Diagnosis of Alzheimer's disease by feature weighted-LSTM: a preliminary study of temporal features in brain resting-state fMRI.The long short-term memory network (LSTM) is widely used in time series data processing as a temporal recursive network.  To integrate the temporal and spatial characteristics of brain regions, this paper proposes a model called feature weighted-LSTM (FW-LSTM).  The feature weight is defined by spatial characteristics calculating the frequency of connectivity of each brain region and further integrated into the LSTM.  The FW-LSTM model on the Alzheimer's disease neuroimaging initiative (ADNI) dataset is used to extract the time-varying characteristics of 90 brain regions for Alzheimer's disease (AD) classification.  It outperformed the one-dimensional convolutional neural networks (1D-CNN) model and LSTM model, which only used temporal features of brain regions.
LSTM
To integrate the temporal and spatial characteristics of brain regions, this paper proposes a model called feature weighted-LSTM (FW-LSTM).  The FW-LSTM model on the Alzheimer's disease neuroimaging initiative (ADNI) dataset is used to extract the time-varying characteristics of 90 brain regions for Alzheimer's disease (AD) classification. 
FW-LSTM
It outperformed the one-dimensional convolutional neural networks (1D-CNN) model and LSTM model, which only used temporal features of brain regions.
1D-CNN