Exploration of LICA detections in resting state fMRI

Authors:
Darya Chyzhyk;Ann K. Shinn;Manuel Grañ/a
Affiliations:
Dept. CCIA, UPV/EHU, San Sebastian, Spain;McLean Hospital, Belmont, Massachusetts/ Harvard Medical School, Boston, Massachusetts;Dept. CCIA, UPV/EHU, San Sebastian, Spain
Venue:
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Year:
2011

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Abstract

Lattice Independent Component Analysis (LICA) approach consists of a detection of lattice independent vectors (endmembers) that are used as a basis for a linear decomposition of the data (unmixing). In this paper we explore the network detections obtained with LICA in resting state fMRI data from healthy controls and schizophrenic patients.We compare with the findings of a standard Independent Component Analysis (ICA) algorithm. We do not find agreement between LICA and ICA. When comparing findings on a control versus a schizophrenic patient, the results from LICA show greater negative correlations than ICA, pointing to a greater potential for discrimination and construction of specific classifiers.