平衡帕金森病进展的神经成像模型的实用性和复杂性

IF 8.2 1区医学 Q1 NEUROSCIENCES

NPJ Parkinson's Disease Pub Date : 2025-08-27 DOI:10.1038/s41531-025-01125-6

Valtteri Kaasinen, Thilo van Eimeren

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引用次数: 0

摘要

可靠的进展模型对于帕金森病的临床决策和试验设计至关重要。我们讨论了PET和SPECT数据中的线性、指数和s形模式，强调了生物标志物和临床轨迹之间的不匹配。我们提出了更具适应性的建模策略，以改善患者分层，支持试验结果，并将成像生物标志物与现实世界的疾病复杂性相结合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Balancing practicality and complexity in neuroimaging models of Parkinson’s disease progression

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Balancing practicality and complexity in neuroimaging models of Parkinson’s disease progression

Reliable progression models are essential for clinical decision-making and trial design in Parkinson’s disease. We discuss linear, exponential, and sigmoidal patterns in PET and SPECT data, emphasizing the mismatch between biomarker and clinical trajectories. We propose more adaptable modeling strategies to improve patient stratification, support trial outcomes, and align imaging biomarkers with real-world disease complexity.

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来源期刊

NPJ Parkinson's Disease Medicine-Neurology (clinical)

CiteScore

9.80

自引率

5.70%

发文量

156

审稿时长

11 weeks

期刊介绍： npj Parkinson's Disease is a comprehensive open access journal that covers a wide range of research areas related to Parkinson's disease. It publishes original studies in basic science, translational research, and clinical investigations. The journal is dedicated to advancing our understanding of Parkinson's disease by exploring various aspects such as anatomy, etiology, genetics, cellular and molecular physiology, neurophysiology, epidemiology, and therapeutic development. By providing free and immediate access to the scientific and Parkinson's disease community, npj Parkinson's Disease promotes collaboration and knowledge sharing among researchers and healthcare professionals.