Abstract
The analysis of fMRI allows mapping the brain and identifying brain regions activated by a particular task. Prior to the analysis, several steps are carried out to prepare the data. One of these is the spatial smoothing whose aim is to eliminate the noise which can cause errors in the analysis. The most common method to perform this is by using a Gaussian filter, in which the extent of smoothing is assumed to be equal across the image. As a result some regions may be under-smoothed, while others may be over-smoothed. Thus, we suggest smoothing the images adaptively using a Wiener filter which allows varying the extent of smoothing according to the changing characteristics of the image. Therefore, we compared the effects of the smoothing with a wiener filter and with a Gaussian Kernel. In general, the results obtained with the adaptive filter were better than those obtained with the Gaussian filter.
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References
D’Esposito, M., Zarahn, E., Aguirre, G.K.: Event-Related functional MRI: implications for cognitive Psychology. Psychological Bulletin 125, 155–164 (1999)
Huettel, S.A., Song, A.W., McCarthy, G.: Functional magnetic resonance imaging. Sinauer Associates, Sunderland (2004)
Lindquist, M., Wager, T.: Spatial smoothing in fmri using prolate spheroidal wave functions. Human Brain Mapping 29, 1276–1287 (2008)
Van De Ville, D., Blu, T., Unser, M.: Surfing the brain: An overview of wavelet-based techniques for fmri data analysis. IEEE Engineering in Medicine and Biology Magazine 25, 65–78 (2006)
Poline, J., Mazoyer, B.: Analysis of individual brain activation maps using hierarchical description and multiscale detection. IEEE Transactions in Medical Imaging 4, 702–710 (1994)
Worsley, K.J., Marrett, S., Neelin, P., Vandal, A.C., Friston, K.J., Evans, A.C.: A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping 4, 58–73 (1996)
Shafie, K., Sigal, B., Siegmund, D., Worsley, K.: Rotation space random fields with an application to fmri data. Annals of Statistics 31, 1732–1771 (2003)
Yue, Y., Loh, J.M., Lindquist, M.A.: Adaptive spatial smoothing of fMRI images. Statistics and Its Interface 3, 3–13 (2010)
Tabelow, K., Polzehl, J., Voss, H.U., Spokoiny, V.: Analyzing fMRI experiments with structural adaptive smoothing procedures. NeuroImage 33, 55–62 (2006)
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, 140–151 (2001)
Serra-Grabulosa, J.M., Adan, A., Falcon, C., Bargallo, N.: Glucose and caffeine effects on sustained attention: an exploratory fMRI study. Human Psychopharmacology Clinical and Experimental 25(7-8), 543–552 (2010)
Adan, A., Serra-Grabulosa, J.M.: Effects of caffeine and glucose, alone and combined, on cognitive performance. Human Psychopharmacology Clinical and Experimental 25(4), 310–317 (2010)
Cornblatt, B.A., Lezenweger, M.F., Erlenmeyer-Kimling, L.: The Continuous Performance Test, Identical Pairs Version: II. Contrasting attentional profiles in schizophrenic and depressed patients. Psychiatry Research 29, 65–85 (1989)
Lim, J.S.: Two-Dimensional Signal and Image Processing. Prentice Hall (1990)
Calhoun, V.D., Adali, T., Pearlson, G.D.: Independent component analysis applied to fMRI data: a generative model for validating results. The Journal of VLSI Signal Processing 37, 281–291 (2004)
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002)
Yoo, S.S., Paralkar, G., Panych, L.P.: Neural substrates associated with the concurrent performance of dual working memory tasks. The International Journal of Neuroscience 114(6), 613–631 (2004)
Bartés-Serrallonga, M., Solé-Casals, J., Adan, A., Falcon, C., Bargallo, N., Serra-Grabulosa, J.M.: Statistical analysis of functional MRI data using independent component analysis. In: International Conference on Neural Computation Theory and Applications, pp. 430–436 (2011)
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Bartés-Serrallonga, M., Serra-Grabulosa, J.M., Adan, A., Falcón, C., Bargalló, N., Solé-Casals, J. (2015). Smoothing FMRI Data Using an Adaptive Wiener Filter. In: Madani, K., Correia, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2012. Studies in Computational Intelligence, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-11271-8_21
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DOI: https://doi.org/10.1007/978-3-319-11271-8_21
Publisher Name: Springer, Cham
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