Exploration of LICA Detections in Resting State fMRI

  • Darya Chyzhyk
  • Ann K. Shinn
  • Manuel Graña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)


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.


Functional Connectivity Independent Component Analysis Schizophrenia Patient Default Mode Network Independent Component Analysis 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Darya Chyzhyk
    • 1
  • Ann K. Shinn
    • 2
  • Manuel Graña
    • 1
  1. 1.Computational Intelligence Group Dept. CCIA, UPV/EHUSan SebastianSpain
  2. 2.McLean Hospital, Belmont, Massachusetts; Harvard Medical SchoolBostonUS

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