Psychiatry Investig Search


Psychiatry Investig > Volume 21(6); 2024 > Article
Oh, Kim, Park, Jang, Kim, and Kwon: Constructing the KOR152 Korean Young Adult Brain Atlas Utilizing the State-of-the-Art Method for the Age-Specific Population



Spatial normalization is an essential process for comparative analyses that heavily depends on the standard brain template used. Brain morphological differences are observed in different populations due to genetic and environmental factors, causing mismatches in regions when the data are normalized to different population templates. Recent studies have indicated differences between Caucasian and East Asian populations as well as within East Asian populations, suggesting the necessity of population-specific brain templates. Thus, this study aimed to construct a Korean young adult age-specific brain template utilizing an advanced method of template construction to update the currently available Korean template.


The KOR152 template was constructed via affine and nonlinear iterative procedures based on prior studies. We compared the morphological features of different population templates (MNI152, Indian_157, and CN200). The distance and volumetric changes before and after registering the data to these templates were calculated for registration accuracy.


The KOR152 global brain features revealed a shorter overall length than the other population templates. The registration accuracy by distance and volumetric change was significantly lower than that of the other population templates, implying that the KOR152 was more accurate than other templates for the young adult Korean population.


This study provided evidence for the need for a population-specific template that may be more appropriate for structural and functional studies in Korean populations.


The magnetic resonance imaging (MRI) brain template is a standardized anatomical reference image that captures average neuroanatomical features of a particular population or an age group to align different images to a standard space by accounting for various shapes, sizes, and orientations [1]. To conduct a comparative analysis, it is important to standardize and reorient brain images to a brain template. Standardization, which is part of the preprocessing step, involves the spatial normalization of individual images to a specific brain template. Various brain templates are available based on age and race, with additional templates accounting for regular updates due to methodological advancements [2,3]. A suitable brain template is selected based on the similarity to the population under study, with preferences given to templates with minimum deformational changes from the original images. This ensures that the template accurately reflects the neuroanatomy of the specific population, leading to more precise analysis and interpretation of brain imaging data.
The Montreal Neurological Institute (MNI) brain template is commonly adapted internationally due to the wide availability of the MNI152 template in various analysis tools and software [4-6]. However, the MNI152 template was constructed for the Caucasian population, which has morphological differences from other ethnic groups, especially those in East Asian countries [7-10]. For example, the Korean template revealed features that were shorter in length and height than did the MNI152 template [11]. Additionally, a task fMRI study demonstrated discrepancies in registering to the Chinese and MNI templates, with different brain activation percentages (72.8% and 80.7%, respectively) and spatial accuracy (the correct location being deeper in the interhemispheric fissure) [12]. These findings imply that misregistration to anatomically different Caucasian brain templates could lead to inaccurate analysis of non-Caucasian populations. Thus, attempts have been made to produce brain templates specific to the East Asian population [6,11].
Furthermore, recent studies have elucidated the need for population-specific brain template construction. Differences in brain morphology, such as shorter and narrower features, within the East Asian population have been found in the Indian_157 template when compared to the Chinese_56 template [10,13]. In addition, a brain microstructural study in Chinese, Malay and Indian neonate populations suggested differences in spinal-cerebellar and cortico-striatal-thalamic circuits among different populations [14]. Further studies have revealed that these differences are due to environmental, genetic, and cultural differences, such as perceptualizing languages [12,15,16]. Together, these studies suggest differences within East Asian countries and ethnicities, emphasizing the need for a population-specific Korean brain template.
For Koreans, two templates are available: the Korean template utilizing 1.5T MR images and the KNE96 template representing the elderly population [11,17]. However, recent advancements in MR techniques and methodology have highlighted the need to update the Korean brain template to represent the Korean population more accurately. For Korean template construction, a conventional method was used wherein one subject was chosen as a reference for affinely registering other brain images, introducing bias toward the selected individual. In contrast, the new methodology utilizes multiple iterations of normalization with a larger sample to generate an unbiased minimally deformed template, which is needed for the Korean population [2]. Moreover, data from individuals ranging in age from 18 to 55 years were used for Korean template construction. Pertinently, research has indicated that brain volume changes over the course of a lifespan [18]. Brain volumes tend to increase rapidly during childhood, reaching a plateau in early adulthood; then, a gradual volumetric loss begins in the late 30s. Hence, the KNE96 template was constructed to better represent the elderly population [17].
Consequently, this study aimed to construct a young adult age-specific Korean brain template that better represents the structural features of the Korean population utilizing the most recent template construction method.



Images from a total of 224 healthy native Koreans were acquired from two separate brain MRI T1 datasets for this study (Table 1). The first dataset (KOR152 dataset) included 152 healthy individuals scanned at Seoul National University Hospital (SNUH). The validation dataset acquired from the Seoul National University (SNU) MRI Center consisted of 72 healthy individuals. All subjects were screened by certified psychiatrists using the SCID-I Nonpatient Edition to rule out any psychiatric illnesses. Individuals with a potential family history (first- to third-degree biological relatives) of psychotic disorders were excluded. Further exclusion criteria included individuals showing signs of substance abuse/dependence, neurological disease, or significant head injury.
All subjects provided written informed consent after receiving a comprehensive explanation of the study procedure (IRB no. H-1201-008-392 and 1905/001-010). For minors, consent was formally obtained from both the participants and their parents. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of SNUH (IRB no. H-2312-013-1488).

Image acquisition and preprocessing

Structural images of all subjects were obtained with Siemens 3T Magnetom Trio MRI scanners using a T1-weighted 3D magnetization-prepared rapid gradient echo sequence (MPRAGE) on both the SNUH (first dataset, 152 subjects) and SNU (validation dataset, 72 subjects) campuses. The T1-weighted MPRAGE images were acquired with the following sequence parameters: for the first dataset, repetition time (TR)=1.67 s, echo time (TE)=1.89 ms, inversion time (TI)=0.9 s, field of view (FOV)=250 mm, flip angle=9°, voxel size=1 mm3, and slices=208; for the validation dataset, TR=2.4 s, TE=2.19 ms, TI=1 s, FOV=272 mm, flip angle=8°, voxel size=0.8 mm3, and slices=224.
The T1 images of all individuals were preprocessed using Advanced Normalization Tools (ANTs) [19,20]. Brain images were extracted using antsBrainExtraction, and to cross-check the accuracy of the process, two neuroscientists conducted a visual inspection. Next, the brain images were processed to remove inhomogeneities created during scanning. This step involved tissue segmentation into three classes: grey matter, white matter, and cerebrospinal fluid, followed by N4 bias field correction [21].

Brain template construction

We constructed a Korean age-specific brain template based on a previously published high-performance T1-weighted brain template constructed from MIITRA [2] images. Korean age-specific brain template construction involved an iterative process [22] based on the symmetric groupwise normalization method using the buildtemplateparallel (BTP) script [23].
First, preprocessed T1 images of each subject were rigidly aligned to the MNI template (1 mm×1 mm×1 mm) to standardize the images in Talairach space. The initial template (A0 image) was constructed by averaging rigidly registered images. Next, affine registration was conducted with rigidly registered individual T1 images as the input and A0 images as the reference. The mutual information and cross-correlation were selected as the cost function for optimization, followed by minimum deformation construction using the BTP script. The transformation matrix and deformations derived via affine registration were concatenated and applied to preprocessed T1 images to obtain an initial template for affine registration (A1n image). The affine registration process was iterated until convergence was reached, with a Pearson cross-correlation similarity index of 0.9995 between the initial template (A1n-1) and successive iterated templates (A1n).
The final step involved nonlinear registration with the aid of the final A1n image template and rigidly registered T1 images as input. The same method was applied for nonlinear registration, in which all transformations and deformations were concatenated to create an initial template for nonlinear registration (A2n image), ultimately yielding a final template (KOR152) with a Pearson cross-correlation similarity index of 0.9995 after the uttermost iteration.
Additionally, a gender-specific template KOR152 was created using the method described above: KOR152_M for males and KOR152_F for females. These gender-specific templates were constructed to account for potential morphological differences between males and females in the Korean population.

Global brain feature measurements

After the KOR152 template was constructed, the global brain features were measured between different templates using Medical Image Processing Analysis and Visualization software []. Four global features (anterior commissure [AC]-posterior commissure [PC] distance, length, width, and height) were measured as described by Bhalerao [13] and Tang [8] (Figure 1A). Using the sagittal plane, the distance from the AC to the PC was initially measured. The midpoint of the AC-PC distance was used to measure the length of the brain in the axial plane. The height of the brain was measured at the midpoint of the AC-PC line in the coronal plane, and the width of the brain was measured when the midpoint was aligned in both the coronal and the axial planes. Additionally, the ratios of width to length (W/L), height to length (H/L), and height to width (H/W) were calculated for comparative analysis and evaluation of the templates.


To validate the precision of the KOR152 template, a distinct validation dataset was subjected to registration to different templates (KOR152, MNI152, Indian_157, and CN200) [15,24]. The preprocessing step detailed in the above section (‘Image acquisition and preprocessing’ section) was performed on the validation dataset. The displacement magnitude (dis-Mag) was used to assess the change in distance before and after registration for each template utilizing Euclidean distance [18]. Additionally, the log of the Jacobian determinant (log-Jacob) was calculated to evaluate the volume changes during registration to each template. Differences in dis-Mag and log-Jacob between different templates were statistically significant according to multivariate analysis of variance and analysis of variance results.
To assess the sample size of the KOR152 template, standard templates with various sample sizes were constructed using the KOR152 dataset. A random bootstrapping method was applied to create five different sample-sized templates, with five different samples for each size [2]. A total of twenty-five standard templates were constructed using the procedure described in the aforementioned template construction section, with sample sizes of 20, 40, 60, 80, and 100. The accuracy of each standard template was measured by registering the template to the validation dataset and calculating dis-Mag and log-Jacob before and after registration to each standard template.


Global brain feature measurements

Compared to features of the MNI152 template, the KOR152 template was slightly shorter in length and height but uniform in width (Table 2 and Figure 1B). Compared to those of other Asian brain templates, the AC-PC length and height were largely in line, albeit relatively shorter in length. Despite these variations, the W/L, H/L, and H/W ratios showed consistency across all templates. The KOR152 was more circular in axial view and more elongated in coronal view compared to Indian_157 and MNI152 templates, as indicated in W/L and H/W ratios. In contrast, the KOR152 exhibited a less circular axial view and appeared less elongated in the coronal view compared to the CN200. The KOR152 template was generally smaller than the other brain templates, but the morphological characteristics of the KOR152 template seemed to be positioned between the MNI152 and CN200 templates.


The T1 images from the validation dataset were normalized to four distinct population templates (Indian_157, MNI152, CN200, and KOR152) to evaluate template variability between different populations. The changes in distance before and after normalization to each template are depicted as dis-Mag in Figure 2A and B, along with the changes in volume determined using log-Jacob (Figure 2C and D). When the data were normalized to the KOR152 template, the least distance change (dis-Mag) was observed compared to that of the other three templates: Indian_157, MNI152, and CN200. Dis-Mag significantly differed between the KOR152 template and Indian_157 (p<0.001), MNI152 (p<0.001), and CN200 (p<0.001) templates after Bonferroni correction. A significant difference was also observed between the Indian_157 and MNI152 templates (p<0.001), the Indian_157 and CN200 templates (p<0.001), and the MNI152 and CN200 templates (p<0.001). Differences were most pronounced in the cerebellum and the inferior temporal cortex (Figure 2A). In the CN200 template, distance changes were generally minimal; however, a major change was noted at the edge of the cerebellum in both hemispheres and in the inferior temporal cortex. When registered to the Indian_157 template, a pattern similar to that of the CN200 emerged but generally showed dispersed changes throughout the cerebellum. For the MNI152 template, there was a general increase in distance change throughout the brain area, with greater attention given to the frontal lobe and cerebellum. Finally, the KOR152 template showed minimal changes in distance, with some alterations at the edges of the inferior temporal lobe.
For the volumetric change (log-Jacob), the KOR152 template exhibited the least volumetric change, followed by the changes in the Indian_157, CN200 and MNI152 templates (Figure 2D). The statistical analysis results significantly differed between the KOR152 and Indian_157 (p<0.01) templates, and an even greater difference, after Bonferroni correction, was obtained when comparing the KOR152 to the MNI152 and CN200 templates (p<0.001). Like in dis-Mag, major areas of volumetric change were visible in the frontal lobe, cerebellum and inferior temporal lobe (Figure 2C), validating the variability when normalizing to different brain templates.
To validate the sample size used for KOR152 template construction, we created templates with different sample sizes, which revealed a decrease in average dis-Mag and average log-Jacob values as the sample size increased (Figure 3). Templates constructed with a smaller sample size showed increased variability and greater divergence from the original data in both dis-Mag and log-Jacob. The KOR152 template showed the smallest changes in both measurements, confirming that an appropriate sample size was used for template construction.


In this study, the KOR152 template was constructed using a state-of-the-art method for creating age-specific brain templates in Korean populations. Global brain feature analysis revealed that the KOR152 template generally had a shorter length than the other population templates evaluated (MNI152, Indian_157, and CN200). Further investigation revealed significant differences in volumetric and distance changes before and after registration to different population templates. The KOR152 template showed the smallest volumetric and distance changes compared to those changes in the MNI152, Indian_157, and CN200 templates. Additionally, the current iterative method for nonlinear and affine registration has shown improved precision and adequacy by requiring a suitable number of samples in the range of 120-150, which is lower than previously mentioned by Yang et al. [24] suggesting an adequate sample size. Overall, these findings highlight the importance of creating population-specific templates as well as appropriate sample sizes for template construction to ensure accurate and reliable results in neuroimaging studies.
All the global brain feature measurements except the width were smaller in the KOR152 compared to the MNI152 template. Variations in global brain characteristics influence the overall shape of the brain, leading to a more rounded appearance in the axial view and a more oval shape in the coronal view in the KOR152 template. Such morphological differences between the KOR152 and MNI152 templates indicate that the MNI152 template may not be appropriate for spatial normalization in the Korean population. Similarly, the Indian_157 and CN200 templates had uniform AC-PC lengths and heights of the KOR15 template but differed in overall length and width, suggesting modest morphological variations among Asian populations. In axial and coronal views, the overall shape of the CN200 template was most rounded and oval-shaped, respectively, followed by the KOR152 and Indian_157 templates. This finding illustrated subtle morphological differences among Asian populations, with the KOR152 template positioned between the Indian_157 and CN200 templates. These disparities may contribute to discrepancies, underscoring the morphological distinctions within East Asian populations. Thus, further validation analyses were performed to assess the accuracy and applicability of different templates in the Korean population.
The mean distance change (dis-Mag) before and after registering different templates to the validation dataset revealed distinct differences, with the foremost difference observed in the CN200 template followed by the Indian_157, MNI152, and KOR152 templates. The inferior temporal lobe and cerebellum were mostly affected by distance changes throughout the different population templates. The CN200 template showed minimal differences, specifically at the edge of the cerebellum and inferior temporal cortex. This finding is consistent with the wider width of global brain features, indicating substantial changes in the width compared to the original data when registered to the CN200 template. The Indian_157 template, on the other hand, exhibited slightly greater variation across the brain, particularly in the cerebellum. These findings are in parallel with previous studies that have demonstrated distinctive differences within Asian populations [10,18,24]. Additionally, registration to the MNI152 template revealed widespread changes in distance across the brain, indicating significant deviations from the original data. Finally, the KOR152 template with minimal overall changes in distance highlights the necessity of using population-specific brain templates for accurate analysis.
Volumetric alterations (obtained via log-Jacob analysis) also demonstrated similar patterns. The frontal lobe, inferior temporal lobe, and cerebellum emerged as the primary brain regions that underwent significant changes in volume. Unlike the distance change, the use of the MNI152 template showed the most volumetric changes among the different templates, followed by the CN200, Indian_157, and KOR152 templates; thus, the MNI152 template may not be suitable for accurate analysis of brain volume in the Korean population. Moreover, significant differences within Asian templates in distance and volume changes underscore the importance of utilizing population-specific brain templates for precise and reliable analysis in neuroimaging studies. Therefore, it is crucial to use population-specific brain templates when analyzing neuroimaging data, as demonstrated by the distinct differences in distance and volume changes observed.
In addition, sample size effects were analyzed to validate the sample size used to construct the KOR152 template. A random bootstrapping method was applied to create templates with sample sizes of 20, 40, 60, 80, and 100. The distance and volume changes (determined by the dis-Mag and log-Jacob analyses, respectively) from each sample template were calculated to compare the values with those of the KOR152 template. The analysis of sample size effects demonstrated that as the sample size increased, the distance and volume changes in the brain templates decreased. This illustrated that with the current template construction method, smaller changes can be observed with a sample size of 152 compared to previous studies that demonstrated a sample size of 200 [24].
The limitations of this study included the use of a registration algorithm for registering the validation datasets into different templates. The accuracy of spatial normalization is known to vary depending on the template used as well as the default settings of registration algorithms provided by different programs [25]. Our study adapted the ANTs SyN method for the registration algorithm [17,26], which is widely available and has outstanding registration performance. Second, the number of samples used for template construction should be noted. Although a previous study of the state-of-the-art method showed that the sample size plateaued at approximately 120 and 150, further studies could be beneficial for template construction if an even larger sample size were used to validate the suitability of the sample size effect on the construction. Finally, the effect of sex should be interpreted with caution due to the disproportionate female-to-male ratio used for the construction of the KOR152 template. To ensure that minimal confounding factors affected template construction, we validated the results with a proportionate dataset of males and females, which revealed the appropriateness of our KOR152 template to the Korean population. However, further study could be beneficial for validating the effect of sex.
In conclusion, this study has important implications. The MNI152 template and the Korean template [11] were constructed with a 1.5T scanner with bias due to manual segmentation techniques. 3T scanner images and the state-of-the-art method for template construction were used to capture Korean-specific morphological features with greater accuracy. Furthermore, in alignment with prior studies, it is evident that differences exist within the Asian population. This further emphasizes the necessity for a population-specific template to ensure accuracy in neuroimaging studies. The KOR152 T1-weighted template is available for download at


Availability of Data and Material

T he KOR152 brain template is available at:

Conflicts of Interest

Jun Soo Kwon, a contributing editor of the Psychiatry Investigation, was not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.

Author Contributions

Conceptualization: Jun Soo Kwon, Harin Oh, Minah Kim. Data curation: Sunghyun Park, Moonyoung Jang. Formal analysis: Harin Oh, Jongrak Kim. Investigation: all authors. Methodology: Harin Oh, Jongrak Kim. Software: Harin Oh. Validation: Minah Kim, Jun Soo Kwon. Writing— original draft: Harin Oh. Writing—review & editing: Jongrak Kim, Sunghyun Park, Moonyoung Jang, Minah Kim, Jun Soo Kwon.

Funding Statement

T his research was supported by the Basic Science Research Program, the Bio & Medical Technology Development Program, the Brain Science Convergence Research Program through the National Research Foundation of Korea (NRF) and the Basic Research Program of the Korea Brain Research Institute (KBRI). This research was funded by the Ministry of Science & ICT (2020M3E5D9079910, 2021M3A9E408078412, RS-2023-00266120, and 21-BR-03-01).


We would like to thank Kwak YB for helping with the initial analysis and the reviewers and editors for their valuable comments.

Figure 1.
Differences in global brain features (GBFs) between templates. A: Guidelines for GBF measurement with T1 KOR152 images. Distance from the anterior commissure to the posterior commissure (green line) was measured in the sagittal plane. Length (yellow line) and width (blue line) on the axial plane and height (red line) on the coronal plane are shown. The GBF measurements involved only brain areas and excluded nonbrain areas for accuracy. B: Length and width differences between brain templates are shown.
Figure 2.
Differences between population templates when registered to the same Korean population. Mean distance change (dis-Mag) before and after registration to each template. Specifying the average warp file in the axial view (z=46) (A) and overall violin plots showing significant differences. Mean volumetric change (log-Jacob) before and after registration to each template (B). Average warp file (z=57) (C) and violin plot for each template for comparison (D). **p<0.01; ***p<0.001, Bonferroni corrected.
Figure 3.
The effect of sample size was investigated with respect to mean distance (dis-Mag) (A) and volumetric change (log-Jacob) (B). For each sample size, five templates were constructed by the random bootstrapping method. The dis-Mag and log-Jacob values were calculated individually for each template and averaged by sample size for validation analysis.
Table 1.
Participant demographic information
KOR152 dataset (N=152) Validation dataset (N=72)
Age (yr) 24.1±6.1 (17-48) 22.63±3.16 (18-34)
 Male 100 39
 Female 52 33

Data are presented as mean±standard deviation (range) or N (%).

Table 2.
Global brain feature measurements in different brain templates
KOR152 MNI152 Indian_157 CN200 KOR152_M KOR152_F
AC-PC length (mm) 26 28 26 26 26 26
Length (mm) 156 170 161 158 158 153
Width (mm) 137 137 131 143 138 136
Height (mm) 98 108 98 98 98 98
W/L 0.878 0.806 0.814 0.905 0.873 0.889
H/L 0.628 0.635 0.609 0.620 0.620 0.641
H/W 0.715 0.788 0.748 0.685 0.710 0.721

All the measurements are in mm except for the ratios. AC, anterior commissure; PC, posterior commissure; W/L, width to length; H/L, height to length; H/W, height to width


1. Lancaster JL, Fox PT. Talairach space as a tool for intersubject standardization in the brain. In: Bankman IN, editor. Handbook of medical imaging. Orlando: Academic Press, 2000, p. 555-567.

2. Ridwan AR, Niaz MR, Wu Y, Qi X, Zhang S, Kontzialis M, et al. Development and evaluation of a high performance T1-weighted brain template for use in studies on older adults. Hum Brain Mapp 2021;42:1758-1776.
pmid pmc
3. Lalys F, Haegelen C, Ferre JC, El-Ganaoui O, Jannin P. Construction and assessment of a 3-T MRI brain template. Neuroimage 2010;49:345-354.
4. Mazziotta JC, Toga AW, Evans A, Fox P, Lancaster J. A probabilistic atlas of the human brain: theory and rationale for its development: the international consortium for brain mapping (ICBM). Neuroimage 1995;2:89-101.
5. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, et al. A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 2001;356:1293-1322.
pmid pmc
6. Mandal PK, Mahajan R, Dinov ID. Structural brain atlases: design, rationale, and applications in normal and pathological cohorts. J Alzheimers Dis 2012;31(Suppl 3):S169-S188.
pmid pmc
7. Chee MW, Zheng H, Goh JO, Park D, Sutton BP. Brain structure in young and old East Asians and Westerners: comparisons of structural volume and cortical thickness. J Cogn Neurosci 2011;23:1065-1079.
8. Tang Y, Hojatkashani C, Dinov ID, Sun B, Fan L, Lin X, et al. The construction of a Chinese MRI brain atlas: a morphometric comparison study between Chinese and Caucasian cohorts. Neuroimage 2010;51:33-41.
9. Uchiyama HT, Seki A, Tanaka D, Koeda T, Jcs Group. A study of the standard brain in Japanese children: morphological comparison with the MNI template. Brain Dev 2013;35:228-235.
10. Rao NP, Jeelani H, Achalia R, Achalia G, Jacob A, Bharath RD, et al. Population differences in brain morphology: need for population specific brain template. Psychiatry Res Neuroimaging 2017;265:1-8.
11. Lee JS, Lee DS, Kim J, Kim YK, Kang E, Kang H, et al. Development of Korean standard brain templates. J Korean Med Sci 2005;20:483-488.
pmid pmc
12. Jao T, Chang CY, Li CW, Chen DY, Wu E, Wu CW, et al. Development of NTU standard Chinese brain template: morphologic and functional comparison with MNI template using magnetic resonance imaging. Annu Int Conf IEEE Eng Med Biol Soc 2009;2009:4779-4782.
13. Bhalerao GV, Parlikar R, Agrawal R, Shivakumar V, Kalmady SV, Rao NP, et al. Construction of population-specific Indian MRI brain template: morphometric comparison with Chinese and Caucasian templates. Asian J Psychiatr 2018;35:93-100.
14. Bai J, Abdul-Rahman MF, Rifkin-Graboi A, Chong YS, Kwek K, Saw SM, et al. Population differences in brain morphology and microstructure among Chinese, Malay, and Indian neonates. PLoS One 2012;7:e47816
pmid pmc
15. Nisbett RE, Miyamoto Y. The influence of culture: holistic versus analytic perception. Trends Cogn Sci 2005;9:467-473.
16. Nisbett RE, Masuda T. Culture and point of view. Proc Natl Acad Sci U S A 2003;100:11163-11170.
pmid pmc
17. Lee H, Yoo BI, Han JW, Lee JJ, Oh SY, Lee EY, et al. Construction and validation of brain MRI templates from a Korean normal elderly population. Psychiatry Investig 2016;13:135-145.
18. Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B, et al. Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology 2000;216:672-682.
19. Avants BB, Tustison N, Johnson H. Advanced normalization tools (ANTS): release 2.x [Internet]. Available at: Accessed February 19, 2022.

20. Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 2011;54:2033-2044.
21. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010;29:1310-1320.
pmid pmc
22. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 2011;54:313-327.
23. Avants BB, Yushkevich P, Pluta J, Minkoff D, Korczykowski M, Detre J, et al. The optimal template effect in hippocampus studies of diseased populations. Neuroimage 2010;49:2457-2466.
24. Yang G, Zhou S, Bozek J, Dong HM, Han M, Zuo XN, et al. Sample sizes and population differences in brain template construction. Neuroimage 2020;206:116318
25. Chen T, Yuan M, Tang J, Lu L. Digital analysis of smart registration methods for magnetic resonance images in public healthcare. Front Public Health 2022;10:896967
pmid pmc
26. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 2008;12:26-41.
Facebook Twitter Linked In Google+
METRICS Graph View
  • 0 Crossref
  •   Scopus
  • 624 View
  • 40 Download


Browse all articles >

Editorial Office
#522, 27, Seochojungang-ro 24-gil, Seocho-gu, Seoul 06601, Korea
Tel: +82-2-717-0892    E-mail:                

Copyright © 2024 by Korean Neuropsychiatric Association.

Developed in M2PI

Close layer
prev next