Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease
Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease
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DOI：
10.3389/fpsyt.2020.00360
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发表时间：
2020-05
影响因子：
4.7
通讯作者：
Fabian Wagner;M. Duering;B. Gesierich;C. Enzinger;S. Ropele;P. Dal-Bianco;Florian Mayer;R. Schmidt;M. Koini
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文献类型：
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作者：
Fabian Wagner;M. Duering;B. Gesierich;C. Enzinger;S. Ropele;P. Dal-Bianco;Florian Mayer;R. Schmidt;M. Koini
对灰质形态的共同变异的研究可能会定义神经退行性疾病，超出了通过局部脑容量的孤立评估所能检测到的范围。因此，我们的目标是 (1) 识别区分阿尔茨海默病 (AD) 患者和健康对照 (HC) 的 SCN（结构协方差网络），(2) 通过比较和现有标记研究其诊断准确性，以及 (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 20 个 SCN 中识别判别网络。该算法在 104 名 AD 患者和 104 名年龄匹配的 HC 的主要样本上进行了训练，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了验证。 20 个 SCN 中只有两个对 AD 和对照之间的区分有显着贡献。这些是颞视 SCN 和次级体感 SCN。他们的诊断准确率在原始队列中为 74%，在独立样本中为 80%。 SCN 的诊断准确性与传统体积 MRI 标记（包括全脑体积和海马体积）的诊断准确性相当。与传统 MRI 标记相比，SCN 并未显着提高诊断准确性。我们发现颞 SCN 与基线的言语记忆相关。没有发现与认知功能的其他关联。 SCN 未能预测平均 18 个月内认知能力下降的过程。我们的结论是 SCN 具有诊断潜力，但超出传统 MRI 标记的诊断信息增益有限。
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.