记忆门诊患者的常规脑磁图检查：一种机器学习方法。
Routine magnetoencephalography in memory clinic patients: A machine learning approach.
作者信息
Gouw Alida A, Hillebrand Arjan, Schoonhoven Deborah N, Demuru Matteo, Ris Peterjan, Scheltens Philip, Stam Cornelis J
机构信息
Alzheimer Center and Department of Neurology, VU University medical center, Amsterdam UMC Amsterdam The Netherlands.
Department of Clinical Neurophysiology and MEG Center Neuroscience Campus Amsterdam VU University Medical Center Amsterdam UMC Amsterdam The Netherlands.
出版信息
Alzheimers Dement (Amst). 2021 Sep 18;13(1):e12227. doi: 10.1002/dad2.12227. eCollection 2021.
INTRODUCTION
We report the routine application of magnetoencephalography (MEG) in a memory clinic, and its value in the discrimination of patients with Alzheimer's disease (AD) dementia from controls.
METHODS
Three hundred sixty-six patients visiting our memory clinic underwent MEG recording. Source-reconstructed MEG data were visually assessed and evaluated in the context of clinical findings and other diagnostic markers. We analyzed the diagnostic accuracy of MEG spectral measures in the discrimination of individual AD dementia patients (n = 40) from subjective cognitive decline (SCD) patients (n = 40) using random forest models.
RESULTS
Best discrimination was obtained using a combination of relative theta and delta power (accuracy 0.846, sensitivity 0.855, specificity 0.837). The results were validated in an independent cohort. Hippocampal and thalamic regions, besides temporal-occipital lobes, contributed considerably to the model.
DISCUSSION
MEG has been implemented successfully in the workup of memory clinic patients and has value in diagnostic decision-making.
引言
我们报告了脑磁图（MEG）在记忆门诊的常规应用，以及其在区分阿尔茨海默病（AD）痴呆患者与对照者方面的价值。
方法
366名到我们记忆门诊就诊的患者接受了MEG记录。对源重建的MEG数据进行了视觉评估，并结合临床发现和其他诊断标志物进行了评估。我们使用随机森林模型分析了MEG频谱测量在区分个体AD痴呆患者（n = 40）与主观认知下降（SCD）患者（n = 40）方面的诊断准确性。
结果
使用相对θ波和δ波功率的组合获得了最佳区分效果（准确率0.846，敏感性0.855，特异性0.837）。结果在一个独立队列中得到了验证。除颞枕叶外，海马体和丘脑区域对该模型有很大贡献。
讨论
MEG已成功应用于记忆门诊患者的检查工作，并在诊断决策中具有价值。
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