He Hengda, Razlighi Qolamreza R, Gazes Yunglin, Habeck Christian, Stern Yaakov
Cognitive Neuroscience Division, Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
Quantitative Neuroimaging Laboratory, Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
bioRxiv. 2024 Dec 13:2024.12.10.627818. doi: 10.1101/2024.12.10.627818.
The deposition of amyloid-β (Aβ) protein in the human brain is a hallmark of Alzheimer's disease and is related to cognitive decline. However, the relationship between early Aβ deposition and future cognitive impairment remains poorly understood, particularly concerning its spatial distribution and network-level effects. Here, we employed a cross-validated machine learning approach and investigated whether integrating subject-specific brain connectome information with Aβ burden measures improves predictive validity for subsequent cognitive decline. Baseline regional Aβ pathology measures from positron emission tomography (PET) imaging predicted prospective cognitive decline. Incorporating structural connectome, but not functional connectome, information into the Aβ measures improved predictive performance. We further identified a neuropathological signature pattern linked to future cognitive decline, which was validated in an independent cohort. These findings advance our understanding of how Aβ pathology relates to brain networks and highlight the potential of network-based metrics for Aβ-PET imaging to identify individuals at higher risk of cognitive decline.
淀粉样β蛋白（Aβ）在人脑中的沉积是阿尔茨海默病的一个标志，并且与认知能力下降有关。然而，早期Aβ沉积与未来认知障碍之间的关系仍知之甚少，尤其是关于其空间分布和网络水平的影响。在此，我们采用了一种交叉验证的机器学习方法，并研究了将个体特异性脑连接组信息与Aβ负荷测量相结合是否能提高对后续认知能力下降的预测效度。来自正电子发射断层扫描（PET）成像的基线区域Aβ病理测量预测了前瞻性认知能力下降。将结构连接组而非功能连接组信息纳入Aβ测量中可提高预测性能。我们进一步确定了一种与未来认知能力下降相关的神经病理特征模式，该模式在一个独立队列中得到了验证。这些发现推进了我们对Aβ病理与脑网络之间关系的理解，并突出了基于网络的Aβ-PET成像指标在识别认知能力下降风险较高个体方面的潜力。
