Altered dynamic functional network connectivity in levodopa-induced dyskinesia of Parkinson's disease

Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates

BACKGROUND

Despite prior studies on early-stage Parkinson's disease (PD) brain connectivity and temporal patterns, differences between tremor-dominant (TD) and postural instability/gait difficulty (PIGD) motor subtypes remain poorly understood. Our study aims to understand the contribution of altered brain network dynamics to heterogeneous motor phenotypes in PD for improving personalized treatment.

METHODS

Electroencephalography (EEG) microstate dynamics were firstly used to capture spatiotemporal brain network changes. A deep learning model was developed to classify PD motor subtypes where spatial variability and electrode location data were incorporated into the analysis.

RESULTS

Compared to healthy individuals, both PD-TD and PD-PIGD patients showed increased local segregation of brain regions. The PD-PIGD subtype had more severe and extensive disorganization in microstate A dynamics, suggesting greater disruption in auditory and motor-related networks. Incorporating spatial information significantly improved the accuracy of subtype classification, with an AUC of 0.972, indicating that EEG microstate dynamic spatial patterns reflect distinct PD motor pathologies. The increased spatial variability in the PD-PIGD group was more closely associated with motor impairments.

CONCLUSIONS

This study presents a novel framework for differentiating PD motor subtypes and emphasizes dynamic brain network features as potential markers for understanding motor symptom variability in PD, which may contribute to the development of personalized treatment strategies.

TRIAL REGISTRATION

ChiCTR2300067657.

Genetic analyses identify brain functional networks associated with the risk of Parkinson's disease and drug-induced parkinsonism

Brain functional networks are associated with parkinsonism in observational studies. However, the causal effects between brain functional networks and parkinsonism remain unclear. We aimed to assess the potential bidirectional causal associations between 191 brain resting-state functional magnetic resonance imaging (rsfMRI) phenotypes and parkinsonism including Parkinson's disease (PD) and drug-induced parkinsonism (DIP). We used Mendelian randomization (MR) to assess the bidirectional associations between brain rsfMRI phenotypes and parkinsonism, followed by several sensitivity analyses for robustness validation. In the forward MR analyses, we found that three rsfMRI phenotypes genetically determined the risk of parkinsonism. The connectivity in the visual network decreased the risk of PD (OR = 0.391, 95% CI = 0.235 ~ 0.649, P = 2.83 × 10-4, P_FDR = 0.039). The connectivity of salience and motor networks increased the risk of DIP (OR = 4.102, 95% CI = 1.903 ~ 8.845, P = 3.17 × 10-4, P_FDR = 0.044). The connectivity of limbic and default mode networks increased the risk of DIP (OR = 14.526, 95% CI = 3.130 ~ 67.408, P = 6.32 × 10-4, P_FDR = 0.0437). The reverse MR analysis indicated that PD and DIP had no effect on brain rsfMRI phenotypes. Our findings reveal causal relationships between brain functional networks and parkinsonism, providing important interventional and therapeutic targets for different parkinsonism.

Levodopa therapy affects brain functional network dynamics in Parkinson's disease

Levodopa (L-dopa) therapy is the most effective pharmacological treatment for motor symptoms of Parkinson's disease (PD). However, its effect on brain functional network dynamics is still unclear. Here, we recruited 26 PD patients and 24 healthy controls, and acquired their resting-state functional MRI data before (PD-OFF) and after (PD-ON) receiving 400 mg L-dopa. Using the independent component analysis and the sliding-window approach, we estimated the dynamic functional connectivity (dFC) and examined the effect of L-dopa on the temporal properties of dFC states, the variability of dFC and functional network topological organization. We found that PD-ON showed decreased mean dwell time in sparsely connected State 2 than PD-OFF, the transformation of the dFC state is more frequent and the variability of dFC was decreased within the auditory network and sensorimotor network in PD-ON. Our findings provide new insights to understand the dynamic neural activity induced by L-dopa therapy in PD patients.

Coherent Changes in Neural Motor Network Activity during Levodopa-Induced Dyskinesia in a Rat Model of Parkinson's Disease

BACKGROUND

Long-term use of levodopa, a metabolic precursor of dopamine (DA) for alleviation of motor symptoms in Parkinson's disease (PD), can cause a serious side effect known as levodopa-induced dyskinesia (LID). With the development of LID, high-frequency gamma oscillations (~100 Hz) are registered in the motor cortex (MCx) in patients with PD and rats with experimental PD. Studying alterations in the activity within major components of motor networks during transition from levodopa-off state to dyskinesia can provide useful information about their contribution to the development of abnormal gamma oscillations and LID.

METHODS

Freely moving rats with unilateral 6-hydroxydopamine hydrobromide (6-OHDA)-induced nigral DA cell lesions were administered a high dose of levodopa for 7 days. Local field potentials (LFPs) and neuronal activity were recorded from electrodes implanted in the motor cortex (MCx), ventromedial nucleus of the thalamus (VM), and substantia nigra pars reticulata nucleus (SNpr).

RESULTS

Levodopa reduced the power of beta oscillations (30-36 Hz) associated with bradykinesia in PD rats in three divisions of the motor neural network (MCx, VM, and SNpr) and prompted subsequent emergence of robust high-frequency gamma oscillations (80-120 Hz) in VM and MCx, but not SNpr, LFPs. Gamma oscillations were strongly associated with the occurrence of abnormal involuntary movements (AIMs) and accompanied by an increase in spiking rates in the VM and MCx and enlarged spike-LFP synchronization with cortical gamma oscillations (68% in the VM and 34% in the MCx). In contrast, SNpr LFPs did not exhibit gamma oscillations during LID, and neuronal activity in most recordings (87%) was largely decreased and not synchronized with VM or MCx LFPs. Administration of the antidyskinetic drug 8-hydroxy-2-(dipropylamino)-tetraline hydrobromide (8-OH-DPAT) restored the initial characteristics of LFPs (30-36 Hz oscillations), rates of neuronal activity, and bradykinesia. Inhibition of VM neurons by the gamma-aminobutyric acid (GABA-A)-agonist muscimol during LID eliminated high gamma oscillations in the MCx and VM, but not dyskinesia, suggesting that gamma oscillations are not critical for the expression of AIMs. In contrast, chemogenetic activation of SNpr neurons during LID eliminated both gamma oscillations and dyskinesia.

CONCLUSIONS

These findings suggest that levodopa treatment leads to crucial reduction of inhibitory control over motor networks due to a large decline in spiking of most SNpr GABAergic projecting neurons, which causes persistent hyperactivity in motor circuits, leading to the appearance of thalamocortical gamma oscillations and LID.

Functional motor network abnormalities associated with levodopa-induced dyskinesia in Parkinson's disease: A systematic review

Parkinson's disease (PD) can be effectively treated with levodopa and dopamine agonists but leads to levodopa-induced dyskinesia (LID) in most patients in the long run. Various functional brain mapping techniques are used to explore alterations in motor networks associated with LID. This pre-registered review (PROSPERO: CRD42022320830) summarizes the motor network abnormalities reported in functional brain mapping studies of patients with LID. We included studies using functional MRI, EEG, PET, SPECT, or TMS and included at least 10 LID patients. For completeness, we included studies of 5-9 patients with LID in a table. Some of these were also incorporated into the review if other studies used the same method. Thirty studies met our pre-defined criteria. Patients with LID showed stronger motor-related activation and functional connectivity of motor and premotor cortical areas and the putamen after levodopa intake relative to PD patients without LID. Decreased activation was found in the right inferior frontal cortex. TMS studies showed increased cortical excitability and blunted cortical plasticity in patients with LID, while "inhibitory" repetitive TMS of prefrontal motor control areas and cerebellum produced transient anti-dyskinetic effects. Overall, sample sizes were small, the number of studies per mapping modality was limited, and most studies lacked independent replication. The alterations associated with LID encompass changes in functional activity, connectivity, cortical excitability, and plasticity in motor execution and motor control networks. A comprehensive understanding of how LID manifests at the motor network level will guide the future development of stimulation-based network therapies for LID.

Dynamic Functional Network Connectivity in Acute Incomplete Cervical Cord Injury Patients and Its Associations With Sensorimotor Dysfunction Measures

BACKGROUND

Dynamic functional network connectivity (dFNC) captures temporal variations in functional connectivity during magnetic resonance imaging acquisition. However, the neural mechanisms driving dFNC alterations in the brain networks of patients with acute incomplete cervical cord injury (AICCI) remain unclear.

METHODS

This study included 16 AICCI patients and 16 healthy controls. Initially, independent component analysis was employed to extract whole-brain independent components from resting-state functional magnetic resonance imaging data. Subsequently, a sliding time window approach, combined with k-means clustering, was used to estimate dFNC states for each participant. Finally, a correlation analysis was conducted to examine the association between sensorimotor dysfunction scores in AICCI patients and the temporal characteristics of dFNC.

RESULTS

Independent component analysis was employed to extract 26 whole-brain independent components. Subsequent dynamic analysis identified 4 distinct connectivity states across the entire cohort. Notably, AICCI patients demonstrated a significant preference for State 3 compared to healthy controls, as evidenced by a higher frequency and longer duration spent in this state. Conversely, State 4 exhibited a reduced frequency and shorter dwell time in AICCI patients. Moreover, correlation analysis revealed a positive association between sensorimotor dysfunction and both the mean dwell time and the fraction of time spent in State 3.

CONCLUSIONS

Patients with AICCI demonstrate abnormal connectivity within dFNC states, and the temporal characteristics of dFNC are associated with sensorimotor dysfunction scores. These findings highlight the potential of dFNC as a sensitive biomarker for detecting network functional changes in AICCI patients, providing valuable insights into the dynamic alterations in brain connectivity related to sensorimotor dysfunction in this population.

Stage-dependent differential impact of network communication on cognitive function across the continuum of cognitive decline in Parkinson's disease

OBJECTIVE

Our objective was to explore the patterns of resting-state network (RSN) connectivity alterations and investigate how the influences of individual-level network connections on cognition varied across clinical stages without assuming a constant relationship.

METHODS

108 PD patients with continuum of cognitive decline (PD-NC = 46, PD-MCI = 43, PDD = 19) and 34 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological tests. Independent component analysis (ICA) and graph theory analyses (GTA) were employed to explore RSN connection changes. Additionally, stage-dependent differential impact of network communication on cognitive performance were examined using sparse varying coefficient modeling.

RESULTS

Compared to HCs, the dorsal attention network (DAN) and dorsal sensorimotor network (dSMN) were central networks with decreased connections in PD-NC and PD-MCI stage, while the lateral visual network (LVN) emerged as a central network in patients with dementia. Additionally, connectivity of the cerebellum network (CBN) increased in the PD-NC and PD-MCI stages. GTA demonstrated decreased nodal metrics for DAN and dSMN, coupled with an increase for CBN. Moreover, the degree centrality (DC) values of DAN and dSMN exhibited a stage-dependent differential impact on cognitive performance across the continuum of cognitive decline.

CONCLUSION

Our findings suggest that across the progression of cognitive impairment, the LVN gradually transitions into a core node with reduced connectivity, while the enhancement of connections in CBN diminishes. Furthermore, the non-linear relationship between the DC values of RSNs and cognitive decline indicates the potential for tailored interventions targeting specific stages.

Dynamic functional network connectivity in patients with a mismatch between white matter hyperintensity and cognitive function

Objective

White matter hyperintensity (WMH) in patients with cerebral small vessel disease (CSVD) is strongly associated with cognitive impairment. However, the severity of WMH does not coincide fully with cognitive impairment. This study aims to explore the differences in the dynamic functional network connectivity (dFNC) of WMH with cognitively matched and mismatched patients, to better understand the underlying mechanisms from a quantitative perspective.

Methods

The resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive function scale assessment of the patients were acquired. Preprocessing of the rs-fMRI data was performed, and this was followed by dFNC analysis to obtain the dFNC metrics. Compared the dFNC and dFNC metrics within different states between mismatch and match group, we analyzed the correlation between dFNC metrics and cognitive function. Finally, to analyze the reasons for the differences between the mismatch and match groups, the CSVD imaging features of each patient were quantified with the assistance of the uAI Discover system.

Results

The 149 CSVD patients included 20 cases of "Type I mismatch," 51 cases of Type I match, 38 cases of "Type II mismatch," and 40 cases of "Type II match." Using dFNC analysis, we found that the fraction time (FT) and mean dwell time (MDT) of State 2 differed significantly between "Type I match" and "Type I mismatch"; the FT of States 1 and 4 differed significantly between "Type II match" and "Type II mismatch." Correlation analysis revealed that dFNC metrics in CSVD patients correlated with executive function and information processing speed among the various cognitive functions. Through quantitative analysis, we found that the number of perivascular spaces and bilateral medial temporal lobe atrophy (MTA) scores differed significantly between "Type I match" and "Type I mismatch," while the left MTA score differed between "Type II match" and "Type II mismatch."

Conclusion

Different mechanisms were implicated in these two types of mismatch: Type I affected higher-order networks, and may be related to the number of perivascular spaces and brain atrophy, whereas Type II affected the primary networks, and may be related to brain atrophy and the years of education.

Intrinsic brain functional connectivity predicts treatment-related motor complications in early Parkinson's disease patients

BACKGROUND

Treatment-related motor complications may develop progressively over the course of Parkinson's disease (PD).

OBJECTIVE

We investigated intrinsic brain networks functional connectivity (FC) at baseline in a cohort of early PD patients which successively developed treatment-related motor complications over 4 years.

METHODS

Baseline MRI images of 88 drug-naïve PD patients and 20 healthy controls were analyzed. After the baseline assessments, all PD patients were prescribed with dopaminergic treatment and yearly clinically re-assessed. At the 4-year follow-up, 36 patients have developed treatment-related motor complications (PD-Compl) whereas 52 had not (PD-no-Compl). Single-subject and group-level independent component analyses were used to investigate FC changes within the major large-scale resting-state networks at baseline. A multivariate Cox regression model was used to explore baseline predictors of treatment-related motor complications at 4-year follow-up.

RESULTS

At baseline, an increased FC in the right middle frontal gyrus within the frontoparietal network as well as a decreased connectivity in the left cuneus within the default-mode network were detected in PD-Compl compared with PD-no-Compl. PD-Compl patients showed a preserved sensorimotor FC compared to controls. FC differences were found to be independent predictors of treatment-related motor complications over time.

CONCLUSION

Our findings demonstrated that specific FC differences may characterize drug-naïve PD patients more prone to develop treatment-related complications. These findings may reflect the presence of an intrinsic vulnerability across frontal and prefrontal circuits, which may be potentially targeted as a future biomarker in clinical trials.

Helicobacter pylori infection and Parkinson's Disease: etiology, pathogenesis and levodopa bioavailability

Parkinson's disease (PD), a neurodegenerative disorder with an unknown etiology, is primarily characterized by the degeneration of dopamine (DA) neurons. The prevalence of PD has experienced a significant surge in recent years. The unidentified etiology poses limitations to the development of effective therapeutic interventions for this condition. Helicobacter pylori (H. pylori) infection has affected approximately half of the global population. Mounting evidences suggest that H. pylori infection plays an important role in PD through various mechanisms. The autotoxin produced by H. pylori induces pro-inflammatory cytokines release, thereby facilitating the occurrence of central inflammation that leads to neuronal damage. Simultaneously, H. pylori disrupts the equilibrium of gastrointestinal microbiota with an overgrowth of bacteria in the small intestinal known as small intestinal bacterial overgrowth (SIBO). This dysbiosis of the gut flora influences the central nervous system (CNS) through microbiome-gut-brain axis. Moreover, SIBO hampers levodopa absorption and affects its therapeutic efficacy in the treatment of PD. Also, H. pylori promotes the production of defensins to regulate the permeability of the blood-brain barrier, facilitating the entry of harmful factors into the CNS. In addition, H. pylori has been found to induce gastroparesis, resulting in a prolonged transit time for levodopa to reach the small intestine. H. pylori may exploit levodopa to facilitate its own growth and proliferation, or it can inflict damage to the gastrointestinal mucosa, leading to gastrointestinal ulcers and impeding levodopa absorption. Here, this review focused on the role of H. pylori infection in PD from etiology, pathogenesis to levodopa bioavailability.

Convergent and divergent intra- and internetwork connectivity in Parkinson's disease with wearing-off

OBJECTIVE

Our study aimed to explore the functional connectivity alterations between cortical nodes of resting-state networks in Parkinson's disease (PD) patients with wearing-off (WO) at different levels.

METHODS

Resting-state functional magnetic resonance imaging was performed on 36 PD patients without wearing-off (PD-nWO), 30 PD patients with wearing-off (PD-WO), and 35 healthy controls (HCs) to extract functional networks. Integrity, network, and edge levels were calculated for comparison between groups. UPDRS-III, MMSE, MOCA, HAMA, and HAMD scores were collected for further regression analysis.

RESULTS

We observed significantly reduced connectivity strength in the dorsal attention network and limbic network in the PD-WO group compared with the HC group. The PD-WO group showed a decreased degree of functional connectivity at 12 nodes, including the bilateral orbital part of the superior frontal gyrus, right olfactory cortex, left medial orbital part of the superior frontal gyrus, bilateral gyrus rectus, right parahippocampal gyrus, right thalamus, left Heschl's gyrus, right superior temporal gyrus part of the temporal pole, left middle temporal gyrus part of the temporal pole, and right inferior temporal gyrus. Furthermore, the PD-WO group showed a significantly lower degree of functional connectivity in the left orbital part of the superior frontal gyrus and right gyrus rectus than the PD-nWO group. Internetwork analysis indicated reduced functional connectivity in five pairs of resting-state networks.

CONCLUSION

Our results demonstrated altered intra- and internetwork connections in PD patients with WO. These findings will facilitate a better understanding of the distinction between the network changes in PD pathophysiology.

Altered dynamic functional network connectivity in drug-naïve Parkinson's disease patients with excessive daytime sleepiness

Background

Excessive daytime sleepiness (EDS) is a frequent nonmotor symptoms of Parkinson's disease (PD), which seriously affects the quality of life of PD patients and exacerbates other nonmotor symptoms. Previous studies have used static analyses of these resting-state functional magnetic resonance imaging (rs-fMRI) data were measured under the assumption that the intrinsic fluctuations during MRI scans are stationary. However, dynamic functional network connectivity (dFNC) analysis captures time-varying connectivity over short time scales and may reveal complex functional tissues in the brain.

Purpose

To identify dynamic functional connectivity characteristics in PD-EDS patients in order to explain the underlying neuropathological mechanisms.

Methods

Based on rs-fMRI data from 16 PD patients with EDS and 41 PD patients without EDS, we applied the sliding window approach, k-means clustering and independent component analysis to estimate the inherent dynamic connectivity states associated with EDS in PD patients and investigated the differences between groups. Furthermore, to assess the correlations between the altered temporal properties and the Epworth sleepiness scale (ESS) scores.

Results

We found four distinct functional connectivity states in PD patients. The patients in the PD-EDS group showed increased fractional time and mean dwell time in state IV, which was characterized by strong connectivity in the sensorimotor (SMN) and visual (VIS) networks, and reduced fractional time in state I, which was characterized by strong positive connectivity intranetwork of the default mode network (DMN) and VIS, while negative connectivity internetwork between the DMN and VIS. Moreover, the ESS scores were positively correlated with fraction time in state IV.

Conclusion

Our results indicated that the strong connectivity within and between the SMN and VIS was characteristic of EDS in PD patients, which may be a potential marker of pathophysiological features related to EDS in PD patients.

Voxel-based morphometry of grey matter structures in Parkinson's Disease with wearing-off

Our study aimed to investigate the grey matter (GM) changes using voxel-based morphometry (VBM) in Parkinson's disease (PD) patients with wearing-off (WO). 3D-T1-weighted imaging was performed on 48 PD patients without wearing-off (PD-nWO), 39 PD patients with wearing-off (PD-WO) and 47 age and sex-matched healthy controls (HCs). 3D structural images were analyzed by VBM procedure with Statistical Parametric Mapping (SPM12) to detect grey matter volume. Widespread areas of grey matter changes were found in patients among three groups (in bilateral frontal, temporal lobes, lingual gyrus, inferior occipital gyrus, right precuneus, right superior parietal gyrus and right cerebellum). Grey matter reductions were found in frontal lobe (right middle frontal gyrus, superior frontal gyrus and precentral gyrus), right parietal lobe (precuneus, superior parietal gyrus, postcentral gyrus), right temporal lobe (superior temporal gyrus, middle temporal gyrus), bilateral lingual gyrus and inferior occipital gyrus in PD-WO group compared with the PD-nWO group. Our results suggesting that wearing-off may be associated with grey matter atrophy in the cortical areas. These findings may aid in a better understanding of the brain degeneration process in PD with wearing-off.

Dynamic functional connectivity reveals hyper-connected pattern and abnormal variability in freezing of gait of Parkinson's disease

BACKGROUND

Freezing of gait (FOG) is an intractable and paroxysmal gait disorder that seriously affects the quality of life of Parkinson's disease (PD) patients. Emerging studies have reported abnormal brain activity of distributed networks in FOG patients, whereas ignoring the intrinsic dynamic fluctuations of functional connectivity. The purpose of this study was to examine the dynamic functional network connectivity (dFNC) of PD-FOG.

METHODS

In total, 52 PD patients with FOG (PD-FOG), 73 without FOG (PD-NFOG) and 38 healthy controls (HCs) received resting state functional magnetic resonance imaging (rs-fMRI). Sliding window method, k-means clustering and graph theory analysis were employed to retrieve dynamic characteristics of PD-FOG. Partial correlation analysis was conducted to verify whether the dFNC was related to freezing gait severity.

RESULTS

Seven brain networks were identified and configured into seven states. Compared to PD-NFOG, significant spatial pattern was identified for state 2 in freezers, showing increased functional coupling between default mode network (DMN) and basal ganglia network (BG), as a concrete manifestation of increased precuneus-caudate coupling. The mean dwell time and fractional window of state 2 had a positive correlation with FOG severity. Furthermore, PD-FOG group exhibited lower variance in nodal efficiency of independent components (IC) 7 (left precuneus).

CONCLUSIONS

Our study suggested that aberrant coupling of precuneus-caudate and disrupted variability of precuneus efficiency might be associated to the neural mechanisms of FOG.