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ISCAS
2008
IEEE

Group learning using contrast NMF : Application to functional and structural MRI of schizophrenia

9 years 12 months ago
Group learning using contrast NMF : Application to functional and structural MRI of schizophrenia
— Non-negative Matrix factorization (NMF) has increasingly been used as a tool in signal processing in the last couple of years. NMF, like independent component analysis (ICA) is useful for decomposing high dimensional data sets into a lower dimensional space. Here, we use NMF to learn the features of both structural and functional magnetic resonance imaging (sMRI/fMRI) data. NMF can be applied to perform group analysis of imaging data and we apply it to learn the spatial patterns which linearly covary among subjects for both sMRI and fMRI. We add an additional contrast term to NMF (called co-NMF) to identify features distinctive between two groups. We apply our approach to a dataset consisting of schizophrenia patients and healthy controls. The results from co-NMF make sense in light of expectations and are improved compared to the NMF results. Our method is general and may prove to be a useful tool for identifying differences between multiple groups.
Vamsi K. Potluru, Vince D. Calhoun
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where ISCAS
Authors Vamsi K. Potluru, Vince D. Calhoun
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