Characterizing dynamic local functional connectivity in the human brain

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Deng, Lifu ^a   Sun, Junfeng ^a   Cheng, Lin ^a   Tong, Shanbao ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN FUNCTION; BRAIN REGION; HUMAN; REST; STOCHASTIC MODEL; ANATOMY AND HISTOLOGY; BRAIN; CONNECTOME; FEMALE; IMAGE PROCESSING; MALE; MIDDLE AGED; MOTOR ACTIVITY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; STATISTICS AND NUMERICAL DATA; TASK PERFORMANCE;

BRAIN; CONNECTOME; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; MOTOR ACTIVITY; REST; TASK PERFORMANCE AND ANALYSIS;

EID: 84971241648     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep26976     Document Type: Article

References (54)

1. van den Heuvel, M. P., Hulshoff Pol, H. E. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20, 519-534, doi: 10.1016/j.euroneuro.2010.03.008 (2010).
2. Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S., Menon, R. S. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Human Brain Mapping 34, 2154-2177, doi: 10.1002/hbm.22058 (2013).
3. Hutchison, R. M. et al. Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage 80, 360-378, doi: 10.1016/j.neuroimage.2013.05.079 (2013).
4. Allen, E. a. et al. Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex 24, 663-676, doi: 10.1093/cercor/bhs352 (2014).
5. Elton, A., Gao, W. Task-related modulation of functional connectivity variability and its behavioral correlations. Hum Brain Mapp 36, 3260-3272, doi: 10.1002/hbm.22847 (2015).
6. Chen, J. E., Chang, C., Greicius, M. D., Glover, G. H. Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics. NeuroImage 111, 476-488, doi: 10.1016/j.neuroimage.2015.01.057 (2015).
7. Kucyi, A., Davis, K. D. Dynamic functional connectivity of the default mode network tracks daydreaming. NeuroImage 100, 471-480, doi: 10.1016/j.neuroimage.2014.06.044 (2014).
8. Jones, D. T. et al. Non-stationarity in the "resting brain's" modular architecture. PLoS ONE 7, e39731-e39731, doi: 10.1371/journal. pone.0039731 (2012).
9. Sako?lu, Ü. et al. A method for evaluating dynamic functional network connectivity and task-modulation: Application to schizophrenia. Magnetic Resonance Materials in Physics, Biology and Medicine 23, 351-366, doi: 10.1007/s10334-010-0197-8 (2010).
10. Liao, W. et al. Dynamical intrinsic functional architecture of the brain during absence seizures. Brain structure & function 219, 2001-2015, doi: 10.1007/s00429-013-0619-2 (2014).
11. Liao, W. et al. DynamicBC: a MATLAB toolbox for dynamic brain connectome analysis. Brain Connectivity 4, 780-790 (2014).
12. Betzel, R. F., Fukushima, M., He, Y., Zuo, X.-N., Sporns, O. Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks. NeuroImage 127, 287-297, doi: 10.1016/j.neuroimage.2015.12.001 (2015).
13. Hindriks, R. et al. Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage 127, 242-256, doi: 10.1016/j.neuroimage.2015.11.055 (2016).
14. Zang, Y., Jiang, T., Lu, Y., He, Y., Tian, L. Regional homogeneity approach to fMRI data analysis. NeuroImage 22, 394-400, doi: 10.1016/j.neuroimage.2003.12.030 (2004).
15. Deshpande, G., LaConte, S., Peltier, S., Hu, X. Integrated local correlation: a new measure of local coherence in fMRI data. Human Brain Mapping 30, 13-23, doi: 10.1002/hbm.20482 (2009).
16. Long, X.-Y. et al. Default mode network as revealed with multiple methods for resting-state functional MRI analysis. Journal of Neuroscience Methods 171, 349-355, doi: 10.1016/j.jneumeth.2008.03.021 (2008).
17. Tomasi, D., Volkow, N. D. Functional connectivity density mapping. Proceedings of the National Academy of Sciences of the United States of America 107, 9885-9890, doi: 10.1073/pnas.1001414107 (2010).
18. Jiang, L. et al. Toward neurobiological characterization of functional homogeneity in the human cortex: regional variation, morphological association and functional covariance network organization. Brain structure & function 220, 2485-2507, doi: 10.1007/s00429-014-0795-8 (2015).
19. Liu, H. et al. Decreased regional homogeneity in schizophrenia: a resting state functional magnetic resonance imaging study. Neuroreport 17, 19-22 (2006).
20. Liu, Z. et al. Decreased regional homogeneity in insula and cerebellum: a resting-state fMRI study in patients with major depression and subjects at high risk for major depression. Psychiatry research 182, 211-215, doi: 10.1016/j.pscychresns.2010.03.004 (2010).
21. Yao, Z., Wang, L., Lu, Q., Liu, H., Teng, G. Regional homogeneity in depression and its relationship with separate depressive symptom clusters: a resting-state fMRI study. Journal of affective disorders 115, 430-438, doi: 10.1016/j.jad.2008.10.013 (2009).
22. Zhang, Z. et al. Altered spontaneous activity in Alzheimer's disease and mild cognitive impairment revealed by Regional Homogeneity. NeuroImage 59, 1429-1440, doi: 10.1016/j.neuroimage.2011.08.049 (2012).
23. Shukla, D. K., Keehn, B., Müller, R. A. Regional homogeneity of fMRI time series in autism spectrum disorders. Neuroscience Letters 476, 46-51, doi: 10.1016/j.neulet.2010.03.080 (2010).
24. Lopez-Larson, M. P., Anderson, J. S., Ferguson, M. A., Yurgelun-Todd, D. Local brain connectivity and associations with gender and age. Developmental cognitive neuroscience 1, 187-197, doi: 10.1016/j.dcn.2010.10.001 (2011).
25. Dai, X.-J. et al. Gender differences in brain regional homogeneity of healthy subjects after normal sleep and after sleep deprivation: a resting-state fMRI study. Sleep medicine 13, 720-727, doi: 10.1016/j.sleep.2011.09.019 (2012).
26. Wang, L., Song, M., Jiang, T., Zhang, Y., Yu, C. Regional homogeneity of the resting-state brain activity correlates with individual intelligence. Neuroscience Letters 488, 275-278, doi: 10.1016/j.neulet.2010.11.046 (2011).
27. Jiang, L., Zuo, X. N. Regional Homogeneity: A Multimodal, Multiscale Neuroimaging Marker of the Human Connectome. The Neuroscientist, doi: 10.1177/1073858415595004 (2015).
28. Hudetz, A. G., Liu, X., Pillay, S. Dynamic Repertoire of Intrinsic Brain States Is Reduced in Propofol-Induced Unconsciousness. Brain Connectivity 5(1), 10-22, doi: 10.1089/brain.2014.0230 (2014).
29. Beckmann, C. F., DeLuca, M., Devlin, J. T., Smith, S. M. Investigations into resting-state connectivity using independent component analysis. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 360, 1001-1013, doi: 10.1098/rstb.2005.1634 (2005).
30. Smith, S. M. et al. Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America 106, 13040-13045, doi: 10.1073/pnas.0905267106 (2009).
31. Shen, X. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neuroscience 18, 1-11, doi: 10.1038/nn.4135 (2015).
32. de Pasquale, F. et al. A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain. Neuron 74, 753-764, doi: 10.1016/j.neuron.2012.03.031 (2012).
33. Zuo, X.-N. et al. Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. NeuroImage 65, 374-386, doi: 10.1016/j.neuroimage.2012.10.017 (2013).
34. Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 80, 62-79, doi: 10.1016/j. neuroimage.2013.05.041 (2013).
35. Moeller, S. et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magnetic Resonance in Medicine 63, 1144-1153, doi: 10.1002/mrm.22361 (2010).
36. Van Essen, D. C. et al. The Human Connectome Project: A data acquisition perspective. NeuroImage 62, 2222-2231, doi: 10.1016/j. neuroimage.2012.02.018 (2012).
37. Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105-124, doi: 10.1016/j.neuroimage.2013.04.127 (2013).
38. Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., Smith, S. M. FSL. NeuroImage 62, 782-790, doi: 10.1016/j. neuroimage.2011.09.015 (2012).
39. Jenkinson, M., Bannister, P., Brady, M., Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825-841 (2002).
40. Fischl, B. FreeSurfer. NeuroImage 62, 774-781, doi: 10.1016/j.neuroimage.2012.01.021 (2012).
41. Chao-Gan, Y., Yu-Feng, Z. DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Frontiers in systems neuroscience 4, 13, doi: 10.3389/fnsys.2010.00013 (2010).
42. Ashburner, J. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95-113, doi: 10.1016/j.neuroimage.2007.07.007 (2007).
43. Song, X.-W. et al. REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing. PLoS ONE 6, e25031-e25031, doi: 10.1371/journal.pone.0025031 (2011).
44. Leonardi, N., Van De Ville, D. On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage 104, 430-436, doi: http://dx.doi.org/10.1016/j.neuroimage.2014.09.007 (2015).
45. Zalesky, A., Breakspear, M. Towards a statistical test for functional connectivity dynamics. NeuroImage 114, 466-470, doi: http://dx.doi.org/10.1016/j.neuroimage.2015.03.047 (2015).
46. Sun, J., Hong, X., Tong, S. Phase synchronization analysis of eeg signals: An evaluation based on surrogate tests. IEEE Transactions on Biomedical Engineering 59, 2254-2263, doi: 10.1109/TBME.2012.2199490 (2012).
47. Kiebel, S. J., Poline, J. B., Friston, K. J., Holmes, a. P., Worsley, K. J. Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model. NeuroImage 10, 756-766, doi: 10.1006/nimg.1999.0508 (1999).
48. Beckmann, C. F., Smith, S. M. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE transactions on medical imaging 23, 137-152, doi: 10.1109/tmi.2003.822821 (2004).
49. Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23 Suppl 1, S208-219, doi: 10.1016/j.neuroimage.2004.07.051 (2004).
50. Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273-289, doi: 10.1006/nimg.2001.0978 (2002).
51. Chan, M. Y., Park, D. C., Savalia, N. K., Petersen, S. E., Wig, G. S. Decreased segregation of brain systems across the healthy adult lifespan. Proceedings of the National Academy of Sciences 111, E4997-E5006, doi: 10.1073/pnas.1415122111 (2014).
52. Benjamini, Y., Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing on JSTOR. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289-300 (1995).
53. Zalesky, A. et al. Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 50, 970-983, doi: 10.1016/j. neuroimage.2009.12.027 (2010).
54. Zhang, Z. et al. Altered functional-structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain 134, 2912-2928, doi: 10.1093/brain/awr223 (2011).