Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features.

Patients with Parkinson's Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD.

Motor symptoms in Parkinson's disease are related to the interplay between cortical curvature and thickness.

Introduction: Brain atrophy in Parkinson's disease occurs to varying degrees in different brain regions, even at the early stage of the disease. While cortical morphological features are often considered independently in structural brain imaging studies, research on the co-progression of different cortical morphological measurements could provide new insights regarding the progression of PD. This study's aim was to examine the interplay between cortical curvature and thickness as a function of PD diagnosis, motor symptoms, and cognitive performance.

Does outpatient palliative care improve patient-centered outcomes in Parkinson's disease: Rationale, design, and implementation of a pragmatic comparative effectiveness trial.

Patients with Parkinson's disease and related disorders (PDRD) and their families have considerable unmet needs including non-motor symptom management, caregiver support, spiritual wellbeing, advance care planning, and end-of-life care. There is increasing interest in applying palliative care (PC) models to better meet these needs. While PC has been shown to improve care and quality of life (QOL) for people with cancer and heart failure, few studies have evaluated the role of PC for people with PDRD.