Before revision: Decoding specific task states from human fMRI ( functional magnetic resonance imaging ) signals constitutes a major goal of neuroimaging. Previous studies of task-state classification have focused largely on processed fMRI characteristics (e.g. functional connectivity patterns, activity patterns) or a single fMRI volume. Here, we assembled a novel deep neural network (DNN) for classifying directly from 4-D fMRI time series, retaining both spatial and temporal information of fMRI signals .
After revision: Decoding specific task states from human fMRI ( functional magnetic resonance imaging (fMRI) of the human brain. However, the number of dimensions of fMRI signals are too large (hundreds of thousands of voxels in one volume) to be efficiently learnt by the MVPA. Researchers thus need to reduce the features to tens or hundreds of dimensions through feature selection/extraction that requires expert knowledge and may lead to a selection bias. In this study, we propose a deep neural network (DNN) for classifying directly from 4-D fMRI time series, retaining both spatial and temporal information of fMRI signals .