Results on a Lattice Computing Based Group Analysis of Schizophrenic Patients on Resting State fMRI

  • Darya Chyzhyk
  • Manuel Graña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7931)


We work on the definition of Lattice Computing approach to identify functional networks in resting state fMRI data (rsfMRI) looking for biomarkers of cognitive or neurodegenerative diseases. The approach uses Lattice Auto-Associative Memories (LAAM) to compute a reduced ordering h-function that can be thresholded or processed by morphological operators for network detection. Group analysis is performed on the templates corresponding to each class of subjects computed by averaging their spatially normalized rsfMRI data. We inspect the Tanimoto coefficients computing the similarity between compared networks to decide the appropriate threshold. Results on a dataset of healthy controls, schizophrenia patients with and without auditory hallucinations show that the approach is able to find functionally connected cluster differences discriminating the subjects suffering auditory hallucination.


Functional Connectivity Independent Component Analysis Schizophrenia Patient Independent Component Analysis Functional Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Darya Chyzhyk
    • 1
  • Manuel Graña
    • 1
  1. 1.Grupo de Inteligencia Computacional (GIC)Universidad del País Vasco (UPV/EHU)San SebastianSpain

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