neuroanatomical database of normal japanese brains

2003 Special issue

Neuroanatomical database of normal Japanese brains

Kazunori Satoa, Yasuyuki Takia, Hiroshi Fukudaa,b, Ryuta Kawashimab,c,*

aIDAC, Tohoku University, 4-1 Seiryocho, Aobaku Sendai 980-8575, JapanbAoba Brain Imaging Research Center, TAO, Sendai 980-8575, Japan

cNICHe, Tohoku University, 4-1 Seiryocho, Aramakiaza Aoba, Aobaku, Sendai 980-8579, Japan

Received 21 October 2002; revised 3 June 2003; accepted 3 June 2003

Abstract

To develop a high-resolution in vivo human neuroanatomy database for Japanese brains, a data set on 1547 normal subjects between the

ages of 16 and 79 years has been collected. First, we determined individual reference brains of normal Japanese for each age and gender

group. We found there exists cross-generational changes in brain shape, that is, the young generation has a shorter and wider brain than the

older generation. Second, we determined the effect of aging on the volume of gray matter and white matter by voxel based morphometry. A

significant negative correlation between the gray matter volume and age was observed. We assume that this kind of database is quite useful to

investigators who are engaged in basic neuroscience, clinical diagnostics and evaluation of neuropsychiatric disorders in patients.

q 2003 Elsevier Ltd. All rights reserved.

Keywords: Magnetic resonance imaging; Human; Brain; Database; Reference brain; Age related changes; Gray matter; White matter

1. Introduction

The shape of the cerebral hemispheres is not determined

by accidental phenomena, but is sculpted by complex

interactions between numerous genetic and environmental

processes during life. For example, neurogenesis, migration,

myelination, and development of connectivity are major

events influencing gyrification and hemispheric shape

during fetal and early postnatal periods (Van Essen,

1996). In the previous neuroimaging investigations, there

are several anatomical studies of gyral pattern and its

intersubject variability (Evans, Collins, & Holmes, 1996; Le

Goualher et al., 2000; Royackkers, Desvignes, Fawal, &

Ravenu, 1999; Zilles et al., 1997). In addition, there exists a

few studies that assess brain shape using deformation based

morphometry using relatively small number of subjects

(Ashburner et al., 1998; Gaser, Nenadic, Buchsbaum,

Hazlett, & Buchsbaum, 2001).

There is no doubt that quantitative results from a large

data set of normal brain imaging could be used to estimate

and eliminate the effects of group differences in behavioral

measures on the dependent anatomic measures. For

example, although using a relatively small number of

samples, Leonard et al. (1999) showed the relationship

between structural abnormalities and schizophrenia, and

Zilles, Kawashima, Dabringhaus, Fukuda, and Schormann

(2001) showed inter-ethnic differences in hemispheric shape

using three-dimensional measurements of brain anatomy by

magnetic resonance imaging (MRI). Identifying and con-

trolling relevant variables that correlate with brain anatomy

would be an essential element in establishing the credibility

of derived, quantitative MRI measures that cannot be

validated by simply having an experienced radiologist

inspect a scan using his or her own internalized database of

experience to define normality. However, to our knowledge,

there exists no comprehensive database that can be used for

the complete, individual subjects, neuroimaging data sets

for the human brain anatomy in an interactive, efficient and

simple manner. It is obvious that such a system would

increase the value of both clinical and research funds spent

on these important and costly studies.

Therefore, the primary neuroinformatics purpose of this

study was to develop a high-resolution in vivo human

neuroanatomy database for Japanese brains. To use this

database, we selected an individual reference (standard)

brain of each age group and each gender group, which

has the least deviation in brain shape with respect to

0893-6080/$ - see front matter q 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/j.neunet.2003.06.004

Neural Networks 16 (2003) 1301–1310

www.elsevier.com/locate/neunet

* Corresponding author. Address: NICHe, Tohoku University,

Aramakiaza Aoba, Aobaku, Sendai 980-8571, Japan. Tel./fax: þ81-22-

217-4088.

E-mail address: [email protected] (R. Kawashima).

http://www.elsevier.com/locate/neunet

the database and then evaluated age-related structural

changes of cerebral hemispheres.

2. Methods

2.1. Development of anatomical brain imaging data

Samples. The subjects were Japanese volunteers

recruited by the Aoba Brain Imaging Research Center,

Sendai, Japan. All were normal and right-handed. The group

consisted of 772 men (age range: 16–79 years) and 775

women (age range: 18–79 years) (Table 1). Data from

subjects who had a history or symptoms of a central nervous

system disease of any kind or brain injury were excluded

from the database. Each subject was interviewed by medical

doctors of the Institute of Development, Aging and Cancer

(IDAC), Tohoku University. Written informed consent was

obtained from each subject after a full explanation of the

purpose and procedures of the study according to the

declaration of Helsinki, 1991, prior to MRI. Approval for

these experiments was obtained from the Institutional

Review Board of the IDAC, Tohoku University.

Imaging. Three different images of the brain were

obtained from each subject using the same 0.5 T MR

scanner (Signa contour, GE-Yokogawa Medical Systems,

Tokyo). Protocols of MRI are summarized in Table 2. Prior

to further computational procedures, all MR images were

filmed in a conventional format and inspected by experi-

enced radiologists in IDAC. Subjects with any abnorma-

lities including brain tumor, infarction, arachnoid cyst, and

normal variations (e.g. mega cisterna magna, and cavum

septum pellucidum) were excluded from this study. Data

from subjects with multiple and extensive lesions in the

white matter were also excluded from the database.

Data processing. All MRIs were transferred to the

Montreal Neurological Institute and were processed auto-

matically in Silicon Graphics workstations as follows. First,

intensity nonuniformity in MRI data was corrected by the

nonparametric nonuniform intensity normalization method

(Sled, Zijdenbos, & Evans, 1998). Next, MRIs were

transformed into the Talairach stereotactic space (Talairach

& Tournoux, 1988) using nine linear parameters, namely

three scaling, three rotations, and three translations (Collins,

Neelin, Peters, & Evans, 1994). In this process, we used an

average brain derived from 305 normal brains for

the standardization procedure (Collins et al., 1994). Then,

tissues were classified with an artificial neural network

classifier (Evans, Frank, Antel, & Miler, 1997). This method

involved classifying each pixel according to its combination

of intensities from each image type. In this study, the

cerebellum and the brain stem were automatically excluded

from MR images using the same masks (Collins et al.,

1998). Following these processes, the MRI maps of the

binary gray matter, white matter and CSF masks were

obtained.

Data Storage. The database consists of three com-

ponents, that is, the raw image data set (i.e. 3D

multispectral MRI structural studies of an individual

subject), the processed image data set, and the associated

text data set. All image data sections are a stack of

images in both ANALYZE and MINC formats. In order

to protect subject confidentiality, patient information was

stripped from all data. Text data provide information on

each subject, e.g. subject ID, experimental protocol ID,

age, gender, health status, assessments (e.g. handedness),

and medication status. Information that can identify an

individual subject, e.g. name, contact address, and

birthday, is stored in the hidden layer of the database,

where only an administrator can access.

2.2. Selection for individual reference brain

Subjects. In this analysis, subjects in the database were

divided into 10 groups according to age and sex; that is, the

groups with members in their decade of age for each gender.

To reduce the calculation time, every 50 subjects were

selected arbitrarily from each group and used for the

analysis.

Image processing. Image data processing was performed

on raw T1-weighted images of each age–sex group. Tissues

other than those of the brain were removed from

T1-weighted images of brains of all the subjects by the

automatic brain extraction method using erosion-dilation

treatment (Miura et al., 2002). One brain was selected as a

template brain and all the other brains in the group were

spatially normalized to this template brain with 12-

parameter-affine-transformation of Automated Image

Registration (AIR) 5 (Woods, Grafton, Holmes, Cherry, &

Mazziotta, 1998). The mean value of cost functions

(standard deviation of ratio images) for each transformation

to this template was calculated. Then, another subject’s

brain was selected as the next template brain, and the same

process was executed. All the brains in the group were used

Table 1

Number of subjects in each age group

Age

,19 20–29 30–39 40–49 50–59 60–69 70 ,

Male 51 179 117 131 158 135 31

Female 9 120 112 159 224 131 20

Table 2

MRI protocols

Protocol TR

(ms)

TE

(ms)

FA Matrix Size

(mm)

T1-weighted SPGR 40 7 30 256 £ 256 £ 124 1 £ 1 £ 1.5

T2-weighted SE 2860 15 – 256 £ 256 £ 63 1 £ 1 £ 3

Proton density SE 2860 120 – 256 £ 256 £ 63 1 £ 1 £ 3

K. Sato et al. / Neural Networks 16 (2003) 1301–13101302

as the template brain and the means of cost functions were

calculated. Hereby, we obtained 50 means of cost functions

in one group. Fig. 1 shows an example of the means of cost

functions for the group of females in their seventh decade. A

brain that yields a minimum mean of cost functions was

selected as the reference subject in the group. Finally, all

selected brains were spatially normalized to the Talairach

space using a 7-parameter global rescaling model which

keeps the shape of each brain.

In the next step, all the brains in a group were spa-

tially transformed to the selected reference brain, and

the transformation matrices were obtained. From these

matrices, the transformation matrices, which define the

average size and shape of the brains in the group, were

calculated with AIR. Using this transformation, the brain of

the reference subject was transformed to the average shape

and size in this group. The transformed brain was

determined as the reference (standard) brain of this age–

sex group.

2.3. Evaluation of age-related structural changes of brain

In this analysis, brain MRIs of 350 males (mean (SD)

age, 45.5 (15.6) years; age range, 16–79 years) and 410

females (mean (SD) age, 47.5 (13.4) years; age range,

19–78 years) were analyzed. Statistical analysis of the local

volume change of the gray matter and white matter was

carried out by statistical parametric mapping (SPM)

(SPM99, Wellcome Department of Cognitive Neurology,

London, UK) (Friston et al., 1995) in Matlab (Math Works,

Natick, MA, USA). The stereotactically normalized gray

matter and white matter binary images were smoothed using

a 20-mm full-width at half-maximum isotropic Gaussian

kernel. Smoothing made the data more normally distributed

(Ashburner & Friston, 2000). The voxel values in the

resulting smoothed gray or white matter images were

referred to as the gray or white matter concentration. To

investigate regionally the specific effects of age on the

imaging data, simple regression analysis was performed.

The voxel value was used as a dependent variable and age

was used as an independent variable. For the correction of

multiple comparisons, height threshold was corrected and

Fig. 1. An example showing cost functions of 50 subjects (Female: aged

60–69). The subject numbers are sorted with respect to cost. The subject

with minimum mean, indicated with an arrow was selected as a standard

subject in this group.

Fig. 2. Examples of trans-axial slices of female reference brains. The left side of the image shows the left hemisphere of the brain.

K. Sato et al. / Neural Networks 16 (2003) 1301–1310 1303

p , 0:05 was considered significant. The results of SPM

image analysis were superimposed on structural images on

horizontal slices, which were the average images of all male

or female subjects’ normalized T1-weighted images, to

facilitate correlation with anatomy.

3. Results


Figs. 2 and 3 show examples of trans axial sections of

generated Japanese reference brain for each age group of

females and males, respectively. Figs. 4 and 5 represent a 3D

view of these reference brains of female and male,

respectively. The ratio of maximum width (right–left) to

maximum length (anterior–posterior) of the reference brains

was 0.867 in second-decade-females, 0.828 in sixth-decade-

females, 0.860 in second-decade-males, and 0.831 in sixth-

decade-males. Both in male and in female, a younger

reference brain had a higher ratio than an older reference

brain, which means that young brains are shorter and wider

than older brains. In each group, the left hemisphere was

larger than the right hemisphere. All the data of normal

Japanese reference brains will be presented on our web site

(http://www.idac.tohoku.ac.jp/JHBP/).


The cross-sectional analysis of the correlation between

gray matter volume and age showed a significant

negative correlation between the gray matter volume

and age in both female ðR2 ¼ 0:38; p , 0:001Þ and male

ðR2 ¼ 0:58; p , 0:001Þ (Fig. 6), while the volume of the

white matter did not show significant change with age

in female ðR2 ¼ 0:02; p ¼ 0:702Þ and male ðR2 ¼

0:02; p ¼ 0:748Þ (Fig. 7). As for the correlation between

gray matter volume and age, the slope of regression

line of male is significantly steeper than that of female

ðp , 0:05Þ:

The local gray matter concentration of almost all cerebral

cortices and the basal ganglia showed a significant negative

correlation with age (Fig. 8). In particular, strong corre-

lations were found between age and the local gray matter

concentration of the left superior temporal gyrus and the left

precentral gyrus in female, and that of the bilateral superior

temporal gyri in male.

The bilateral periventricular regions of lateral ventri-

culus and the third ventricle showed significant negative

correlations with age in both genders (Fig. 9). In particular,

strong correlations of white matter concentration with age

were found in bilateral lateral periventricular regions, the

left corpus callosum in female, and in bilateral lateral

periventricular regions and the left fasciculus occipito-

frontalis in male.

The bilateral white matter of subcortical regions showed

significant positive correlations with age in each gender

(Fig. 10). In particular, strong correlation with age was

found in the white matter of the right cuneus, white matter

of the light superior temporal gyrus, and the left fornix in

female, and in the white matter of the left cuneus, and

bilateral external capsule in male.

Fig. 3. Examples of trans-axial slices of male reference brains. The left side of the image shows the left hemisphere of the brain.


http://www.idac.tohoku.ac.jp/JHBP/

Fig. 4. 3D view of female reference brains. Images are front, left, back, and dorsal views, from left to right.

Fig. 5. 3D view of male reference brains. Images are front, left, back, and dorsal views, from left to right.


4. Discussion

4.1. Brain imaging database

To our knowledge, this is the first report describing the

anatomical brain image database of normal Japanese. Our

system has been designed not only for the storage but also

for the sharing of raw anatomical MRI data. There is no

doubt that an electronic data sharing system has become an

important tool in many scientific research fields. It is true

that sharing raw data with the scientific community

increases the value of the data and provides current

knowledge. Although, it is also true that there are several

problems in raw data sharing (Koslow, 2002). The most

problematic issue regarding the distribution of a raw

structural MRI data set is the potential for compromising

a subject’s confidentiality, since the experimental subject’s

face could be reconstructed from the raw data sets. We,

therefore, have made our decision to alter or eliminate facial

structures from the data set prior to distribution of raw

image data.

Several approaches can be used in the development of

databases to accommodate the diversity of data types and

structures required to adequately represent brain structure

and function. Fox and colleagues have developed one of the

first human brain imaging databases, named BrainMap

database (http://www.brainmapdbj.org), which was

Fig. 6. Correlation between gray matter volume and aging in female and

male. Vertical and horizontal axes represent volume of gray matter in mm3

and age of subject, respectively. Dots and bars indicate mean and standard

deviation of gray matter volume of each decade subjects, respectively.Fig. 7. Correlation between white matter volume and aging in female and

male. Vertical and horizontal axes represent volume of white matter in mm3

and age of subject, respectively. Dots and bars indicate mean and standard

deviation of white matter volume of each decade subjects, respectively.


http://www.brainmapdbj.org

designed to provide an indexing system that maps both

context and content as a multidimensional space for the

functional brain-imaging literature (Fox & Lancaster, 1994;

Fox, Mikiten, Davis, & Lancaster, 1994). Usually, the

image data are excluded and only boundary information is

retained. An International Consortium for Brain Mapping

(ICBM) has been developing a 4D probabilistic atlas and

reference system for the human brain (Mazziotta et al.,

2001). They have collected anatomical brain images of

more than 7000 subjects between the ages of 18 and 90

years from seven countries, and stored these data sets in

a digital brain image library (http://www.loni.ucla.edu/

ICBM/). Our Japanese brain database project has joined in

this project since 1999. In the ICBM project, data on each

subject includes detailed demographic, clinical, behavioral,

and imaging information. In addition, DNA samples have

been collected from 5800 subjects for genotyping, and a

component of the program uses postmortem tissues to

determine the probabilistic distribution of microscopic cyto-

and chemoarchitectural regions in the human brain. The

fMRI Data Center of Dartmouth College has been

developing a functional imaging database (Van Horn et al.,

Fig. 8. Color-coded SPM indicating areas showing significantly negative correlation between local gray matter and age. The right side of the image represents

the left side of the brain. Color scales indicate t-score. The numbers at bottom left in the slices, that is 28, 7, 22 and 37, indicate z-coordinate labels in Talairach

stereotactic space (Talairach & Tournoux, 1988).

Fig. 9. Color-coded SPM indicating areas showing significantly negative correlation between local white matter and age. Other details are the same as Fig. 7.


http://www.loni.ucla.edu/ICBM/

http://www.loni.ucla.edu/ICBM/

2001). They are collecting raw functional imaging data from

peer-reviewed publications, and put these raw data into a

database that will allow for the mining of highly

heterogeneous and voluminous functional MRI data

(http://www.fmridc.org/).


To our knowledge, this is the first report showing

individual reference brains of normal Japanese for each age

and gender group. Our reference brains, that deviate least

from the others in terms of mean square intensity,

characterized the average shape of the brain of each

group. It is interesting and important to note that, although

we could not perform statistical analysis, we found a clear

tendency of cross-generational changes in brain shape.

These kinds of variability in brain shape between individ-

uals, gender, as well as ethnic groups have been reported

previously (Zilles et al., 1997, 2001). The biological

significance of this tendency shown in our data is as yet

unclear. However, since the shape of the brain is sculptured

by complex interactions between numerous genetically and

environmentally influenced processes in life (Zilles et al.,

2001), we assume that significant environmental changes

may have occurred during the past 50 years in Japan.

Recently, functional imaging study has become the

major focus of neuroscience research, and it provides much

knowledge on functional organization of the human brain.

These functional imaging data are usually mapped to a

spatial reference system (Talairach & Tournoux, 1988), that

is, an individual reference brain selected as the anatomically

least-deviating specimen from a large sample of brains

(Roland et al., 1994), or a mean brain constructed by

averaging individual brains (Evans et al., 1996), in order to

match individual size and shape of the brain. For this

purpose, many different linear and nonlinear procedures

have been proposed for normalizing an individual brain in a

standard size and shape. Although, the brain size can be

easily normalized by linear transformations, normalization

of the brain shape is difficult even when nonlinear

procedures are used. Our results can provide useful criteria

for the evaluation of spatial standard reference systems.

Because the shape of the brain differs between Japanese and

European (Zilles et al., 2001), as well as between young and

older subjects, a reference system for each age group of each

race might be necessary for functional imaging studies.


There was a significant negative correlation between the

gray matter volume and age in each gender, while the

volume of the white matter did not show significant change

with age in each gender. Our results indicate that the essence

of brain atrophy with aging is the volume reduction of the

gray matter. This finding is consistent with a previous study

that showed morphometrical change of the normal Euro-

pean adult brain with aging (Good et al., 2001).

The gray matter concentration of almost all cerebral

cortices and basal ganglia showed significant negative

correlations with age. From several previous studies, normal

age-related loss of gray matter volume has been attributed to

decreased perfusion (Melamed, Lavy, Bentin, Cooper, &

Rinot, 1980; Meyer et al., 1999; Takeda, Matsuzawa, &

Matsui, 1988), neuronal shrinkage and/or loss with

decreased cortical synaptic densities (Anderson, Hubbard,

Coghill, & Slidders, 1983; Masliah, Mallory, Hansen,

DeTeresa, & Terry, 1993; Terry, DeTeresa, & Hansen,

1987), which are probably related to neuronal apoptosis

Fig. 10. Color-coded SPM indicating areas showing significantly positive correlation between local white matter and age. Other details are the same as Fig. 7.


http://www.fmridc.org/

(Bredesen, 1995; Charriaut-Marlangue et al., 1996).

Recently, Van Laere et al. (2001) showed that the left

superior temporal gyrus showed significant negative

correlation between perfusion and aging. Their results are

in line with our results that the gray matter concentration of

the left superior temporal gyrus showed most significant

negative correlation with age. Their results combined with

results of this study suggest that the strong negative

correlation between the gray matter concentration of left

superior temporal gyrus and age may be due to decreased

perfusion.

The factors that influence the volume change of white

matter are the loss of axons associated with the loss of

neurons, loss of myelin, and decrease in the numbers of glial

cells. Previous studies using autopsied normal human brains

showed that approximately 10% of all neocortical neurons

are lost over the life span in both genders (Pakkenberg &

Gundersen, 1997), and that the total volume of myelinated

fibers in elderly subjects was lower than that in young

subjects (Tang, Nyengaard, Pakkenberg, & Gundersen,

1997). On the other hand, a parallel process of capillary

network and swelling of perivascular spaces may increase

the white matter volume (Meier-Ruge, Ulrich, Bruhlmann,

& Meier, 1992). There was a substantial increase in glial

population in the paracortical white matter of the visual

cortex of older subjects compared with younger subjects

(Devaney & Johnson, 1980). In our results, there was no

significant correlation between the white matter volume and

age, while a significant local change of white matter

concentration with aging was observed; concentration of the

entire periventricular white matter showed significant

negative correlation with age, while the entire subcortical

white matter showed significant positive correlation with

age. These phenomena cannot be explained by loss of

axons, loss of myelin, or decrease in the numbers of glial

cells. From our results, we consider that white matter was

moved to lateral region due to atrophy of gray matter with

aging. We consider that local change of white matter

concentration is affected by that of gray matter concen-

tration. A study supports our hypothesis in that study,

correlations between structural change and the degree of

Alzheimer disease progression in patients was analyzed

(Janke et al., 2001). As the disease progressed, atrophy of

the gray matter was observed, otherwise the most prominent

structural change of the white matter was not atrophy or

volume growth, but volume movement.

Acknowledgements

We thank Drs Alan Evans, Alex Zijdenbos, Peter Neelin

and Jason Lerch at Montreal Neurological Institute, McGill

University, for image data processing, Drs Ryoi Goto,

Shuichi Ono, Shigeo Kinomura, Manabu Nakagawa,

Motoaki Sugiura, and Jobu Watanabe at IDAC, Tohoku

University for data collection and analyses of image data,

and Drs Kazuhito Shida and Yoshiyuki Kawazoe at IMR,

Tohoku University for development of the database system.

This research is supported by grants from the Telecommu-

nications Advancement Organization of Japan, as well as

the National Institute of Mental Health, the National

Institute of Neurological Disorders and Stroke, the National

Institute on Drug Abuse, and the National Cancer Institute

of USA.

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neuroanatomical database of normal japanese brains

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