On real data, we demonstrate that integrating connectivity information inferred from diffusion tensor imaging (DTI) data provides higher classification accuracy ...
Aug 31, 2012 · In this paper, we propose incorporating connectivity into sparse classifier learning so that both local and long-range connections can be.
PDF | In recent years, sparse regularization has become a dominant means for handling the curse of dimensionality in functional magnetic resonance.
On real data, we demonstrate that integrating connectivity information inferred from diffusion tensor imaging (DTI) data provides higher classification accuracy ...
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Connectivity-informed sparse classifiers for fMRI brain decoding, in International Workshop on Pattern Recognition in NeuroImaging (London: ), 101–104 ...
This study employs a sparsity approach on resting-state fMRI to discern relevant brain region connectivity for predicting Autism.
Connectivity-Informed Sparse Classifiers for fMRI Brain Decoding by Bernard Ng, Viviana Siless, Gael Varoquaux, Jean-Baptiste Poline, Bertrand.
Feb 17, 2017 · Connectivity-informed sparse classifiers for fMRI brain decoding, in Second International Workshop on Pattern Recognition in NeuroImaging ...
Functional connectivity using resting-state fMRI has emerged as an important research tool for understanding normal brain function as well as changes occurring ...
Connectivity-informed Sparse Classifiers for fMRI Brain Decoding · Bernard Ng , Viviana Siless , Gaël Varoquaux , Jean-Baptiste Poline , Bertrand Thirion.