Predicting the Progression of Mild Cognitive Impairment to Alzheimer's Dementia Using Recurrent Neural Networks With a Series of Neuropsychological Tests

Abstract

Background and purpose: The prevalence of Alzheimer's dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments for this neurodegenerative disease have been established. Predicting AD is therefore becoming more important, because early diagnosis is the best way to prevent its onset and delay its progression.

Methods: Mild cognitive impairment (MCI) is the stage between normal cognition and AD, with large variations in its progression. The disease can be effectively managed by accurately predicting the probability of MCI progressing to AD over several years. In this study we used the Alzheimer's Disease Neuroimaging Initiative dataset to predict the progression of MCI to AD over a 3-year period from baseline. We developed and compared various recurrent neural network (RNN) models to determine the predictive effectiveness of four neuropsychological (NP) tests and magnetic resonance imaging (MRI) data at baseline.

Results: The experimental results confirmed that the Preclinical Alzheimer's Cognitive Composite score was the most effective of the four NP tests, and that the prediction performance of the NP tests improved over time. Moreover, the gated recurrent unit model exhibited the best performance among the prediction models, with an average area under the receiver operating characteristic curve of 0.916.

Conclusions: Timely prediction of progression from MCI to AD can be achieved using a series of NP test results and an RNN, both with and without using the baseline MRI data.

Keywords: Alzheimer's disease; dementia; mild cognitive impairment; neural network models; neuropsychological tests.

Fig. 1. Study population and overall study process. AUC, area under the receiver operating characteristic curve; GRU, gated recurrent unit; LSTM, long short-term memory; MCI, mild cognitive impairment; RNN, recurrent neural network.

Fig. 2. Model overview. The input for each time point consists of NP test results of baseline, 12 months, and 24 months, and all inputs include CD and MRI data of baseline. CD, clinical data; MRI, magnetic resonance imaging; NP, neuropsychological; RNN, recurrent neural network.

Fig. 3. Performance comparisons for each model with varying NP tests (with data in the baseline). ADAS13, 13-item cognition subscale of the Alzheimer’s Disease Assessment Scale; AUC, area under the receiver operating characteristic curve; GRU, gated recurrent unit; LSTM, long short-term memory; M12, 12 months; M24, 24 months; M36, 36 months; MMSE, Mini-Mental State Examination; mPACC, modified Preclinical Alzheimer Cognitive Composite; NP, neuropsychological; RNN, recurrent neural network.

Fig. 4. Performance comparisons for each model with varying NP tests (without data in the baseline). ADAS13, 13-item cognition subscale of the Alzheimer’s Disease Assessment Scale; AUC, area under the receiver operating characteristic curve; GRU, gated recurrent unit; LSTM, long short-term memory; M12, 12 months; M24, 24 months; M36, 36 months; MMSE, Mini-Mental State Examination; mPACC, modified Preclinical Alzheimer Cognitive Composite; NP, neuropsychological; RNN, recurrent neural network.