for age, sex, and years of education (all P’s < 0.05).

FIGURE 3:

Tau deposition in the cortical and subcortical meta-ROIs is associated with the PSPRS total scores, DTI-ALPS indexes, GM atrophy, and midbrain volumes. A, The heatmap shows Pearson correlation matrix of tau depositions, clinical assessments, and MRI parameters. B, Three-dimensional render views show that tau deposition in the cortical meta-ROIs has significantly negative correlations with the GM volumes in the medial frontal, parietal, and temporal regions. The color bar indicates the significance of the associations. C, Tau deposition in the cortical meta-ROIs shows a significantly negative correlation with the DTI-ALPS indexes. D, Tau deposition in the subcortical meta-ROIs shows a significantly negative correlation with the midbrain volumes. E–F, The PSPRS total scores show significantly negative correlations with the midbrain volumes (E) and DTI-ALPS indexes (F).

In the VBM analyses, the SUVRs from the cortical meta-ROIs showed significantly negative correlations with the GM volumes in the bilateral parietal, temporal, medial frontal, and anterior cingulate regions (Fig. 3B). No significant correlation was found between the SUVRs from the subcortical meta-ROIs and GM volumes.

In DTI-ALPS index analysis, the SUVRs from the cortical but not the subcortical meta-ROIs showed a significantly negative correlation (P = 0.03 and P = 0.07, respectively) (Fig. 3C). In the regional analysis, the SUVRs from the parietal, temporal, and occipital lobes showed significantly negative correlations with the DTI-ALPS indexes (P = 0.03, P = 0.03, and P = 0.04, respectively). In the midbrain volume analysis, the SUVRs from the subcortical but not cortical meta-ROIs showed a significantly negative correlation with midbrain volumes (P = 0.01 and P = 0.63, respectively) (Fig. 3D). In the regional analysis, the SUVRs from the anterior and posterior putamen, globus pallidum, midbrain, red nucleus, raphe nucleus, and dentate nucleus showed significantly negative correlations with the midbrain volumes (all P’s < 0.05).

The Associations Between MRI Parameters and Clinical Parameters

The midbrain volume showed a significantly negative correlation with the PSPRS total scores (P < 0.01) (Fig. 3E). The DTI-ALPS indexes showed a significantly negative correlation with the PSPRS total scores but not the MMSE scores (P = 0.01 and P = 0.22, respectively) (Fig. 3F).

Mediation Ana