NPJ Parkinson's Disease最新文献

Impulse control disorders (ICDs) affect up to 45% of Parkinson's disease (PD) patients, yet their neural mechanisms remain unclear. Using multimodal PET and resting-state fMRI in 23 PD patients (11 PDICD + , 12 PD-ICD-) and 14 healthy controls, we identified specific brain pathways underlying ICDs. PDICD+ patients showed steeper delay discounting and altered functional connectivity, including enhanced posterior parietal coupling within executive networks and disrupted salience-executive interactions. Critically, aberrant right supplementary motor area-amygdala connectivity was linked to ICD severity and decisional impulsivity. Path analysis revealed that increased SMA 5-HT₂ₐ receptor availability was associated with enhanced SMA-amygdala coupling, which in turn was positively associated with ICD symptoms. By linking serotonergic dysfunction to disrupted motor-limbic networks and impulsive behavior, this study identifies targetable pathways for managing a common non-motor complication of PD.

While the level of α-synuclein oligomers (α-SOs) in the CSF of patients with Parkinson's disease (PD) is consistently increased, its pathogenic role in PD remains poorly understood. This study focuses on the role of CSF-derived α-SOs in PD pathology. We demonstrated that CSF-derived α-synuclein enters the brain via perivascular spaces, which was more abundant in the olfactory bulb (OB) than in the substantia nigra (SN). We also found that neuroinflammation was more pronounced in the OB than in the SN following α-SOs injection. α-SOs-treated mice exhibited an early and persistent loss of dopaminergic (DA) neurons in the OB, along with olfactory deficit. Conversely, DA neuron loss in the SN occurred later and was associated with motor dysfunction. Furthermore, reducing α-SOs dose alleviated OB pathology. These findings suggest that perivascular spread of CSF-derived α-SOs induces region-specific PD-like pathology, indicating that removing CSF-derived α-SOs could slow PD progression.

Deep brain stimulation (DBS) is established for Parkinson's disease (PD), but conventional DBS (cDBS) may yield suboptimal symptom control and side effects, particularly on gait. Adaptive DBS (aDBS), adjusting stimulation to subthalamic beta activity, may offer superior outcomes, though programming remains incompletely defined. Between January and April 2025, we offered dual threshold aDBS to 20 consecutive PD patients with chronic cDBS and a Percept neurostimulator. Nine were eligible; exclusions were due to signal artifacts, absence of a distinct beta peak, or stimulation settings incompatible with aDBS. By July 2025, five remained on chronic aDBS, one reverted to cDBS by preference, and three were still in optimization. In this manuscript, we outline our aDBS programming principles and preliminary clinical efficacy. On unblinded MDS-UPDRS III, aDBS yielded a ~35% greater motor improvement than cDBS, with gait showing the most consistent benefits. Dual threshold aDBS appears clinically advantageous, though current technical and programming constraints may limit widespread adoption.

Cognitive decline is a major non-motor complication in early Parkinson's disease (PD), but predicting its progression remains challenging. Using data from 193 participants in the Early Parkinson's Disease Longitudinal Singapore (PALS) cohort, we evaluated whether repeated blood biomarker measurements (baseline, year 3, year 5)-neurofilament light chain (NfL) and total tau (t-tau)-could improve prediction of cognitive decline, defined as a one-point annual or sustained two-year drop in Montreal Cognitive Assessment scores. We applied three variable selection methods and five machine learning models across seven feature sets. Overall, 23% of participants experienced cognitive decline over five years. The XGBoost model trained on Random Forest-selected variables achieved the highest performance (AUC = 0.806), a substantial improvement over the baseline-only model (AUC = 0.560). Key predictors included diastolic blood pressure and summaries of t-tau and NfL. Time-varying biomarkers improved predictions over baseline data alone, supporting their integration with machine learning for early cognitive risk assessment in PD.

Cognitive impairment varies across sporadic Parkinson's disease (PD) and the common genetic subtypes glucocerebrosidase (GBA1) and leucine-rich repeat kinase 2 (LRRK2) PD and is influenced by Apolipoprotein E (APOE) polymorphisms and Alzheimer's disease (AD) co-pathology. However, the effects of APOE genotype, Aβ42 and tau on cognitive decline across these PD subtypes remain unclear. Using pooled longitudinal data across the PPMI and CPP cohorts, we examined the effects of APOE genotype and cerebrospinal fluid (CSF) Aβ42 and tau on cognitive decline across sporadic PD, GBA1-PD, LRRK2-PD, and healthy control (HC) subjects. Whereas in sporadic PD the APOE ε4 allele was associated with faster cognitive decline than APOE ε3 or ε2 alleles, no APOE effect was observed in GBA1-PD or LRRK2-PD. While lower baseline CSF Aβ42 was linked to faster cognitive decline in all groups, higher baseline CSF pTau was associated with faster decline in sporadic PD and LRRK2-PD but not in GBA1-PD. These findings underscore differential vulnerabilities to APOE genotype and AD-related biomarkers among PD subtypes, a critical consideration for clinical trials targeting cognitive decline in PD.