Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression.

Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 PD patients and 366 controls, aiming to disentangle PD-specific neuroanatomical alterations. The results reveal that these neuroanatomical alterations in PD are correlated with individual differences in dopamine transporter binding deficit, neurodegeneration biomarkers, and clinical severity and progression. The correlation with clinical severity is verified in an external cohort. Notably, certain proteins in the cerebrospinal fluid are strongly associated with PD-specific features, particularly those involved in the immune function. The most notable neuroanatomical alterations are observed in both subcortical and temporal regions. Our findings provide deeper insights into the patterns of brain atrophy in PD and potential underlying molecular mechanisms, paving the way for earlier patient stratification and the development of treatments to slow down neurodegeneration.

Analysis of diffusion changes in cerebral tissues of Parki̇nson's patients who underwent subthalamic nucleus deep brain stimulation: Correlation of improvements in motor and neuropsychiatric symptoms.

OBJECTIVE: Parkinson's disease (PD) as a neurodegenerative disorder characterized by a reduction in both the quantity and functionality of dopaminergic neurons. This succinctly highlights the central pathological feature of PD and its association with dopaminergic neuron degeneration, which underlies the motor and non-motor symptoms of the disease. This study aims to elucidate the nuances of apparent diffusion coefficient (ADC) changes in different cerebral regions by after the bilateral subthalamic nucleus (STN) deep brain stimulation (DBS) surgery of PD, as well as to investigate their potential interactions with the motor and neuropsychiatric spectrum. METHODS: Patients who underwent STN-DBS surgery for PD between 2017 and 2019 were included in this study. The results of diffusion magnetic resonance imaging (MRI), Unified Parkinson Disease Rating Scale (UPDRS) III scores, Beck and Hamilton depression tests were recorded before and at the 3rd month of postoperative stimulation. The data obtained were evaluated with the Wilcoxon signed rank test. Result of the statistical tests were within the 95 % confidence interval and p values were significant below 0.05. RESULTS: Our study was conducted with a total of 13 patients, 8 men and 5 women. As a result of measurements made in a total of 32 different regions, especially in the motor and neuropsychiatric areas of the brain, an increase in ADC values was found in all areas. ADC changes of eight localizations such as left corpus callosum, right corona radiata, left corona radiata, hippocampus, right insula, left superior cerebellar peduncle, left caudate nucleus and left putamen were statistically significant. UPDRS III scores improved by 57 % (p <0.05), and Beck and Hamilton depression scores by 25 % and 33 %, respectively (p> 0.05). CONCLUSIONS: This article implicate that bilateral STN-DBS surgery potentially exerts beneficial effects on both motor and neuropsychiatric symptomatology in individuals with PD. We believe that this therapeutic mechanism is hypothesized to involve modulation of diffusion alterations within distinct cerebral tissues.

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

Purpose: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia and mild cognitive impairment with high accuracy. Prior studies also demonstrated retinal differences in individuals with PD. Herein, we developed a convolutional neural network (CNN) to classify multimodal retinal imaging from either a Parkinson's disease (PD) or control group. Methods: We trained a CNN to receive retinal image inputs of optical coherence tomography (OCT) ganglion cell-inner plexiform layer (GC-IPL) thickness color maps, OCT angiography 6 × 6-mm en face macular images of the superficial capillary plexus, and ultra-widefield (UWF) fundus color and autofluorescence photographs to classify the retinal imaging as PD or control. The model consists of a shared pretrained VGG19 feature extractor and image-specific feature transformations which converge to a single output. Model results were assessed using receiver operating characteristic (ROC) curves and bootstrapped 95% confidence intervals for area under the ROC curve (AUC) values. Results: In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing. Our best CNN variant achieved an AUC of 0.918. UWF color photographs were the most effective imaging input, and GC-IPL thickness maps were the least contributory. Conclusions: Using retinal images, our pilot CNN was able to identify individuals with PD and serves as a proof of concept to spur the collection of larger imaging datasets needed for clinical-grade algorithms. Translational Relevance: Developing machine learning models for automated detection of Parkinson's disease from retinal imaging could lead to earlier and more widespread diagnoses.

Alpha synuclein PET imaging, a step closer to in vivo neuropathology in Parkinson's disease and related disorders.

In this issue of Neuron, Endo et al.1 develop a PET tracer capable of detecting alpha-synuclein (ɑ-syn). With validation in animal models and humans, this tracer brings us closer to being able to monitor the synuclein aggregation process and associated pathological changes in Parkinson's disease (PD) and other synucleinopathies.

Parkinson's image detection and classification based on deep learning.

OBJECTIVE: There are two major issues in the MRI image diagnosis task for Parkinson's disease. Firstly, there are slight differences in MRI images between healthy individuals and Parkinson's patients, and the medical field has not yet established precise lesion localization standards, which poses a huge challenge for the effective prediction of Parkinson's disease through MRI images. Secondly, the early diagnosis of Parkinson's disease traditionally relies on the subjective judgment of doctors, which leads to insufficient accuracy and consistency. This article proposes an improved YOLOv5 detection algorithm based on deep learning for predicting and classifying Parkinson's images. METHODS: This article improves the YOLOv5s network as the basic framework. Firstly, the CA attention mechanism was introduced to enable the model to dynamically adjust attention based on local features of the image, significantly enhancing the sensitivity of the model to PD related small pathological features; Secondly, replace the dynamic full dimensional convolution module to optimize the multi-level extraction of image features; Finally, the coupling head strategy is adopted to improve the execution efficiency of classification and localization tasks separately. RESULTS: We validated the effectiveness of the proposed method using a dataset of 582 MRI images from 108 patients. The results show that the proposed method achieves 0.961, 0.974, and 0.986 in Precision, Recall, and mAP, respectively, and the experimental results are superior to other algorithms. CONSLUSION: The improved model has achieved high accuracy and detection accuracy, and can accurately detect and recognize complex Parkinson's MRI images. SIGNIFICANCE: This algorithm has shown good performance in the early diagnosis of Parkinson's disease and can provide clinical assistance for doctors in early diagnosis. It compensates for the limitations of traditional methods.

Exploring brain asymmetry in early-stage Parkinson's disease through functional and structural MRI.

OBJECTIVE: This study explores the correlation between asymmetrical brain functional activity, gray matter asymmetry, and the severity of early-stage Parkinson's disease (PD). METHODS: Ninety-three early-stage PD patients (ePD, H-Y stages 1-2.5) were recruited, divided into 47 mild (ePD-mild, H-Y stages 1-1.5) and 46 moderate (ePD-moderate, H-Y stages 2-2.5) cases, alongside 43 matched healthy controls (HCs). The study employed the Hoehn and Yahr (H-Y) staging system for disease severity assessment and utilized voxel-mirrored homotopic connectivity (VMHC) for analyzing brain functional activity asymmetry. Asymmetry voxel-based morphometry analysis (VBM) was applied to evaluate gray matter asymmetry. RESULTS: The study found that, relative to HCs, both PD subgroups demonstrated reduced VMHC values in regions including the amygdala, putamen, inferior and middle temporal gyrus, and cerebellum Crus I. The ePD-moderate group also showed decreased VMHC in additional regions such as the postcentral gyrus, lingual gyrus, and superior frontal gyrus, with notably lower VMHC in the superior frontal gyrus compared to the ePD-mild group. A negative correlation was observed between the mean VMHC values in the superior frontal gyrus and H-Y stages, UPDRS, and UPDRS-III scores. No significant asymmetry in gray matter was detected. CONCLUSIONS: Asymmetrical brain functional activity is a significant characteristic of PD, which exacerbates as the disease severity increases, resembling the dissemination of Lewy bodies across the PD neurological framework. VMHC emerges as a potent tool for characterizing disease severity in early-stage PD.

Development of Peptide-Based Probes for Molecular Imaging of the Postsynaptic Density in the Brain.

The postsynaptic density (PSD) comprises numerous scaffolding proteins, receptors, and signaling molecules that coordinate synaptic transmission in the brain. Postsynaptic density protein 95 (PSD-95) is a master scaffold protein within the PSD and one of its most abundant proteins and therefore constitutes a very attractive biomarker of PSD function and its pathological changes. Here, we exploit a high-affinity inhibitor of PSD-95, AVLX-144, as a template for developing probes for molecular imaging of the PSD. AVLX-144-based probes were labeled with the radioisotopes fluorine-18 and tritium, as well as a fluorescent tag. Tracer binding showed saturable, displaceable, and uneven distribution in rat brain slices, proving effective in quantitative autoradiography and cell imaging studies. Notably, we observed diminished tracer binding in human post-mortem Parkinson's disease (PD) brain slices, suggesting postsynaptic impairment in PD. We thus offer a suite of translational probes for visualizing and understanding PSD-related pathologies.

Motor cortex activation mediates associations between striatal dopamine depletion and manual dexterity in Parkinson's disease.

INTRODUCTION: Parkinson's disease (PD) presents with a progressive decline in manual dexterity, attributed to dysfunction in the basal ganglia-thalamus-cortex loop, influenced by dopaminergic deficits in the striatum. Recent research suggests that the motor cortex may play a pivotal role in mediating the relationship between striatal dopamine depletion and motor function in PD. Understanding this connection is crucial for comprehending the origins of manual dexterity impairments in PD. Therefore, our study aimed to explore how motor cortex activation mediates the association between striatal dopamine depletion and manual dexterity in PD. MATERIALS AND METHODS: We enrolled 26 mildly affected PD patients in their off-medication phase to undergo [18F]FDOPA PET/CT scans for evaluating striatal dopaminergic function. EEG recordings were conducted during bimanual anti-phase finger tapping tasks to evaluate motor cortex activity, specifically focusing on Event-Related Desynchronization in the beta band. Manual dexterity was assessed using the Purdue Pegboard Test. Regression-based mediation analysis was conducted to examine whether motor cortex activation mediates the association between striatal dopamine depletion and manual dexterity in PD. RESULTS: Mediation analysis revealed a significant direct effect of putamen dopamine depletion on manual dexterity for the affected hand and assembly tasks (performed with two hands), with motor cortex activity mediating this association. In contrast, while caudate nucleus dopamine depletion showed a significant direct effect on manual dexterity, motor cortex mediation on this association was not observed. CONCLUSION: Our study confirms the association between striatum dopamine depletion and impaired manual dexterity in PD, with motor cortex activity mediating this relationship.

Frontoparietal-Striatal Network and Nucleus Basalis Modulation in Patients With Parkinson Disease and Gait Disturbance.

BACKGROUND AND OBJECTIVES: Neural computations underlying gait disorders in Parkinson disease (PD) are multifactorial and involve impaired expression of stereotactic locomotor patterns and compensatory recruitment of cognitive functions. This study aimed to clarify the network mechanisms of cognitive contribution to gait control and its breakdown in patients with PD. METHODS: Patients with PD were instructed to walk at a comfortable pace on a mat with pressure sensors. The characterization of cognitive-motor interplay was enhanced by using a gait with a secondary cognitive task (dual-task condition) and a gait without additional tasks (single-task condition). Participants were scanned using 3-T MRI and 123I-ioflupane SPECT. RESULTS: According to gait characteristics, cluster analysis assisted by a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding, categorized 56 patients with PD into 3 subpopulations. The preserved gait (PG) subgroup (n = 23) showed preserved speed and variability during gait, both with and without additional cognitive load. Compared with the PG subgroup, the mildly impaired gait (MIG) subgroup (n = 16) demonstrated deteriorated gait variability with additional cognitive load and impaired speed and gait variability without additional cognitive load. The severely impaired gait (SIG) subgroup (n = 17) revealed the slowest speed and highest gait variability. In addition, group differences were found in attention/working memory and executive function domains, with the lowest performance in the SIG subgroup than in the PG and MIG subgroups. Using resting-state functional MRI, the SIG subgroup demonstrated lower functional connectivity of the left and right frontoparietal network (FPN) with the caudate than the PG subgroup did (left FPN, d = 1.21, p < 0.001; right FPN, d = 1.05, p = 0.004). Cortical thickness in the FPN and 123I-ioflupane uptake in the striatum did not differ among the 3 subgroups. By contrast, the severity of Ch4 density loss was significantly correlated with the level of functional connectivity degradation of the FPN and caudate (left FPN-caudate, r = 0.27, p = 0.04). DISCUSSION: These findings suggest that the functional connectivity of the FPN with the caudate, as mediated by the cholinergic Ch4 projection system, underlies the compensatory recruitment of attention and executive function for damaged automaticity in gait in patients with PD.

Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions.

A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.

Exploring the cognitive assessment potential of MRI-based volumetric hippocampal segmentation in Parkinson's disease.

PURPOSE: To investigate the potential of magnetic resonance imaging (MRI)-based total and segmental hippocampus volume analysis in the assessment of cognitive status in Parkinson's disease (PD). METHODS: We divided participants into three groups Group A-Parkinson patients (Pp) with normal cognitive status (n = 25), Group B-Pp with dementia (n = 17), and Group C-healthy controls (n = 37). Three-dimensional T1W Fast Spoiled Gradient Recalled Echo images were used for Volbrain hippocampus subfield segmentation. We used the "Winterburn" protocol, which divides the hippocampus into five segments, Cornu Ammonis (CA),CA2/CA3, CA4/dentate gyrus, stratum radiatum, lacunosum, and moleculare, and subiculum. RESULTS: A total of 79 participants were included in the study, consisting of 42 individuals with PD (64.2% male) and 37 healthy controls (54.1% male). The mean age of PD was 60.9 ± 10.7 years and the mean age of control group was 59.27 ± 12.3 years. Significant differences were found in total hippocampal volumes between Group A and B (p = .047. Statistically significant group differences were found in total, right, and left CA1 volumes (analysis of variance [ANOVA]: F(2,76) = 8.098, p = .001; F(2,76) = 7.628, p = .001; F(2,76) = 5.084, p = .008, respectively), as well as in total subiculum volumes (ANOVA: F(2,76) = 4.368, p = .016). Post hoc tests showed that total subiculum volume was significantly lower in individuals with normal cognitive status (0.474 ± 0.116 cm3) compared to healthy controls (0.578 ± 0.151 cm3, p = .013). CONCLUSION: Volumetric hippocampal MRI can be used to assess the cognitive status of Pp. Longitudinal studies that evaluate Pp who progress from normal cognition to dementia are required to establish a causal relationship.

Retinal hyperspectral imaging in mouse models of Parkinson's disease and healthy aging.

Retinal hyperspectral imaging (HSI) is a non-invasive in vivo approach that has shown promise in Alzheimer's disease. Parkinson's disease is another neurodegenerative disease where brain pathobiology such as alpha-synuclein and iron overaccumulation have been implicated in the retina. However, it remains unknown whether HSI is altered in in vivo models of Parkinson's disease, whether it differs from healthy aging, and the mechanisms which drive these changes. To address this, we conducted HSI in two mouse models of Parkinson's disease across different ages; an alpha-synuclein overaccumulation model (hA53T transgenic line M83, A53T) and an iron deposition model (Tau knock out, TauKO). In comparison to wild-type littermates the A53T and TauKO mice both demonstrated increased reflectivity at short wavelengths ~ 450 to 600 nm. In contrast, healthy aging in three background strains exhibited the opposite effect, a decreased reflectance in the short wavelength spectrum. We also demonstrate that the Parkinson's hyperspectral signature is similar to that from an Alzheimer's disease model, 5xFAD mice. Multivariate analyses of HSI were significant when plotted against age. Moreover, when alpha-synuclein, iron or retinal nerve fibre layer thickness were added as a cofactor this improved the R2 values of the correlations in certain groups. This study demonstrates an in vivo hyperspectral signature in Parkinson's disease that is consistent in two mouse models and is distinct from healthy aging. There is also a suggestion that factors including retinal deposition of alpha-synuclein and iron may play a role in driving the Parkinson's disease hyperspectral profile and retinal nerve fibre layer thickness in advanced aging. These findings suggest that HSI may be a promising translation tool in Parkinson's disease.

Discovery and Evaluation of Imidazo[2,1-b][1,3,4]thiadiazole Derivatives as New Candidates for α-Synuclein PET Imaging.

α-synuclein (α-syn) pathologies are central to the development of synucleinopathies including Parkinson's disease (PD). Positron emission tomography (PET) imaging of α-syn pathologies is one strategy to facilitate the diagnosis, understanding, and treatment of synucleinopathies, but has been restricted by the lack of specific α-syn PET probes. In this work, we identified 2,6-disubstituted imidazo[2,1-b][1,3,4]thiadiazole (ITA) as a new α-syn-binding scaffold. Through autoradiography studies, we discovered an iodinated lead compound [125I]ITA-3, with moderate binding affinity (IC50 = 55 nM) to α-syn pathologies in human PD brain sections. Modified from [125I]ITA-3, we developed a potential PET tracer, [18F]FITA-2 (radiochemical yield >25%, molar activity >110 GBq/µmol), which demonstrated clear signals in α-syn-rich regions in human PD brain tissues (IC50 = 245 nM), good brain uptake (SUVpeak = 2.80 ± 0.45), and fast clearance rate in rats. Overall, [18F]FITA-2 appears to be a promising candidate for α-syn PET imaging and merits further development.

Fractional Anisotropy is a More Sensitive Diagnostic Biomarker Than Mean Kurtosis for Patients with Parkinson Disease with Cognitive Dysfunction: A Diffusional Kurtosis Map Tract-Based Spatial Statistics Study.

BACKGROUND AND PURPOSE: There is heterogeneity of white matter damage in Parkinson's disease patients with different cognitive states. Our aim was to find sensitive diffusional kurtosis imaging biomarkers to differentiate the white matter damage pattern of mild cognitive impairment and dementia. MATERIALS AND METHODS: Nineteen patients with Parkinson disease with mild cognitive impairment and 18 patients with Parkinson disease with dementia were prospectively enrolled. All participants underwent MR examination with 3D-T1-weighted image and diffusional kurtosis imaging sequences. Demographic data were compared between the 2 groups. Voxelwise statistical analyses of diffusional kurtosis imaging parameters were performed using tract-based spatial statistics. The receiver operator characteristic curve of significantly different metrics was graphed. The correlation of significantly different metrics with global cognitive status was analyzed. RESULTS: Compared with the Parkinson disease with mild cognitive impairment group, the fractional anisotropy and mean kurtosis values decreased in 4 independent clusters in the forceps minor, forceps major, inferior fronto-occipital fasciculus, and the inferior and superior longitudinal fasciculus in patients with Parkinson disease with dementia; the mean diffusivity decreased in 1 cluster in the forceps minor. The fractional anisotropy value in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus would be the diffusional kurtosis imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and patients with Parkinson disease with dementia, with the best diagnostic efficiency of 0.853. The fractional anisotropy values in the forceps minor (ß = 84.20, P < .001) and years of education (ß = 0.38, P = .014) were positively correlated with the Montreal Cognitive Assessment. CONCLUSIONS: The diffusional kurtosis imaging-derived fractional anisotropy and mean kurtosis can detect the different white matter damage patterns of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia. Fractional anisotropy is more sensitive than mean kurtosis in the differential diagnosis; fractional anisotropy derived from diffusional kurtosis imaging could become a promising imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia.

Pathologic Substrates of Structural Brain Network Resilience and Topology in Parkinson Disease Decedents.

BACKGROUND AND OBJECTIVES: In Parkinson disease (PD), α-synuclein spreading through connected brain regions leads to neuronal loss and brain network disruptions. With diffusion-weighted imaging (DWI), it is possible to capture conventional measures of brain network organization and more advanced measures of brain network resilience. We aimed to investigate which neuropathologic processes contribute to regional network topologic changes and brain network resilience in PD. METHODS: Using a combined postmortem MRI and histopathology approach, PD and control brain donors with available postmortem in situ 3D T1-weighted MRI, DWI, and brain tissue were selected from the Netherlands Brain Bank and Normal Aging Brain Collection Amsterdam. Probabilistic tractography was performed, and conventional network topologic measures of regional eigenvector centrality and clustering coefficient, and brain network resilience (change in global efficiency upon regional node failure) were calculated. PSer129 α-synuclein, phosphorylated-tau, ß-amyloid, neurofilament light-chain immunoreactivity, and synaptophysin density were quantified in 8 cortical regions. Group differences and correlations were assessed with rank-based nonparametric tests, with age, sex, and postmortem delay as covariates. RESULTS: Nineteen clinically defined and pathology-confirmed PD (7 F/12 M, 81 ± 7 years) and 15 control (8 F/7 M, 73 ± 9 years) donors were included. With regional conventional measures, we found lower eigenvector centrality only in the parahippocampal gyrus in PD (d = -1.08, 95% CI 0.003-0.010, p = 0.021), which did not associate with underlying pathology. No differences were found in regional clustering coefficient. With the more advanced measure of brain network resilience, we found that the PD brain network was less resilient to node failure of the dorsal anterior insula compared with the control brain network (d = -1.00, 95% CI 0.0012-0.0015, p = 0.018). This change was not directly driven by neuropathologic processes within the dorsal anterior insula or in connected regions but was associated with higher Braak α-synuclein staging (rs = -0.40, p = 0.036). DISCUSSION: Although our cohort might suffer from selection bias, our results highlight that regional network disturbances are more complex to interpret than previously believed. Regional neuropathologic processes did not drive regional topologic changes, but a global increase in α-synuclein pathology had a widespread effect on brain network reorganization in PD.

Attention-enhanced dilated convolution for Parkinson's disease detection using transcranial sonography.

BACKGROUND: Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis. METHODS: This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities. RESULTS: The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models. CONCLUSION: The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.

Gray matter volume alterations in de novo Parkinson's disease: A mediational role in the interplay between sleep quality and anxiety.

OBJECTIVE: Parkinson's disease (PD) is increasingly recognized for its non-motor symptoms, among which emotional disturbances and sleep disorders frequently co-occur. The commonality of neuroanatomical underpinnings for these symptoms is not fully understood. This study is intended to investigate the differences in gray matter volume (GMV) between PD patients with anxiety (A-PD) and those without anxiety (NA-PD). Additionally, it seeks to uncover the interplay between GMV variations and the manifestations of anxiety and sleep quality. METHODS: A total of 37 A-PD patients, 43 NA-PD patients, and 36 healthy controls (HCs) were recruited, all of whom underwent voxel-based morphometry (VBM) analysis. Group differences in GMV were assessed using analysis of covariance (ANCOVA). Partial correlation between GMV, anxiety symptom, and sleep quality were analyzed. Mediation analysis explored the mediating role of the volume of GMV-distinct brain regions on the relationship between sleep quality and anxiety within the PD patient cohort. RESULTS: A-PD patients showed significantly lower GMV in the fusiform gyrus (FG) and right inferior temporal gyrus (ITG) compared to HCs and NA-PD patients. GMV in these regions correlated negatively with Hamilton Anxiety Rating Scale (HAMA) scores (right ITG: r = -0.690, p < 0.001; left FG: r = -0.509, p < 0.001; right FG: r = -0.576, p < 0.001) and positively with sleep quality in PD patients (right ITG: r = 0.592, p < 0.001; left FG: r = 0.356, p = 0.001; right FG: r = 0.470, p < 0.001). Mediation analysis revealed that GMV in the FG and right ITG mediated the relationship between sleep quality and anxiety symptoms, with substantial effect sizes accounted for by the right ITG (25.74%) and FG (left: 11.90%, right: 15.59%). CONCLUSION: This study has shed further light on the relationship between sleep disturbances and anxiety symptoms in PD patients. Given the pivotal roles of the FG and the ITG in facial recognition and the recognition of emotion-related facial expressions, our findings indicate that compromised sleep quality, under the pathological conditions of PD, may exacerbate the reduction in GMV within these regions, impairing the recognition of emotional facial expressions and thereby intensifying anxiety symptoms.

Cholinergic nucleus degeneration and its association with gait impairment in Parkinson's disease.

BACKGROUND: The contribution of cholinergic degeneration to gait disturbance in Parkinson's disease (PD) is increasingly recognized, yet its relationship with dopaminergic-resistant gait parameters has been poorly investigated. We investigated the association between comprehensive gait parameters and cholinergic nucleus degeneration in PD. METHODS: This cross-sectional study enrolled 84 PD patients and 69 controls. All subjects underwent brain structural magnetic resonance imaging to assess the gray matter density (GMD) and volume (GMV) of the cholinergic nuclei (Ch123/Ch4). Gait parameters under single-task (ST) and dual-task (DT) walking tests were acquired using sensor wearables in PD group. We compared cholinergic nucleus morphology and gait performance between groups and examined their association. RESULTS: PD patients exhibited significantly decreased GMD and GMV of the left Ch4 compared to controls after reaching HY stage > 2. Significant correlations were observed between multiple gait parameters and bilateral Ch123/Ch4. After multiple testing correction, the Ch123/Ch4 degeneration was significantly associated with shorter stride length, lower gait velocity, longer stance phase, smaller ankle toe-off and heel-strike angles under both ST and DT condition. For PD patients with HY stage 1-2, there were no significant degeneration of Ch123/4, and only right side Ch123/Ch4 were corrected with the gait parameters. However, as the disease progressed to HY stage > 2, bilateral Ch123/Ch4 nuclei showed correlations with gait performance, with more extensive significant correlations were observed in the right side. CONCLUSIONS: Our study demonstrated the progressive association between cholinergic nuclei degeneration and gait impairment across different stages of PD, and highlighting the potential lateralization of the cholinergic nuclei's impact on gait impairment. These findings offer insights for the design and implementation of future clinical trials investigating cholinergic treatments as a promising approach to address gait impairments in PD.

Dynamic properties in functional connectivity changes and striatal dopamine deficiency in Parkinson's disease.

Recent studies in Parkinson's disease (PD) patients reported disruptions in dynamic functional connectivity (dFC, i.e., a characterization of spontaneous fluctuations in functional connectivity over time). Here, we assessed whether the integrity of striatal dopamine terminals directly modulates dFC metrics in two separate PD cohorts, indexing dopamine-related changes in large-scale brain network dynamics and its implications in clinical features. We pooled data from two disease-control cohorts reflecting early PD. From the Parkinson's Progression Marker Initiative (PPMI) cohort, resting-state functional magnetic resonance imaging (rsfMRI) and dopamine transporter (DaT) single-photon emission computed tomography (SPECT) were available for 63 PD patients and 16 age- and sex-matched healthy controls. From the clinical research group 219 (KFO) cohort, rsfMRI imaging was available for 52 PD patients and 17 age- and sex-matched healthy controls. A subset of 41 PD patients and 13 healthy control subjects additionally underwent 18F-DOPA-positron emission tomography (PET) imaging. The striatal synthesis capacity of 18F-DOPA PET and dopamine terminal quantity of DaT SPECT images were extracted for the putamen and the caudate. After rsfMRI pre-processing, an independent component analysis was performed on both cohorts simultaneously. Based on the derived components, an individual sliding window approach (44 s window) and a subsequent k-means clustering were conducted separately for each cohort to derive dFC states (reemerging intra- and interindividual connectivity patterns). From these states, we derived temporal metrics, such as average dwell time per state, state attendance, and number of transitions and compared them between groups and cohorts. Further, we correlated these with the respective measures for local dopaminergic impairment and clinical severity. The cohorts did not differ regarding age and sex. Between cohorts, PD groups differed regarding disease duration, education, cognitive scores and L-dopa equivalent daily dose. In both cohorts, the dFC analysis resulted in three distinct states, varying in connectivity patterns and strength. In the PPMI cohort, PD patients showed a lower state attendance for the globally integrated (GI) state and a lower number of transitions than controls. Significantly, worse motor scores (Unified Parkinson's Disease Rating Scale Part III) and dopaminergic impairment in the putamen and the caudate were associated with low average dwell time in the GI state and a low total number of transitions. These results were not observed in the KFO cohort: No group differences in dFC measures or associations between dFC variables and dopamine synthesis capacity were observed. Notably, worse motor performance was associated with a low number of bidirectional transitions between the GI and the lesser connected (LC) state across the PD groups of both cohorts. Hence, in early PD, relative preservation of motor performance may be linked to a more dynamic engagement of an interconnected brain state. Specifically, those large-scale network dynamics seem to relate to striatal dopamine availability. Notably, most of these results were obtained only for one cohort, suggesting that dFC is impacted by certain cohort features like educational level, or disease severity. As we could not pinpoint these features with the data at hand, we suspect that other, in our case untracked, demographical features drive connectivity dynamics in PD. PRACTITIONER POINTS: Exploring dopamine's role in brain network dynamics in two Parkinson's disease (PD) cohorts, we unraveled PD-specific changes in dynamic functional connectivity. Results in the Parkinson's Progression Marker Initiative (PPMI) and the KFO cohort suggest motor performance may be linked to a more dynamic engagement and disengagement of an interconnected brain state. Results only in the PPMI cohort suggest striatal dopamine availability influences large-scale network dynamics that are relevant in motor control.

Insular and limbic abnormal functional connectivity in early-stage Parkinson's disease patients with autonomic dysfunction.

Autonomic symptoms in Parkinson's disease result from variable involvement of the central and peripheral systems, but many aspects remain unclear. The analysis of functional connectivity has shown promising results in assessing the pathophysiology of Parkinson's disease. This study aims to investigate the association between autonomic symptoms and cortical functional connectivity in early Parkinson's disease patients using high-density EEG. 53 early Parkinson's disease patients (F/M 18/35) and 49 controls (F/M 20/29) were included. Autonomic symptoms were evaluated using the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score. Data were recorded with a 64-channel EEG system. We analyzed cortical functional connectivity, based on weighted phase-lag index, in θ-α-ß-low-γ bands. A network-based statistic was used to perform linear regression between Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and functional connectivity in Parkinson's disease patients. We observed a positive relation between the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and α-functional connectivity (network τ = 2.8, P = 0.038). Regions with higher degrees were insula and limbic lobe. Moreover, we found positive correlations between the mean connectivity of this network and the gastrointestinal, cardiovascular, and thermoregulatory domains of Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction. Our results revealed abnormal functional connectivity in specific areas in Parkinson's disease patients with greater autonomic symptoms. Insula and limbic areas play a significant role in the regulation of the autonomic system. Increased functional connectivity in these regions might represent the central compensatory mechanism of peripheral autonomic dysfunction in Parkinson's disease.