Neurol India Home 

Year : 2019  |  Volume : 67  |  Issue : 1  |  Page : 229--234

Construction of Indian human brain atlas

Jayanthi Sivaswamy1, Alphin J Thottupattu1, Raghav Mehta2, R Sheelakumari3, Chandrasekharan Kesavadas3,  
1 Center for Visual Information Technology, International Institute of Information Technology (IIIT), Hyderabad, Telangana, India
2 Center for Visual Information Technology, International Institute of Information Technology (IIIT), Hyderabad, Telangana; Probabilistic Vision Group, Centre for Intelligent Machines, Department of electrical and Computer Engineering, McGill University, Montreal, QC, Canada
3 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India

Correspondence Address:
Dr. Jayanthi Sivaswamy
Center for Visual Information Technology, IIIT, Hyderabad, Telangana


Context: A brain magnetic resonanace imaging (MRI) atlas plays an important role in many neuroimage analysis tasks as it provides an atlas with a standard coordinate system which is needed for spatial normalization of a brain MRI. Ideally, this atlas should be as near to the average brain of the population being studied as possible. Aims: The aim of this study is to construct and validate the Indian brain MRI atlas of young Indian population and the corresponding structure probability maps. Settings and Design: This was a population-specific atlas generation and validation process. Materials and Methods: 100 young healthy adults (M/F = 50/50), aged 21–30 years, were recruited for the study. Three different 1.5-T scanners were used for image acquisition. The atlas and structure maps were created using nonrigid groupwise registration and label-transfer techniques. Comparison and Validation: The generated atlas was compared against other atlases to study the population-specific trends. Results: The atlas-based comparison indicated a signifi cant difference between the global size of Indian and Caucasian brains. This difference was noteworthy for all three global measures, namely, length, width, and height. Such a comparison with the Chinese and Korean brain templates indicate all 3 to be comparable in length but signifi cantly different (smaller) in terms of height and width. Conclusions: The findings confirm that there is significant difference in brain morphology between Indian, Chinese, and Caucasian populations.

How to cite this article:
Sivaswamy J, Thottupattu AJ, Mehta R, Sheelakumari R, Kesavadas C. Construction of Indian human brain atlas.Neurol India 2019;67:229-234

How to cite this URL:
Sivaswamy J, Thottupattu AJ, Mehta R, Sheelakumari R, Kesavadas C. Construction of Indian human brain atlas. Neurol India [serial online] 2019 [cited 2021 May 17 ];67:229-234
Available from:

Full Text

The significant difference in the shape and size of human brains across different races poses a great challenge for functional and structural comparison in neuroscience research.[1] Hence, spatial normalization is a critical preprocessing step in automated neuroimage analysis techniques such as voxel-based morphometry (VBM). Spatial normalization involves matching the brain of individual subjects to a standard coordinate framework using various registration methods.[2] Such a representation of the brain using a standard coordinate framework is known as a brain atlas. Among other things, a brain atlas facilitates comparison and/or combining of findings from different brain imaging modalities (functional or structural, e.g., functional MRI (fMRI), diffusion tensor imaging (DTI), T1, T2, fluid-attenuated inversion recovery (FLAIR), etc.), different brain states (healthy or diseased), and different subjects.[3]

The Talairach and Tournoux atlas, also popularly known as the Talairach atlas, is the oldest and the most widely used brain atlas.[4] This atlas is a set of drawings created manually from the postmortem brain sections of a 60-year-old French woman. A digital atlas is typically created by registering the volumes, which is a process of spatially aligning images. In a groupwise registration method, a common coordinate system is identified for a group of images such that all images are at a minimum equal deformation distance from the coordinate system. An atlas is then created by warping these images into this coordinate system and taking their average. The Montreal Neurological Institute (MNI) and the International Consortium for Brain Mapping (ICBM) created the first digital human brain atlas using the brain MRI volume of 305 young normal Caucasian subjects (male[M]/female[F] = 239/66), in the year 1993.[5] This atlas was created using a 12-parameter, linear, groupwise registration method. In recent years, MNI and ICBM have released two more brain atlases, namely, MNI-152[6] and ICBM-452.[7] The MNI-152 is also widely used as a standard coordinate system in many neuroscience studies. Atlases have also been constructed from multiple scans of an individual subject, examples of which are Colin 27[8] and French atlas.[9]

However, these brain atlases do not account for differences across phenotypic groups (e.g., age, gender, race, or disease conditions) as they are created using brain MRI of mostly Caucasian population. Of late, many studies have shown that there is a morphological difference across phenotypes. Specifically, this difference relative to the Caucasians has been reported for the East Asian populations [1],[10] as well as for the African-American ones.[11] These studies have motivated the creation of population-specific brain atlases, as spatial normalization of non-Caucasian brain to Caucasian atlas can result in measurement errors.[12] Initial efforts at such population-specific brain atlases resulted in the Chinese atlas, created using 56 right-handed male subjects (age range: 1.81-24.6 years)[1] and the Korean atlas, constructed using MRI and positron-emission tomography images of 78 (49 male and 29 female) right-handed subjects (age range: 19.4-44.6 years).[13] An atlas with 27 subjects (M/F = 17/10) has also been constructed to study population-dependent variations in brain morphology among people from India. However, subjects considered are largely males from a local region.[14]

 Materials and Methods

The study, as approved by the Institute Review Board, involved collecting MRI volumes of young adults after obtaining an informed consent in writing. The work described has been carried out in accordance with the Code of Ethics of the World Medical Association.


100 young healthy adults (M/F = 50/50), aged 21–30 years, were recruited for the study. The volunteers were largely senior undergraduate and postgraduate students hailing from different geographical locations of India. The age distribution of the volunteers is shown in [Figure 1]. Each subject underwent a medical examination to rule out any psychological or neurological disorders. All images were examined by an experienced neuroradiologist to ascertain that no structural abnormalities were present.{Figure 1}

Image acquisition

MR images of these subjects were acquired at three different hospitals in Hyderabad. The subjects were divided into three groups with each group having approximately equal male and female subjects. The first group (17M/16F) was scanned using a Siemens 1.5-T MRI scanner with T1 MPRAGE sequence, second group (16M/17F) was scanned using a GE 1.5-T MRI scanner with T1 BRAVO sequence, while the last group (17M/17F) was scanned using a Phillips 1.5-T MRI scanner for T1 3D TFE sequence. All MRI volumes were acquired in the sagittal plane. The quality of the MRI volumes was assessed and approved by an experienced radiologist. Scanning parameters can be found in [Table 1].{Table 1}

Data preprocessing

Every MRI volume was preprocessed using a standard pipeline consisting of N4-Bias field correction [15] and denoising with nonlocal mean filtering.[16] As the data were collected from three different scanner manufacturers with different acquisition parameters, intensity range of all the volumes varied significantly. Intensity standardization was done using a histogram-matching-based method [17] as it is also an internal part of the advanced normalization tools (ANTs) package used for the Atlas construction (

All the volumes were automatically aligned with respect to their anterior commissure (AC)–posterior commissure (PC) line using an algorithm proposed by Ardekani et al.,[18] available with automatic resistration and toolbox (ART) package ( This was further corrected by a senior radiologist using the medical image processing, analysis, and visualization (MIPAV) tool (

Atlas construction

An atlas was constructed with all the 100 T1 MRI volumes by a nonrigid groupwise registration method [19] using the ANT's toolbox. This method aids in the derivation of an optimal atlas which is unbiased with respect to both shape and appearance in the diffeomorphic space. The atlas derived from 100 volumes will henceforth be referred to as the IBA100 atlas. Two gender-specific atlases (IBA50M for male and IBA50F for female) were also constructed using the same pipeline.

Structure map creation

The 100 MRI volumes used for atlas creation were automatically segmented/marked for 7 structure pairs using label transfer:[20] thalamus, putamen, pallidum, hippocampus, amygdala, caudate nucleus, and accumbens area. These 3D markings were verified and corrected by a medical expert in the three views. The corrected volumes were cross-checked by another medical expert. The experts used ITK-SNAP tool (an interactive software application that permits navigation of 3-D medical imag edseveloped by researchers at University of Pennsylvania and Utah, USA), for correcting the markings of seven structure pairs. The markings were first transferred to the atlas space and the coregistered structure markings combined to generate a probability map for each structure.


The constructed atlases and structure maps are given in [Figure 2] and [Figure 3], respectively. They were analyzed at both global and local structure levels.{Figure 2}{Figure 3}

Global brain feature comparison across different population atlases

The constructed atlas was validated against atlases available for other populations.[1],[3],[4],[5],[6],[7],[13],[21],[22] The global brain features such as length, width, height, and volume of whole brain were measured on the atlas and compared. These were defined on the cerebrum as follows: length: distance between extreme anterior and posterior points; width: distance between extreme right (R) and left (L) points; and height: distance between extreme superior (S) and inferior (I) points. These measurements were performed by a senior neuroradiologist. Further measurements made were the width-to-length (W/L), height-to-length (H/L), and height-to-width (H/W) ratio: the distance between AC and PC of the brain.

We automatically segmented brain MRI volume in tissue probability class of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using FAST [23] algorithm of FSL toolbox.[7] The total brain volume was computed as the net volume of all the voxels for which sum of the three tissue probabilities was more than 0.5. The tabulated measurements in [Table 2] indicate that the brain size of western population (MNI305, MNI152, and ICBM452) is significantly higher compared to the eastern population (Chinese2020 and Korean96).{Table 2}

While the Indian brain is closer to the Chinese2020 brain in length, it differs in terms of width and height, whereas the difference in all dimensions is low when compared to the Korean96. Thus, the brain size varies even among the eastern population.

Based on the tabulated results for IBA100 atlas, it can be observed that the Indian brain, on an average, is smaller in height, width, and volume compared to MNI152,[6] Chinese2020,[22] and Korean96[21] atlases.

Gender-specific atlas analysis

Brain size and shape have been reported to vary across gender.[21],[24] In order to verify if this holds for the Indian population, a gender-based separation and analysis was done of the 100 subjects for the whole brain as well as GM, WM, and CSF volumes. The global brain-size difference across gender was assessed using an independent t-test (two-tailed). Results are listed in [Table 3].{Table 3}

Based on these results, we observe that on average, the male brain is larger than female brain in all the three dimensions, namely, length, width, and height. Consequently, the male brain also has a higher total volume as well as higher CSF, GM, and WM volumes relative to the female brain. However, W/L, H/L, and H/W ratios are comparable for both genders. These findings are consistent with reports on other populations.

Structure level brain feature comparison with other structure maps

Next, we compared IBA100 with two different population atlases at the structure level, namely, LPBA40[25] and Chinese2020.[22] This comparison is motivated by the fact that atlases are popular in automated structure segmentation [26],[27] and selection of the structure atlas could be important. The Chinese2020 atlas is labeled by the automated anatomical labeling (AAL) atlas,[28] where 45 anatomical volumes of interest in each hemisphere are marked, and the LPBA40 has manual markings for 56 structures. IBA100 has manual markings for six structures in two hemispheres. Two of these structure pairs, namely, hippocampus and putamen, are common with other two population atlases and hence their average volumes (measured in the respective atlas space) were compared. These averages are tabulated as percentage of the total brain volume in [Table 4]. The size of structures of Indian population is more similar to that in Chinese population. Overall, however, there is a significant difference in the structure volumes across the three atlases. Thus, variation across population appears to also hold at the structure level.{Table 4}

Atlas validation by linear registration

Validation was done using linear registration as it compensated for any of the global differences, such as rotation, scaling, translation, affine, etc., between the volume from validation set and the atlas volume. This allowed us to compare global brain features of the validation set before and after registration to an atlas space. For this purpose, MRIs of 15 additional volunteers (8 males, 7 females) were collected. They were divided into three groups of five volunteers. Each group was scanned using either GE, Siemens, or Phillips scanner using parameters mentioned in [Table 1].

Brain MRI volumes Iv of these 15 volunteers (5 male, 24.50 2.67) were aligned to each of the 3 different atlases, namely, IBA100, Chinese2020, and MNI152, using a 12-parameter transformation as implemented in advanced normalization tools (ANTs). The registered result is denoted in general as Ivr. The global brain features in Iv and Ivr were then compared using a paired t-test to obtain three P values. These are denoted as P1, P2, and P3, for MNI152, Chinese2020, and IBA100, respectively. The global deformations required to register a brain to an atlas can thus be quantitatively evaluated. A similar validation method was recently used [22] where it was reported that comparatively smaller global deformation was required to register brains of Chinese subjects to the Chinese2020.

In [Table 5], we can observe that P1 and P2 have lower values for all the global brain features. This indicates that the distributions of Iv and Ivr obtained with MNI152 or Chinese2020 are significantly different implying a high global deformation. This is not true when registration is to IBA100 as P3 has higher values for all the brain features, indicating marginal difference between Iv and Ivr. This shows that IBA100 atlas is near to the average Indian brain.{Table 5}

Structure map validation with expert marking

The probability map, created by using the 100 coregistered volumes, is used to create a maximum likelihood structure map. The maximum probability structure map is registered with each of the 15 validation volumes, used in 3.4, and the labels are transferred to get the corresponding labels for the 15 validation volumes.[20] On the same 15 volumes, we got markings from experts for two structure pairs: hippocampus and putamen. These markings were done by one medical expert and cross-checked by a senior expert. We analyzed whether there was a good agreement between population-specific structure-map-based segmentation and the expert markings for the two structure pair segmentations. For comparison, we used dice coefficient and volume statistics of two structure pairs – hippocampus and putamen, and the details are given in [Table 6].{Table 6}


Our work is mainly focused on studying the importance of population-specific atlases. The result of this study indicates that there is a significant difference between global size of Indian (IBA100) and Caucasian brain (MNI152). This difference was noteworthy for all three global measures, that is, length, width, and height. Comparison of Indian atlas (IBA100) with Chinese (Chinese2020) and Korean (KNE96) shows that Indian brain is comparable in terms of length with both the atlases, while it was significantly smaller in terms of height and width. From the analysis based on the structure maps (IBA100, LPBA40, Chinese2020), we can conclude that significant difference extends to the structure level. IBA100 is more comparable to atlases of its geographical neighbors, namely, the Chinese (Chinese2020) and Korean (KNE96) than distant Caucasian population (MNI152).

Validation results of IBA100 against MNI152, Chinese2020, and Korean96 indicated a few positive aspects when dealing with a new volume from the Indian population: less global deformation is required to register to IBA100 atlas, and there is more similarity in size at global and structural levels with IBA100 relative to other atlases. We have released the following products publicly via

IBA100 brain atlasMale and Female atlases (IBA50M, IBA50F)Tissue probability maps: WM, GM, CSFMaximum likelihood structure maps of seven cortical structures.

Spatial mismatch and mislocalization between brain structures have been reported when MNI-152 atlas was used instead of population-specific atlas.[12] Voxel-based morphometry (VBM) and fMRI analysis require spatial normalization and hence should benefit from using IBA100, when the volume to be analyzed is from the Indian population.

The study is limited by the number of subjects (100) but the findings are compelling enough to motivate future work on building an atlas with a much larger as well as more heterogeneous mix set of subjects with a diverse set of education levels. The latter is reported to affect the brain anatomy.[29] Similarly, deriving atlases for different age groups will also be of interest to check the effect of age-related factors on brain anatomy.

Financial support and sponsorship

Department of Science and Technology, Govt. of India, under Grant SR/CSRI/194/2013(G).

Conflicts of interest

There are no conflicts of interest.


1Tang 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.
2Ashburner J, Friston KJ. Voxel-based morphometry: The methods. Neuroimage 2000; 11:805-21.
3Evans AC, Janke AL, Collins DL, Baillet S. Brain templates and atlases. Neuroimage 2012;62:911-22.
4Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain. 3-Dimensional proportional system: An approach to cerebral imaging 1988;39:145.
5Evans AC, Collins DL, Mills S, Brown E, Kelly R, Peters TM. 3D statistical neuroanatomical models from 305 MRI volumes. In Nuclear Science Symposium and Medical Imaging Conference, 1993 IEEE Conference Record. (IEEE), 1813-7.
6Mazziotta 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;356:1293-322.
7Mazziotta 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.
8Collins DL, Holmes CJ, Peters TM, Evans AC. Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping 1995;3:190-208.
9Lalys F, Haegelen C, Ferre JC, El-Ganaoui O, Jannin P. Construction and assessment of a 3-TMRI brain template. Neuroimage 2010;49:345-54.
10Uchiyama 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;35:228-35.
11Isamah N, Faison W, Payne ME, MacFall J, Steffens DC, Beyer JL, et al. Variability in frontotemporal brain structure: The importance of recruitment of African Americans in neuroscience research. PLoS One 2010;5:e13642.
12Jao 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. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE (IEEE), 4779-82.
13Lee JS, Lee DS, Kim J, Kim YK, Kang E, Kang H, et al. Development of Korean standard brain templates. J Korean Med Sci 2005;20:483-8.
14Rao 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.
15Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: Improved N3 bias correction. IEEE TransMed Imaging 2010;29:1310-20.
16Manjón, JV, Coupé P, Martí-Bonmatí L, Collins DL, Robles M. A daptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging 2010;31:192-203.
17Avants 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;49:2457–66.
18Ardekani BA, Bachman AH. Model-based automatic detection of the anterior and posterior commissures on MRI scans. Neuroimage 1999;46:677-82.
19Avants B, Sundaram T, Duda J, Gee J, Ng L. Non-rigid image registration. In: Yoos TS (editor) Insight into Images. Principles and Practice for Segmentation, Registration and Image Analysis. Wellesey: A.K Peters. 2004;307-48.
20Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, et al. Automated Talairach atlas labels for functional brain mapping. Human Brain Mapp 2000;10:120-31.
21Lee 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;13:135-45.
22Liang P, Shi L, Chen N, Luo Y, Wang X, Liu K, et al. Construction of brain atlases based on a multi-center MRI dataset of 2020 Chinese adults. Sci Rep 2015;5. doi: 10.1038/srep18216.
23Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001;20:45-57.
24Xing W, Nan C, Zhen Tao Z, Rong X, Luo J, Zhuo Y, et al. Probabilistic MRI brain anatomical atlases based on 1,000 Chinese subjects. PloS One 2013;8:e50939.
25Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, et al. Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 2008;39:1064-80.
26Khan AR, Wang L, Beg MF. Freesurfer-initiated fully-automated subcortical brain segmentation in MRI using large deformation diffeomorphic metric mapping. Neuroimage 2008;41:735-46.
27Pohl KM, Bouix S, Nakamura M, Rohlfing T, McCarley RW, Kikinis R, et al. A hierarchical algorithm for MR brain image parcellation. IEEE Trans Med Imaging 2007;26:1201-12.
28Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 2002;15:273-89.
29Coffey C, Saxton J, Ratcliff G, Bryan R, Lucke J. Relation of education to brain size in normal aging. Neurology 1999;53:189-96.