Full regularization path for sparse principal component analysis
Proceedings of the 24th international conference on Machine learning
Expectation-maximization for sparse and non-negative PCA
Proceedings of the 25th international conference on Machine learning
Shape modeling and analysis with entropy-based particle systems
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Functional Segmentation of fMRI Data Using Adaptive Non-negative Sparse PCA (ANSPCA)
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
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Functional magnetic resonance imaging (fMRI) has become increasingly used for studying functional integration of the brain. However, the large inter-subject variability in functional connectivity renders detection of representative group networks very difficult. In this paper, we propose a new iterative method that we refer to as "group replicator dynamics," for detecting sparse functional networks that are common across subjects within a group. The proposed method uses replicator dynamics, which we show to be equivalent to non-negative sparse PCA, and incorporates group information for identifying common networks across subjects with subject-specific weightings of the identified brain regions reflecting individual differences. Finding a separate network for each subject, as opposed to employing traditional averaging approaches, permits statistical testing of group significance. We validated our method on synthetic data, and applying it to real fMRI data detected task-specific group networks that conform well with prior neuroscience knowledge.