Connectome-based predictive modelling estimates individual cognitive status in Parkinson's disease

作者：Alexander Tobias Ysbæk-Nielsen · 发表于：Parkinsonism & Related Disorders · 年份：2024 · DOI：10.1016/j.parkreldis.2024.106020 · 被引用次数：9 · 研究领域：Functional Brain Connectivity Studies、Parkinson's Disease Mechanisms and Treatments、Neurological disorders and treatments

INTRODUCTION: The progressive nature of Parkinson's disease (PD) affords emphasis on accurate early-stage individual-level assessment of risk and intervention appropriateness. In PD, cognitive impairment (CI) may follow or precede motor symptoms but are generally underdetected. In addition to impeding daily functioning and quality of life, CIs increase the risk for later conversion to dementia, providing a pressing need to develop novel tools to detect and interpret them. Connectome-based predictive modelling (CPM) is an emerging machine-learning approach to individual prediction that holds translational promise due to its noninvasiveness and simple implementation. The aim of this study was to investigate CPM's potential to predict and understand CIs in PD. METHODS: Resting-state functional connectivity from 58 patients with PD of varying cognitive status was used to train a CPM-model to predict a global cognitive composite (GCC) score. The model was validated using cross-validation, permutation testing, and internal stability analyses. The combined predictive strength of two brain connectivity networks, positive and negative, directly and inversely correlated with GCC, respectively, was assessed. RESULTS: < .05. Separately, the positive and negative networks were similar in performance, rs ≥ .58, ps < .05, but varied in anatomical distribution. CONCLUSIONS: This study identified a connectome predictive of cognitive scores in PD, with features overlapping with established and...