A weighted small world network measure for assessing functional connectivity

Journal of Neuroscience Methods

Volumn 212, Issue 1, 2013, Pages 133-142

  Bolaños, Marcos ^a   Bernat, Edward M ^b   He, Bin ^c   Aviyente, Selin ^a  

^a Michigan State University   (United States)

^b FLORIDA STATE UNIVERSITY   (United States)

^c University of Minnesota   (United States)

Author keywords

Electroencephalogram; Error related negativity; Graph theory; Phase synchronization; Small world measure; Time frequency analysis

Indexed keywords

ARTICLE; BRAIN FUNCTION; BRAIN REGION; CALCULATION; COGNITION; ELECTROENCEPHALOGRAPHY; ERROR RELATED NEGATIVITY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; MATHEMATICAL ANALYSIS; MATHEMATICAL PARAMETERS; NEUROPHYSIOLOGY; OSCILLATION; PRIORITY JOURNAL; SOCIAL NETWORK;

ALGORITHMS; BRAIN; BRAIN MAPPING; BRAIN WAVES; COMPUTER SIMULATION; HUMANS; MODELS, NEUROLOGICAL; NERVE NET; SIGNAL PROCESSING, COMPUTER-ASSISTED; WEIGHTS AND MEASURES;

EID: 84868221926     PISSN: 01650270     EISSN: 1872678X     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2012.10.004     Document Type: Article

References (58)

1. Achard S., Salvador R., Whitcher B., Suckling J., Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 2006, 26:63-72.
2. Aviyente S., Bernat E.M., Evans W.S., Sponheim S.R. A phase synchrony measure for quantifying dynamic functional integration in the brain. Hum Brain Mapp 2011, 32:80-93.
3. Aviyente S., Mutlu A.Y. A time-frequency based approach to phase and phase synchrony estimation. IEEE Trans Signal Process 2011, 59:3086-3098.
4. Barrat A., Barthelemy M., Pastor-Satorras R., Vespignani A. The architecture of complex weighted networks. Proc Natl Acad Sci U S A 2004, 101:3747-3752.
5. Bassett D.S., Bullmore E. Small-world brain networks. Neuroscientist 2006, 12:512-523.
6. Bassett D.S., Meyer-Lindenberg A., Achard S., Duke T., Bullmore E. Adaptive reconfiguration of fractal small-world human brain functional networks. Proc Natl Acad Sci 2006, 103:19518-19523.
7. Bernat E.M., Nelson L.D., Steele V.R., Gehring W.J., Patrick C.J. Externalizing psychopathology and gain-loss feedback in a simulated gambling task: dissociable components of brain response revealed by time-frequency analysis. J Abnorm Psychol 2011, 120:352-364.
8. Bernat E.M., Williams W.J., Gehring W.J. Decomposing ERP time-frequency energy using PCA. Clin Neurophysiol 2005, 116:1314-1334.
9. Botvinick M.M., Braver T.S., Barch D.M., Carter C.S., Cohen J.D. Conflict monitoring and cognitive control. Psychol Rev 2001, 108:624.
10. Botvinick M.M., Cohen J.D., Carter C.S. Conflict monitoring and anterior cingulate cortex: an update. Trends Cogn Sci 2004, 8:539-546.
11. Brown J.W., Braver T.S. A computational model of risk, conflict, and individual difference effects in the anterior cingulate cortex. Brain Res 2008, 1202:99-108.
12. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009, 10:186-198.
13. Caldarelli G., Capocci A., Garlaschelli D. A self-organized model for network evolution. Eur Phys J B 2008, 64:585-591.
14. Cavanagh J.F., Cohen M.X., Allen J.J.B. Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring. J Neurosci 2009, 29:98-105.
15. Choi H., Williams W.J. Improved time-frequency representation of multicomponent signals using exponential kernels. IEEE Trans Acoust 1989, 17:862-871.
16. Douw L., Schoonheim M.M., Landi D., van der Meer M.L., Geurts J.J.G., Reijneveld J.C., et al. Cognition is related to resting-state small-world network topology: an magnetoencephalographic study. Neuroscience 2011, 175:169-177.
17. Estrada E., Hatano N. Communicability in complex networks. Phys Rev E 2008, 77:036111.
18. Fagiolo G. Clustering in complex directed networks. Phys Rev E 2007, 76:026107.
19. Fagiolo G., Reyes J., Schiavo S. On the topological properties of the world trade web: a weighted network analysis. Appl Phys Financ Anal 2008, 387:3868-3873.
20. Fallani F.D.V., Astolfi L., Cincotti F., Mattia D., Marciani M.G., Salinari S., et al. Cortical functional connectivity networks in normal and spinal cord injured patients: evaluation by graph analysis. Hum Brain Mapp 2007, 28:1334-1346.
21. Friston K.J. Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp 1994, 2:56-78.
22. Friston K.J. Functional and effective connectivity: a review. Brain Connect 2011, 1:13-36.
23. Gehring W.J., Willoughby A.R. Are all medial frontal negativities created equal? Toward a richer empirical basis for theories of action monitoring. Errors, conflicts and the brain. Curr Opin Perform Monitor 2004, 14-20.
24. Genovese C.R., Lazar N.A., Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 2002, 15:870-878.
25. Grindrod P. Range-dependent random graphs and their application to modeling large small-world Proteome datasets. Phys Rev E 2002, 66:066702.
26. Hall J.R., Bernat E.M., Patrick C.J. Externalizing psychopathology and the error-related negativity. Psychol Sci 2007, 18:326-333.
27. He Bin. Brain electric source imaging: scalp Laplacian mapping and cortical imaging. Crit Rev Biomed Eng 1999, 27:149.
28. Honey C.J., K.ã¶tter R., Breakspear M., Sporns O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci 2007, 104:10240.
29. Kalna G. A clustering coefficient for weighted networks, with application to gene expression data. AI Commun 2007, 20:263-271.
30. Kayser J., Tenke C.E. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clin Neurophysiol 2006, 117:348-368.
31. Kayser J., Tenke C.E. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates. Clin Neurophysiol 2006, 117:369-380.
32. Lachaux J.P., Rodriguez E., Martinerie J., Varela F.J. Measuring phase synchrony in brain signals. Hum Brain Mapp 1999, 8:194-208.
33. Latora V., Marchiori M. Economic small-world behavior in weighted networks. Eur Phys J B 2003, 32:249-263.
34. Liao W., Ding J., Marinazzo D., Xu Q., Wang Z., Yuan C., et al. Small-world directed networks in the human brain: multivariate Granger causality analysis of resting-state fMRI. Neuroimage 2011, 54:2683-2694.
35. Liu Z., He B. fMRI-EEG integrated cortical source imaging by use of time-variant spatial constraints. Neuroimage 2008, 39:1198-1214.
36. MacDonald A.W., Cohen J.D., Stenger V.A., Carter C.S. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 2000, 288:1835.
37. Meunier D., Lambiotte R., Bullmore E.T. Modular and hierarchically modular organization of brain networks. Front Neurosci 2010, 4.
38. Miller E.K., Cohen J.D. An integrative theory of prefrontal cortex function. Annu Rev Neurosci 2001, 24:167-202.
39. Onnela J.P., Saramaki J., Kertesz J., Kaski K. Intensity and coherence of motifs in weighted complex networks. Phys Rev E 2005, 71:065103.
40. Pereda E., Quiroga R.Q., Bhattacharya J. Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol 2005, 77:1-37.
41. Reijneveld J.C., Ponten S.C., Berendse H.W., Stam C.J. The application of graph theoretical analysis to complex networks in the brain. Clin Neurophysiol 2007, 118:2317-2331.
42. Rihaczek A. Signal energy distribution in time and frequency. IEEE Trans Inform Theory 1968, 14:369-374.
43. Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010, 52:1059-1069.
44. Saramaki J., Kivela M., Onnela J.P., Kaski K., Kertesz J. Generalizations of the clustering coefficient to weighted complex networks. Phys Rev E 2007, 75:02715.
45. Sporns O., Chialvo D.R., Kaiser M., Hilgetag C.C. Organization, development and function of complex brain networks. Trends Cogn Sci 2004, 8:418-425.
46. Sporns O., Honey C.J., Kötter R. Identification and classification of hubs in brain networks. PLoS One 2007, 2:e1049.
47. Stam C.J. Functional connectivity patterns of human magnetoen-cephalographic recordings: a 'small-world' network. Neurosci Lett 2003, 355:25-28.
48. Stam C.J., De Haan W., Daffertshofer A., Jones B.F., Manshanden I., Van Cappellen van Walsum A.M., et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain 2009, 132:213.
49. Stam C.J., de Haan W., Daffertshofer A., Jones B.F., Manshanden I., van Walsum A.M.V., et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain 2009, 132:213-224.
50. Stam C.J., Jones B.F., Nolte G., Breakspear M., Scheltens P. Small-world networks and functional connectivity in Alzheimer's disease. Cereb Cortex 2007, 17:92-99.
51. Supekar K., Menon V., Rubin D., Musen M., Greicius M.D. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS Comput Biol 2008, 4:e1000100.
52. Tenke CE, Kayser J. Generator localization by current source density (CSD): implications of volume conduction and field closure at intracranial and scalp resolutions. Clin Neurophysiol 2012; in press.
53. Trujillo L.T., Allen J.J.B. Theta EEG dynamics of the error-related negativity. Clin Neurophysiol 2007, 118:645-668.
54. Watts D.J., Strogatz S.H. Small world. Nature 1998, 393:440-442.
55. Yan C., Gong G., Wang J., Wang D., Liu D., Zhu C., et al. Sex- and brain size-related small-world structural cortical networks in young adults: a DTI tractography study. Cereb Cortex 2011, 21:449.
56. Yang Y., Xinghuo Y. Weighted small world complex networks: smart sliding mode control. Proceedings of the intelligent computing 5th international conference on emerging intelligent computing technology and applications 2009, 935-944.
57. Zachary W.W. An information flow model for conflict and fission in small groups. J Anthropol Res 1977, 452-473.
58. Zhang B., Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005, 4. Article 17.