「様式３－B」

課題番号（Project number）61

Development and aging of human functional and structuralbrain networks studied with a large brain MRI database［１］組織（Research group）
代表者（Principal Investigator (PI)）：
瀧 靖之（東北大学加齢医学研究所）
対応者（Host researcher at IDAC）：
呉 凱（華南理工大学）
分担者（Co-investigator）：
研究費(Expenditure report of research funds)：
物件費 250,000YEN［２］研究経過（Research setup）
Graph theoretical analysis of neuroimaging
data is growing rapidly and could potentiallyprovide a relatively simple but powerfulquantitative framework to describe and compare
whole human brain structural and functionalnetworks under diverse experimental and clinicalconditions. Prof. Taki’s group has built alarge-scale of Japanese brain MRI database(around 2500 subjects aged from 6-80) with avariety of imaging modalities, including s-MRI,
DTI, and rs-fMRI. Prof. Taki and I proposaled a
new research regarding lifespan changes ofhuman brain connectivity, which yields truly
integrative and comprehensive descriptions of thestructural and functional organization of thehuman brain. We construct structural brain
networks by the measurement of regional graymatter volume (RGMV) using sMRI and themeasurementofanatomicalconnection
probabilities using DTI. We employ rs-fMRI toconstruct functional brain networks bymeasuring the temporal correlations in timecourses (also referred to as functionalconnectivity).
One of our previous studies demonstrate
topological organization of functional brain

networks in healthy children using resting-state
fMRI and indicate network properties in relation toage, sex, and intelligence [1]. The recent progress ofour study was to investigate age- and sex- relateddifferences in structural cortical networks derived
from DTI in healthy children.

［３］成果（Research outcomes）（３－１）研究成果（Results）
The sample consisted of 291 subjects (146 boys,145 girls; age range, 5.6-18.4 years) [1]. All images
were collected using a 3-T Philips Intera Achieva
scanner. The diffusion-weighted data were acquiredby using a spin-echo EPI sequence. Additionally, adata set with no diffusion weighting (b value=0s/mm2) (b=0 image) was acquired.
We constructed structural cortical networks
using DTI by the methodology described in aprevious studies [2]. Briefly, the whole brain wasparcellated into 1024 regions with equal size,
defined by a high-resolution parcellation ofAutomated Anatomical Labeling (AAL) atlas [3].
The number of interconnecting fibers was definedas the weight of the network edge, which results ina symmetric 1024*1024 matrix for each subject.
Finally, a threshold of (T=3) was set to the weight of
the network edge. To characterize the global andregional properties of structural cortical networks,
we computed graph measures as follow: clustering
coefficient, path length, local efficiency, globalefficiency, small-worldness, modularity, mean
anatomical distance, regional nodal efficiency,
regional strength of anatomical distance [4].
We applied a general linear model (GLM) toanalyze the effects of age, sex, and theirinteraction on the network parameters. To detectthe development trajectories of the linear andquadratic age-related changes in each network
parameter, we used two multiple linearregressions (Model I and II) that modeled meanvalue, age, and age2 as predictors, with sex as acovariate. We then determined the best model
among the two regressions based on Akaike’s
information criterion (AIC).
Significant age- and sex-related differences were
found in both global and regional network
properties. Global efficiency and mean anatomical
distance were positive correlated with age, whilepath length and modularity were negative
correlated with age (p<0.05). Girls showedsignificantly higher values in clustering coefficient,localefficiency,
globalefficiency,andsmall-worldness, compared with boys. Boys showedsignificantly higher values in path length,compared with girls (p<0.05). Global hubs definedby regional nodal efficiency were predominately infrontal and parietal brain regions (Figure 1).

Importantly, the global hubs pattern wasnon-significantly correlated with age and thus kept
relatively stable across the age range of 6-18 years.
Age-related increases in the regional nodal
efficiency were found in the medial prefrontal,
lateral frontal, medial temporal, parietal, andoccipital brain regions (Figure 2). Significant
sex-related differences in the regional nodal
efficiency were found in various brain regions,primarily in the medial prefrontal, parietal, andoccipital brain regions.
To our knowledge, this is the first study to reveal
age- and sex-related changes in topologicalorganization of structural cortical networks in alarge number of healthy children. Our findings ofthe network topology of structural cortical networks
with normal development bring new insights into
the understanding of brain maturation andcognitive development during childhood andadolescence.

Figure 1. Global hubs in structural corticalnetworks.
Global hubs defined as the brain regions showing higher
values in regional nodal efficiency (> mean + SD). The
correlation between age and the Pearson correlation
coefficient, which was defined as the association of hub

Figure 2. Significant age-related changes inregional nodal properties.

distribution between the global hubs shown in (A) and thosein each individual. Note that the results were calculated after

Significant age-related differences in regional nodal efficiency

adjusting for the effects of brain size and sex, using a general(p<0.05, FDR-corrected). Significant age-related differences inlinear model.

regional strength of anatomical distance (p<0.05,FDR-corrected)

（ ３ － ２ ） 波 及 効 果 と 発 展 性 な ど （ Futureperspectives）
With the support of this project, Prof. Taki’s
group and my group have established amechanism of international joint research. The
group of Prof. Taki focuses on the construction of ahuge database of MRI images and its applicationin the studies of normal aging, development, andlife style related topics. My group is engaged indeveloping new methods of human connectome
analysis and machine learning and applying
these methods into the studies of healthy subjects
and patients with brain diseases. We hope that
this kind joint research could be approached inthe future.

［４］成果資料（List of Papers）（１）Wu K , Taki Y, Sato K, Wu X, Xue J, Du X,Takeuchi H, Thyreau B, Fukuda H, Kawashima R.
Age- and sex- related differences in structural cortical
networks in healthy children. 21st Annual Meeting ofthe Organization for Human Brain Mapping, June
14-18, 2015, Honolulu, USA.
（ ２ ） Chihiro Kondo, Koichi Ito, Kai Wu,
Kazunori Sato, Yasuyuki Taki, Hiroshi Fukuda,Takafumi Aoki. Age Estimation Method Using
Brain Local Features for T1-WeightedImages.37th Annual International Conference ofthe IEEE Engineering in Medicine and Biology
Society, pp. 666--669, August 2015.
（３）近藤千裕, 伊藤康一, 呉凱, 佐藤和則, 瀧靖之,福田寛, 青木孝文.ADNI データベースの脳ネットワーク解析に関する検討.電子情報通信学会医用画像研 究 会 , Vol. 115, No. 301, pp. 67--72,
November2015.
（４）近藤千裕, 伊藤康一, 呉凱, 佐藤和則, 瀧靖之,
福田寛, 青木孝文.脳局所特徴量に基づく年齢推定
手法と ADNI データベースを用いた性能評価.電子情報通信学会医用画像研究会, Vol. 115, No. 139, pp.
79--84, July 2015.
（５）近藤千裕, 伊藤康一, 呉凱, 佐藤和則, 瀧靖之,福田寛, 青木孝文.経時変化のある脳 MRI 画像デー
タベースを用いた年齢推定手法の性能評価.電子情報通信学会医用画像研究会, Vol. 114, No. 482, pp.
23--28, March 2015.
（６）近藤千裕, 伊藤康一, 呉凱, 佐藤和則，瀧靖之,福田寛, 青木孝文.脳 MRI 画像の局所特徴量に基づく年齢推定手法と縦断データベースを用いた性能評

価 . 情 報 処 理 学 会 第 77 回 全 国 大 会 , pp.
2-359--2-360, March 2015.
（７）近藤千裕, 伊藤康一, 呉凱, 佐藤和則, 瀧靖之,福田寛, 青木孝文.脳 MRI 画像の局所特徴量に基づく年齢推定手法とその評価.電子情報通信学会医用画 像 研 究 会 , Vol. 114, No. 311, pp. 11--16,
November 2014.
（８）近藤千裕, 伊藤康一, 呉凱, 佐藤和則, 瀧靖之,福田寛, 青木孝文.脳 MRI 画像の局所特徴量に基づく年齢推定手法の検討.平成 26 年度電気関係学会東北支部連合大会, No. 2H01, p. 208, August 2014.
（９）近藤千裕, 伊藤康一, 呉凱, 佐藤和則, 瀧靖之,福田寛, 青木孝文.脳局所特徴量に基づく年齢推定
手法と脳 MRI 画像データベースを用いた性能評価.
映像情報メディア学会技術報告, Vol. 38, No. 32, pp.
15--18, August 2014.
（１０）Minyue Chi, Shengwen Guo, Yuping Ning,Jie Li, Haochen Qi, Minjian Gao, Xiuyong Wu,
Junwei Xue, Xin Du, Jiexin Wang, Xiaowei Hu,Yangbo Guo, Yuling Yang, Hongjun Peng, and Kai
Wu. Discriminative Analysis of Major Depressive
Disorder and Anxious Depression Using Support
Vector Machine. J. Comput. Theor. Nanosci. 12(7),1395-1401, 2015.
（１１）Shengwen Guo, Chunren Lai, WenshengWang, Lina Cheng, Kai Wu, Guiying Cen,
Minyue Chi. Classification using multiply corticalfeatures of temporal lobe epilepsy patients.
Journal of Southeast University (Natural ScienceEdition). Vol.45, No.6, pp. 1057-1060, 2015.11.
Reference:
1. Wu, K., et al., Topological organization offunctional brain networks in healthy children:differences in relation to age, sex, andintelligence. PLoS ONE, 2013. 8(2): p. e55347.
2. Shu, N., et al., Diffusion Tensor Tractography
Reveals Disrupted Topological Efficiency inWhite Matter Structural Networks in Multiple
Sclerosis. Cereb Cortex, 2011. 21(11): p.
2565-2577.
3. Zalesky, A., et al., Whole-brain anatomical

networks: does the choice of nodes matter?
Neuroimage, 2010. 50(3): p. 970-83.
4. Rubinov, M. and O. Sporns, Complex network

measures of brain connectivity: uses andinterpretations. Neuroimage, 2010. 52(3): p.
1059-69.