Department of Brain and Cognitive Sciences and Center for Visual Science at the University of Rochester, Rochester, NY 14627, USA.
Neurobiol Aging. 2011 Oct;32(10):1742-55. doi: 10.1016/j.neurobiolaging.2009.11.010. Epub 2009 Dec 14.
Predicting which individuals will progress to Alzheimer's disease (AD) is important in both clinical and research settings. We used brain Event-Related Potentials (ERPs) obtained in a perceptual/cognitive paradigm with various processing demands to predict which individual Mild Cognitive Impairment (MCI) subjects will develop AD versus which will not. ERP components, including P3, memory "storage" component, and other earlier and later components, were identified and measured by Principal Components Analysis. When measured for particular task conditions, a weighted set of eight ERP component_conditions performed well in discriminant analysis at predicting later AD progression with good accuracy, sensitivity, and specificity. The predictions for most individuals (79%) had high posterior probabilities and were accurate (88%). This method, supported by a cross-validation where the prediction accuracy was 70-78%, features the posterior probability for each individual as a method of determining the likelihood of progression to AD. Empirically obtained prediction accuracies rose to 94% when the computed posterior probabilities for individuals were 0.90 or higher (which was found for 40% of our MCI sample).
预测哪些个体将发展为阿尔茨海默病（AD）在临床和研究环境中都很重要。我们使用具有不同处理需求的感知/认知范式中的大脑事件相关电位（ERPs）来预测哪些轻度认知障碍（MCI）患者将发展为 AD，哪些不会。通过主成分分析确定和测量 ERP 成分，包括 P3、记忆“存储”成分和其他早期和晚期成分。当针对特定任务条件进行测量时，一组加权的 8 个 ERP 成分条件在判别分析中表现良好，具有较高的准确性、敏感性和特异性，可以很好地预测 AD 的后期进展。对于大多数个体（79%），预测的后验概率很高，并且准确（88%）。该方法通过交叉验证得到支持，其中预测准确率为 70-78%，通过为每个个体计算后验概率作为确定向 AD 进展的可能性的方法。当个体的计算后验概率为 0.90 或更高时（在我们的 MCI 样本中占 40%），通过经验获得的预测准确率上升到 94%。
