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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chyzyk, D., Termenon, M., Savio, A.: A Comparison of VBM Results by SPM, ICA and LICA. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds.) HAIS 2010. LNCS, vol. 6077, pp. 429–435. Springer, Heidelberg (2010)
Cordes, D., Haughton, V., Carew, J.D., Arfanakis, K., Maravilla, K.: Hierarchical clustering to measure connectivity in fmri resting-state data. Magnetic Resonance Imaging 20(4), 305–317 (2002)
Craddock, R.C., Holtzheimer III, P.E., Hu, X.P., Mayberg, H.S.: Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine 62, 1619–1628 (2009)
Demirci, O., Stevens, M.C., Andreasen, N.C., Michael, A., Liu, J., White, T., Pearlson, G.D., Clark, V.P., Calhoun, V.D.: Investigation of relationships between fMRI brain networks in the spectral domain using ICA and granger causality reveals distinct differences between schizophrenia patients and healthy controls. NeuroImage 46(2), 419–431 (2009)
Graña, M.: A brief review of lattice computing. In: Proc. WCCI, pp. 1777–1781 (2008)
Graña, M., Chyzyk, D., García-Sebastián, M., Hernández, C.: Lattice independent component analysis for functional magnetic resonance imaging. Information Sciences (2010) (in press)
Graña, M., Savio, A.M., Garcia-Sebastian, M., Fernandez, E.: A lattice computing approach for on-line fmri analysis. Image and Vision Computing (2009) (in press)
Schmalz, M.S., Ritter, G.X., Urcid, G.: Autonomous single-pass endmember approximation using lattice auto-associative memories. Neurocomputing 72(10-12), 2101–2110 (2009)
Liu, Y., Wang, K., Yu, C., He, Y., Zhou, Y., Liang, M., Wang, L., Jiang, T.: Regional homogeneity, functional connectivity and imaging markers of alzheimer’s disease: A review of resting-state fmri studies. Neuropsychologia 46(6), 1648–1656 (2008); Neuroimaging of Early Alzheimer’s Disease
Maldonado, J.O., Hernandez, C., Graña, M., Villaverde, I.: Two lattice computing approaches for the unsupervised segmentation of hyperspectral images. Neurocomputing 72(10-12), 2111–2120 (2009)
Mingoia, G., Wagner, G., Langbein, K., Scherpiet, S., Schloesser, R., Gaser, C., Sauer, H., Nenadic, I.: Altered default-mode network activity in schizophrenia: A resting state fmri study. Schizophrenia Research 117(2-3), 355–356 (2010); 2nd Biennial Schizophrenia International Research Conference
Northoff, G., Duncan, N.W., Hayes, D.J.: The brain and its resting state activity–experimental and methodological implications. Progress in Neurobiology 92(4), 593–600 (2010)
Dosenbach, N.U.F., et al.: Prediction of individual brain maturity using fmri. Science 329, 1358–1361 (2010)
Pereira, F., Mitchell, T., Botvinick, M.: Machine learning classifiers and fMRI: A tutorial overview. NeuroImage 45(supplement 1), S199–S209 (2009); Mathematics in Brain Imaging
Remes, J.J., Starck, T., Nikkinen, J., Ollila, E., Beckmann, C.F., Tervonen, O., Kiviniemi, V., Silven, O.: Effects of repeatability measures on results of fmri sica: A study on simulated and real resting-state effects. NeuroImage (2010) (in Press) Corrected proof
van den Heuvel, M.P., Pol, H.E.H.: Exploring the brain network: A review on resting-state fmri functional connectivity. European Neuropsychopharmacology 20(8), 519–534 (2010)
Calhoun, T.V.D., Adali, T.: Unmixing fmri with independent component analysis. IEEE Engineering in Medicine and Biology Magazine 25(2), 79–90 (2006)
Vercammen, A., Knegtering, H., den Boer, J.A., Liemburg, E.J., Aleman, A.: Auditory hallucinations in schizophrenia are associated with reduced functional connectivity of the temporo-parietal area. Biological Psychiatry 67(10), 912–918 (2010); Anhedonia in Schizophrenia
Yao, Z., Wang, L., Lu, Q., Liu, H., Teng, G.: Regional homogeneity in depression and its relationship with separate depressive symptom clusters: A resting-state fmri study. Journal of Affective Disorders 115(3), 430–438 (2009)
Zhou, Y., Liang, M., Jiang, T., Tian, L., Liu, Y., Liu, Z., Liu, H., Kuang, F.: Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fmri. Neuroscience Letters 417(3), 297–302 (2007)
Zhou, Y., Shu, N., Liu, Y., Song, M., Hao, Y., Liu, H., Yu, C., Liu, Z., Jiang, T.: Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia. Schizophrenia Research 100(1-3), 120–132 (2008)
Zou, Q.-H., Zhu, C.-Z., Yang, Y., Zuo, X.-N., Long, X.-Y., Cao, Q.-J., Wang, Y.-F., Zang, Y.-F.: An improved approach to detection of amplitude of low-frequency fluctuation (alff) for resting-state fmri: Fractional alff. Journal of Neuroscience Methods 172(1), 137–141 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chyzhyk, D., Shinn, A.K., Graña, M. (2011). Exploration of LICA Detections in Resting State fMRI. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-21326-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21325-0
Online ISBN: 978-3-642-21326-7
eBook Packages: Computer ScienceComputer Science (R0)