The measurements of the similarity of dynamic brain functional network_Journal of Biomedical Engineering

Authors：

HE Yongquan ¹ , ZHANG Li ² , FANG Shan ² , ZENG Yaqin ² , YANG Wei ² , CHEN Weidong ³ , SHAO Yuling ² , CHENG Ruidong ² , YE Xiangming ² ,  XU Dongrong ^1,4

1. Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China;
2. Department of Rehabilitation Medicine, Zhejiang Province People’s Hospital, Hangzhou Medical College, Hangzhou 310014, P. R. China;
3. Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, P. R. China;
4. Columbia University & New York State Psychiatric Institute, New York, NY 10032, USA;

Corresponding author：

XU Dongrong, Email: drxudr@gmail.com

Keywords：

Brain functional network; Dynamic evolution; Graph theory; Similarity; Functional magnetic resonance imaging

DOI：

10.7507/1001-5515.202103079

Video：

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Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the “evolutional” and “structural” properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.

Citation： HE Yongquan, ZHANG Li, FANG Shan, ZENG Yaqin, YANG Wei, CHEN Weidong, SHAO Yuling, CHENG Ruidong, YE Xiangming, XU Dongrong. The measurements of the similarity of dynamic brain functional network. Journal of Biomedical Engineering, 2022, 39(2): 237-247. doi: 10.7507/1001-5515.202103079 Copy

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1. Lee H, Lee D K, Park K, et al. Default mode network connectivity is associated with long-term clinical outcome in patients with schizophrenia. Neuroimage Clin, 2019, 22: 101805.
2. Li Q G, Zhao C, Shan Y, et al. Dynamic neural network changes revealed by voxel-based functional connectivity strength in left basal ganglia Ischemic stroke. Front Neurosci, 2020, 14: 526645.
3. Vecchio F, Miraglia F, Maria Rossini P. Connectome: Graph theory application in functional brain network architecture. Clin Neurophysiol Pract, 2017, 2: 206-213.
4. Yin D, Song F, Xu D, et al. Altered topological properties of the cortical motor-related network in patients with subcortical stroke revealed by graph theoretical analysis. Hum Brain Mapp, 2014, 35(7): 3343-3359.
5. Farahani F V, Karwowski W, Lighthall N R. Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review. Front Neurosci, 2019, 13: 585.
6. Zhu J, Wang C, Liu F, et al. Alterations of functional and structural networks in schizophrenia patients with auditory verbal hallucinations. Front Hum Neurosci, 2016, 10: 114.
7. Miri Ashtiani S N, Daliri M R, Behnam H, et al. Altered topological properties of brain networks in the early MS patients revealed by cognitive task-related fMRI and graph theory. Biomed Signal Proces, 2018, 40: 385-395.
8. Gonzalez-Burgos L, Pereira J B, Mohanty R, et al. Cortical networks underpinning compensation of verbal fluency in normal aging. Cereb Cortex, 2021, 31(8): 3832-3845.
9. Damaraju E, Allen E A, Belger A, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin, 2014, 5: 298-308.
10. Chen J, Sun D, Shi Y, et al. Alterations of static functional connectivity and dynamic functional connectivity in motor execution regions after stroke. Neurosci Lett, 2018, 686: 112-121.
11. Behfar Q, Behfar S K, Von Reutern B, et al. Graph theory analysis reveals resting-state compensatory mechanisms in healthy aging and prodromal Alzheimer’s disease. Front Aging Neurosci, 2020, 12: 576627.
12. Hua K, Wang T, Li C, et al. Abnormal degree centrality in chronic users of codeine-containing cough syrups: a resting-state functional magnetic resonance imaging study. Neuroimage Clin, 2018, 19: 775-781.
13. Ng K K, Lo J C, Lim J K W, et al. Reduced functional segregation between the default mode network and the executive control network in healthy older adults: A longitudinal study. Neuroimage, 2016, 133: 321-330.
14. Wang J, Wu Z, Zhao Y, et al. Research on similarity measurement method for large-scale dynamic networks. J Comput Sci Tech, 2019, 13(9): 1543-1552.
15. Liljestrom M, Stevenson C, Kujala J, et al. Task- and stimulus-related cortical networks in language production: Exploring similarity of MEG- and fMRI-derived functional connectivity. Neuroimage, 2015, 120: 75-87.
16. Zhu J, Zhao W, Zhang C, et al. Disrupted topological organization of the motor execution network in alcohol dependence. Psychiatry Res Neuroimaging, 2018, 280: 1-8.
17. Li M, Dahmani L, Wang D, et al. Co-activation patterns across multiple tasks reveal robust anti-correlated functional networks. Neuroimage, 2021, 227: 117680.
18. Ramkiran S, Sharma A, Rao N P. Resting-state anticorrelated networks in Schizophrenia. Psychiatry Res Neuroimaging, 2019, 284: 1-8.
19. Mitra A, Raichle M E. Principles of cross-network communication in human resting state fMRI. Scand J Psychol, 2018, 59(1): 83-90.
20. Sun F, Zhao Z, Lan M, et al. Abnormal dynamic functional network connectivity of the mirror neuron system network and the mentalizing network in patients with adolescent-onset, first-episode, drug-naive schizophrenia. Neurosci Res, 2021, 162: 63-70.
21. Patil A U, Ghate S, Madathil D, et al. Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition. Sci Rep, 2021, 11: 165.
22. Geng H, Xu P, Sommer I E, et al. Abnormal dynamic resting-state brain network organization in auditory verbal hallucination. Brain Struct Funct, 2020, 225(8): 2315-2330.
23. Jann K, Gee D G, Kilroy E, et al. Functional connectivity in BOLD and CBF data: similarity and reliability of resting brain networks. Neuroimage, 2015, 106: 111-122.
24. Zhu S, Fang Z, Hu S, et al. Resting state brain function analysis using concurrent BOLD in ASL perfusion fMRI. PLoS One, 2013, 8(6): e65884.
25. Fuxman Bass J I, Diallo A, Nelson J, et al. Using networks to measure similarity between genes: association index selection. Nat Methods, 2013, 10(12): 1169-1176.
26. Levandowsky M, Winter D. Distance between Sets. Nature, 1971, 234(5323): 34-35.
27. Wilson R C, Zhu P. A study of graph spectra for comparing graphs and trees. Pattern Recogn, 2008, 41(9): 2833-2841.
28. 孔维梁, 韩淑云, 黄宏涛. 基于用户概要扩展的协同过滤算法. 计算机应用研究, 2017(5): 1379-1383.
29. Chouakria A D, Nagabhushan P N. Adaptive dissimilarity index for measuring time series proximity. Adv Data Anal Classi, 2007, 1(1): 5-21.
30. Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn Reson Med, 1995, 34(6): 910-914.
31. Darras K F A, Deppe F, Fabian Y, et al. High microphone signal-to-noise ratio enhances acoustic sampling of wildlife. PeerJ, 2020, 8: e9955.
32. Mihai P G, Otto M, Domin M, et al. Brain imaging correlates of recovered swallowing after dysphagic stroke: A fMRI and DWI study. Neuroimage Clin, 2016, 12: 1013-1021.
33. Mekbib D B, Zhao Z, Wang J, et al. Proactive motor functional recovery following immersive virtual reality-based limb mirroring therapy in patients with subacute stroke. Neurotherapeutics, 2020, 17(4): 1919-1930.
34. Bassett D S, Wymbs N F, Porter M A, et al. Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci U S A, 2011, 108(18): 7641-7646.
35. Wang J, Zuo X, Dai Z, et al. Disrupted functional brain connectome in individuals at risk for Alzheimer’s disease. Biol Psychiatry, 2013, 73(5): 472-481.
36. 郑昊敏, 温忠麟, 吴艳. 心理学常用效应量的选用与分析. 心理科学进展, 2011, 19(12): 1868-1878.
37. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 2010, 52(3): 1059-1069.

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The measurements of the similarity of dynamic brain functional network

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