Abstract
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.
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Chyzhyk, D., Graña, M. (2013). Results on a Lattice Computing Based Group Analysis of Schizophrenic Patients on Resting State fMRI. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Computation in Engineering and Medical Applications. IWINAC 2013. Lecture Notes in Computer Science, vol 7931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38622-0_14
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DOI: https://doi.org/10.1007/978-3-642-38622-0_14
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