Resilience in neuroscience generally refers to an individual’s capacity to counteract the adverse effects of a neuropathological condition. While resilience mechanisms in Alzheimer’s disease are well-investigated, knowledge regarding its quantification, neurobiological underpinnings, network adaptations, and long-term effects in Parkinson’s disease is limited. Our study involved 151 Parkinson’s patients from the Parkinson’s Progression Marker Initiative Database with available Magnetic Resonance Imaging, Dopamine Transporter Single-Photon Emission Computed Tomography scans, and clinical information. We used an improved prediction model linking neuropathology to symptom severity to estimate individual resilience levels. Higher resilience levels were associated with a more active lifestyle, increased grey matter volume in motor-associated regions, a distinct structural connectivity network and maintenance of relative motor functioning for up to a decade. Overall, the results indicate that relative maintenance of motor function in Parkinson’s patients may be associated with greater neuronal substrate, allowing higher tolerance against neurodegenerative processes through dynamic network restructuring.

神经科学中的复原力通常是指个体抵抗神经病理状况不利影响的能力。虽然阿尔茨海默病的恢复机制得到了充分研究，但有关其量化、神经生物学基础、网络适应和帕金森病的长期影响的知识仍然有限。我们的研究涉及来自帕金森病进展标记计划数据库的 151 名帕金森病患者，并提供可用的磁共​​振成像、多巴胺转运蛋白单光子发射计算机断层扫描和临床信息。我们使用改进的预测模型将神经病理学与症状严重程度联系起来，以估计个体的恢复水平。较高的复原力水平与更积极的生活方式、运动相关区域灰质体积的增加、独特的结构连接网络以及长达十年的相对运动功能的维持有关。总体而言，结果表明帕金森病患者运动功能的相对维持可能与更大的神经元基质有关，从而通过动态网络重组对神经退行性过程具有更高的耐受性。