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Developing a Korean Standard Brain Atlas on the basis of Statistical and Probabilistic Approach and Visualization tool for Functional image analysis  

Koo, B.B. (Department of Biomedical Engineering, Hanyang University)
Lee, J.M. (Department of Biomedical Engineering, Hanyang University)
Kim, J.S. (Department of Biomedical Engineering, Hanyang University)
Lee, J.S. (Department of Nuclear Medicine, Seoul National University College of Medicine)
Kim, I.Y. (Department of Biomedical Engineering, Hanyang University)
Kim, J.J. (Deparment of Psychiatry, Yonsei University College of Medicine)
Lee, D.S. (Department of Nuclear Medicine, Seoul National University College of Medicine)
Kwon, J.S. (Department of Nuclear Medicine, Seoul National University College of Medicine)
Kim, S.I. (Department of Biomedical Engineering, Hanyang University)
Publication Information
The Korean Journal of Nuclear Medicine / v.37, no.3, 2003 , pp. 162-170 More about this Journal
Abstract
The probabilistic anatomical maps are used to localize the functional neuro-images and morphological variability. The quantitative indicator is very important to inquire the anatomical position of an activated legion because functional image data has the low-resolution nature and no inherent anatomical information. Although previously developed MNI probabilistic anatomical map was enough to localize the data, it was not suitable for the Korean brains because of the morphological difference between Occidental and Oriental. In this study, we develop a probabilistic anatomical map for Korean normal brain. Normal 75 blains of T1-weighted spoiled gradient echo magnetic resonance images were acquired on a 1.5-T GESIGNA scanner. Then, a standard brain is selected in the group through a clinician searches a brain of the average property in the Talairach coordinate system. With the standard brain, an anatomist delineates 89 regions of interest (ROI) parcellating cortical and subcortical areas. The parcellated ROIs of the standard are warped and overlapped into each brain by maximizing intensity similarity. And every brain is automatically labeledwith the registered ROIs. Each of the same-labeled region is linearly normalize to the standard brain, and the occurrence of each legion is counted. Finally, 89 probabilistic ROI volumes are generated. This paper presents a probabilistic anatomical map for localizing the functional and structural analysis of Korean normal brain. In the future, we'll develop the group specific probabilistic anatomical maps of OCD and schizophrenia disease.
Keywords
Statistical and Probabilistic Anatomical Map; Standard Brain template; Brain Atlas; Functional Activation Labeling;
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1 Brett M, Johnsrude IS, Owen AM. The problem of functional localization in the human brain. Nature reviews. Neuroscience 2002;3:243-249
2 Dinov ID, Mega MS, Thompson PM, Lee L, Woods RP, Holmes CJ, et al. Analyzing fimctional brain images in a probabilistic atlas: a validation of subvolume thresholding. J. Compo Assist. Tomogr. 2000;24: 128-138
3 Yoon UC, Lee JM, Kim JJ, Kim IY, Kwon JS, Kim SI. Adaptable Fuzzy C-means for ImClassification as a Preprocessing Procedure of Brain Parcellation. J. digital imaging : the official journal of the Society for Computer Applications in Radiology. 2001;14(2): 238-240
4 Lee JM, Koo BB, Kim JS, Kim IY, Kim SJ. A Novel Automatic Algorithm for Selecting a Standard Brain in a Data Set Using Simple Structure Analysis in Talairach Coordinate System. SCAR, 2003
5 Mazziotta JC, Toga AW, Evans AC, Fox P, Lancaster J. A Probabilistic Atlas of the Human Brain; Theory and Rationalefor Its Development. Neurolmage 1995;2:89-101
6 Collins DL. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 1994;18:192-205
7 Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P. Multimodaiity Image Registration by Maximization of Mutual Information. IEEE trans. on Medical Imaging 1997;16(2):187-198
8 Evans AC, Collins DL, Milner B. MRI-based stereotactic atlas from 250 young normal subjects. Neurosci. Abstr. 1992;18:408
9 Christensen GE, Rabbitt RD, Miller MI. Deformable templates using large deformation kinematics. IEEE Trans on Image Processing. 1996;5(9):1435-1447
10 Desmond JE, Lim KO. On- and Offline Talairach Registration for Structural and Functional MRI Studies. Human Brain Mapping 1997;5(1):58-73
11 Fischl B, Sereno MI, Tootell R, Dale AM. High resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping 1999;8:272-284
12 Zilles K, Kawashima R, Dabrinhaus A, Fukuda H, Schonnann T. Hemispheric Shape of Europian and Japanese Brains. Neurolmage 2001;13:262-271
13 Kim JJ, Crespo-Facorro B, Anderson NC, O'Leary DS, Zhang B. An MRI-Based Parcellation Method for the Temporal Lobe. Neurolmage 2000;11:271-288
14 Crespo-Facorro B, Kim JJ, Anderson NC, O'Leary DS, Wiser AK. Human Frontal Cortex: An MRI-Based Parcellation Method. Neurolrnage 1999;10:500-519
15 Evans AC. Anatomical mapping of functional activation in stereotactic coordinate space. Neurolmage 1992;1:43-53
16 Evans AC, Kamber M, Collins DL, MacDonald D. An MRI-based probabilistic atlas of neuroanatomy. In: Magnetic Resonance Scanning and Eplilepsy. S. Shorvon, Ed. New York: Plenum; 1994. p. 263-274
17 Mega MS, Thompson PM. Sulcal variability in the Alzheimer's brain: correlations with cognition. Neurology 1998;50: 145-151
18 Ashbumer J, Friston KJ. Nonlinear spatial normalization using basis fimctions. Human brain Mapping 1999;7(4):254-266
19 Talairach J, Toumoux P. Co-planar stereotacxic atlas of the human brain. New York: Thieme Medical Publishers; 1998