He Bing, Zhang Shu, Risacher Shannon L, Saykin Andrew J, Yan Jingwen
Biomedical Engineering and Informatics, Indiana University Indianapolis, 535 W Michigan St., Indianapolis, Indiana 46202, USA,
Department of Computer Science, University of California Los Angeles 404 Westwood Plaza Engineering IV, Los Angeles, CA 90095, USA,
Pac Symp Biocomput. 2025;30:664-674. doi: 10.1142/9789819807024_0047.
Alzheimer's disease (AD) is a neurodegenerative disorder that results in progressive cognitive decline but without any clinically validated cures so far. Understanding the progression of AD is critical for early detection and risk assessment for AD in aging individuals, thereby enabling initiation of timely intervention and improved chance of success in AD trials. Recent pseudotime approach turns cross-sectional data into "faux" longitudinal data to understand how a complex process evolves over time. This is critical for Alzheimer, which unfolds over the course of decades, but the collected data offers only a snapshot. In this study, we tested several state-of-the-art pseudotime approaches to model the full spectrum of AD progression. Subsequently, we evaluated and compared the pseudotime progression score derived from individual imaging modalities and multi-modalities in the ADNI cohort. Our results showed that most existing pseudotime analysis tools do not generalize well to the imaging data, with either flipped progression score or poor separation of diagnosis groups. This is likely due to the underlying assumptions that only stand for single cell data. From the only tool with promising results, it was observed that all pseudotime, derived from either single imaging modalities or multi-modalities, captures the progressiveness of diagnosis groups. Pseudotime from multi-modality, but not the single modalities, confirmed the hypothetical temporal order of imaging phenotypes. In addition, we found that multi-modal pseudotime is mostly driven by amyloid and tau imaging, suggesting their continuous changes along the full spectrum of AD progression.
阿尔茨海默病（AD）是一种神经退行性疾病，会导致进行性认知衰退，但迄今为止尚无任何经过临床验证的治愈方法。了解AD的进展对于衰老个体中AD的早期检测和风险评估至关重要，从而能够及时开展干预并提高AD试验成功的几率。最近的伪时间方法将横断面数据转化为“虚拟”纵向数据，以了解一个复杂过程是如何随时间演变的。这对于在数十年间发展的阿尔茨海默病至关重要，因为收集到的数据只是一个快照。在本研究中，我们测试了几种最先进的伪时间方法来模拟AD进展的全谱。随后，我们在ADNI队列中评估并比较了从个体成像模态和多模态得出的伪时间进展分数。我们的结果表明，大多数现有的伪时间分析工具对成像数据的通用性不佳，要么进展分数颠倒，要么诊断组分离不佳。这可能是由于其仅适用于单细胞数据的潜在假设。从唯一有前景结果的工具中观察到，从单一成像模态或多模态得出的所有伪时间都捕捉到了诊断组的进展性。来自多模态而非单模态的伪时间证实了成像表型的假设时间顺序。此外，我们发现多模态伪时间主要由淀粉样蛋白和tau成像驱动，表明它们在AD进展的全谱中持续变化。
