Skip to main content

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

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7931))

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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Craddock, R., Holtzheimer III, P., Hu, X., Mayberg, H.: Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine 62, 1619–1628 (2009)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. Dosenbach, N.U.F., et al.: Prediction of individual brain maturity using fmri. Science 329, 1358–1361 (2010)

    Article  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. 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 56(2), 554–569 (2011)

    Article  Google Scholar 

  8. Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J.: A method for making group inferences from functional mri data using independent component analysis. Human Brain Mapping 14(3), 140–151 (2001)

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. Pereira, F., Mitchell, T., Botvinick, M.: Machine learning classifiers and fMRI: A tutorial overview. NeuroImage 45(1, suppl. 1) S199–S209 (2009); Mathematics in Brain Imaging

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Vercammen, A., Knegtering, H., den Boer, J., 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

    Article  Google Scholar 

  17. Graña, M., Chyzhyk, D.: Hybrid multivariate morphology using lattice auto-associative memories for resting-state fmri network discovery. In: IEEE 2012 12th International Conference on Hybrid Intelligent Systems (HIS), pp. 537–542 (2012)

    Google Scholar 

  18. Ritter, G.X., Sussner, P., Diaz-de-Leon, J.L.: Morphological associative memories. IEEE Transactions on Neural Networks 9(2), 281–293 (1998)

    Article  Google Scholar 

  19. Ritter, G.X., Diaz-de-Leon, J.L., Sussner, P.: Morphological bidirectional associative memories. Neural Networks 12(6), 851–867 (1999)

    Article  Google Scholar 

  20. Velasco-Forero, S., Angulo, J.: Supervised ordering in \({\rm i}\!{\rm r}^p\): Application to morphological processing of hyperspectral images. IEEE Transactions on Image Processing 20(11), 3301–3308 (2011)

    Article  MathSciNet  Google Scholar 

  21. Liu, D., Yan, C., Ren, J., Yao, L., Kiviniemi, V.J., Zang, Y.: Using coherence to measure regional homogeneity of resting-state fmri signal. Frontiers in Systems Neuroscience 4(24) (2010)

    Google Scholar 

  22. Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M.: Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London - Series B: Biological Sciences 360(1457), 1001–1013 (2005)

    Article  Google Scholar 

  23. Shinn, A.K., Baker, J.T., Cohen, B.M., Ongur, D.: Functional connectivity of left heschl’s gyrus in vulnerability to auditory hallucinations in schizophrenia. Schizophrenia Research 143(2-3), 260–268 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38622-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38621-3

  • Online ISBN: 978-3-642-38622-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics