Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks.
Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks.
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DOI：
10.1038/s41380-022-01886-z
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发表时间：
2023-06
影响因子：
11
通讯作者：
Hosseini, S. M. Hadi
中科院分区：
文献类型：
--
作者：
Avelar-Pereira, Barbara;Belloy, Michael E.;O'Hara, Ruth;Hosseini, S. M. Hadi
阿尔茨海默病 (AD) 是一种多因素和异质性疾病，这使得早期检测成为一项挑战。研究尝试结合生物标志物来改善 AD 检测并预测进展。然而，大多数现有的工作报告都是并行结果或比较标准化结果，但没有同时分析数据。我们测试了一个多维网络框架，应用于 ADNI 的 490 名受试者（认知正常 [CN] = 147；轻度认知障碍 [MCI] = 287；AD = 56），以创建能够捕获 AD 异质性和进展的单一模型。首先，我们为结构磁共振成像、β淀粉样蛋白正电子发射断层扫描、脑脊液、认知和遗传学数据构建了主题相似性网络，然后应用多层社区检测来寻找跨模式具有共同相似性的群体。对个体进行纵向随访，AD 受试者平均随访 4.5 年。我们的研究结果表明，多层社区检测可以准确识别当前和未来的 AD（约 90%），并且还能够识别临床误诊的病例。在所有发展为 AD 或恢复为 CN 的 MCI 参与者中，多层模型分别正确识别了 90.8% 和 88.5% 的病例。我们在整个样本中以及在检查无 AD 病理学受试者（即淀粉样蛋白阴性）的多模态数据时观察到类似的亚型。最后，这些结果还使用独立的测试集进行了验证。总之，多层框架成功地检测了 AD，并通过识别具有相似的神经、认知、病理和遗传学信息多学科特征的亚型，提供了对该疾病异质性的独特见解。
Alzheimer’s disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-β positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied multilayer community detection to find groups with shared similarities across modalities. Individuals were also followed-up longitudinally, with AD subjects having, on average, 4.5 years of follow-up. Our findings show that multilayer community detection allows for accurate identification of present and future AD (≈90%) and is also able to identify cases that were misdiagnosed clinically. From all MCI participants who developed AD or reverted to CN, the multilayer model correctly identified 90.8% and 88.5% of cases respectively. We observed similar subtypes across the full sample and when examining multimodal data from subjects with no AD pathology (i.e., amyloid negative). Finally, these results were also validated using an independent testing set. In summary, the multilayer framework is successful in detecting AD and provides unique insight into the heterogeneity of the disease by identifying subtypes that share similar multidisciplinary profiles of neurological, cognitive, pathological, and genetics information.
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影响因子：
3.2
作者：
Crane, Paul K.;Carle, Adam;Gibbons, Laura E.;Insel, Philip;Mackin, R. Scott;Gross, Alden;Jones, Richard N.;Mukherjee, Shubhabrata;Curtis, S. McKay;Harvey, Danielle;Weiner, Michael;Mungas, Dan
通讯作者：
Mungas, Dan
影响因子：
15.9
作者：
Ewers M;Sperling RA;Klunk WE;Weiner MW;Hampel H
通讯作者：
Hampel H
影响因子：
14.5
作者：
Dong, Aoyan;Toledo, Jon B.;Davatzikos, Christos
通讯作者：
Davatzikos, Christos
影响因子：
4
作者：
Edmonds, Emily C.;Delano-Wood, Lisa;Bondi, Mark W.
通讯作者：
Bondi, Mark W.
影响因子：
--
作者：
Aizenstein, Howard Jay;Nebes, Robert D.;Saxton, Judith A.;Price, Julie C.;Mathis, Chester A.;Tsopelas, Nicholas D.;Ziolko, Scott K.;James, Jeffrey A.;Snitz, Beth E.;Houck, Patricia R.;Bi, Wenzhu;Cohen, Ann D.;Lopresti, Brian J.;DeKosky, Steven T.;Halligan, Edythe M.;Klunk, William E.
通讯作者：
Klunk, William E.