Differential Diagnosis among Alzheimer s Disease, Mild Cognitive Impairment, and Normal Subjects Using Resting-State fMRI Data Extracted from Multi-Subject Dictionary Learning Atlas: A Deep Learning-Based Study

Purpose: A powerful imaging method for evaluating brain patches is resting-state functional Magnetic Resonance (rs-fMRI) Imaging, in which the subject is at rest. Artificial Neural Networks (ANN) are one of the several Alzheimer s Disease (AD) analysis and diagnosis methods used in this study. We investigate ANNs ability to diagnose AD using rs-fMRI data. Materials and Methods: The acquisition of functional and structural magnetic resonance imaging was applied for 15 AD, 17 mild cognitive impairment, and ten normal healthy participants. Time series of blood oxygen level-dependent were extracted from the multi-subject dictionary learning brain atlas after pre-processing. This study develops a one-dimensional Convolutional Neural Network (CNN) using extracted signals of the functional atlas for differential diagnosis of AD. Results: Applying the proposed method to rs-fMRI signals for classifying three classes of Alzheimer’s patients resulted in overall accuracy, F1-score, and precision of 0.685, 0.663, and 0.681, respectively. Using 39 regions in the brain and proposing a quite simple network than most of the available deep learning-based methods are the main advantages of this model. Conclusion: rs-fMRI signal recognition based on a functional atlas with the application of a deep neural network has a pattern recognition capability that can make a differential diagnosis with an acceptable level of accuracy and precision. Therefore, deep neural networks can be considered as a tool for the early diagnosis of AD.