Alzheimer’s disease (AD) is a slowly progressing disease for which there is no known therapeutic cure at present. Ongoing research around the world is actively engaged in the quest for identifying markers that can help predict the future cognitive state of individuals so that measures can be taken to prevent the onset or arrest the progression of the disease. Researchers are interested in both biological and neuropsychological markers that can serve as good predictors of the future cognitive state of individuals. The goal of this study is to identify non-invasive, inexpensive markers and develop neural network models that learn the relationship between those markers and the future cognitive state. To that end, we use the renowned Alzheimer’s Disease Neuroimaging Initiative (ADNI) data for a handful of neuropsychological tests to train Recurrent Neural Network (RNN) models to predict future neuropsychological test results and Multi-Level Perceptron (MLP) models to diagnose the future cognitive states of trial participants based on those predicted results. The results demonstrate that the predicted cognitive states match the actual cognitive states of ADNI test subjects with a high level of accuracy. Therefore, this novel two-step technique can serve as an effective tool for the prediction of Alzheimer’s disease progression. The reliance of the results on inexpensive, non-invasive tests implies that this technique can be used in countries around the world including those with limited financial resources.

中文翻译：

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使用神经心理学测试早期检测阿尔茨海默病：使用神经网络的预测-诊断方法

阿尔茨海默病 (AD) 是一种进展缓慢的疾病，目前尚无已知的治愈方法。世界各地正在进行的研究正在积极寻求识别有助于预测个人未来认知状态的标记，以便采取措施预防疾病的发作或阻止疾病的进展。研究人员对可以作为个人未来认知状态的良好预测指标的生物学和神经心理学标记感兴趣。本研究的目标是识别非侵入性、廉价的标记，并开发神经网络模型来学习这些标记与未来认知状态之间的关系。为此，我们使用著名的阿尔茨海默氏病神经影像学倡议 (ADNI) 数据进行少量神经心理学测试，以训练循环神经网络 (RNN) 模型来预测未来的神经心理学测试结果和多级感知器 (MLP) 模型来诊断未来的认知试验状态参与者根据这些预测结果。结果表明，预测的认知状态与 ADNI 测试对象的实际认知状态高度匹配。因此，这种新颖的两步技术可以作为预测阿尔茨海默病进展的有效工具。结果对廉价、非侵入性测试的依赖意味着该技术可用于世界各国，包括那些财政资源有限的国家。