Stone David B, Ryman Sephira G, Hartman Alexandra P, Wertz Christopher J, Vakhtin Andrei A
AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd., and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics.
The Mind Research Network, Lovelace Biomedical Research Institute, Albuquerque, NM, United States.
Front Aging Neurosci. 2021 Jul 23;13:711579. doi: 10.3389/fnagi.2021.711579. eCollection 2021.
Identifying biomarkers that can assess the risk of developing Alzheimer's Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients.
识别能够评估患阿尔茨海默病(AD)风险的生物标志物仍然是一项重大挑战。在本研究中,我们调查了34名后来转化为AD的轻度认知障碍(MCI)患者和53名病情稳定的MCI患者的脑白质完整性水平。我们使用扩散张量成像(DTI)和自动纤维定量分析来获取20条主要白质束的扩散特性。为了确定哪些白质束和扩散测量指标与AD转化最为相关,我们使用支持向量机(SVM)根据每条白质束的扩散特性分别对AD转化型和非转化型MCI患者进行分类。我们发现,来自七条白质束的扩散率测量指标可预测AD转化,其中轴向扩散率是最具预测性的扩散测量指标。进一步分析表明,右侧扣带回海马束的中央和海马旁终末区域、右侧额枕下束的中央区域以及左侧下纵束的后部和前部区域的白质变化是MCI转化为AD的最佳预测指标。基于这些白质束区域的支持向量机准确率达到了0.75。这些发现为MCI患者提供了更多潜在的AD风险生物标志物。