Cortical atrophy is associated with cognitive impairment in Parkinson's disease: a combined analysis of cortical thickness and functional connectivity

doi:10.21203/rs.3.rs-1626602/v1

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Cortical atrophy is associated with cognitive impairment in Parkinson's disease: a combined analysis of cortical thickness and functional connectivity

https://doi.org/10.21203/rs.3.rs-1626602/v1

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We aimed to perform a combined analysis of cortical thickness and functional connectivity to explore their association with cognitive impairment in Parkinson's disease (PD). A total of 53 PD and 15 healthy control subjects were enrolled. PD patients were divided into PD with normal cognition (PD-NC, n = 25), PD with mild cognitive impairment (PD-MCI, n = 11), and PD with dementia (PDD, n = 17). Cognitive status was assessed with the Mini-Mental State Examination (MMSE). Anatomical magnetic resonance imaging and resting-state functional connectivity analysis were performed in all subjects. First, surface-based morphometry measurements of cortical thickness and voxels with cortical thickness reduction were detected. Then, regions showing reduced thickness were analyzed for changes in resting-state functional connectivity in PD involving cognitive impairment (PD-MCI and PDD groups together). Our results showed that, compared with PD-NC, patients with cognitive impairment showed decreased cortical thickness in the left superior temporal, left lingual, right insula, and right fusiform regions. PD-MCI patients showed these alterations in the right lingual region. Widespread cortical thinning was detected in PDD subjects, including the left superior temporal, left fusiform, right insula, and right fusiform areas. We found that cortical thinning in the left superior temporal, left fusiform, and right temporal pole regions positively correlated with MMSE score. In the resting-state functional connectivity analysis, we found a decrease in functional connectivity between the cortical atrophic brain areas mentioned above and cognition-related brain networks, as well as an increase in functional connectivity between those region and the cerebellum. Alterations in cortical thickness may result in a dysfunction of resting-state functional connectivity, contributing to cognitive decline in patients with PD.

Parkinson's disease

cognitive impairment

cortical thickness

resting-state functional connectivity

Parkinson's disease (PD) is a chronic neurodegenerative condition common in middle-aged and elderly subjects, affecting 8 to 18 in every 100,000 people every year(de Lau & Breteler, 2006). PD involves mainly the extrapyramidal system and manifests as motor symptoms, such as muscular rigidity, static tremor, bradykinesia, and postural gait disorder. Many PD patients also manifest non-motor symptoms, including autonomic dysfunction, psychiatric disturbances, sensory disorders, and cognitive impairment. Cognitive impairment in PD can range from mild cognitive impairment (PD-MCI) to dementia (PD-associated dementia, PDD). PD-MCI is an independent risk factor for PDD. PDD has been associated with increased risk of disability(Aarsland, Larsen, Tandberg, & Laake, 2000) and mortality(Levy et al., 2002), amounting to a heavy social and economic burden. Early screening for cognitive impairment and its biological markers is crucial to identify PD patients at higher risk of disease progression and worse outcomes.

In recent years, a number of magnetic resonance imaging (MRI) studies have shown promise for the prediction of cognitive function decline in PD. Previous MRI studies using voxel-based morphometry showed a correlation of grey matter atrophy with disease progression and cognitive impairment in PD(Ibarretxe-Bilbao, Junque, Marti, & Tolosa, 2011; Ibarretxe-Bilbao, Tolosa, Junque, & Marti, 2009). However, surface-based morphometry measurements of cortical thickness may be more sensitive than voxel-based morphometry for the identification of regional cortical thinning associated with PD(Pereira et al., 2012). Previous work found regional thinning in left temporal-occipital and right parietal-frontal areas in PD patients with cognitive disorder compared to patients without it(Pagonabarraga et al., 2013), while another study did not find this difference(Pereira et al., 2014).

Recently, several studies have examined so-called ‘resting state’ networks(Schindlbeck & Eidelberg, 2018; Wu & Hallett, 2013). Resting state networks are characterized by organized basal activity during rest or passive visual fixation and by low-frequency signal fluctuations. Analysis of these resting state networks suggest the existence of an underlying structural core of functional connectivity (FC) networks in the human brain(van den Heuvel & Hulshoff Pol, 2010). Previous work(Amboni et al., 2015; Baggio et al., 2015; Wolters et al., 2019; Zhan et al., 2018) showed that cognitive impairment in PD correlates with reduced connectivity in networks relevant to cognition, most prominently the default-mode, executive control, and salient networks. The default-mode network, including the hippocampus, medial temporal, posterior cingulate cortex, precuneus, medial prefrontal cortex, and inferior parietal lobule(Buckner, Andrews-Hanna, & Schacter, 2008), has been postulated to play roles in many cognitive processes, such as the theory of mind, social cognition, executive function, and episodic recall(Laird et al., 2011). Patients with PDD show disrupted connectivity in the default-mode network(Rektorova, Krajcovicova, Marecek, & Mikl, 2012). Executive control networks are found in the dorsolateral prefrontal cortex and posterior parietal cortex. PD patients show alterations in executive control network functionality, leading to defects in focused attention, executive functions, and behavioral regulation(Dong et al., 2020). Salient networks include the insula and anterior cingulate cortex, integrating the up-down information from past experiences(Suzuki & Amaral, 1994). Disruptions in FC involving these networks may be useful biomarkers for identifying PD with cognitive impairment.

Previous imaging studies have found alterations in cortical thickness and FC in patients with PD-MCI. However, their conclusions are based on separated studies and none of them combined both analyses at different stages of PD cognitive impairment. In the present study, we analyzed neuroimaging data of PD patients in two steps: 1) surface-based morphometry measurements of cortical thickness and voxels with cortical thickness reduction were detected; then 2) regions of interest as defined in step 1 were analyzed to search for abnormalities in FC between voxels with reduced cortical thickness and whole-brain voxels in resting-state FC analysis. We expect that our study will guide the discovery of neuroimaging markers and underlying mechanisms related to cognitive impairment in PD.

Participants

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the First Affiliated Hospital of Kunming Medical University. All participants provided written informed consent for their anonymized clinical data to be analyzed and published for research purposes.

The flow chart of this study is shown in Figure 1. Study subjects were enrolled from a series of outpatient clinics and from the neurology department of the First Affiliated Hospital of Kunming Medical University between June 2020 and October 2021 in this cross-sectional study. PD patients were diagnosed according to the 2015 criteria of the International Parkinson's and Motor Disorders Association (Postuma et al., 2015). Cognitive status was assessed by the Mini-Mental State Examination (MMSE)(Dubois et al., 2007) in both PD patients and healthy controls. PD patients were stratified into three groups: those with normal cognition (PD-NC), PD-MCI, and PDD. In some analyses, the PD-MCI and PDD groups were aggregated to represent “PD patients with cognitive impairment”. The diagnoses of PD-MCI or PDD were made according to the Diagnostic Criteria by the Movement Disorder Society Task Force Guidelines(Emre et al., 2007; Litvan et al., 2012). Patients who did not satisfy the criteria for PD-MCI or PDD were defined as PD without cognitive impairment.

Patients were excluded if they presented any of the following: atypical or secondary symptoms associated with PD, including multiple progressive supranuclear palsy, multiple system atrophy, corticobasal ganglionic degeneration, vascular parkinsonism, or neuroleptic agent-related parkinsonism; previous history of additional neurological or psychological disorders; any MRI-associated contraindications; left-handedness; or history of intracranial surgery for PD.

As healthy controls, individuals were recruited who had normal cognitive function, as assessed by the MMSE no psychological or neurological disorder; and no current chronic condition.

Neuropsychological tests

Study assessments were performed during ‘ON’ medication conditions (best motor), without stopping any ongoing medication. Motor symptom severity was assessed using the Hoehn and Yahr (H-Y) scale(Goetz et al., 2004) and the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III)(Goetz, 2010). Drug intake was recorded, and levodopa-equivalent daily dose in patients with PD was calculated as recommended(Tomlinson et al., 2010).

Rapid eye movement sleep behavior disorder (RBD) was diagnosed based on the Rapid Eye Movement Sleep Behavior Disorder screening questionnaire (RBDSQ)(Bušková et al., 2019). Across the 10 items of the RBDSQ, the total possible score is 13, and a score > 5 was considered suggestive of RBD(Bušková et al., 2019). Patients' roommates were asked whether the patient had nightmares, talked or moved while sleeping. The severity of depression in PD patients was assessed using the Hamilton Depression Scale (HAMD)(Broen et al., 2015), while severity of anxiety was assessed using the Hamilton Anxiety Scale (HAMA) (Kummer, Cardoso, & Teixeira, 2010).

Image acquisition and processing

Image acquisition

All subjects underwent MRI exams on a 3.0-T whole body scanner (Discovery 750w, GE Healthcare, USA) at the Imaging Department of the First Affiliated Hospital of Kunming Medical University, using standard head coils as transmitting and receiving coils. Participants lay on their back with their head on a foam pad, and they wore rubber earplugs to reduce noise. They were instructed to breathe calmly and not to think about anything specific or fall asleep. T1-weighted, T2-weighted or fluid attenuated inversion recovery MRI was performed, as required, in order to exclude any pathological condition. The MRI parameters were as follows: repetition time = 8.2 ms, echo time = 3.2 ms, turn angle = 12°, inversion time = 450 ms, matrix = 256 × 256, field-of-view = 256 mm × 256 mm, and slice thickness = 1 mm. In addition, RS-fMRI images were obtained using an echo planar imaging using the follow parameters: 36 slices, slice thickness: 3mm, no gap, voxel size: 3.5×3.5×4mm, volumes: 240, repetition time: 2000ms, Echo time: 30ms, turn angle: 90°, field-of-view: 224mm, matrix: 64×64.

Image processing

Cortical thickness

The analysis of surface-based morphometry relied on FreeSurfer software version 6.0.0 (http://surfer.nmr.mgh.harvard.edu/). The main steps of the analysis were as follows. First, MRIcron software was used to convert the Digital Imaging and Communications in Medicine (DICOM) format of 3D-T1 images into the 4D-Neuroimaging Informatics Technology Initiative (NIfTI) format for subsequent processing and analysis. Then, using Linux Ubuntu, NIfTI data were imported into FreeSurfer 6.0.0 for automatic processing. This automatic processing included head movement correction, removal of structures external to the brain tissue (such as the skull), automatic conversion into Talairach space, automatic segmentation of subcortical structure, gray normalization, mosaic of gray matter boundary, automatic topological correction, surface deformation of brain tissue, surface expansion after the cortical images were generated, and transformation to a spherical distribution template. The cortical thickness, defined as the distance from the grey/white matter boundary to the corresponding pial surface, was calculated for each brain region after processing with a Gaussian smoothing nucleus (full width at half maximum = 10). Finally, all reconstructed data sets were visually inspected to ensure accuracy of registration, skull dissection, segmentation, and cortical surface reconstruction.

Resting-state functional connectivity

We used the CONN toolsbox for SPM version 17 (http://www.nitrc.org/projects/conn, RRID: SCR_009550) for the analysis of resting-state data. The first 10 images were discarded to eliminate T1 equilibrium artifacts in the time series, and preprocessed using the CONN preprocessing pipeline. Motion correction was performed on the image using realignment and de-distortion, followed by slice time correction. The functional image was then co-registered with the structural T1 image, the space was normalized to a 2-mm space in Montreal Neurological Institute (MNI) coordinates, and smoothed using an 8-mm full-width and half-height Gaussian kernel.

Statisticcal analysis

Analysis of clinical data

SPSS version 25.0 (IBM, Armonk, NY, USA) was used for analysis of clinical data. For normally distributed data, the mean ± standard deviation (SD) was reported. Differences across more than two groups were assessed for significance using multivariate analysis of variance, while pairwise comparisons were assessed using multiple post-hoc comparisons. For skewed data, the median and 25-75% percentiles were reported, and differences were assessed for significance using the Mann-Whitney U test (two groups) or Kruskal-Wallis test (more than two groups). Frequencies were expressed as percentages, and differences were assessed using the chi-squared or Fisher tests. P < 0.05 was considered statistically significant.

Image data analysis

The "mri_glmfit" routine in Freesurfer was used for cortical thickness analysis and correlation analysis. Vertex-by-vertex comparison was performed with a general linear model, where age and education were included as covariables. The cluster-wise P-value of the corrected results was calculated by multiple-comparison correction based on 1000 iterations of Monte Carlo simulation. A cluster-wise P-value < 0.05 was considered statistically significant.

According to the changes in the cortical thickness among the groups, we extracted the corresponding MNI coordinates as regions of interest (ROIs), and the radius was set to 10 mm. Then, the FC was analyzed between each ROI and whole brain voxels using the SPM12-based CONN toolbox. The mean blood oxygen level-dependent time series was computed across all voxels within each ROI. Bivariate correlation analyses were used to determine the linear association of the blood oxygen level-dependent time series between each pair of sources, and a Fisher Z transformation was applied. Individual seed-to-voxel maps were entered into a second-level analysis. A whole-brain height threshold of P < 0.001 (uncorrected) was used to identify areas showing significant changes in FC. A false discovery rate-corrected threshold of P < 0.05 at this height threshold was applied for all reported clusters.

Demographic and clinical characteristics of study patients

Fifteen healthy control subjects and 53 PD patients were recruited (25 PD-NC, 11 PD-MCI, and 17 PDD). Sex, H-Y grading, HAMA, HAMD, and RBD were similar among the PD groups. Compared with healthy controls and PD-NC patients, the PD-MCI and PDD groups were older and longer disease duration, higher UPDRSIII score, and higher levodopa-equivalent daily dose (Table 1). MMSE scores decreased in the trend controls > PD-NC > PD-MCI > PDD, and differences between groups were significant (Table 1).

Analysis of images

Healthy controls versus PD patients with cognitive impairment

PD patients with cognitive impairment (PD-MCI and PDD) showed reduced cortical thickness in the left insula, left parstriangularis, right superior temporal, and right lingual compared with healthy controls (Table 2, Figure 2A). When we defined the left insula MNI coordinates as the ROI, PD patients with cognitive impairment showed markedly higher connectivity between the left insula and several brain areas (vermis 10, left cerebellum 9, vermis 9, and brain-stem) compared with the healthy control group (Table 3, Figure 3A).

When the right lingual was defined as the ROI, PD patients with cognitive impairment presented higher connectivity between the right lingual and left superior parietal lobule compared with healthy controls. No regions showed decreased connectivity to the right lingual region (Table 3, Figure 3A). Other regions did not show significant FC differences between healthy controls and PD patients with cognitive impairment.

Comparison of PD patients with or without cognitive impairment

PD patients with cognitive impairment showed reduced cortical thickness in the left superior temporal, left lingual, right insula, and right fusiform areas compared with those without cognitive impairment (Table 2, Figure 2B). When the right insula MNI coordinates were defined as the ROI, patients with cognitive impairment showed decreased FC between right insula and the following brain regions: right superior parietal lobule, lateral occipital cortex (right superior division), supramarginal gyrus (right posterior division), right postcentral gyrus, supramarginal gyrus (right anterior division), and lateral occipital cortex (left superior division) (Table 3, Figure 3B). No regions showed increased FC with the right insula. Other regions did not show significant FC differences between patients with or without cognitive impairment.

PD-NC versus PD-MCI

The PD-MCI group presented decreased cortical thickness in the right lingual compared to the PD-NC group (Table 2, Figure 4A). Compared to PD-NC patients, PD-MCI patients showed higher resting-state FC between the right lingual and the following brain regions: right cerebellum crus 2, left cerebellum crus 2, and vermis 7 (Table 3, Figure 5A). No regions of reduced FC to the right lingual region were detected in PD-MCI patients relative to PD-NC patients.

PD-NC versus PDD

Widespread cortical thinning was detected in PDD compared to PD-NC, including the left superior temporal, left fusiform, right insula, and right fusiform areas(fusiform1 and 2)(Table 2, Figure 4B). Based on the left superior temporal as ROI, the PDD group showed higher connectivity between the left superior temporal and several brain areas (left cerebellum crus 1 and 2) and decreased connectivity between the left superior temporal and right superior parietal lobule and right postcentral gyrus, compared with the PD-NC group (Table 3, Figure 5B).

When the right insula was defined as the ROI, PDD patients showed decreased right insula FC to the cingulate gyrus (anterior division) and right paracingulate gyrus (Table 3, Figure 5B). No regions showed increased connectivity to the right insula.

When the right fusiform 1 was defined as the ROI, the PDD group showed decreased right fusiform FC to the superior temporal gyrus (left anterior division), left planum temporale, left Heschl's gyrus, left central opercular cortex, and left planum polare compared with the PD-NC group (Table 3, Figure 5B). No regions showed increased connectivity to the right fusiform 1 region in the PDD group.

When the left fusiform was considered the ROI, the PD-NC and PDD groups showed similar FC.

Correlation analysis

MMSE scores of PD patients positively correlated with cortical thickness in the left superior temporal, left fusiform, and right temporal pole areas (Table 4, Figure 6). H-Y score negatively correlated with cortical thickness in the left transverstemporal, left superior temporal, and right precuneus areas (Table 4, Figure 7A). The UPDRSIII score negatively correlated with cortical thickness in the left superior temporal, left insula, right superior temporal, and right fusiform areas (Table 4, Figure 7B).

In this work, we explored associations among cerebral cortical thickness, FC, and cognitive impairment in PD. By combining structural and functional connectivities, our findings suggest that alterations in cortical thickness and resting-state FC may contribute to cognitive decline in PD. In the present study, We used regions presenting cortical atrophy as ROIs for the first time, show that atrophy in cortical thickness may result in dysfunction of resting-state functional connectivity. (Achard, Salvador, Whitcher, Suckling, & Bullmore, 2006; Tononi, Edelman, & Sporns, 1998). In addition, we found increased connectivity between the cortical atrophic brain areas and the cerebellar regions in PD with cognitive impairment, which suggests that the cerebellum in particular may be involved in cognitive dysfunction in PD. Furthermore, we associated the decrease in cortical thickness with disease progression in PD patients.

Our data on cortical thickness in PD-associated cognitive impairment are consistent with previous studies showing widespread temporal, frontal, and occipital lobe cortical thinning(C. H. Chen, Lee, & Lin, 2020; Hwang et al., 2013; Lee et al., 2014; Mak et al., 2015; Trufanov, Odinak, Litvinenko, Rezvantsev, & Voronkov, 2013). We also found widespread cortical thinning, involving the superior temporal, lingual, fusiform, and insula regions, which points to impairment in multiple cognitive domains. The temporal lobe is closely related to memory and also affects executive function and visuospatial function through the frontotemporal and occipito-temporal loops(Gratwicke, Jahanshahi, & Foltynie, 2015). Lingual and fusiform regions are posterior visual cortical components, and their atrophy correlates with visual perceptual dysfunction(Goldman, Williams-Gray, Barker, Duda, & Galvin, 2014). Insular lesions in human patients have been shown to impair performance on tasks requiring cognitive flexibility(Hodgson et al., 2007). Our results found that cortical thinning in the left superior temporal, left fusiform, and right temporal pole areas positively correlated with global cognitive decline. This is consistent with the regions known to be involved in higher-order cognitive functions. Previous work(Zarei et al., 2013) also found a positive correlation between MMSE scores and cortical thickness in the anterior temporal, dorsolateral prefrontal, posterior cingulate, fusiform, and occipitotemporal cortex. In addition, longitudinal studies(Ibarretxe-Bilbao et al., 2012; Williams-Gray et al., 2009) have linked gradual thinning in the temporal-occipital cortex to deterioration that can eventually lead to dementia. These considerations suggest that the decrease in cortical thickness may be a promising marker for predicting cognitive decline in PD(Zarei et al., 2013).

The underlying mechanisms behind the cortical thinning that we and others have observed remains unclear, and several hypotheses have been formulated to explain it. First, progression of PD is associated with progressive cortico-striatal circuitry dysfunction, which can be tracked by monitoring glucose metabolism in cortical regions using positron emission tomography(Braak et al., 2003). Thus, dysfunction of projections between cortico-striatal regions may lead to chronic “disuse” of the cortex, leading in turn to cortical thinning. Another hypothesis is that abnormal phosphorylation of α-synuclein and Tau occurs at cortical synapses in PD patients with cognitive dysfunction(González-Redondo et al., 2014), which contributes to cortical mitochondrial dysfunction, leading to neuronal death and, potentially, cortical atrophy(Ferrer, 2009). A third hypothesis is that cortical thinning arises not from neuronal death but from cellular shrinkage and reduction in dendritic arborization(Morrison & Hof, 1997). Indeed, in PD-MCI, the topographic distribution of cortical thinning is consistent with the regions where hypometabolism and hypoperfusion occur(Abe et al., 2003). This leads to reduced size of neuronal cell bodies, reduced dendritic arborization and/or loss of presynaptic terminals, which can result in cortical atrophy.(Braak, Rüb, Jansen Steur, Del Tredici, & de Vos, 2005; Pellicano et al., 2012)

Although the atrophy changes in our patients were relatively limited, they affected cortical regions in areas important for information integration(Achard et al., 2006; Tononi et al., 1998). Analysis of normal cerebral cortical thickness revealed the existence of a group of posteromedial cortical areas that form dense interconnected brain networks(Hagmann et al., 2008). Executive control, default-mode, and salient networks are thought to be mainly related to cognitive function, and these networks depend on, and regulate each other, to ensure normal cognitive function(Pievani, de Haan, Wu, Seeley, & Frisoni, 2011). Cortical atrophy of brain regions at important nodes leads to disruption of brain network connections and dysfunction, which gives rise to cognitive impairment in PD(Tononi et al., 1998). This observation was confirmed in our resting-state FC analysis using atrophic brain regions as ROIs. Our results showed that PD patients with cognitive impairment showed weak FC in insula with executive control and default-mode networks. The PDD group showed weak FC in superior temporal with executive control and default-mode networks; weak FC in insula with the salient network; and weak FC in fusiform with default-mode and executive control networks. The insula is an important node of the salient and ventral attention networks. Moreover, dysfunction in the loops of midbrain ventral tegmental area and the insula contributes to executive dysfunction in PD(Oades & Halliday, 1987), and specific disruption of projections to the insular cortex contribute to worsening executive impairment and PDD. This may result because insula cannot effectively recruit other cognitive networks, such as the fronto-parietal network(Gratwicke et al., 2015). In this way, our study and literature establish strong correlations between atrophy of the insula cortex and progression to PDD. Similarly, our work supports the idea that atrophy of the temporal lobe, lingual, and fusiform regions disrupts the default-mode, executive control, and salient networks, thereby contributing to cognitive impairment in PD.

In our study, PD patients with cognitive dysfunction presented stronger FC between the cortical atrophic brain areas and the cerebellum than healthy controls or PD-NC patients. This implicates the cerebellum may be particularly involved in adaptive changes that occur in PD with cognitive dysfunction in response to local cortical atrophic change. The cerebellum is known to influence motor activity and cognition through the cerebello-thalamo-cortical circuit(Middleton & Strick, 2001). Our patients showed alterations in FC involving voxels related to executive control and default-mode networks, and these alterations likely affect executive ability, visuospatial function, memory, and attention(Li, Su, Li, Jin, & Chen, 2018). To overcome these defects in executive ability and visuospatial function, PD patients may compensate by strengthening their abilities in certain functions (e.g. volition, planning, and purposive action) and in self-referential processing (e.g. recollection of past experiences). This may explain why our PD patients with cognitive impairment showed increased cerebellar FC with executive control and default-mode networks. Similarly, we previously found that PD patients with visual disorders showed stronger FC involving the cerebellar vermis X, visual networks and default-mode networks than patients without visuospatial disorders(Yin et al., 2021). Patients with visual disorders also showed increased FC within the intracerebellar network between the vermis X and left cerebellum 8, and left cerebellum 9 (Yin et al., 2021). The connectivity in another work showed stronger connectivity between the cerebellum and frontoparietal networks in PDD(Zhan et al., 2018). These observations strongly implicate the cerebellum in cognitive dysfunction in PD. Further work should examine whether PD patients strengthen FC between the cerebellum and other networks as a compensatory mechanism.

Our data indicate that the decrease in mean cortical thickness in superior temporal, precuneus, insula, and fusiform regions may be useful for assessing PD severity, confirming previous observations(Burton, McKeith, Burn, Williams, & O'Brien, 2004; Mak et al., 2015; Zarei et al., 2013). PD symptoms and disease severity are typically assessed using the UPDRS score and H-Y staging(S. Chen et al., 2016). UPDRS-III scores in PD patients negatively correlate with the thickness of the left and right fusiform gyrus and left temporal pole(Zarei et al., 2013), while H-Y stage negatively correlates with the thickness of the posterior cingulate cortex and the temporal cortex(Gao et al., 2018). The anatomical distribution of these cortical thinning is consistent with changes in cortical glucose metabolism and perfusion decreases observed in the early stage of the disease, which gradually extend to multiple cortical regions as the disease progresses(Borghammer et al., 2010; González-Redondo et al., 2014). These results further illustrate the feasibility of using MRI biomarkers to assess the pathophysiology of PD motor dysfunction.

Our study presents some limitations. First, despite the rigorous exclusion of MRI data affected by head motion and other preprocessing steps to reduce motion artifacts, such artifacts may still have influenced the results. Second, due to motor dysfunction and poor cooperation of PD patients, comprehensive cognitive assessment was not performed, so our study could not provide more detailed understanding of the characteristics and progression of cognitive dysfunction. Third, the sample was relatively small, and the cross-sectional design prevented us from determining causal relationships. Our findings should be verified and extended in large, longitudinal investigations.

Alterations in cortical thickness may disrupt resting-state FC and thereby contribute to cognitive decline in PD. Our results may guide the discovery of neuroimaging markers and underlying mechanisms related to cognitive impairment in PD.

Acknowledgements

We thank the patients with Parkinson's disease and the healthy control group for their participation in our study.

Funding Sources

This work was supported by the Yunnan Province Clinical Research Center for Neurological Diseases(Xinglong Yang, 202002AA100204), Clinical Research Center for Geriatric Diseases of Yunnan Province - diagnosis and treatment of geriatric comorbidity and clinical translational research(Xinglong Yang, 202102AA310069), National Natural Science Foundation of China (Xinglong Yang, 81960242), Chengdu Medical College - Chengdu Seventh People's Hospital Joint Scientific Research Fund(Baiyuan Yang, 2020LHJYZD-02), Yunnan Applied Basic Research Project (Xinglong Yang, 2019FE001-048, 202001AT070001), Special clinical research project for young and middle-aged Doctors in Lingnan Neurology Department(Xinglong Yang,).

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the First Affiliated Hospital of Kunming Medical University.

Consent to participate

All participants provided written informed consent for their anonymized clinical data to be analyzed and published for research purposes.

Consent to Publish

If the manuscript is accepted, we agree to publish it.

Author contributions

Yongyun Zhu , Baiyuan Yang and Chuanbin Zhou contribute equally.

Author contributions included conception and study design (Yongyun Zhu, Baiyuan Yang, Chuanbin Zhou, Ailan Pang and Xinglong Yang), data collection or acquisition (Chao Gao, Yanfei Hu, Wei Fang Yin, Kangfu Yin, Yangfan Zhu, Guoliang Jiang and Hui Ren), statistical analysis (Yongyun Zhu, Baiyuan Yang and Chuanbin Zhou), interpretation of results (Yongyun Zhu, Baiyuan Yang, Chuanbin Zhou, Ailan Pang and Xinglong Yang), drafting the manuscript work or revising it critically for important intellectual content (Yongyun Zhu, Baiyuan Yang, Chuanbin Zhou, Ailan Pang and Xinglong Yang) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).

Availability of data and material

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Table 1

Clinical and demographic characteristics of the study participants.
Characteristic	HC (n = 15)	PD-NC (n = 25)	PD-MCI (n = 11)	PDD (n = 17)	P value
Age, years	63.4 ± 10.2	59.2 ± 10.8	64.9 ± 10.0	69.3 ± 10.3	0.015*
Male	6 (40.0)	13 (52.0)	5 (45.5)	12 (70.6)	0.335
Disease duration, years	NA	2.00 (1.75, 3.60)	1.00 (1.00, 5.00)	5.00 (2.00, 7.00)	0.030*
H-Y grade	NA	2.00 (1.00, 2.50)	2.00 (1.00, 2.00)	3.00 (1.90, 3.00)	0.201
UPDRSIII	NA	22.0 (18.0, 32.5)	36.0 (15.0, 47.0)	43.0 (28.5, 51.5)	0.006*
LEDD	NA	358.52 ± 182.72	282.29 ± 132.30	467.67 ± 181.46	0.024*
HAMA	NA	15.60 ± 11.78	20.91 ± 11.18	20.35 ± 11.84	0.158
HAMD	NA	12.36 ± 9.43	15.64 ± 8.26	18.18 ± 10.44	0.309
RBD, %	NA	28.0	36.4	52.9	0.449
MMSE	28.87 ± 0.99	26.92 ± 2.74	21.91 ± 3.48	15.41 ± 5.13	< 0.001*
* P value < 0.05.Values are n (%), median (interquartile range) or mean ± SD, unless otherwise noted.
Abbreviations: HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression Scale; HC,Healthy controls;LEDD, levodopa-equivalent daily doses; MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination; NA, not applicable; NC, normal cognition; PD, Parkinson’s disease; PDD, Parkinson’s disease-associated dementia; RBD, rapid eye movement sleep behavioral disorder; UPDRS, Unified Parkinson’s Disease Rating Scale.

Table 2

Comparison of cortical thickness between groups.
Study group	Brain area	MNI coordinates			Cluster size	Vertex	P value
Study group	Brain area	X	Y	Z	Cluster size	Vertex	P value
HC > CI	Left insula	-34.5	-28.5	21.3	459.38	1380	0.009*
	Left parstriangularis	-51.1	31.0	2.5	360.18	568	0.039*
	Right superiortemporal	47.3	-1.9	-17.8	836.87	1805	< 0.001*
	Right lingual	27.3	-62.9	-4.3	714.82	1174	< 0.001*
NC > CI	Left superiortemporal	-40.2	-8.9	-16.4	702.58	1744	< 0.001*
	Left lingual	-28.1	-55.2	-6.9	515.64	1078	0.004*
	Right insula	37.2	2.2	-20.0	789.06	1955	< 0.001*
	Right fusiform	32.1	-54.3	-6.0	761.14	1284	< 0.001*
NC > MCI	Right lingual	25.5	-67.9	-3.5	362.99	595	0.012*
NC > PDD	Left superiortemporal	-39.2	-8.2	-16.1	861.44	2109	< 0.001*
	Left fusiform	-35.1	-42.0	-15.6	706.10	1444	< 0.001*
	Right insula	36.1	6.1	-13.6	1144.42	2635	< 0.001*
	Right fusiform1	32.7	-52.8	-6.3	940.81	1623	< 0.001*
	Right fusiform2	36.1	-13.8	-33.2	384.76	678	0.032
* P value < 0.05.
Abbreviations: CI, cognitive impairment; HC, healthy controls; MCI, mild cognitive impairment; MNI, Montreal Neurological Institute; NC, normal cognition; PD, Parkinson’s disease; PDD, Parkinson’s disease-associated dementia.

Table 3

Analysis of functional connectivity between regions of interest (ROI) and whole-brain voxels in groups.
	Seed	Cluster	MNI (x, y, z)	Cluster size	Cluster P-FDR
Controls vs. PD-CI (PD-CI increased)	Insula L	Vermis10, Cerebellum Crus9 L, Vermis9, Brain-Stem	+ 06, -45, -27	36	0.019*
Controls vs. PD-CI (PD-CI increased)	Lingual R	SPL L	-24, -45, + 45	30	0.044*
PD-NC vs. PD-CI (PD-NC increased)	Insula R	SPL R, sLOC R, pSMG R, Postcentral R, aSMG R	+ 39, -42, + 48	196	< 0.001*
PD-NC vs. PD-CI (PD-NC increased)		sLOC L	-24, -69, + 27	44	0.015*
PD-NC vs. PD-MCI (PD-MCI increased)	Lingual R	Cerebellum Crus2 R, Cerebellum Crus2 L, Vermis7	+ 03, -84, -27	31	0.029*
PD-NC vs. PDD (PDD decreased)	Superior temporal L	SPL R, Postcentral R	+ 33, -42, + 57	37	0.017*
	Insula R	AC, Paracingulate R	+ 6, + 18, +30	117	< 0.001*
	Fusiform1 R	aSTG L, Planum Temporale L, Heschl's L, Central Opercular L, Planum Polare L	-54, -21, +3	42	0.029*
PDNC vs. PDD (PDD increased)	Superior temporal L	Cerebellum Crus1 L, Cerebellum Crus2 L	-12, -84, -24	38	0.017*
Abbreviations: AC, cingulate gyrus, anterior division; aSMG, supramarginal gyrus, anterior division; aSTG, superior temporal gyrus, anterior division; CI, cognitive impairment; L, left; MCI, mild cognitive impairment; MNI, Montreal Neurological Institute; NC, normal cognition; PD, Parkinson’s disease; PDD, Parkinson’s disease-associated dementia; pSMG, supramarginal gyrus, posterior division; FDR, false discovery rate-corrected; R, right; sLOC, lateral occipital cortex, superior division; SPL, superior parietal lobule.

Table 4

Correlation of cortical thickness with clinical and neuropsychological assessments of Parkinson's disease (PD) patients.
Assessment	Brain area	MNI coordinates			Cluster size	Vertex	P value
Assessment	Brain area	X	Y	Z	Cluster size	Vertex	P value
MMSE(positive correlation)	Left superior temporal	-40.6	-9.4	-16.3	1424.78	3023	< 0.001*
	Left fusiform	-34.4	-41.6	-16.6	677.65	1370	< 0.001*
	Right temporal pole	32.5	3.4	-29.5	3214.56	6545	< 0.001*
H-Y grading (negative correlation)	Left transverstemporal	-49.9	-15.2	2.1	583.01	1393	0.002*
	Left superior temporal	-43.4	12.2	-30.7	532.99	943	0.003*
	Right precuneus	21.7	-58.7	13.8	485.94	949	0.008*
UPDRSIII(negative correlation)	Left superior temporal	-50.1	10.1	-19.0	638.36	1234	< 0.001*
	Left insula	-30.7	-27.7	13.2	618.76	1579	0.001*
	Right superior temporal	47.7	-18.3	-2.7	856.70	1810	< 0.001*
	Right fusiform	31.4	-55.9	-5.5	350.40	707	0.049*
Abbreviations: H-Y, Hoehn and Yahr; MMSE, Mini-Mental State Examination; MNI, Montreal Neurological Institute; UPDRSIII, Unified Parkinson’s Disease Rating Scale Part III.

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Cortical atrophy is associated with cognitive impairment in Parkinson's disease: a combined analysis of cortical thickness and functional connectivity

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