Biomarker For Parkinson's Disease Research Articles

Skip-AttSeqNet: Leveraging skip connection and attention-driven Seq2seq model to enhance eye movement event detection in Parkinson’s disease

To address the limitations of traditional algorithms in detecting eye movement events, particularly in Parkinson’s disease (PD) patients, this study introduces Skip-AttSeqNet. It presents an innovative approach combining skip-connected, one-dimensional convolutional neural networks with an attention-enhanced, bidirectional long short-term memory network. This hybrid architecture significantly advances smooth pursuit (SP) event detection, as evidenced by its performance on both the GazeCom dataset and a unique dataset of PD patient eye movements. Key innovations in this work include the utilization of skip connections and attention mechanisms, along with optimized training–validation set division, collectively enhancing the model’s accuracy while mitigating overfitting. Skip-AttSeqNet outperforms existing algorithms, achieving a 3.2% higher sample-level F1 score and a notable 6.2% increase in event-level F1 scores for SP detection. Furthermore, we established a smooth-pursuit experimental paradigm and identified significant differences in saccade and SP features between PD patients and healthy older adults through statistical analysis using the Mann–Whitney test. These findings underscore the potential of eye movement metrics as biomarkers for PD, thereby not only strengthening PD diagnosis but also enriching the intersection of computer vision and biomedical research domains.

Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning.

Parkinson's disease (PD) is a multifactorial disease that lacks reliable biomarkers for its diagnosis. It is now clear that aging is the greatest risk factor for developing PD. Therefore, it is necessary to identify novel biomarkers associated with aging in PD. In this study, we downloaded aging-related genes from the Human Ageing Gene Database. To screen and verify biomarkers for PD, we used whole-blood RNA-Seq data from 11 PD patients and 13 healthy control (HC) subjects as a training dataset and three datasets retrieved from the Gene Expression Omnibus (GEO) database as validation datasets. Using the limma package in R, 1435 differentially expressed genes (DEGs) were found in the training dataset. Of these genes, 29 genes were found to occur in both DEGs and 307 aging-related genes. By using machine learning algorithms (LASSO, RF, SVM, and RR), Venn diagrams, and LASSO regression, four of these genes were determined to be potential PD biomarkers; these were further validated in external validation datasets and by qRT-PCR in the peripheral blood mononuclear cells (PBMCs) of 10 PD patients and 10 HC subjects. Based on the biomarkers, a diagnostic model was developed that had reliable predictive ability for PD. Two of the identified biomarkers demonstrated a meaningful correlation with immune cell infiltration status in the PD patients and HC subjects. In conclusion, four aging-related genes were identified as robust diagnostic biomarkers and may serve as potential targets for PD therapeutics.

Enhancing early Parkinson’s disease detection through multimodal deep learning and explainable AI: insights from the PPMI database

Parkinson’s is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining different 3D architectures with the novel Excitation Network (EN) and supported by Explainable Artificial Intelligence (XAI) techniques. Utilizing data from the Parkinson’s Progression Markers Initiative, this study introduces a joint co-learning approach for multimodal fusion, enabling end-to-end training of deep neural networks and facilitating the learning of complementary information from both imaging and clinical modalities. DenseNet with EN outperformed other models, showing a substantial increase in accuracy when supplemented with clinical data. XAI methods, such as Integrated Gradients for ResNet and DenseNet, and Attention Heatmaps for Vision Transformer (ViT), revealed that DenseNet focused on brain regions believed to be critical to prodromal pathophysiology, including the right temporal and left pre-frontal areas. Similarly, ViT highlighted the lateral ventricles associated with cognitive decline, indicating their potential in the Prodromal stage. These findings underscore the potential of these regions as early-stage PD biomarkers and showcase the proposed framework's efficacy in predicting subtypes of PD and aiding in early diagnosis, paving the way for innovative diagnostic tools and precision medicine.

Metabolomics Unveils Disrupted Pathways in Parkinson's Disease: Toward Biomarker-Based Diagnosis.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by diverse symptoms, where accurate diagnosis remains challenging. Traditional clinical observation methods often result in misdiagnosis, highlighting the need for biomarker-based diagnostic approaches. This study utilizes ultraperformance liquid chromatography coupled to an electrospray ionization source and quadrupole time-of-flight untargeted metabolomics combined with biochemometrics to identify novel serum biomarkers for PD. Analyzing a Brazilian cohort of serum samples from 39 PD patients and 15 healthy controls, we identified 15 metabolites significantly associated with PD, with 11 reported as potential biomarkers for the first time. Key disrupted metabolic pathways include caffeine metabolism, arachidonic acid metabolism, and primary bile acid biosynthesis. Our machine learning model demonstrated high accuracy, with the Rotation Forest boosting model achieving 94.1% accuracy in distinguishing PD patients from controls. It is based on three new PD biomarkers (downregulated: 1-lyso-2-arachidonoyl-phosphatidate and hypoxanthine and upregulated: ferulic acid) and surpasses the general 80% diagnostic accuracy obtained from initial clinical evaluations conducted by specialists. Besides, this machine learning model based on a decision tree allowed for visual and easy interpretability of affected metabolites in PD patients. These findings could improve the detection and monitoring of PD, paving the way for more precise diagnostics and therapeutic interventions. Our research emphasizes the critical role of metabolomics and machine learning in advancing our understanding of the chemical profile of neurodegenerative diseases.

Association between plasma Netrin-1 levels and motor and nonmotor symptoms in Parkinson's disease.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by dopaminergic neuron degeneration and diverse motor and nonmotor symptoms. Early diagnosis and intervention are crucial but challenging due to reliance on clinical presentation. Recent research suggests potential biomarkers for early detection, including plasma netrin-1 (NTN-1), a protein implicated in neuronal survival. This cross-sectional study recruited 105 PD patients and 65 healthy controls, assessing plasma NTN-1 levels and correlating them with clinical characteristics. Statistical analyses explored associations between NTN-1 levels and PD symptoms, considering demographic factors. PD patients exhibited significantly lower plasma NTN-1 levels compared to controls. NTN-1 demonstrated moderate potential as a PD biomarker. Positive correlations were found between NTN-1 levels and motor, depression, and cognitive symptoms. Multiple regression analysis revealed disease duration and NTN-1 levels as key factors influencing symptom severity. Gender also impacted symptom scores. Reduced plasma NTN-1 levels correlate with PD severity, suggesting its potential as a biomarker. However, further research is needed to elucidate the roles of NTN-1 in PD pathophysiology and validate its diagnostic and therapeutic implications. Understanding the involvement of NTN-1 may lead to personalized management strategies for PD.

Simultaneous Increase of Mean Susceptibility and Mean Kurtosis in the Substantia Nigra as an MRI Neuroimaging Biomarker for Early-Stage Parkinson's Disease: A Systematic Review and Meta-Analysis.

Parkinson's disease (PD) is the second most common neurodegenerative disorder. Early detection is crucial for treatment and slowing disease progression. Simultaneous alterations in mean susceptibility (MS) from quantitative susceptibility mapping (QSM) and mean kurtosis (MK) from diffusion kurtosis imaging (DKI) can serve as reliable neuroimaging biomarkers for early-stage PD (ESPD) in the basal ganglia nuclei, including the substantia nigra (SN), putamen (PUT), globus pallidus (GP), and caudate nucleus (CN). Systematic review and meta-analysis. One hundred eleven patients diagnosed with ESPD and 81 healthy controls (HCs) were included from four studies that utilized both QSM and DKI in both subject groups. Three-dimensional multi-echo gradient echo sequence for QSM and spin echo planar imaging sequence for DKI at 3 Tesla. A systematic review and meta-analysis using PRISMA guidelines searched PubMed, Web of Science, and Scopus. Random-effects model, standardized mean difference (SMD) to compare MS and MK between ESPD patients and HCs, I2 statistic for heterogeneity, Newcastle-Ottawa Scale (NOS) for risk of bias, and Egger's test for publication bias. A P-value <0.05 was considered significant. MS values were significantly higher in SN (SMD 0.72, 95% CI 0.31 to 1.12), PUT (SMD 0.68, 95% CI 0.29 to 1.07), and GP (SMD 0.53, 95% CI 0.19 to 0.87) in ESPD patients compared to HCs. CN did not show a significant difference in MS values (P = 0.15). MK values were significantly higher only in SN (SMD = 0.72, 95% CI 0.16 to 1.27). MK values were not significantly different in PUT (P = 1.00), GP (P = 0.97), and CN (P = 0.59). Studies had high quality (NOS 7-8) and no publication bias (P = 0.967). Simultaneous use of MS and MK may be useful as an early neuroimaging biomarker for ESPD detection and its differentiation from HCs, with significant differences observed in the SN. 2 TECHNICAL EFFICACY: Stage 2.

Association of CSF α-Synuclein Seeding Amplification Assay Results With Clinical Features of Possible and Probable Dementia With Lewy Bodies.

The clinical diagnosis of dementia with Lewy bodies (DLB) depends on identifying significant cognitive decline accompanied by core features of parkinsonism, visual hallucinations, cognitive fluctuations, and REM sleep behavior disorder (RBD). Hyposmia is one of the several supportive features. α-Synuclein seeding amplification assays (αSyn-SAAs) may enhance diagnostic accuracy by detecting pathologic αSyn seeds in CSF. In this study, we examine how different clinical features associate with CSF αSyn-SAA positivity in a large group of clinically diagnosed participants with DLB. Cross-sectional and longitudinal CSF samples from the multicentered observational cohort study of the DLB Consortium and similar studies within the Parkinson's Disease Biomarker Program, contributed by academic medical centers in the United States, underwent αSyn-SAA testing. Participants included those clinically diagnosed with DLB and 2 control cohorts. Associations between core DLB features and olfaction with αSyn-SAA positivity were evaluated using logistic regression. CSF samples from 191 participants diagnosed with DLB (mean age 69.9 ± 6.8, 15% female), 50 age-matched and sex-matched clinical control participants, and 49 younger analytical control participants were analyzed. Seventy-two percent (137/191) of participants with DLB had positive αSyn-SAAs vs 4% of the control groups. Among participants with DLB, those who were αSyn-SAA-positive had lower Montreal Cognitive Assessment scores (18.8 ± 5.7 vs 21.2 ± 5.2, p = 0.01), had worse parkinsonism on the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (33.8 ± 15.1 vs 25.6 ± 16.4, p = 0.001), were more likely to report RBD (114/133 [86%] vs 33/53 [62%], p < 0.0001), and had worse hyposmia on the University of Pennsylvania Smell Identification Test (UPSIT) (94/105 [90%] below 15th percentile vs 14/44 [32%], p < 0.0001). UPSIT percentile had the highest area under the curve (0.87, 95% CI 0.81-0.94) in predicting αSyn-SAA positivity and participants scoring at or below the 15th percentile of age and sex normative values had 18.3 times higher odds (95% CI 7.52-44.6) of having a positive αSyn-SAA test. Among 82 participants with longitudinal CSF samples, 81 (99%) had the same αSyn-SAA result for initial and follow-up specimens. A substantial proportion of clinically diagnosed participants with DLB had negative αSyn-SAA results. Hyposmia was the strongest clinical predictor of αSyn-SAA positivity. Hyposmia and αSyn-SAA may have utility in improving the diagnostic assessment of individuals with potential DLB. This study provided Class III evidence that CSF αSyn-SAA distinguishes patients with clinically diagnosed DLB from normal controls.

Early detection of dopaminergic dysfunction and glymphatic system impairment in Parkinson's disease

PurposeThis study aimed to assess the glymphatic function and its correlation with clinical characteristics and the loss of dopaminergic neurons in Parkinson's disease (PD) using hybrid positron emission tomography (PET)-magnetic resonance imaging (MRI) combined with diffusion tensor image analysis along the perivascular space (DTI-ALPS), choroid plexus volume (CPV), and enlarged perivascular space (EPVS) volume. MethodsTwenty-five PD patients and thirty matched healthy controls (HC) participated in the study. All participants underwent 18F-fluorodopa (18F-DOPA) PET-MRI scanning. The striatal standardized uptake value ratio (SUVR), DTI-ALPS index, CPV, and EPVS volume were calculated. Furthermore, we also analysed the relationship between the DTI-ALPS index, CPV, EPVS volume and striatal SUVR as well as clinical characteristics of PD patients. ResultsPD patients demonstrated significantly lower values in DTI-ALPS (t = 3.053, p = 0.004) and larger CPV (t = 2.743, p = 0.008) and EPVS volume (t = 2.807, p = 0.008) compared to HC. In PD group, the ALPS-index was negatively correlated with the Unified Parkinson's Disease Rating Scale III (UPDRS-III) scores (r = −0.730, p < 0.001), and positively correlated with the mean putaminal SUVR (r = 0.560, p = 0.007) and mean caudal SUVR (r = 0.459, p = 0.032). Moreover, the mean putaminal SUVR was negatively associated with the UPDRS-III scores (r = −0.544, p = 0.009). ConclusionDTI-ALPS has the potential to uncover glymphatic dysfunction in patients with PD, with this dysfunction correlating strongly with the severity of disease, together with the mean putaminal and caudal SUVR. PET- MRI can serve as a potential multimodal imaging biomarker for early-stage PD.

Alzheimer's Disease Related Biomarkers Were Associated with Amnestic Cognitive Impairment in Parkinson's Disease: A Cross-Sectional Cohort Study.

Cognitive impairment is common in patients with Parkinson's disease (PD) and occurs through multiple mechanisms, including Alzheimer's disease (AD) pathology and the involvement of α-synucleinopathies. We aimed to investigate the pathological biomarkers of both PD and AD in plasma and neuronal extracellular vesicles (EVs) and their association with different types of cognitive impairment in PD patients. A total of 122 patients with PD and 30 healthy controls were included in this cross-sectional cohort study between March 2021 and July 2023. Non-dementia PD patients were divided into amnestic and non-amnestic groups according to the memory domain of a neuropsychological assessment. Plasma and neuronal EV biomarkers, including α-synuclein (α-syn), beta-amyloid (Aβ), total tau (T-tau), phosphorylated tau181 (p-tau181), and glial fibrillary acidic protein (GFAP), were measured using a single-molecule array and a chemiluminescence immunoassay, respectively. Neuronal EV but not plasma α-syn levels, were significantly increased in PD as compared to healthy controls, and they were positively associated with UPDRS part III scores and the severity of cognitive impairment. A lower plasma Aβ42 level and higher neuronal EV T-tau level were found in the amnestic PD group compared to the non-amnestic PD group. The results of the current study demonstrate that neuronal EV α-syn levels can be a sensitive biomarker for assisting in the diagnosis and disease severity prediction of PD. Both AD and PD pathologies are important factors in cognitive impairment associated with PD, and AD pathologies are more involved in amnestic memory deficit in PD.

Exploring circular RNAs as biomarkers for Parkinson's disease and their expression changes after aerobic exercise rehabilitation.

Circular RNAs (circRNAs) are circularized single-stranded ribonucleic acids that interacts with DNA, RNA, and proteins to play critical roles in cell biology. CircRNAs regulate microRNA content, gene expression, and may code for specific peptides. Indeed, circRNAs are differentially expressed in neurodegenerative disorders like Parkinson's disease (PD), playing a potential role in the mechanisms of brain pathology. The RNA molecules with aberrant expression in the brain can cross the blood-brain barrier and reach the bloodstream, which enable their use as non-invasive PD disease biomarker. Promising targets with valuable discriminatory ability in combined circRNA signatures include MAPK9_circ_0001566, SLAIN1_circ_0000497, SLAIN2_circ_0126525, PSEN1_circ_0003848, circ_0004381, and circ_0017204. On the other hand, regular exercises are effective therapy for mitigating PD symptoms, promoting neuroprotective effects with epigenetic modulation. Aerobic exercises slow symptom progression in PD by improving motor control, ameliorating higher functions, and enhancing brain activity and neuropathology. These improvements are accompanied by changes circRNA expression, including hsa_circ_0001535 (circFAM13B) and hsa_circ_0000437 (circCORO1C). The sensitivity of current methods for detecting circulating circRNAs is considered a limitation. While amplification kits already exist for low-abundant microRNAs, similar kits are needed for circRNAs. Alternatively, the use of digital PCR can help overcome this constraint. The current review examines the potential use of circRNAs as non-invasive biomarkers of PD and to assess the effects of rehabilitation. Although circRNAs hold promise as targets for PD diagnosis and therapeutics, further validation is needed before their clinical implementation.

Alpha Synuclein Toxicity and Non-Motor Parkinson's.

Parkinson's disease (PD) is a common multisystem neurodegenerative disorder affecting 1% of the population over the age of 60 years. The main neuropathological features of PD are the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of alpha synuclein (αSyn)-rich Lewy bodies both manifesting with classical motor signs. αSyn has emerged as a key protein in PD pathology as it can spread through synaptic networks to reach several anatomical regions of the body contributing to the appearance of non-motor symptoms (NMS) considered prevalent among individuals prior to PD diagnosis and persisting throughout the patient's life. NMS mainly includes loss of taste and smell, constipation, psychiatric disorders, dementia, impaired rapid eye movement (REM) sleep, urogenital dysfunction, and cardiovascular impairment. This review summarizes the more recent findings on the impact of αSyn deposits on several prodromal NMS and emphasizes the importance of early detection of αSyn toxic species in biofluids and peripheral biopsies as prospective biomarkers in PD.

Evaluation of a Liquid Chromatography-Tandem Mass Spectrometry Method for the Analysis of Glucosylceramide and Galactosylceramide Isoforms in Cerebrospinal Fluid of Parkinson's Disease Patients.

Mutations in GBA1, encoding glucocerebrosidase beta 1 (GCase), are the most common genetic risk factor for Parkinson's disease (PD). GCase dysfunction leads to an accumulation of glucosylceramide (GluCer) substrates in different organs and fluids. Despite the challenges in quantifying GluCer isoforms in biological samples, their potential clinical interest as PD biomarkers justifies the development of robust assays. An extensively evaluated high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method for quantifying 14 GluCer and galactosylceramide (GalCer) isoforms in human cerebrospinal fluid (CSF) samples is presented. Sample pretreatment, HPLC, and MS/MS parameters were optimized. Evaluation was performed according to the recommendations of the Clinical and Laboratory Standards Institute and European Medicines Agency guidelines. Four 7-point calibration curves were generated, with a linearity interval from 2.5 to 200 nM (R2 ≥ 0.995). The limit of quantification was set at 5 nM. Between-run precision and accuracy were up to 12.5 and 9%, respectively. After method validation, we measured the levels of GluCer and GalCer isoforms in CSF human samples, including 6 healthy controls (HC), 22 idiopathic GBA1 wild-type PD (iPD) patients, and 5 GBA1-associated PD (PD-GBA) patients. GluCer/GalCer median ratios were found to be higher in the CSF of PD-GBA patients, particularly in severe GBA1 mutations, than those in iPD and HC. The observed trends in GluCer/GalCer ratios among groups provide novel information for the comprehensive analysis of sphingolipids as potential biomarkers of PD.

A candidate reference measurement procedure for the quantification of α-synuclein in cerebrospinal fluid using an SI traceable primary calibrator and multiple reaction monitoring.

α-synuclein aggregation is an important hallmark of neurodegenerative diseases such as Parkinson's disease (PD) and Lewy body dementia. α-synuclein has been increasingly used as a diagnostic biomarker in PD and other synucleinopathies. Current clinical assays rely on antibody-based immunoassays to detect α-synuclein, which possess high sensitivity, afford high throughput and require small sample volumes. The utility of these assays, however, may be compounded by the specificity, selectivity and batch-to-batch heterogeneity of the antibody used, which can lead to deviations in the total amount of the protein measured when comparing results among different laboratories. Similarly, current mass spectrometry-based quantification methods for α-synuclein lack well-defined, value assigned calibrators to ensure comparability of measurements. Therefore, there is still an unmet need for the standardisation of clinical measurements for α-synuclein that can be achieved by the development of reference measurement procedures (RMPs) utilising calibrators traceable to the SI (International System of Units). Here, we report a candidate RMP for α-synuclein, using an SI traceable primary calibrator and an isotope dilution mass spectrometry (IDMS) approach to address this need. The gravimetrically prepared primary calibrator was traceably quantified utilising a combination of amino acid analysis (AAA) and quantitative nuclear magnetic resonance (qNMR) for value assignment. An optimised targeted sample clean-up procedure involving a non-denaturing Lys-C digestion and solid-phase extraction strategy was devised, followed by the development of a targeted multiple reaction monitoring (MRM) method for the quantification of α-synuclein in cerebrospinal fluid (CSF). This candidate RMP was then deployed for the sensitive detection and accurate quantification of multiple proteotypic α-synuclein peptides in patient derived CSF samples. The LC-MS based results were subsequently compared to immunoassay data to assess the overall performance of our approach. The development and adoption of this candidate RMP, along with the availability of the SI traceable primary calibrator will allow for reliable quantifications of α-synuclein in CSF by an LC-MS based assay. The RMP will potentially contribute towards the standardisation of this important biomarker and may lead to future interlaboratory comparisons.

Skin α-Synuclein Seeding Activity in Patients with Type 1 Gaucher Disease.

Patients with type 1 Gaucher disease (GD1) have a significantly increased risk of developing Parkinson's disease (PD). The objective of this study was to evaluate skin α-synuclein (αSyn) seeding activity as a biomarker for GD1-related PD (GD1-PD). This single-center study administered motor and cognitive examinations and questionnaires of nonmotor symptoms to adult patients with GD1. Optional skin biopsy was performed for skin αSyn seed amplification assay (αSyn SAA) using real-time quaking-induced conversion assay. Forty-nine patients were enrolled, and 36 underwent skin biopsy. Two study participants had PD. Ten participants were αSyn SAA positive (27.8%), 7 (19.4%) were intermediate, and 19 (52.8%) were negative. Positive αSyn seeding activity was observed in the single GD1-PD case who consented to biopsy. αSyn SAA positivity was associated with older age (p = 0.043), although αSyn SAA positivity was more prevalent in patients with GD1 than historic controls. Longitudinal follow-up is required to determine whether skin αSyn seeding activity can be an early biomarker for GD1-PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Combined MR quantitative susceptibility mapping and multi-shell diffusion in Parkinson's disease.

Quantitative susceptibility mapping (QSM), neurite orientation dispersion and density imaging (NODDI), and the g-ratio have separately shown differences between Parkinson's disease (PD) and healthy controls. The g-ratio has, however, not been studied in PD in the substantia nigra (SN) and the putamen. A combination of these methods could also potentially be a complementary imaging biomarker for PD. This study aimed to assess the diagnostic performance of QSM, NODDI, the g-ratio, and a combined QSM-NODDI imaging marker in the SN and putamen of PD patients. In this prospective study, the diagnostic performance of median region of interest values was compared in a cohort of 15 participants with PD and 14 healthy controls after manual segmentation. The diagnostic performance was assessed using the area under curve (AUC) for the receiving operator characteristic. Median QSM in the contralateral SN identified PD with AUC 0.77, and median isotropic volume fraction identified PD in the ipsilateral SN with AUC 0.68. A combined NODDI-QSM marker improved diagnostic performance (AUC 0.80). No significant differences were found in the g-ratio. A combination of median QSM and median isotropic volume fraction improves the differentiation of PD from healthy controls and is a potential biomarker in the diagnostics of PD. This confirms previously reported results indicating that combining QSM and NODDI modestly improves differentiation of PD.

Exploring the complexity of EEG patterns in Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20Hz) and beta 2 (20-30Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.

Increased mean diffusivity of the caudal motor SNc identifies patients with REM sleep behaviour disorder and Parkinson’s disease

Idiopathic rapid eye movement sleep behaviour disorder (iRBD)—a Parkinson’s disease (PD) prodrome—might exhibit neural changes similar to those in PD. Substantia nigra pars compacta (SNc) degeneration underlies motor symptoms of PD. In iRBD and early PD (ePD), we measured diffusion MRI (dMRI) in the caudal motor SNc, which overlaps the nigrosome-1—the earliest-degenerating dopaminergic neurons in PD—and in the striatum. Nineteen iRBD, 26 ePD (1.7 ± 0.03 years), and 46 age-matched healthy controls (HCs) were scanned at Western University, and 47 iRBD, 115 ePD (0.9 ± 0.01 years), and 56 HCs were scanned through the Parkinson’s Progression Markers Initiative, using 3T MRI. We segmented the SNc and striatum into subregions using automated probabilistic tractography to the cortex. We measured mean diffusivity (MD) and fractional anisotropy (FA) along white-matter bundles and subregional surfaces. We performed group-level and classification analyses. Increased caudal motor SNc surface MD was the only iRBD-HCs and ePD-HCs difference replicating across datasets (padj < 0.05). No iRBD-ePD differences emerged. Caudal motor SNc surface MD classified patient groups from HCs at the single-subject level with good-to-excellent balanced accuracy in an independent sample (0.91 iRBD and 0.86 iRBD and ePD combined), compared to fair performance for total SNc surface MD (0.72 iRBD and ePD). Caudal motor SNc surface MD correlated significantly with MDS-UPDRS-III scores in ePD patients. Using dMRI and automated segmentation, we detected changes suggesting altered microstructural integrity in iRBD and ePD in the nigrostriatal subregion known to degenerate first in PD. Surface MD of the caudal motor SNc presents a potential measure for inclusion in neuroimaging biomarkers of iRBD and PD.

Preliminary study of substantia nigra analysis by tensorial feature extraction.

Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients. We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition. Furthermore, we apply a feature selection method that excludes common features between PD and non-PD. We collect neuromelanin images of 263 patients: 124 PD and 139 non-PD patients and divide them into training and testing datasets for experiments. We then experimentally evaluate the classification accuracy of the substantia nigra between PD and non-PD patients using the proposed feature extraction method and linear discriminant analysis. The proposed method achieves a sensitivity of 0.72 and a specificity of 0.64 for our testing dataset of 66 non-PD and 42 PD patients. Furthermore, we visualise the important patterns in the substantia nigra by a linear combination of rank-1 tensors with selected features. The visualised patterns include the ventrolateral tier, where the severe loss of neurons can be observed in PD. We develop a new feature-extraction method for the analysis of the substantia nigra towards PD diagnosis. In the experiments, even though the classification accuracy with the proposed feature extraction method and linear discriminant analysis is lower than that of expert physicians, the results suggest the potential of tensorial feature extraction.

Study protocol for the MIND-PD study: a randomized controlled trial to investigate clinical and biological effects of mindfulness-based cognitive therapy in people with Parkinson’s disease

BackgroundPeople with Parkinson’s disease (PD) are very sensitive to the effects of stress. The prevalence of stress-related neuropsychiatric symptoms is high, and acute stress worsens motor symptoms. Animal studies suggest that chronic stress may accelerate disease progression, but evidence for this in humans is lacking. Mindfulness-based interventions (MBIs) train participants to focus on the present moment, on purpose and without judgement. Previous studies suggest that MBIs may alleviate stress and reduce depression and anxiety in PD. We aim to demonstrate the efficacy of Mindfulness-Based Cognitive Therapy (MBCT) as a non-pharmacologic treatment strategy for neuropsychiatric (and motor) symptoms in PD, and to identify the mechanisms underlying stress and stress reduction in PD.MethodsIn a prospective randomized controlled trial (RCT), we investigate whether 8 weeks of MBCT, as compared to care as usual, can reduce symptoms of anxiety and depression in people with PD. We aim to include 124 PD patients, who experience mild-moderate symptoms of anxiety and depression, are eligible for magnetic resonance imaging (MRI) and naïve to mindfulness, and who have a disease duration ≤ 10 years. Every participant is followed for 12 months. Clinical and biochemical assessments take place at baseline (T0), after 2 months (T1), and after 12 months (T2); MRI assessments take place at T0 and T2. Our primary outcome is the total score on the Hospital Anxiety and Depression Scale (HADS) at T1, while correcting for the HADS score at T0, age, and gender. Beyond testing the effects of MBCT on symptoms of anxiety and depression in PD, we explore whether MBCT: (1) has an effect on motor symptom severity, (2) influences cerebral and biochemical markers of stress, and (3) leads to a change in biomarkers of PD progression.DiscussionMIND-PD is one of the first RCTs with a 1-year follow-up to investigate the effects of MBCT on symptoms of anxiety and depression in PD, and to explore possible mechanisms underlying stress and stress reduction in PD. Insight into these mechanisms can pave the way to new treatment methods in the future.Trial registrationClinicalTrials.gov, NCT05779137. Registered on 12 January 2023.

Interocular asymmetry of retinal change in Parkinson's disease

PurposeTo investigate interocular asymmetry (IA) of retinal structure and vessel density in patients with Parkinson's disease (PD) and normal controls (NC). MethodsSeventy-eight subjects including 40 PD patients and 38 NC had completed optical coherence tomography angiography (OCTA) and neurological examinations for three rating scales (UPDRS-III, MMSE and MoCA). The IA was calculated by the absolute value of difference in right and left eyes. The IA of thickness in macular ganglion cell inner-plexiform layer (GCIPL), peripapillary retinal nerve fiber layer (pRNFL), and vessel density of superficial capillary plexus (SCP), deep capillary plexus (DCP), radial peripapillary capillary (RPC) were obtained from OCTA. ResultsThe motor-symptom-onset side of eyes showed lower vessel density in parafovea of SCP (51.09 ± 3.46 vs 49.81 ± 4.16, P = 0.03) and superior hemi of perifovea DCP (49.55 ± 5.81 vs 47.33 ± 5.71, = 0.04). The PD patients showed larger IA in thickness of superior half of pRNFL (5.27 [2.67, 10.87] vs 3.08 [1.62, 5.61], P = 0.02) and parafovea GCIPL (2.40[1.25, 6.35] vs 1.40[0.50, 2.45], P = 0.02). No significant interocular asymmetry was found in vessel density between PD and NC. A higher UPDRS-III scale was associated with larger IA in GCIPL (β = 0.093, P = 0.001) and smaller IA in DCP (β = −0.065, P = 0.037). ConclusionThe motor-symptom-onset side of eyes showed more severe loss of macular vessel density than the other side of eyes. The PD patients showed asymmetrical structural change in GCIPL and pRNFL, which showed the potential as the diagnostic biomarker for PD.