NPJ Parkinson's Disease最新文献

Parkinson’s Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.

Deep brain stimulation (DBS) can ameliorate motor symptoms in Parkinson’s disease (PD), but its mechanism remains unclear. This work constructs a multi-scale brain model using the fMRI data from 27 PD patients with subthalamic DBS and 30 healthy controls. The model fits microscopic coupling parameters in the cortico-basal ganglia-thalamic neural loop to match individual connectivity, finding the “push-pull” effect of basal ganglia network. Specifically, increased GABAergic projection into the thalamus from basal ganglia worsens rigidity, while reduced GABAergic projection within the cortex exacerbates bradykinesia, suggesting that the dopamine deficiency induces the chain coupling variations to “push” the network to an abnormal state. Conversely, DBS can alleviate rigidity by enhancing GABAergic projections within the basal ganglia, and improve bradykinesia by reducing cortical projections to basal ganglia, exhibiting that DBS “pulls” the network to a healthy state. This work combines the microscopic and macroscopic neural information for understanding PD and its treatment.

Alterations in subcortical brain regions are linked to motor and non-motor symptoms in Parkinson's disease (PD). However, associations between clinical expression and regional morphological abnormalities of the basal ganglia, thalamus, amygdala and hippocampus are not well established. We analyzed 3D T1-weighted brain MRI and clinical data from 2525 individuals with PD and 1326 controls from 22 global sources in the ENIGMA-PD consortium. We investigated disease effects using mass univariate and multivariate models on the medial thickness of 27,120 vertices of seven bilateral subcortical structures. Shape differences were observed across all Hoehn and Yahr (HY) stages, as well as correlations with motor and cognitive symptoms. Notably, we observed incrementally thinner putamen from HY1, caudate nucleus and amygdala from HY2, hippocampus, nucleus accumbens, and thalamus from HY3, and globus pallidus from HY4-5. Subregions of the thalami were thicker in HY1 and HY2. Largely congruent patterns were associated with a longer time since diagnosis and worse motor symptoms and cognitive performance. Multivariate regression revealed patterns predictive of disease stage. These cross-sectional findings provide new insights into PD subcortical degeneration by demonstrating patterns of disease stage-specific morphology, largely consistent with ongoing degeneration.

Resting tremor (RT) is a Parkinson’s disease (PD) symptom with an unclear relationship to the dopaminergic system. We analysed data from 432 subjects from the Parkinson’s Progression Markers Initiative, 57 additional PD patients and controls and 86 subjects referred for dopamine transporter single-photon emission computed tomography (DaT-SPECT). Caudate binding ratio (CBR), but not putamen binding ratio, was higher in RT patients. Furthermore, higher baseline CBR was linked to RT development. In the smaller cohorts, a 4–6 Hz oscillation-based metric from inertial sensors correlated with RT amplitude, distinguished controls from patients with reduced DaT binding and correlated with CBR in the latter group. In silico modelling uncovered that higher CBR in RT patients explained correlations between RT and DaT-SPECT found in several datasets, supporting a spurious origin for ipsilateral correlations between CBR and RT. These results suggest that caudate dopaminergic terminals integrity is a feature of RT with potential pathophysiological implications.

To identify circRNAs associated with Parkinson’s disease (PD) we leveraged two of the largest publicly available studies with longitudinal clinical and blood transcriptomic data. We performed a cross-sectional study utilizing the last visit of each participant (N = 1848), and a longitudinal analysis that included 1166 participants with at least two time points. We identified 192 differentially expressed circRNAs, with effects that were sustained during disease, in mutation carriers, and diverse ancestry. The 192 circRNAs were leveraged to distinguish between PD and healthy participants with a ROC AUC of 0.797. Further, 71 circRNAs were sufficient to distinguish between genetic PD (AUC₇₁ = 0.954) and, at-risk participants (AUC₇₁ = 0.929) and healthy controls, supporting that circRNAs have the potential to aid the diagnosis of PD. Finally, we identified five circRNAs highly correlated with symptom severity. Overall, we demonstrated that circRNAs play an important role in PD and can be clinically relevant to improve diagnostic and monitoring.

Individual variability exists in parkinsonian motor symptoms despite a similar degree of nigrostriatal dopamine depletion in Parkinson’s disease (PD), called motor reserve. We enrolled 397 patients newly diagnosed with PD who underwent dual-phase ¹⁸F-FP-CIT PET upon initial assessment. Individual motor reserve was estimated based on initial parkinsonian motor symptoms and striatal dopamine transporter availability using a residual model. Patients with low motor reserve (the lowest quartile group, n = 100) exhibited decreased uptake in the occipital region compared to those with high motor reserve (the highest quartile group, n = 100) on early-phase ¹⁸F-FP-CIT PET images. Patients with high motor reserve had a lower risk of conversion to dementia than the those with low motor reserve, whereas the effect of PD groups on the risk of dementia conversion was not mediated by occipital hypoperfusion. These findings suggest that cerebral hypoperfusion in the occipital region is associated with low motor reserve in patients with PD.

Calcium ion (Ca²⁺) homeostasis is crucial for neuron function and neurotransmission. This study focused on the actions mediated by the CB₁ receptor (CB₁R), the most abundant G protein-coupled receptor (GPCR) in central nervous system (CNS) neurons, over by the AT₁R, which is one of the few G protein-coupled CNS receptors able to regulate cytoplasmic Ca²⁺ levels. A functional interaction suggesting a direct association between these receptors was detected. AT₁-CB₁ receptor heteromers (AT₁CB₁Hets) were identified in HEK-293T cells by bioluminescence resonance energy transfer (BRET²). Functional interactions within the AT₁-CB₁ complex and their potential relevance in Parkinson’s disease (PD) were assessed. In situ proximity ligation assays (PLA) identified AT₁CB₁Hets in neurons, in which an important finding was that Ca²⁺ level increase upon AT₁R activation was reduced in the presence of cannabinoids acting on CB₁Rs. AT₁CB₁Het expression was quantified in samples from the 6-hydroxydopamine (6-OHDA) hemilesioned rat model of PD in which a lower expression of AT₁CB₁Hets was observed in striatal neurons from lesioned animals (versus non-lesioned). AT₁CB₁Het expression changed depending on both the lesion and the consequences of levodopa administration, i.e., dyskinesias versus lack of involuntary movements. A partial recovery in AT₁CB₁Het expression was detected in lesioned animals that developed levodopa-induced dyskinesias. These findings support the existence of a compensatory mechanism mediated by AT₁CB₁Hets that modulates susceptibility to levodopa-induced dyskinesias in PD. Therefore, cannabinoids may be useful in reducing calcium dyshomeostasis in dyskinesia.

Mitochondrial dysfunction plays an important role in Parkinson's disease (PD), with mitochondrial DNA copy number (mtDNA-CN) emerging as a potential marker for mitochondrial health. We investigated the links between blood mtDNA-CN and PD severity and risk using the Accelerating Medicines Partnership program for Parkinson's Disease dataset, replicating our results in the UK Biobank. Our findings reveal that reduced blood mtDNA-CN levels are associated with heightened PD risk and increased severity of motor symptoms and olfactory dysfunction. We estimated blood cell composition using complete blood cell profile when available or RNA-sequencing data as a surrogate. After adjusting for blood cell composition, the associations between mtDNA-CN and PD risk and clinical symptoms became non-significant. Bidirectional Mendelian randomization analysis also found no evidence of a direct causal relationship between blood mtDNA-CN and PD susceptibility. Hence peripheral inflammatory immune responses rather than mitochondrial dysfunction underpin these previously identified associations in PD.

This study aimed to identify potential markers that can predict Parkinson’s disease with mild cognitive impairment (PDMCI). We retrospectively collected general demographic data, clinically relevant scales, plasma samples, and neuroimaging data (T1-weighted magnetic resonance imaging (MRI) data as well as resting-state functional MRI [Rs-fMRI] data) from 173 individuals. Subsequently, based on the aforementioned multimodal indices, a support vector machine was employed to investigate the machine learning (ML) classification of PD patients with normal cognition (PDNC) and PDMCI. The performance of 29 classifiers was assessed based on various combinations of indicators. Results demonstrated that the optimal classifier in the validation set was composed by clinical + Rs-fMRI+ neurofilament light chain, exhibiting a mean Accuracy of 0.762, a mean area under curve of 0.840, a mean sensitivity of 0.745, along with a mean specificity of 0.783. The ML algorithm based on multimodal data demonstrated enhanced discriminative ability between PDNC and PDMCI patients.

Deep brain stimulation (DBS) is an established treatment for motor disorders like Parkinson’s disease, but its mechanisms and effects on neurons and networks are not fully understood, limiting research-driven progress. This review presents a framework that combines neurophysiological insights and translational research to enhance DBS therapy, emphasizing biomarkers, device technology, and symptom-specific neuromodulation. It also examines the role of animal research in improving DBS, while acknowledging challenges in clinical translation.