Frontoparietal network’s coupling of brain function and cerebrospinal fluid dynamics reduced in Parkinson’s disease with cognitive impairment

doi:10.21203/rs.3.rs-7650473/v1

Background

Most Parkinson’s disease patients often develop to cognitive impairment and glymphatic system dysfunction may underlie this symptom. Recent studies proved that the coupling strength of global blood-oxygen-level-dependent (gBOLD) signals and cerebrospinal fluid (CSF) inflow dynamically reflects glymphatic function. As different brain networks contribute distinctly to cognition. We examined Yeo-7 network’s BOLD-CSF coupling in PD to uncover the specific neural mechanisms linking glymphatic dysfunction to cognitive impairment.

Methods

Structural and resting-state functional MRI, clinical motor (Unified Parkinson’s Disease Rating Scale, UPDRS), and cognitive (Mini-mental State Examination, MMSE) assessments were collected from 122 PD patients(45 with mild cognitive impairment(MCI)/ 77 with normal cognition(NC)) and 39 healthy controls (HCs). Based on the Dice coefficient from the Neuroparc OSF registered repository, we mapped ninety AAL brain regions to seven Yeo brain networks. Global and network-specific BOLD-CSF coupling strengths were computed. Statistical analyses compared group differences and examined correlations between BOLD-CSF and clinical measures.

Results

Consistent with prior research, decreased gBOLD-CSF coupling correlated with lower MMSE scores. Compared to HCs, PD-MCI patients showed weaker coupling in the frontoparietal network (FPN) and limbic network (LN). Notably, MMSE scores demonstrated negative correlation specifically with FPN coupling. Among PD subgroups, PD-MCI patients exhibited higher UPDRS-II scores than PD-NC. In the PD-MCI group, UPDRS-II scores positively correlated with global, LN and FPN BOLD-CSF coupling.

Conclusions

These findings support BOLD-CSF coupling strength may serve as a PD-MCI biomarker and reveal distinct pathological features between PD-MCI and PD-NC subtypes.

Parkinson’s disease

cognitive impairment

glymphatic system(GS)

resting-state functional magnetic resonance imaging (rs-fMRI)

cerebrospinal fluid(CSF)

blood oxygen level-dependent(BOLD)

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Its pathological features are the absence of dopaminergic neurons and the abnormal accumulation of α -synuclein (α-syn)¹. Although motor symptoms (such as tremor, rigidity and bradykinesia) are the core diagnostic criteria for PD, approximately 40%-80% of patients will develop into mild cognitive impairment (MCI) and 30% will eventually progress to Parkinson's disease dementia², which significantly reduce the quality of life and increase the burden of care.

Brain imaging plays an important role in exploring the mechanism of cognitive impairment in PD. Existing research found that patients with cognitive impairment has extensive molecular, functional and brain structural changes, such as dopaminergic loss, changes in the integrity of neuronal activity, brain tissue atrophy, etc. The dopamine content can be detected by DAT-SPECT and plays a key role in working memory and cognitive control^{3, 4}. The integrity of neuronal activity can be detected by the fractional amplitude of low-frequency fluctuations (fALFF) in resting-state functional magnetic resonance imaging (rs-fMRI). Studies have found that the fALFF values changed in cognitive-related brain regions of PD patients, suggesting that abnormal neuronal activity is related to cognitive impairment⁵. The reduction of gray matter density, which represents the neuronal connection disruption, occurs in cognitive-related brain regions and has been proved to be associated with the decline of cognitive function⁶.

In recent years, the cerebral lymphatic clearance pathway, which is responsible for waste removal, has been thought to be associated with cognitive impairment severity and aggravation in PD^{7, 8}. Glymphatic system was first proposed by researchers such as Iliff. It is composed of the perivascular spaces around both arteries and veins and the foot processes of astrocytes⁹. It is a broad circulatory pathway in the brain responsible for the direct exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF) in the brain. Dysfunction of lymphatic system drainage can aggravate the accumulation of pathological proteins, such as α-syn, thereby promoting the progression of PD^{10, 11}. At present, researchers use indicators such as expanded perivascular space and diffusion tensor imaging analysis along the perivascular space to evaluate the function of the lymphatic system^12–14. However, these alternative indicators cannot reflect the dynamic changes of the lymphatic system¹⁵. Recently, Kiviniemi et al. made a pioneering discovery using rapid functional magnetic resonance imaging technology that a wide range of low-frequency (< 0.1Hz) rsfMRI blood oxygen level-dependent (BOLD) signals are related to cerebrospinal fluid dynamics¹⁶. Relevant studies have found that changes in whole-brain BOLD (gBOLD) signals are accompanied by slow (< 0.1Hz) but intense changes in cardiac and respiratory activities^17–20. These physiological changes are believed to drive CSF flow. A study by Fultz confirmed this standpoint. He found that during human sleep, large fluctuations in gBOLD signals are accompanied by CSF inflow²¹. Therefore, BOLD-CSF coupling can be used as a direct indicator for quantifying the function of the lymphatic system.

Studies have shown that in PD patients, weaker gBOLD - CSF coupling is associated with lower MoCA score, PD-MCI patients than PD with normal cognition(NC) showed a significantly weaker gBOLD - CSF coupling^{22, 23}. However, inconsistent results have emerged in the studies on gBOLD-CSF coupling and MDS Unified-Parkinson Disease Rating Scale(MDS-UPDRS) scores. Some studies have found that the coupling has no correlation with either the motor or non-motor sub-scores of UPDRS, while another study has found that the coupling has a weak correlation with UPDRS-II and is significantly correlated with the longitudinal changes of the UPDRS-III score. At present, there is no study exploring the relationship between BOLD-CSF coupling of different brain functional networks and cognitive scores, especially whether these networks have a selective effect on cognitive impairment.

Therefore, we analyzed the relationship between the BOLD-CSF coupling strength in the Yeo-7 brain network and cognitive scores, in order to clarify the relationship between the brain network specificity of the BOLD-CSF coupling and cognitive impairment. We also explored the relationship between the coupling strength of specific brain networks and the sub-scores of UPDRS to verify the sources of heterogeneity in previous studies.

Participants

This study has been approved by the Ethics Committee of Guangzhou First People's Hospital (the Second Affiliated Hospital of South China University of Technology). All subjects provided informed consent. The study was carried out in accordance with the principles of the Declaration of Helsinki and its later amendments. PD subjects were all patients admitted to the Department of Neurology of Guangzhou First People's Hospital from March 2020 to August 2024. The inclusion criteria were as follows: (1) PD was diagnosed for the first time; (2) No drug intervention has been carried out; (3) Completed imaging data collection and clinical evaluation. The exclusion criteria were as follows: (1) Poor image quality was not conducive to the analyst; (2) Patients combined with other nervous system disorders. Age - and sex-matched healthy individuals recruited from the community as healthy controls (HCs) (Fig. 1).

Following the inclusion and exclusion criteria, 39 HC and 122 PD were included. According to MMSE scores, PD patients were divided into two groups: MCI and NC.

Clinical assessments

Baseline clinical features included: Baseline age, sex, duration of disease from PD diagnosis to enrollment, Hoehn & Yahr (H-Y) staging, MDS-UPDRS sub-scores (UPDRS-I, UPDRS-II, UPDRS-III, and UPDRS-IV), and Mini-Mental State Examination (MMSE) score. H-Y grading method is used to record the severity of the disease for PD patients²⁴. The first part of UPDRS is used to assess the degree of mental activity, behavior and emotional disorders of patients. The second part of UPDRS is used to assess patients' daily living ability. The third part of UPDRS is used to evaluate patients' motor function; and the fourth part of UPDRS is used to record the complications of patients after treatment²⁵. Participants' cognitive function was scored using MMSE. The lower the MMSE score, the more serious the cognitive dysfunction²⁶. According to the score, mild cognitive impairment was defined as an MMSE score of 21-26, and cognitive normal was defined as an MMSE score of 27 or above.

Imaging data acquisition and preprocessing

MRI data acquisition

MRIs were acquired on a 3 Tesla Siemens system (Erlangen, Germany) equipped with a 16-channel phased-array head coil. 3D T1-weighted MPRAGE sequence was acquired using the following specifications: echo time (TE) = 2.19 ms, repetition time (TR) = 1,900 ms, field of view (FOV) = 250 mm x 250 mm, flip angle = 9°, slice thickness = 1.0 mm and slice gap = 0 mm. Other sequences included rs-fMRI acquisition with a single-shot echo-planar imaging sequence (TE = 21 ms, TR = 2,000 ms, FOV = 224 mm x 224 mm, flip angle = 78°, matrix = 64 x 64, slice thickness = 4.0 mm, voxel size = 3.5 mm x 3.5 mm x 4.0 mm, and comprising 220 time points) and DTI with a spin echo planar imaging sequence (TE = 102 ms, TR = 8700 ms, FOV = 230 mm x 230 mm², number of excitations (NEX) = 1, slice thickness = 2.5 mm, and matrix = 92 x 92).

fMRI data preprocessing

All rs-fMRI data were meticulously processed utilizing the Data Processing & Analysis of Brain Imaging (DPABI) toolbox (version 8.2) ²⁷ alongside Statistical Parametric Mapping (SPM, version 12)²⁸. Pre-processing steps included excluding the first 10 time points to achieve magnetization equilibrium, slice-timing correction, motion correction, spatial smoothing (full width at maximum (FWHM) = 6mm), linear detrending and bandpass filtering (0.01–0.1 Hz). Given their relevance to the current study, no nuisance regression analysis was conducted on gBOLD signals, CSF signals, or motion parameters.

Evaluation of gBOLD–CSF coupling strength

After all preconditioning procedures, we extracted gBOLD, Yeo-7 network BOLD and CSF fMRI signals (Fig2a) from the gray matter and CSF regions to explore the coupling changes. Cortical gray matter regions of interest (ROI) were defined according to the Harvard-Oxford cortical structural atlas, and gBOLD were extracted from the ROI. We obtained a dice score map between Automated Anatomical Labelling (AAL) atlas and the Yeo-7 Networks atlas from the neuroparc OSF registered repository^{29, 30} (Fig3). According to the dice score, the ninety brain regions of AAL were corresponded to the seven brain networks proposed by Thomas Yeo. Then each of Yeo-7 networks was given its own mask generated based on the above map using the REST Image Calculator Toolkit in the Resting-State fMRI Data Analysis Toolkit (REST) ^{31, 32}. Consistent with validation by Fultz et al, CSF signals surrounding the inferior cerebellum were chosen due to their sensitivity to cerebrospinal fluid influx in this region. The ROIs pertaining to CSF were delineated manually on rs-fMRI and subsequently validated with T1WI.

Following the extraction of rs-fMRI signals from both cortical gray matter and CSF, we proceeded to compute the cross-correlation function between the BOLD signals and the CSF signals. This analysis was conducted to evaluate the coupling strength across a range of time lags, specifically from -10 to 10 seconds, utilizing Pearson correlation methods for the assessment. Given the symmetrical amplitude of negative peaks at +6 seconds and positive peaks at -6 seconds, the negative peak's BOLD-CSF correlation at +6 seconds was used to quantify BOLD-CSF coupling strength (Fig2b).

Statistical analysis

Pearson test was used to check the normality of the data. Non-parametric tests were used to compare differences in non-normal distribution data (age, duration of illness, MMSE score and UPDRS scores). Chi-square test was used to examine differences in classification indicators (gender and H-Y staging). The differences of gBOLD-CSF among PD-MCI group, PD-NC group and HC group were compared by ANOVA. Spearman correlation analysis was used to analyze the relationship between gBOLD-CSF coupling and clinical indicators (age and MMSE score), and Bonferroni multiple comparison correction test was used. Statistical analysis and graph generation using GraphPad Prism 8.0. Non-normally distributed data are described by interquartile spacing. The difference was statistically significant with p value <0.05.

Participant characteristics

We finally included 122 PD patients (45 with mild cognitive impairment and 77 with normal cognition) and 39 healthy controls. Table 1 summarized the detailed sample characteristics. There was no significant difference in gender (p=0.883) and age (p=0.586) between PD group and NC group. In the PD subgroup (PD-MCI and PD-NC), there were statistically significant differences in disease time (p = 0.012), H-Y score (p < 0.001), UPDRS-II (p=0.015) and UPDRS-III (p= 0.003). There was no significant difference between UPDRS-I (p=0.106) and UPDRS-Ⅳ (p= 0.835).

Table 1. Participant baseline characteristics

	PD-MCI （N = 45）	PD-NC (N = 77)	HC (N = 39)	P value	MCIvs.NC P value	MCIvs.HC P value	NCvs.HC P value
Male/Female	26/19	41/36	21/18	0.883
Age(years)	63.69±8.31	63.36±7.46	62.23±4.54	0.586
Time duration	5.56±4.61	3.84±3.50		0.012*
H-Y	2.35±0.63	1.96±0.60		＜0.001***
UPDRS-I	2.24±2.11	1.80±2.49		0.106
UPDRS-II	11.10±6.92	7.92±4.39		0.015*
UPDRS-III	29.74±12.40	23.28±12.83		0.003**
UPDRS-IV	1.51±2.11	1.62±2.46		0.835
MMSE	24.11±1.51	28.55±1.08	29.05±1.01	＜0.001***	＜0.001***	0.001**	0.2854
gBOLD-CSF	0.028±0.32	-0.10±0.22	-0.16±0.22	0.003**	0.045*	0.007**	0.402

Data represent the mean and standard deviation (in parentheses) unless otherwise indicated. Significant at p = 0.05. * P <0.05, ** P <0.01, *** P <0.001.

Abbreviation: H-Y, Hoehn & Yahr; UPDRS, Unified Parkinson’s Disease Rating Scale; MMSE, Mini-Mental State Examination.

Association between the gBOLD-CSF coupling and cognition

We investigated whether the gBOLD-CSF coupling was associated with cognitive impairment in PD patients. The statistical results showed that compared with the PD-NC group and the HC group, the coupling intensity of gBOLD-CSF in the PD-MCI group was decreased (less negative)(Fig4a, p=0.045, p=0.006), and the MMSE score was significantly decreased (Fig4b, p < 0.001, p < 0.001), indicating that the lymphatic system was more severely damaged in patients with cognitive impairment. There was no significant difference in MMSE score (p=0.285) and gBOLD-CSF coupling (p=0.402) between PD-NC group and HC group.

Correlation analysis showed that gBOLD-CSF coupling was negatively correlated with MMSE scores for all participants ((Fig4c, r = - 0.164, p = 0.037). That is to say lower MMSE ratings were associated with a reduction in the strength of gBOLD-CSF coupling. Besides, gBOLD-CSF coupling was positively correlated with age in all participants (Fig4d, r = 0.177, p = 0.025). The gBOLD-CSF coupling was relatively weak in older participants.

Analysis of BOLD-CSF in the Yeo-7 brain network

Then, we respectively studied the intergroup differences of BOLD-CSF coupling in the Yeo-7 network (including the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network(VAN), limbic network (LN), frontal-parietal network (FPN), and default network (DN)) and its relationship with cognitive impairment. Compared with HC group, PD-MCI patients had lower coupling in LN (Fig5a, p = 0.001) and FPN (Fig5b, p = 0.03). Moreover, whether in the MCI group or among all the subjects, MMSE and FPN network’s coupling was negatively correlated (Fig5c, PD-MCI: r = - 0.318, p = 0.033; WHOLE: r = -0.165, p = 0.036), while no such results were found in other brain networks.

In the PD subgroup, compared with PD-NC, the UPDRS-II score in the PD-MCI group was higher (Fig6a, p = 0.015). In the PD-MCI group, UPDRS-Ⅱ was positively correlated with BOLD-CSF in the whole brain (r = 0.298, p = 0.0467), the LN (r = 0.306, p = 0.04) and FPN (r = 0.403, p = 0.006) brain networks(Fig6b), while those results were not found in the PD-NC group.

This study explored the relationship between BOLD-CSF coupling and cognitive dysfunction in PD patients. The results showed that the coupling intensity of gBOLD-CSF in PD-MCI patients was significantly lower than that in the cognitive-normal and healthy control groups, and was negatively correlated with the MMSE score. Furthermore, older participants showed weakened gBOLD-CSF coupling, and the reduced coupling of specific brain networks (FPN and LN) was associated with the cognitive decline and UPDRS-Ⅱ in PD-MCI. These findings support the potential role of abnormal cerebrospinal fluid dynamics in cognitive impairment of PD and provide a new perspective for understanding the pathological mechanism of PD.

The relationship between gBOLD-CSF coupling and cognitive impairment in PD

Our research results show that the gBOLD - CSF coupling strength in PD with MCI significantly reduced, and negative correlation with MMSE score. These findings are consistent with Han’s research results, which prompt that abnormal cerebrospinal fluid dynamics may be associated with cognitive decline. As summarized in the introduction, the coupling of BOLD signal and CSF flow during sleep may be involved in the waste clearance process of the lymphatic system. Under normal circumstances, α-syn is cleared by aquaporin-4 (AQP4) in the terminal foot of astrocytes. The coupling decline may lead to the impaired clearance of toxic substances such as α-syn, which is highly correlated with the pathological progression of PD-MCI⁹. In addition, there was no significant difference in gBOLD-CSF coupling and MMSE score between PD-NC and HC group. It indicates that in the early stage of PD, the function of the lymphatic system may be relatively retained. However, as the disease progresses, its function gradually deteriorates, eventually leading to a decline in cognitive function. This discovery suggests that the gBOLD-CSF coupling may serve as a potential biomarker for the progression of cognitive impairment in PD.

The influence of age on gBOLD-CSF coupling

We found that age was positively related with gBOLD - CSF coupling (weaker coupling strength), This is same as the previous studies show that aging is accompanied by a decline in the function of the lymphatic system³³. Aging may lead to reduced cerebrospinal fluid flow, decreased vascular compliance and impaired blood-brain barrier function, thereby affecting the clearance efficiency of the lymphatic system³⁴. In PD patients, age-related decline in lymphoid system function may further intensify the accumulation of pathological proteins and accelerate cognitive decline. Therefore, future research should take into account the regulatory effect of age on the lymphatic system function, especially in elderly patients with neurodegenerative diseases.

Specific brain network coupling anomalies and cognitive impairment

In Yeo - 7 network analysis, we found that BOLD-CSF coupling in FPN and LN significantly reduced in PD-MCI, and the coupling in FPN is negatively correlated with MMSE score. FPN involves executive function and attention regulation, and its dysfunction is closely related to cognitive impairment in PD patients³⁵. LN is involved in emotion and memory processing, and its abnormalities may be related to the mental and behavioral symptoms in PD-MCI patients³⁶. These results suggest that lymphatic system dysfunction in specific brain networks may selectively affect the cognitive domain, and FPN and LN may be vulnerable networks of PD-MCI. Furthermore, in the PD-MCI group, the UPDRS-II score was positively correlated with the BOLD-CSF coupling in the whole brain , FPN and LN. However, no similar results were found in the PD-NC group. This indicates that the severity of motor symptoms may share the same pathological process as cognitive impairment, both being related to dysfunction of the lymphatic system. In particular, this association may be more concentrated in specific brain networks in PD-MCI. This discovery provides a new research direction for the comorbidity mechanism of motor and non-motor symptoms in PD.

Study limitations and future direction

Several limitations of our study should be noted: first of all, the sample size is relatively small, so it leads to statistical selection bias and lower effects. Secondly, due to the incomplete biological data of the included subjects, the correlation between gBOLD-CSF coupling and biomarkers was not explored in this study. In addition, the cross-sectional design cannot determine the causal relationship between gBOLD-CSF coupling and cognitive impairment. Future studies can adopt longitudinal design and combine multiple indicators (such as α-syn, tau protein, inflammatory indicators, etc.) to evaluate the lymphatic system function more comprehensively.

In conclusion, the analysis presented here delineates the relationship between BOLD-CSF coupling in different functional brain regions and cognitive impairment in Parkinson's disease. It was found that the BOLD-CSF coupling in the FPN and LN showed significant differences in PD-MCI. Additionally, it was discovered that UPDRS-II was correlated with the above indicators. This provides the foundation for the precise diagnosis and treatment of cognitive impairment in PD as well as the study of its mechanism.

Ethics approval

Approval was obtained from the ethics committee of Guangzhou First People's Hospital (the Second Affiliated Hospital of South China University of Technology) . The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Consent

Informed consent regarding participating and publishing their data was obtained from all individual participants included in the study.

Data availability

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

Competing Interests

Y. X. and Y. G. are employees of Philips Healthcare and receive salary from that company. Other authors have no competing interests to declare that are relevant to the content of this article.

Funding

This work was supported by the Natural Science Foundation of Guangdong Province (Grant numbers: 2023A1515010606), the Clinical High-Tech and major technical projects of Guangzhou (Grant numbers: 2024C-GX16), Guangzhou Municipal Science and Technology Program key projects (Grant numbers: 2025A03J4222).

Authors’ contribution statements

M.W. contributed to Investigation, Data curation, Methodology, Validation, and Writing - original draft. X.R. contributed to Investigation, Data curation, Methodology, and Validation. X.H. contributed to Investigation, Data curation, and Formal analysis. Z.K., S.B., R.G., Q.Z., and X.Z. contributed to Data curation and Formal analysis. Y.X. and Y.G. contributed to Methodology and Validation. X.W. contributed to Investigation, Funding acquisition, Conceptualization, Supervision, and Writing - review & editing. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

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No competing interests reported.

Frontoparietal network’s coupling of brain function and cerebrospinal fluid dynamics reduced in Parkinson’s disease with cognitive impairment

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