Mukherji Devarshi, Mukherji Manibrata, Mukherji Nivedita
University of Michigan, Ann Arbor, MI, USA.
, Rochester, MI, USA.
Brain Inform. 2022 Sep 27;9(1):23. doi: 10.1186/s40708-022-00169-1.
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.
阿尔茨海默病（AD）是一种进展缓慢的疾病，目前尚无已知的治疗方法。世界各地正在进行的研究积极致力于寻找能够帮助预测个体未来认知状态的标志物，以便采取措施预防疾病的发作或阻止其进展。研究人员对生物标志物和神经心理学标志物都感兴趣，这些标志物可以很好地预测个体的未来认知状态。本研究的目的是识别非侵入性、低成本的标志物，并开发神经网络模型来学习这些标志物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病神经影像学倡议（ADNI）的数据进行一系列神经心理学测试，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，并使用多层感知器（MLP）模型根据这些预测结果诊断试验参与者未来的认知状态。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。结果对低成本、非侵入性测试的依赖意味着该技术可在世界各国使用，包括那些财政资源有限的国家。
