Wagner Fabian, Duering Marco, Gesierich Benno G, Enzinger Christian, Ropele Stefan, Dal-Bianco Peter, Mayer Florian, Schmidt Reinhold, Koini Marisa
Department of Neurology, Medical University of Graz, Graz, Austria.
Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
Front Psychiatry. 2020 May 5;11:360. doi: 10.3389/fpsyt.2020.00360. eCollection 2020.
The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer's disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.
对灰质形态共享变异的研究可能会界定神经退行性疾病，这超出了仅通过孤立评估区域脑容量所能检测到的范围。因此，我们旨在：（1）识别能够区分阿尔茨海默病（AD）患者与健康对照（HC）的结构协方差网络（SCN）；（2）与既定标志物相比，研究其诊断准确性，并高于既定标志物；（3）确定它们是否与认知能力相关。我们应用随机森林算法从一组20个SCN中识别出具有区分性的网络。该算法在由104名AD患者和104名年龄匹配的HC组成的主要样本上进行训练，然后在来自另一个中心的28名AD患者和28名对照的独立样本中进行验证。20个SCN中只有两个对AD与对照之间的区分有显著贡献。它们是一个颞叶SCN和一个次级躯体感觉SCN。在原始队列中，它们的诊断准确率为74%，在独立样本中为80%。SCN的诊断准确率与包括全脑体积和海马体体积在内的传统容积MRI标志物相当。SCN并没有比传统MRI标志物显著提高诊断准确率。我们发现颞叶SCN与基线时的言语记忆相关。未观察到与其他认知功能的关联。SCN未能预测平均18个月内的认知衰退进程。我们得出结论，SCN具有诊断潜力，但超出传统MRI标志物的诊断信息增益有限。
