|Table of Contents|

[1] Mo Zhaoqi, Wang Qiang, Tian Shui, Yan Rui, et al. Evaluating treatment via flexibilityof dynamic MRI community structures in depression [J]. Journal of Southeast University (English Edition), 2017, 33 (3): 273-276. [doi:10.3969/j.issn.1003-7985.2017.03.004]
Copy

Evaluating treatment via flexibilityof dynamic MRI community structures in depression()
动态核磁共振社区结构灵活度指标在抑郁症疗效评估中的应用
Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
33
Issue:
2017 3
Page:
273-276
Research Field:
Computer Science and Engineering
Publishing date:
2017-09-30

Info

Title:
Evaluating treatment via flexibilityof dynamic MRI community structures in depression
动态核磁共振社区结构灵活度指标在抑郁症疗效评估中的应用
Author(s):
Mo Zhaoqi1, Wang Qiang2, Tian Shui1, Yan Rui2, Geng Jiting2, Yao Zhijian2, Lu Qing1
1Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 210096, China
2Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China
莫昭奇1, 王强2, 田水1, 阎锐2, 耿纪婷2, 姚志剑2, 卢青1
1东南大学儿童发展与学习科学教育部重点实验室, 南京 210096; 2南京医科大学附属脑科医院精神科, 南京 210029
Keywords:
dynamic community structure;  magnetic resonance imaging(MRI);  depression;  treatment effect
动态社区结构;  核磁共振成像;  抑郁症;  治疗效果
PACS:
TP3
DOI:
10.3969/j.issn.1003-7985.2017.03.004
Abstract:
The flexibility of dynamic community structure is adopted to analyze the depressive resting-state functional magnetic resonance imaging(rfMRI)signals in order to improve the accuracy of evaluating depression treatment. The rfMRI signals of each brain network were obtained by the independent component correlation algorithm(ICA). Dynamic functional connections were computed with sliding windows and L1 norm. Then, the connections were used to calculate the dynamic community structure via the community-detection algorithm. The result of structure’s community assignment has the general character with the brain activity changing over time. The flexibility index is one of traits of dynamic community structure, meaning the number of times a region changes. In this study, 16 patients who achieved clinical remission joined the experiment and were scanned before and after treatment. Pair permutation tests compare the difference of six brain networks’ flexibility between pre-therapy and post-treatment. The results show that the distribution of the flexibility values declines in a default network and cognitive control network between pre-therapy and post-treatment patients with statistical difference. Therefore, flexibility is a suitable approach to accurately evaluate the depression treatment effect.
为了更有效地评估抑郁症患者治疗前后的改善效果, 使用动态模块化算法探测抑郁症患者静息态脑网络的灵活度属性.使用独立成分分析获得每个被试的特定脑网络分区信号, 通过滑动窗口和L1范数计算动态功能连接矩阵, 然后运用社区探测算法计算功能连接的动态社区结构.最终获得的模块化分配结构具有大脑活动随时间推移的一般特征.灵活性指标是动态社区结构的特征之一, 表征区域变化的次数.本次研究中, 有16名患者实现临床缓解并治疗前后各扫描一次.计算得到的所有患者治疗前后全脑6个网络的灵活度指标组间置换检验结果显示, 患者治疗前和治疗后的默认网络和认知控制网络灵活性度分布存在下降趋势, 且该趋势具有统计学差异.因此这2个网络的灵活度指标可用于抑郁病人治疗效果评估的客观参考.

References:

[1] Chang C, Glover G H. Time-frequency dynamics of resting-state brain connectivity measured with fMRI[J].Neuroimage, 2010, 50(1): 81-98. DOI:10.1016/j.neuroimage.2009.12.011.
[2] Bassett D S, Wymbs N F, Porter M A, et al. Dynamic reconfiguration of human brain networks during learning[J]. Proceedings of the National Academy of Sciences, 2011, 108(18): 7641-7646. DOI:10.1073/pnas.1018985108.
[3] Wei M B, Qin J L, Yan R, et al. Abnormal dynamic community structure of the salience network in depression[J]. Journal of Magnetic Resonance Imaging, 2017, 45(4): 1135-1143. DOI:10.1002/jmri.25429.
[4] Wei M B, Qin J L, Yan R, et al. Association of resting-state network dysfunction with their dynamics of inter-network interactions in depression[J]. Journal of Affective Disorders, 2015, 174: 527-534. DOI:10.1016/j.jad.2014.12.020.
[5] Bassett D S, Porter M A, Wymbs N F, et al. Robust detection of dynamic community structure in networks[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2013, 23(1): 013142. DOI:10.1063/1.4790830.
[6] Yan C G, Zang Y F. DPARSF: A Matlab toolbox for “pipeline” data analysis of resting-state fMRI[J/OL]. Front Syst Neurosci, 2010. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2889691/. DOI:10.3389/fnsys.2010.00013.
[7] Beckmann C F, Mackay C E, Filippini N, et al. Group comparison of resting-state FMRI data using multi-subject ICA and dual regression[J]. Neuroimage, 2009, 47(Sup 1): S148. DOI:10.1016/s1053-8119(09)71511-3.
[8] Wei M B, Qin J L, Yan R, et al. Identifying major depressive disorder using Hurst exponent of resting-state brain networks[J]. Psychiatry Research: Neuroimaging, 2013, 214(3): 306-312. DOI:10.1016/j.pscychresns.2013.09.008.
[9] Allen E A, Damaraju E, Plis S M, et al. Tracking whole-brain connectivity dynamics in the resting state[J]. Cereb Cortex, 2014, 24(3): 663-676. DOI:10.1093/cercor/bhs352.
[10] Marrelec G, Krainik A, Duffau H, et al. Partial correlation for functional brain interactivity investigation in functional MRI[J]. Neuroimage, 2006, 32(1): 228-237. DOI:10.1016/j.neuroimage.2005.12.057.
[11] Mucha P J, Richardson T, Macon K, et al. Community structure in time-dependent, multiscale, and multiplex networks[J]. Science, 2010, 328(5980): 876-878. DOI:10.1126/science.1184819.
[12] Mulders P C, van Eijndhoven P F, Schene A H, et al. Resting-state functional connectivity in major depressive disorder: A review[J]. Neuroscience & Biobehavioral Reviews, 2015, 56: 330-344. DOI:10.1016/j.neubiorev.2015.07.014.
[13] Dutta A, McKie S, Deakin J F W. Resting state networks in major depressive disorder[J]. Psychiatry Research: Neuroimaging, 2014, 224(3): 139-151. DOI:10.1016/j.pscychresns.2014.10.003.

Memo

Memo:
Biographies: Mo Zhaoqi(1992—), male, graduate; Lu Qing(corresponding author), female, doctor, professor, luq@seu.edu.cn.
Foundation items: The National High Technology Research and Development Program of China(863 program)(No.2015AA020509), the National Natural Science Foundation of China(No.81571639, 81371522, 61372032).
Citation: Mo Zhaoqi, Wang Qiang, Tian Shui, et al. Evaluating treatment via flexibility of dynamic MRI community structures in depression[J].Journal of Southeast University(English Edition), 2017, 33(3):273-276.DOI:10.3969/j.issn.1003-7985.2017.03.004.
Last Update: 2017-09-20