[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()

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

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

Keywords:

dynamic community structure;  magnetic resonance imaging(MRI);  depression;  treatment effect

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.

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.

Common functions