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Functional Magnetic Resonance Imaging (fMRI) data consists of time series for each voxel recorded during a cognitive task. In order to extract useful information from this noisy and redundant data, techniques are proposed to select the voxels that are relevant to the underlying cognitive task. We propose a simple and efficient algorithm for decoding the(More)
An information theoretic approach is proposed to estimate the degree of connectivity for each voxel with its neighboring voxels. The neighborhood system is defined by spatial and functional connectivity metrics. Then, a local mesh of variable size is formed around each voxel using spatial or functional neighborhood. The mesh arc weights, called Mesh Arc(More)
In this study, the degree of connectivity for each voxel, which is the unit element of functional Magnetic Resonance Imaging (fMRI) data, with its neighboring voxels is estimated. The neighborhood system is defined by spatial connectivity metrics and a local mesh of variable size is formed around each voxel using spatial neighborhood. Then, the mesh arc(More)
In this study, we propose a new approach to construct a two-level functional brain network. The nodes of the first-level network are the voxels of the functional Magnetic Resonance Images (fMRI) recorded during an object recognition task. The nodes of the network at the second-level are the anatomic regions of the brain. The arcs of the first level are(More)
We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthesizing natural looking motion sequences over long time horizons without catastrophic drift or motion degradation. The model consists of two components, a 3-layer recurrent neural(More)
In this study, the degree of connectivity for each voxel, which is the unit element of functional Magnetic Resonance Imaging (fMRI) data, with its neighboring voxels is estimated. The neighborhood system is defined by spatial connectivity metrics and a local mesh of variable size is formed around each voxel using spatial neighborhood. Then, the mesh arc(More)
We suggest a new approach to estimate a brain network to model cognitive tasks and explore the node degree distribution of this network in anatomic regions. Functional Magnetic Resonance Images are used to estimate the relationship among the voxels. First, a local mesh is formed around each voxel in a predefined neighborhood system. Then, the edge weights(More)
Functional magnetic resonance imaging produces high dimensional data, with a less then ideal number of labelled samples for brain decoding tasks (predicting brain states). In this study, we propose a new deep temporal convolutional neural network architecture with spatial pooling for brain decoding which aims to reduce dimensionality of feature space along(More)
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