Exploring the possibility for early detection of Alzheimer's disease with spatial-domain neural images

Abstract

Mild cognitive impairment (MCI) is an intermediate stage leading to Alzheimer's disease (AD). Diagnosis for MCI patients at an early stage can reduce the chances of developing into a severe condition for cognition. This study aims to identify the healthy control (HC) and MCI through the neural images in the specific time points during the mental tasks by the convolutional neural network (CNN). The signals were acquired by the functional near-infrared spectroscopy (fNIRS). 15 MCI patients and 9 HC subjects are employed in the experiment to perform the N-back task, Stroop task, and verbal fluency task (VFT), respectively. The neural images were generated by the brain map in the specific time points (i.e., 5 sec, 10 sec, 15 sec, 20 sec, 25 sec, 30 sec, 35 sec, 40 sec, 45 sec, 50 sec, 55 sec, 60 sec, and 65 sec). Four layers CNN were applied to classify the neural images of the different time points for three mental tasks (i.e., N-back, Stroop, and VFT). For evaluating the performance of the classifier, we utilized a 5-fold cross-validation method. The CNN results indicated that all the mental tasks obtained a great performance (i.e., averaged accuracy of N-back: 82.59%, Stroop: 85.03%, and VFT: 82.20%). Especially, the highest accuracy of the Stroop task in the 60-sec time point is 98.57%. Thus, these findings demonstrate that the neural images can be useful for the identification of the MCI. The fNIRS could be a next promising non-invasive neural imaging tool for early detection of AD in the clinical field.