Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Investigation of light stimulated mouse brain activation in high magnetic field fMRI using image segmentation methods

  • Kim, Wook (Korea Institute of Radiological and Medical Sciences) ;
  • Woo, Sang-Keun (Korea Institute of Radiological and Medical Sciences) ;
  • Kang, Joo Hyun (Korea Institute of Radiological and Medical Sciences) ;
  • Lim, Sang Moo (Korea Institute of Radiological and Medical Sciences)
  • Received : 2016.08.26
  • Accepted : 2016.12.02
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Magnetic resonance image (MRI) is widely used in brain research field and medical image. Especially, non-invasive brain activation acquired image technique, which is functional magnetic resonance image (fMRI) is used in brain study. In this study, we investigate brain activation occurred by LED light stimulation. For investigate of brain activation in experimental small animal, we used high magnetic field 9.4T MRI. Experimental small animal is Balb/c mouse, method of fMRI is using echo planar image (EPI). EPI method spend more less time than any other MRI method. For this reason, however, EPI data has low contrast. Due to the low contrast, image pre-processing is very hard and inaccuracy. In this study, we planned the study protocol, which is called block design in fMRI research field. The block designed has 8 LED light stimulation session and 8 rest session. All block is consist of 6 EPI images and acquired 1 slice of EPI image is 16 second. During the light session, we occurred LED light stimulation for 1 minutes 36 seconds. During the rest session, we do not occurred light stimulation and remain the light off state for 1 minutes 36 seconds. This session repeat the all over the EPI scan time, so the total spend time of EPI scan has almost 26 minutes. After acquired EPI data, we performed the analysis of this image data. In this study, we analysis of EPI data using statistical parametric map (SPM) software and performed image pre-processing such as realignment, co-registration, normalization, smoothing of EPI data. The pre-processing of fMRI data have to segmented using this software. However this method has 3 different method which is Gaussian nonparametric, warped modulate, and tissue probability map. In this study we performed the this 3 different method and compared how they can change the result of fMRI analysis results. The result of this study show that LED light stimulation was activate superior colliculus region in mouse brain. And the most higher activated value of segmentation method was using tissue probability map. this study may help to improve brain activation study using EPI and SPM analysis.

Keywords

References

  1. Sung Dae Yoon, Hyun Wook Park. "Current Status of Measurement and Analysis of the fMRI" Journal of KIISE, JOK. Vol. 27, pp. 25-32. Apr 2009.
  2. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK. "Advances in functional and structural MR image analysis and implementation as FSL." Neuroimage. Vol. 31, pp. S208-219. Dec. 2004.
  3. Chung JY, Yoon HW, Kim YB, Park HW, Cho ZH. "Susceptibility compensated fMRI study using a tailored RF echo planar imaging sequence." Journal of Magnetic Resonance Imaging. Vol. 1, pp. 221-228. Jan. 2009.
  4. Pautler RG. "Mouse MRI: concepts and applications in physiology." Physiology. Vol. 19, pp. 168-175. Aug. 2004. https://doi.org/10.1152/physiol.00016.2004
  5. Ogawa S, Lee TM, Kay AR, Tank DW. "Brain magnetic resonance imaging with contrast dependent on blood oxygenation." Proceedings of the National Academy of Sciences. Vol. 1, pp. 9868-9872. Dec. 1990.
  6. Nair G, Duong TQ. "Echo‐planar BOLD fMRI of mice on a narrow‐bore 9.4 T magnet." Magnetic resonance in medicine. Vol. 52, pp. 430-434. Aug. 2004. https://doi.org/10.1002/mrm.20158
  7. Stephen J. Sawiak, Nigel I. Wood, Guy B. Williams, A. Jennifer Morton, and T. Adrian Carpenter "Voxel-based morphometry with templates and validation in a mouse model of Huntington's disease," Magnetic Resonance Imaging, Vol. 31, pp. 1522-1531, March 2013. https://doi.org/10.1016/j.mri.2013.06.001
  8. Christopher J. Bailey, Basavaraju G. Sanganahalli, Peter Herman, Hal Blumenfeld, Albert Gjedde and Fahmeed Hyder "Analysis of Time and Space Invariance of BOLD responses in the Rat Visual System", Cerebral Cortex, Vol. 1, pp. 210-222, Jan. 2013.
  9. Williams, K. A., Magnuson, M., Majeed, W., LaConte, S. M., Peltier, S. J., Hu, X., & Keilholz, S. D. (2010). "Comparison of ${\alpha}$-chloralose, medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat." Magnetic resonance imaging. Vol. 28, pp.995-1003. Sep. 2010. https://doi.org/10.1016/j.mri.2010.03.007
  10. Takata N, Yoshida K, Komaki Y, Xu M, Sakai Y, Hikishima K, Mimura M, Okano H, Tanaka KF. "Optogenetic activation of CA1 pyramidal neurons at the dorsal and ventral hippocampus evokes distinct brain-wide responses revealed by mouse fMRI." PLoS one. Vol. 10, pp. e0121417. Mar. 2015. https://doi.org/10.1371/journal.pone.0121417
  11. Le Bihan D, Karni A. "Applications of magnetic resonance imaging to the study of human brain function." Current Opinion in Neurobiology. Vol. 5, pp. 231-237, Apr. 1995.
  12. Bookstein FL. "Voxel-based morphometry should not be used with imperfectly registered images." Neuroimage. Vol. 31, pp. 1454-1462. Dec 2001.
  13. Ashburner J, Neelin P, Collins DL, Evans A, Friston K. "Incorporating prior knowledge into image registration." Neuroimage. Vol. 30, pp. 344-352. Nov. 1997.
  14. Hae-Jeong Park. "Methodological Review on Functional Neuroimaging Using Positron Emission Tomography." Nuclear Medicine Molecular Imaging. Vol. 41, pp. 71-77. Apr. 2007.
  15. Stephen J. Sawiak, Jean-Luc Picq and Marc Dhenain "Voxel-based morphometry analyses of in vivo MRI in the aging mouse lemur primate", Frontiers in Aging Neuroscience, Vol. 6, pp. 82-91, May. 2014.
  16. Sawiak SJ, Wood NI, Williams GB, Morton AJ, Carpenter TA. "Voxel-based morphometry with templates and validation in a mouse model of Huntington's disease." Magnetic resonance imaging. Vol. 31, pp. 1522-1531. Nov. 2013. https://doi.org/10.1016/j.mri.2013.06.001
  17. Büchel, Christian, and K. J. Friston. "Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI." Cerebral cortex. Vol. 7, pp.768-778. Dec. 1997. https://doi.org/10.1093/cercor/7.8.768