Study on Abnormal Topological Properties of Structural Brain Networks of Patients with Depression Comorbid with Anxiety_Journal of Biomedical Engineering

Authors：

WUXiuyong ¹ , WUXiaoming ¹ , PENGHongjun ² , NINGYuping ^3,4 ,  WUKai ^1,4

1. Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, Guangzhou 510006, China;
2. Department of Clinical Psychology, Guang Zhou Brain Hospital (GBH), Guangzhou 510370, China;
3. Department of Neurology, Guang Zhou Brain Hospital (GBH), Guangzhou 510370, China;
4. GBH-SCUT Joint Research Centre for Neuroimgaing, Guangzhou 510370, China;

Corresponding author：

WUKai, Email: kaiwu@scut.edu.cn

Keywords：

Diffusion tensor imaging; Depression comorbid with anxiety; Structural brain networks; Topological properties

DOI：

10.7507/1001-5515.20160091

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This paper is aimed to analyze the topological properties of structural brain networks in depressive patients with and without anxiety and to explore the neuropath logical mechanisms of depression comorbid with anxiety. Diffusion tensor imaging and deterministic tractography were applied to map the white matter structural networks. We collected 20 depressive patients with anxiety (DPA), 18 depressive patients without anxiety (DP), and 28 normal controls (NC) as comparative groups. The global and nodal properties of the structural brain networks in the three groups were analyzed with graph theoretical methods.The result showed that ① the structural brain networks in three groups showed small-world properties and highly connected global hubs predominately from association cortices; ② DP group showed lower local efficiency and global efficiency compared to NC group, whereas DPA group showed higher local efficiency and global efficiency compared to NC group; ③ significant differences of network properties (clustering coefficient, characteristic path lengths, local efficiency, global efficiency) were found between DPA and DP groups; ④ DP group showed significant changes of nodal efficiency in the brain areas primarily in the temporal lobe and bilateral frontal gyrus, compared to DPA and NC groups. The analysis indicated that the DP and DPA groups showed nodal properties of the structural brain networks, compared to NC group. Moreover, the two diseased groups indicated an opposite trend in the network properties. The results of this study may provide a new imaging index for clinical diagnosis for depression comorbid with anxiety.

Citation： WUXiuyong, WUXiaoming, PENGHongjun, NINGYuping, WUKai. Study on Abnormal Topological Properties of Structural Brain Networks of Patients with Depression Comorbid with Anxiety. Journal of Biomedical Engineering, 2016, 33(3): 545-552. doi: 10.7507/1001-5515.20160091 Copy

1.	HE Yong, CHEN Z J, EVANS A C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI[J]. Cereb Cortex, 2007, 17(10):2407-2419.
2.	BULLMORE E, SPORNS O. Complex brain networks:graph theoretical analysis of structural and functional systems[J]. Nat Rev Neurosci, 2009, 10(3):186-198.
3.	VAN DEN HEUVEL M P, FORNITO A. Brain networks in schizophrenia[J]. Neuropsychol Rev, 2014, 24(1):32-48.
4.	LORENZETTI V, ALLEN N B, FORNITO A, et al. Structural brain abnormalities in major depressive disorder:a selective review of recent MRI studies[J]. J Affect Disord, 2009, 117(1/2):1-17.
5.	KOOLSCHIJN P C, VAN HAREN N E, LENSVELT-MULDERS G J, et al. Brain volume abnormalities in major depressive disorder:a meta-analysis of magnetic resonance imaging studies[J]. Hum Brain Mapp, 2009, 30(11):3719-3735.
6.	刘威,李海江,邱江.抑郁症的脑网络失调:来自图论分析的证据[J].心理科学进展,2015,23(1):85-92.
7.	张作记. 行为医学量表手册[M/CD].北京:中华医学电子音像出版社,2005:225-227.
8.	HAMIILTION M. A rating scale for depression[J]. J Neurol Neurosurg Psychiatry, 1960, 2(23):56-62.
9.	HAMIILTION M. The assessment of anxiety by rating scale[J]. Brit J Mesychol, 1959, 1(32):50-55.
10.	CUI Zaixu, ZHONG Suyu, XU Pengfei, et al. PANDA:a pipeline toolbox for analyzing brain diffusion images[J]. Front Hum Neurosci, 2013, 7(7):42.
11.	RUBINOV M, SPORNS O. Complex network measures of brain connectivity:uses and interpretations[J]. Neuroimage, 2010, 52(3):1059-1069.
12.	HUMPHRIES M D, GURNEY K, PRESCOTT T J. The brainstem reticular formation is a small-world,not scale-free,network[J]. Proc Biol Sci, 2006, 26(50):12921-12942.
13.	ACHARD S, SALVADOR R, WHITCHER B, et al. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs[J]. J Neurosci, 2006, 26(1):63-72.
14.	SPORNS O, ZWI J D. The small world of the cerebral cortex[J]. Neuroinformatics, 2004, 2(2):145-162.
15.	BAI Feng, SHU N, YUAN Yonggui, et al. Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment[J]. J Neurosci, 2012, 32(12):4307-4318.
16.	WU Kai, TAKI Y, SATO K, et al. Age-related changes in topological organization of structural brain networks in healthy individuals[J]. Hum Brain Mapp, 2012, 33(3):552-568.
17.	GONG Gaolang, HE Yong, CONCHA L, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography[J]. Cereb Cortex, 2009, 19(3):524-536.
18.	BASSETT D S, BULLMORE E T. Human brain networks in health and disease[J]. Curr Opin Neurol, 2009, 22(4):340-347.
19.	陈建淮, 姚志剑,秦姣龙,等.抑郁症患者脑结构网络局部属性与疾病严重程度相关研究[J].中华行为医学与脑科学杂志,2014,23(10):881-884.
20.	AJILORE O, LAMAR M, KUMAR A. Association of brain network efficiency with aging, depression, and cognition[J]. Am J Geriatr Psychiatry, 2014, 22(2):102-110.
21.	黄海燕, 田兄玲,王俭,等.磁共振成像扩散峰度成像技术对抑郁症患者脑微观结构变化的评估价值[J].中国全科医学,2014,36(36):4386-4391.
22.	SEMINOWICZ D A, MAYBERG H S, MCINTOSH A R, et al. Limbic-frontal circuitry in major depression:a path modeling metanalysis[J]. Neuroimage, 2004, 22(1):409-418.
23.	ZHU Xueling, WANG Xiang, XIAO Jin, et al. Evidence of a dissociation pattern in Resting-State default mode network connectivity in First-Episode, Treatment-Naive major depression patients[J]. Biol Psychiatry, 2012, 71(7):611-617.
24.	DE KWAASTENIET B, RUHE E, CAAN M, et al. Relation between structural and functional connectivity in major depressive disorder[J]. Biol Psychiatry, 2013, 74(1):40-47.
25.	王文杰, 刚宝芝.颞叶病变引起认知功能障碍的特点[J].中华临床医师杂志:电子版,2013,20(20):9326-9328.
26.	INKSTER B, RAO A W, RIDLER K, et al. Structural brain changes in patients with recurrent major depressive disorder presenting with anxiety symptoms[J]. J Neuroimaging, 2011, 21(4):375-382.
27.	ANDREESCU C, WU Minjie, BUTTERS M A, et al. The default mode network in late-life anxious depression[J]. Am J Geriatr Psychiatry, 2011, 19(11):980-983.
28.	CHI M, GUO S W, K W, et al. Using support vector machine to identify imaging biomarkers of major depressive disorder and anxious depression[J]. Communication in Computer and Information Science, 2014, 47(4):63-67.
29.	QI Haochen, NING Yuping, LI Jie, et al. Gray matter volume abnormalities in depressive patients with and without anxiety disorders[J]. Medicine (Baltimore), 2014, 93(29):e345.

1. HE Yong, CHEN Z J, EVANS A C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI[J]. Cereb Cortex, 2007, 17(10):2407-2419.
2. BULLMORE E, SPORNS O. Complex brain networks:graph theoretical analysis of structural and functional systems[J]. Nat Rev Neurosci, 2009, 10(3):186-198.
3. VAN DEN HEUVEL M P, FORNITO A. Brain networks in schizophrenia[J]. Neuropsychol Rev, 2014, 24(1):32-48.
4. LORENZETTI V, ALLEN N B, FORNITO A, et al. Structural brain abnormalities in major depressive disorder:a selective review of recent MRI studies[J]. J Affect Disord, 2009, 117(1/2):1-17.
5. KOOLSCHIJN P C, VAN HAREN N E, LENSVELT-MULDERS G J, et al. Brain volume abnormalities in major depressive disorder:a meta-analysis of magnetic resonance imaging studies[J]. Hum Brain Mapp, 2009, 30(11):3719-3735.
6. 刘威,李海江,邱江.抑郁症的脑网络失调:来自图论分析的证据[J].心理科学进展,2015,23(1):85-92.
7. 张作记. 行为医学量表手册[M/CD].北京:中华医学电子音像出版社,2005:225-227.
8. HAMIILTION M. A rating scale for depression[J]. J Neurol Neurosurg Psychiatry, 1960, 2(23):56-62.
9. HAMIILTION M. The assessment of anxiety by rating scale[J]. Brit J Mesychol, 1959, 1(32):50-55.
10. CUI Zaixu, ZHONG Suyu, XU Pengfei, et al. PANDA:a pipeline toolbox for analyzing brain diffusion images[J]. Front Hum Neurosci, 2013, 7(7):42.
11. RUBINOV M, SPORNS O. Complex network measures of brain connectivity:uses and interpretations[J]. Neuroimage, 2010, 52(3):1059-1069.
12. HUMPHRIES M D, GURNEY K, PRESCOTT T J. The brainstem reticular formation is a small-world,not scale-free,network[J]. Proc Biol Sci, 2006, 26(50):12921-12942.
13. ACHARD S, SALVADOR R, WHITCHER B, et al. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs[J]. J Neurosci, 2006, 26(1):63-72.
14. SPORNS O, ZWI J D. The small world of the cerebral cortex[J]. Neuroinformatics, 2004, 2(2):145-162.
15. BAI Feng, SHU N, YUAN Yonggui, et al. Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment[J]. J Neurosci, 2012, 32(12):4307-4318.
16. WU Kai, TAKI Y, SATO K, et al. Age-related changes in topological organization of structural brain networks in healthy individuals[J]. Hum Brain Mapp, 2012, 33(3):552-568.
17. GONG Gaolang, HE Yong, CONCHA L, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography[J]. Cereb Cortex, 2009, 19(3):524-536.
18. BASSETT D S, BULLMORE E T. Human brain networks in health and disease[J]. Curr Opin Neurol, 2009, 22(4):340-347.
19. 陈建淮, 姚志剑,秦姣龙,等.抑郁症患者脑结构网络局部属性与疾病严重程度相关研究[J].中华行为医学与脑科学杂志,2014,23(10):881-884.
20. AJILORE O, LAMAR M, KUMAR A. Association of brain network efficiency with aging, depression, and cognition[J]. Am J Geriatr Psychiatry, 2014, 22(2):102-110.
21. 黄海燕, 田兄玲,王俭,等.磁共振成像扩散峰度成像技术对抑郁症患者脑微观结构变化的评估价值[J].中国全科医学,2014,36(36):4386-4391.
22. SEMINOWICZ D A, MAYBERG H S, MCINTOSH A R, et al. Limbic-frontal circuitry in major depression:a path modeling metanalysis[J]. Neuroimage, 2004, 22(1):409-418.
23. ZHU Xueling, WANG Xiang, XIAO Jin, et al. Evidence of a dissociation pattern in Resting-State default mode network connectivity in First-Episode, Treatment-Naive major depression patients[J]. Biol Psychiatry, 2012, 71(7):611-617.
24. DE KWAASTENIET B, RUHE E, CAAN M, et al. Relation between structural and functional connectivity in major depressive disorder[J]. Biol Psychiatry, 2013, 74(1):40-47.
25. 王文杰, 刚宝芝.颞叶病变引起认知功能障碍的特点[J].中华临床医师杂志:电子版,2013,20(20):9326-9328.
26. INKSTER B, RAO A W, RIDLER K, et al. Structural brain changes in patients with recurrent major depressive disorder presenting with anxiety symptoms[J]. J Neuroimaging, 2011, 21(4):375-382.
27. ANDREESCU C, WU Minjie, BUTTERS M A, et al. The default mode network in late-life anxious depression[J]. Am J Geriatr Psychiatry, 2011, 19(11):980-983.
28. CHI M, GUO S W, K W, et al. Using support vector machine to identify imaging biomarkers of major depressive disorder and anxious depression[J]. Communication in Computer and Information Science, 2014, 47(4):63-67.
29. QI Haochen, NING Yuping, LI Jie, et al. Gray matter volume abnormalities in depressive patients with and without anxiety disorders[J]. Medicine (Baltimore), 2014, 93(29):e345.

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