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https://observatorio.fm.usp.br/handle/OPI/12113
Title: | LOCAL SPARSE COMPONENT ANALYSIS FOR BLIND SOURCE SEPARATION: AN APPLICATION TO RESTING STATE FMRI |
Authors: | VIEIRA, Gilson; AMARO JR., Edson; BACCALA, Luiz A. |
Citation: | 2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), p.5611-5614, 2014 |
Abstract: | We propose a new Blind Source Separation technique for whole-brain activity estimation that best profits from FMRI's intrinsic spatial sparsity. The Local Sparse Component Analysis (LSCA) combines wavelet analysis, group-separable regularizers, contiguity-constrained clusterization and principal components analysis (PCA) into a unique spatial sparse representation of FMRI images towards efficient dimensionality reduction without sacrificing physiological characteristics by avoiding artificial stochastic model constraints. The LSCA outperforms classical PCA source reconstruction for artificial data sets over many noise levels. A real FMRI data illustration reveals resting-state activities in regions hard to observe, such as thalamus and basal ganglia, because of their small spatial scale. |
Appears in Collections: | Comunicações em Eventos - FM/MDR Comunicações em Eventos - HC/InRad Comunicações em Eventos - LIM/44 |
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