Sparse network-based models for patient classification using fMRI
Maria J Rosa, Liana Portugal, Tim Hahn, Andreas J Fallgatter, Marta I Garrido, John Shawe-Taylor, Janaina Mourao-Miranda
NeuroImage | ACADEMIC PRESS INC ELSEVIER SCIENCE | Published : 2015
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling fr..View full abstract
Awarded by Wellcome Trust (UK)
Awarded by National Council for Scientific and Technological Development (Brazil)
Awarded by Australian Research Council (Discovery Early Career Researcher Award)
This work was funded by the Wellcome Trust (UK) [grant number WT086565/Z/08/Z to JMM and MJR]. The National Council for Scientific and Technological Development (Brazil) (201935/2012-0) to LP, and the Australian Research Council (Discovery Early Career Researcher Award DE130101393 to MIG). The authors thank Prof. Steve Williams and Dr. Andre Marquand (Department of Neuroimaging, Institute of Psychiatry, King's College London) for providing the event-related fMRI dataset.