STATISTICAL ANALYSIS OF FUNCTIONAL MRI DATA USING INDEPENDENT COMPONENT ANALYSIS

M. Bartés-Serrallonga, J. Solé-Casals, A. Adan, C. Falcón, N. Bargalló, J. M. Serra-Grabulosa

Abstract

Functional magnetic resonance imaging (fMRI) is a technique to map the brain, anatomically as well as physiologically, which does not require any invasive analysis. In order to obtain brain activation maps, the subject under study must perform a task or be exposed to an external stimulus. At the same time a large amount of images are acquired using ultra-fast sequences through magnetic resonance. Afterwards, these images are processed and analyzed with statistical algorithms. This study was made in collaboration with the consolidated Neuropsychology Research Group of the University of Barcelona, focusing on applications of fMRI for the study of brain function in images obtained with various subjects. This group performed a study which analyzed fMRI data, acquired with various subjects, using the General Linear Model (GLM). The aim of our work was to analyze the same fMRI data using Independent Component Analysis (ICA) and compare the results with those obtained through GLM. Results showed that ICA was able to find more active networks than GLM. The activations were found in frontal, parietal, occipital and temporal areas.

References

  1. Adan A, Serra-Grabulosa JM ., 2010. Effects of caffeine and glucose, alone and combined, on cognitive performance. Human Psychopharmacology clinical and experimental, 25 (4), 310 - 317.
  2. Ashby, F., 2011. Statistical analysis of fMRI Data. Cambridge, MA: MIT Press
  3. Bush G., Luu P., Posner M. I., 2000. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci., 4 (6), 215 - 222.
  4. Calhoun, V. D., Adali, T., Pearlson, G. D. and Pekar, J. J., 2001,”A Method forMaking Group Inferences From Functional MRI Data Using Independent Component Analysis”, HBM, 14, 140 - 151.
  5. Calhoun, V. D., Adali, T., Pearlson, G. D., 2004, ”Independent component analysis applied to fMRI data: a generative model for validating results”, J. VLSI Signal Process, 37, 281 - 291.
  6. Cornblatt, B. A., Lezenweger, M. F., Erlenmeyer-Kimling, L., 1989. The Continuous Performance Test, Identical Pairs Version: II. Contrasting attentional profiles in schizophrenic and depressed patients. Psychiatry Research, 29, 65 - 85.
  7. Gusnard D. A., Raichle M. E., 2001. Searching for a baseline: Functional imaging and the resting human
  8. brain. Nature Neuroscience Reviews, 2, 685 - 694.
  9. D'Esposito, M., Zarahn, E., Aguirre, G. K., 1999. EventRelated functional MRI: implications for cognitive Psychology. Psychological Bulletin, 125, 155 - 64.
  10. Hyvärinen, A., Oja E., 2000. Independent component analysis: Algorithms and applications. Neural Networks, 13, 411 - 430.
  11. Hyvärinen A., Oja E., Karhunen J., 2001. Independent component analysis, John Wiley & Sons.
  12. McKiernan K. A., Kaufman J. N., Kucera-Thompson J., Binder J. R., 2003. A parametric manipulation of factors affecting task-induced deactivation in functional neuroimaging. Journal of Cognitive Neuroscience, 15, 394 - 408.
  13. Nielsen F. A., Balslev D., Hansen L. K., 2005. Mining the posterior cingulate: segregation between memory and pain components. Neuroimage, 27 (3), 520 - 532.
  14. Nieuwenhuis S., Ridderinkhof K. R., Blom J., Band G.P., Kok A., 2001. Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task. Psychophysiology, 38 (5), 752 - 60.
  15. Posner M. I., DiGirolamo G. J., 1998. Executive attention: Conflict, target detection, and cognitive control. In Parasuraman R. The attentive brain. Cambridge, Mass: MIT Press.
  16. Scott A. Huettel, Allen W. Song, and Gregory McCarthy., 2004. Functional magnetic resonance imaging. Sunderland, MA: Sinauer Associates
  17. Serra-Grabulosa J. M, Adan A, Falcon C , Bargallo N, 2010a Glucose and caffeine effects on sustained attention: an exploratory fMRI study. Human Psychopharmacology clinical and experimental 25 (7- 8), 543 - 552
  18. Serra-Grabulosa J. M., Adan A., Falcón C., Bargalló N., Solé-Casals J., 2010b. Cerebral correlates of the continous performance test-identical pairs version: An fMRI study. In Proceedings of the Third Internationa Conference on Bio-inspired Systems and Signal Processing - BIOSIGNALS.
  19. Stoeckel C., Gough .P. M., Watkins K. E., Devlin J. T., 2009. Supramarginal gyrus involvement in visual word recognition. Cortex, 45 (9),1091 - 1096.
  20. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N. et al. 2002. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273 - 289.
  21. Volz K. G., Schubotz R. I., von Cramon D. Y., 2005. Variants of uncertainty in decision-making and their neural correlates. Brain Res. Bull, 67 (5), 403 - 12.
  22. Weissman D. H., Gopalakrishnan A., Hazlett C. J, Woldorff M. G., 2005. Dorsal Anterior Cingulate Cortex Resolves Conflict from Distracting Stimuli by Boosting Attention toward Relevant Events. Cerebral Cortex, 15, 229 - 237.
Download


Paper Citation


in Harvard Style

Bartés-Serrallonga M., Solé-Casals J., Adan A., Falcón C., Bargalló N. and Serra-Grabulosa J. (2011). STATISTICAL ANALYSIS OF FUNCTIONAL MRI DATA USING INDEPENDENT COMPONENT ANALYSIS . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: Special Session on Challenges in Neuroengineering, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 430-436. DOI: 10.5220/0003723504300436


in Bibtex Style

@conference{special session on challenges in neuroengineering11,
author={M. Bartés-Serrallonga and J. Solé-Casals and A. Adan and C. Falcón and N. Bargalló and J. M. Serra-Grabulosa},
title={STATISTICAL ANALYSIS OF FUNCTIONAL MRI DATA USING INDEPENDENT COMPONENT ANALYSIS},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: Special Session on Challenges in Neuroengineering, (IJCCI 2011)},
year={2011},
pages={430-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003723504300436},
isbn={978-989-8425-84-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: Special Session on Challenges in Neuroengineering, (IJCCI 2011)
TI - STATISTICAL ANALYSIS OF FUNCTIONAL MRI DATA USING INDEPENDENT COMPONENT ANALYSIS
SN - 978-989-8425-84-3
AU - Bartés-Serrallonga M.
AU - Solé-Casals J.
AU - Adan A.
AU - Falcón C.
AU - Bargalló N.
AU - Serra-Grabulosa J.
PY - 2011
SP - 430
EP - 436
DO - 10.5220/0003723504300436