KEYWORDS: Functional magnetic resonance imaging, Data modeling, Convolution, Alzheimer's disease, Neural networks, Brain, Neuroimaging, 3D modeling, Data conversion, Signal processing
In recently years, motivated by the excellent performance in automatic feature extraction and complex patterns detecting from raw data, recently, deep learning technologies have been widely used in analyzing fMRI data for Alzheimer’s disease classification. However, most current studies did not take full advantage of the temporal and spatial features of fMRI, which may result in ignoring some important information and influencing classification performance. In this paper, we propose a novel approach based on deep learning to learn temporal and spatial features of 4D fMRI for Alzheimer’s disease classification. This model is composed of 3D Convolutional Neural Network(3DCNN) and recurrent neural network. Experimental results demonstrated that the proposed approach could discriminate Alzheimer’s patients from healthy controls with a high accuracy rate.
KEYWORDS: Functional magnetic resonance imaging, 3D modeling, Brain, Visualization, Neuroimaging, Brain mapping, Machine learning, Data modeling, 3D visualizations, Sensors
Deep learning methods have proven promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain, however, they lack transparency in their decision making, in the sense that it is not straightforward to visualize the features on which the decision was made. In this study, we investigated the decoding of four sensorimotor tasks based on 3D fMRI according to 3D Convolutional Neural Network (3DCNN), and then adopted Grad-CAM algorithms to provide visual explanation from deep networks so as to support the decoding decision.
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