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Computational Intelligence and Neuroscience
Volume 2012 (2012), Article ID 412512, 21 pages
http://dx.doi.org/10.1155/2012/412512
Research Article

Brain Connectivity Analysis: A Short Survey

1CIML Group, Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany
2IEETA/DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal
3Institute of Experimental Psychology, University of Regensburg, 93040 Regensburg, Germany
4DTSTC, Facultad de Ciencias, Universidad Granada, 18071 Granada, Spain
5DATC/ESTII, Universidad de Granada, 18071 Granada, Spain

Received 8 May 2012; Revised 10 August 2012; Accepted 28 August 2012

Academic Editor: Mark Greenlee

Copyright © 2012 E. W. Lang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. O. Sporns, Connectome, vol. 5, Scholarpedia, 2010.
  2. O. Sporns, G. Tononi, and R. Kötter, “The human connectome: a structural description of the human brain,” PLoS Computational Biology, vol. 1, no. 4, p. e42, 2005. View at Google Scholar · View at Scopus
  3. K. J. Friston, “Functional and effective connectivity in neuroimaging: a synthesis,” Human Brain Mapping, vol. 2, no. 1-2, pp. 56–78, 1994. View at Google Scholar · View at Scopus
  4. K. J. Friston, “Functional and effective connectivity in neuroimaging: a synthesis,” Human Brain Mapping, vol. 2, no. 1-2, pp. 56–78, 1994. View at Google Scholar · View at Scopus
  5. B. Horwitz, “The elusive concept of brain connectivity,” NeuroImage, vol. 19, no. 2, pp. 466–470, 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Johansen-Berg and M. F. S. Rushworth, “Using diffusion imaging to study human connectional anatomy,” Annual Review of Neuroscience, vol. 32, pp. 75–94, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Axer, K. Amunts, D. Grässel et al., “A novel approach to the human connectome: ultra-high resolution mapping of fiber tracts in the brain,” NeuroImage, vol. 54, no. 2, pp. 1091–1101, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. J. C. Reijneveld, S. C. Ponten, H. W. Berendse, and C. J. Stam, “The application of graph theoretical analysis to complex networks in the brain,” Clinical Neurophysiology, vol. 118, no. 11, pp. 2317–2331, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. E. Bullmore and O. Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems,” Nature Reviews Neuroscience, vol. 10, no. 3, pp. 186–198, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. E. T. Bullmore and D. S. Bassett, “Brain graphs: graphical models of the human brain connectome,” Annual Review of Clinical Psychology, vol. 7, pp. 113–140, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Guye, G. Bettus, F. Bartolomei, and P. J. Cozzone, “Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 23, no. 5-6, pp. 409–421, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Fornito, A. Zalesky, I. H. Harding et al., “Whole-brain anatomical networks: does the choice of nodes matter?” NeuroImage, vol. 50, no. 3, pp. 970–983, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. M. D. Fox and M. E. Raichle, “Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging,” Nature Reviews Neuroscience, vol. 8, no. 9, pp. 700–711, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. V. G. van de Ven, E. Formisano, D. Prvulovic, C. H. Roeder, and D. E. J. Linden, “Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest,” Human Brain Mapping, vol. 22, no. 3, pp. 165–178, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. D. He, Z. Zeng, and L. Stone, “Detecting generalized synchrony: an improved approach,” Physical Review E, vol. 67, no. 2, Article ID 026223, 2003. View at Google Scholar
  16. H. Berger, “Über das Elektrenkephalogramm des Menschen,” Archiv für Psychiatrie und Nervenkrankheiten, vol. 87, no. 1, pp. 527–570, 1929. View at Publisher · View at Google Scholar · View at Scopus
  17. M. E. Raichle, A. M. MacLeod, A. Z. Snyder, W. J. Powers, D. A. Gusnard, and G. L. Shulman, “A default mode of brain function,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 2, pp. 676–682, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. M. D. Greicius, B. Krasnow, A. L. Reiss, and V. Menon, “Functional connectivity in the resting brain: a network analysis of the default mode hypothesis,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 1, pp. 253–258, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. S. J. Broyd, C. Demanuele, S. Debener, S. K. Helps, C. J. James, and E. J. S. Sonuga-Barke, “Default-mode brain dysfunction in mental disorders: a systematic review,” Neuroscience and Biobehavioral Reviews, vol. 33, no. 3, pp. 279–296, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. J. S. Damoiseaux, S. A. R. B. Rombouts, F. Barkhof et al., “Consistent resting-state networks across healthy subjects,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 37, pp. 13848–13853, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Mantini, M. G. Perrucci, C. Del Gratta, G. L. Romani, and M. Corbetta, “Electrophysiological signatures of resting state networks in the human brain,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 32, pp. 13170–13175, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. M. D. Greicius, V. Kiviniemi, O. Tervonen et al., “Persistent default-mode network connectivity during light sedation,” Human Brain Mapping, vol. 29, no. 7, pp. 839–847, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. M. J. Jafri, G. D. Pearlson, M. Stevens, and V. D. Calhoun, “A method for functional network connectivity among spatially independent resting-state components in schizophrenia,” NeuroImage, vol. 39, no. 4, pp. 1666–1681, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. E. J. S. Sonuga-Barke and F. X. Castellanos, “Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis,” Neuroscience and Biobehavioral Reviews, vol. 31, no. 7, pp. 977–986, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. M. D. Greicius, K. Supekar, V. Menon, and R. F. Dougherty, “Resting-state functional connectivity reflects structural connectivity in the default mode network,” Cerebral Cortex, vol. 19, no. 1, pp. 72–78, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, “Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 13, pp. 4637–4642, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. L. J. Zhang, G. Yang, J. Yin, Y. Liu, and J. Qi, “Abnormal default-mode network activation in cirrhotic patients: a functional magnetic resonance imaging study,” Acta Radiologica, vol. 48, no. 7, pp. 781–787, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Eichele, S. Debener, V. D. Calhoun et al., “Prediction of human errors by maladaptive changes in event-related brain networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 16, pp. 6173–6178, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. K. D. Singh and I. P. Fawcett, “Transient and linearly graded deactivation of the human default-mode network by a visual detection task,” NeuroImage, vol. 41, no. 1, pp. 100–112, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. S. M. Wilson, I. Molnar-Szakacs, and M. Iacoboni, “Beyond superior temporal cortex: intersubject correlations in narrative speech comprehension,” Cerebral Cortex, vol. 18, no. 1, pp. 230–242, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. S. G. Horovitz, M. Fukunaga, J. A. De Zwart et al., “Low frequency BOLD fluctuations during resting wakefulness and light sleep: a simultaneous EEG-fMRI study,” Human Brain Mapping, vol. 29, no. 6, pp. 671–682, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. F. Esposito, A. Bertolino, T. Scarabino et al., “Independent component model of the default-mode brain function: assessing the impact of active thinking,” Brain Research Bulletin, vol. 70, no. 4–6, pp. 263–269, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. R. Salvador, A. Martínez, E. Pomarol-Clotet et al., “A simple view of the brain through a frequency-specific functional connectivity measure,” NeuroImage, vol. 39, no. 1, pp. 279–289, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. G. Buzsáki and A. Draguhn, “Neuronal olscillations in cortical networks,” Science, vol. 304, no. 5679, pp. 1926–1929, 2004. View at Publisher · View at Google Scholar · View at Scopus
  35. M. D. Fox, A. Z. Snyder, J. M. Zacks, and M. E. Raichle, “Coherent spontaneous activity accounts for trial-to-trial variability in human evoked brain responses,” Nature Neuroscience, vol. 9, no. 1, pp. 23–25, 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. A. M. C. Kelly, L. Q. Uddin, B. B. Biswal, F. X. Castellanos, and M. P. Milham, “Competition between functional brain networks mediates behavioral variability,” NeuroImage, vol. 39, no. 1, pp. 527–537, 2008. View at Publisher · View at Google Scholar · View at Scopus
  37. M. D. Greicius, B. H. Flores, V. Menon et al., “Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus,” Biological Psychiatry, vol. 62, no. 5, pp. 429–437, 2007. View at Publisher · View at Google Scholar · View at Scopus
  38. F. X. Castellanos, D. S. Margulies, C. Kelly et al., “Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit hyperactivity disorder,” Biological Psychiatry, vol. 63, no. 3, pp. 332–337, 2008. View at Publisher · View at Google Scholar · View at Scopus
  39. S. Tinaz, H. E. Schendan, and C. E. Stern, “Fronto-striatal deficit in Parkinson's disease during semantic event sequencing,” Neurobiology of Aging, vol. 29, no. 3, pp. 397–407, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. P. Fransson, B. Skiöld, S. Horsch et al., “Resting-state networks in the infant brain,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 39, pp. 15531–15536, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. D. A. Fair, A. L. Cohen, N. U. F. Dosenbach et al., “The maturing architecture of the brain's default network,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 10, pp. 4028–4032, 2008. View at Publisher · View at Google Scholar · View at Scopus
  42. M. E. Thomason, C. E. Chang, G. H. Glover, J. D. E. Gabrieli, M. D. Greicius, and I. H. Gotlib, “Default-mode function and task-induced deactivation have overlapping brain substrates in children,” NeuroImage, vol. 41, no. 4, pp. 1493–1503, 2008. View at Publisher · View at Google Scholar · View at Scopus
  43. P. Fransson, “How default is the default mode of brain function?. Further evidence from intrinsic BOLD signal fluctuations,” Neuropsychologia, vol. 44, no. 14, pp. 2836–2845, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Van Essen, and M. E. Raichle, “The human brain is intrinsically organized into dynamic, anticorrelated functional networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 27, pp. 9673–9678, 2005. View at Publisher · View at Google Scholar · View at Scopus
  45. M. E. Raichle and A. Z. Snyder, “A default mode of brain function: a brief history of an evolving idea,” NeuroImage, vol. 37, no. 4, pp. 1083–1090, 2007. View at Publisher · View at Google Scholar · View at Scopus
  46. K. Gopinath, W. Ringe, A. Goyal et al., “Striatal functional connectivity networks are modulated by fMRI resting state conditions,” NeuroImage, vol. 54, no. 1, pp. 380–388, 2011. View at Publisher · View at Google Scholar · View at Scopus
  47. A. M. Morcom and P. C. Fletcher, “Does the brain have a baseline? Why we should be resisting a rest,” NeuroImage, vol. 37, no. 4, pp. 1073–1082, 2007. View at Publisher · View at Google Scholar · View at Scopus
  48. M. P. van den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: a review on resting-state fMRI functional connectivity,” European Neuropsychopharmacology, vol. 20, no. 8, pp. 519–534, 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. D. S. Margulies, J. Böttger, X. Long et al., “Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 23, no. 5-6, pp. 289–307, 2010. View at Publisher · View at Google Scholar · View at Scopus
  50. H. Y. Zhang, S. J. Wang, J. Xing et al., “Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer's disease,” Behavioural Brain Research, vol. 197, no. 1, pp. 103–108, 2009. View at Publisher · View at Google Scholar · View at Scopus
  51. C. Sorg, V. Riedl, R. Perneczky, A. Kurz, and A. M. Wohlschläger, “Impact of Alzheimer's disease on the functional connectivity of spontaneous brain activity,” Current Alzheimer Research, vol. 6, no. 6, pp. 541–553, 2009. View at Publisher · View at Google Scholar · View at Scopus
  52. T.-S. Yo, A. Anwander, M. Descoteaux, P. Fillard, C. Poupon, and T. R. Knösche, “Quantifying brain connectivity: a comparative tractography study,” Lecture Notes in Computer Science, vol. 5761, no. 1, pp. 886–893, 2009. View at Publisher · View at Google Scholar · View at Scopus
  53. D. Saur, B. Schelter, S. Schnell et al., “Combining functional and anatomical connectivity reveals brain networks for auditory language comprehension,” NeuroImage, vol. 49, no. 4, pp. 3187–3197, 2010. View at Publisher · View at Google Scholar · View at Scopus
  54. C. Chang and G. H. Glover, “Time-frequency dynamics of resting-state brain connectivity measured with fMRI,” NeuroImage, vol. 50, no. 1, pp. 81–98, 2010. View at Publisher · View at Google Scholar · View at Scopus
  55. M. J. Brookes, J. R. Hale, J. M. Zumer et al., “Measuring functional connectivity using MEG: methodology and comparison with fcMRI,” NeuroImage, vol. 56, no. 3, pp. 1082–1104, 2011. View at Publisher · View at Google Scholar · View at Scopus
  56. J. Cabral, E. Hugues, O. Sporns, and G. Deco, “Role of local network oscillations in resting-state functional connectivity,” NeuroImage, vol. 57, no. 1, pp. 130–139, 2011. View at Publisher · View at Google Scholar · View at Scopus
  57. P. L. Nunez, R. Srinivasan, A. F. Westdorp et al., “EEG coherency I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales,” Electroencephalography and Clinical Neurophysiology, vol. 103, no. 5, pp. 499–515, 1997. View at Publisher · View at Google Scholar · View at Scopus
  58. P. L. Nunez, R. B. Silberstein, Z. Shi et al., “EEG coherency II: experimental comparisons of multiple measures,” Clinical Neurophysiology, vol. 110, no. 3, pp. 469–486, 1999. View at Publisher · View at Google Scholar · View at Scopus
  59. P. L. Nunez, B. M. Wingeier, and R. B. Silberstein, “Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks,” Human Brain Mapping, vol. 13, no. 3, pp. 125–164, 2001. View at Publisher · View at Google Scholar · View at Scopus
  60. R. Srinivasan, W. R. Winter, J. Ding, and P. L. Nunez, “EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics,” Journal of Neuroscience Methods, vol. 166, no. 1, pp. 41–52, 2007. View at Publisher · View at Google Scholar · View at Scopus
  61. S. Palva and J. M. Palva, “New vistas for α-frequency band oscillations,” Trends in Neurosciences, vol. 30, no. 4, pp. 150–158, 2007. View at Publisher · View at Google Scholar · View at Scopus
  62. J. Kaiser and W. Lutzenberger, “Human gamma-band activity: a window to cognitive processing,” NeuroReport, vol. 16, no. 3, pp. 207–211, 2005. View at Publisher · View at Google Scholar · View at Scopus
  63. A. Shmuel and D. A. Leopold, “Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: implications for functional connectivity at rest,” Human Brain Mapping, vol. 29, no. 7, pp. 751–761, 2008. View at Publisher · View at Google Scholar · View at Scopus
  64. D. P. Auer, “Spontaneous low-frequency blood oxygenation level-dependent fluctuations and functional connectivity analysis of the 'resting' brain,” Magnetic Resonance Imaging, vol. 26, no. 7, pp. 1055–1064, 2008. View at Publisher · View at Google Scholar · View at Scopus
  65. S. A. Weiss, D. S. Bassett, D. Rubinstein et al., “Functional brain network characterization and adaptivity during task practice in healthy volunteers and people with Schizophrenia,” Frontiers in Human Neuroscience, vol. 5, p. 81, 2011. View at Google Scholar
  66. A. S. Ghuman, J. R. McDaniel, and A. Martin, “A wavelet-based method for measuring the oscillatory dynamics of resting-state functional connectivity in MEG,” NeuroImage, vol. 56, no. 1, pp. 69–77, 2011. View at Publisher · View at Google Scholar · View at Scopus
  67. K. Li, L. Guo, J. Nie, G. Li, and T. Liu, “Review of methods for functional brain connectivity detection using fMRI,” Computerized Medical Imaging and Graphics, vol. 33, no. 2, pp. 131–139, 2009. View at Publisher · View at Google Scholar · View at Scopus
  68. K. Friston, J. T. Ashburner, S. T. Kiebel, T. E. Nicols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Imaging, Elsevier, Academic Press, New York, NY, USA, 2006.
  69. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, New York, NY, USA, 2001.
  70. A. Cichocki and S.-I. Amari, Adaptive Blind Signal and Image Processing, Wiley, New York, NY, USA, 2002.
  71. J. V. Stone, J. Porrill, N. R. Porter, and I. D. Wilkinson, “Spatiotemporal independent component analysis of event-related fMRI data using skewed probability density functions,” NeuroImage, vol. 15, no. 2, pp. 407–421, 2002. View at Publisher · View at Google Scholar · View at Scopus
  72. F. J. Theis, P. Gruber, I. R. Keck, and E. W. Lang, “Functional MRI analysis by a novel spatiotemporal ICA algorithm,” in Proceedings of the 15th International Conference on Artificial Neural Networks: Biological Inspirations (ICANN '05), W. Duch, Ed., vol. 3696 of Lecture notes in computer science, pp. 677–682, Springer, Warsaw, Poland, September 2005.
  73. F. J. Theis, P. Gruber, I. R. Keck, and E. W. Lang, “A robust model for spatiotemporal dependencies,” Neurocomputing, vol. 71, no. 10–12, pp. 2209–2216, 2008. View at Publisher · View at Google Scholar · View at Scopus
  74. P. Stoika and Y. Selén, “Model order selection,” IEEE Signal Processing Magazine, vol. 21, no. 4, pp. 36–47, 2004. View at Google Scholar
  75. Q. Ding and S. Kay, “Inconsistency of the MDL: on the performance of model order selection criteria with increasing signal-to-noise ratio,” IEEE Transactions on Signal Processing, vol. 59, no. 5, pp. 1959–1969, 2011. View at Publisher · View at Google Scholar · View at Scopus
  76. I. Daubechies, E. Roussos, S. Takerkart et al., “Independent component analysis for brain fMRI does not select for independence,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 26, pp. 10415–10422, 2009. View at Publisher · View at Google Scholar · View at Scopus
  77. V. D. Calhoun, J. Liu, and T. Adali, “A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.,” NeuroImage, vol. 45, no. 1, pp. S163–172, 2009. View at Publisher · View at Google Scholar · View at Scopus
  78. W. W. Seeley, R. K. Crawford, J. Zhou, B. L. Miller, and M. D. Greicius, “Neurodegenerative diseases target large-scale human brain networks,” Neuron, vol. 62, no. 1, pp. 42–52, 2009. View at Publisher · View at Google Scholar · View at Scopus
  79. C. Habas, N. Kamdar, D. Nguyen et al., “Distinct cerebellar contributions to intrinsic connectivity networks,” Journal of Neuroscience, vol. 29, no. 26, pp. 8586–8594, 2009. View at Publisher · View at Google Scholar · View at Scopus
  80. N. Filippini, B. J. MacIntosh, M. G. Hough et al., “Distinct patterns of brain activity in young carriers of the APOE-ε4 allele,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 17, pp. 7209–7214, 2009. View at Publisher · View at Google Scholar · View at Scopus
  81. C. F. Beckmann, C. E. Mackay, N. Filippini, and S. M. Smith, “Group comparison of resting-state FMRI data using multi-subject ICA and dual regression,” NeuroImage, vol. 47, supplement 1, pp. S39–S41, 2009. View at Publisher · View at Google Scholar
  82. V. D. Calhoun, T. Adali, V. B. McGinty, J. J. Pekar, T. D. Watson, and G. D. Pearlson, “fMRI activation in a visual-perception task: network of areas detected using the general linear model and independent components analysis,” NeuroImage, vol. 14, no. 5, pp. 1080–1088, 2001. View at Publisher · View at Google Scholar · View at Scopus
  83. A. Wismüller, O. Lange, D. Auer, and G. Leinsinger, “Model-free functional MRI analysis for detecting low-frequency functional connectivity in the human brains,” in Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 7624, The international society for optics and photonics (SPIE), 2010.
  84. P. Bellec, P. Rosa-Neto, O. C. Lyttelton, H. Benali, and A. C. Evans, “Multi-level bootstrap analysis of stable clusters in resting-state fMRI,” NeuroImage, vol. 51, no. 3, pp. 1126–1139, 2010. View at Publisher · View at Google Scholar · View at Scopus
  85. D. Cordes, V. Haughton, J. D. Carew, K. Arfanakis, and K. Maravilla, “Hierarchical clustering to measure connectivity in fMRI resting-state data,” Magnetic Resonance Imaging, vol. 20, no. 4, pp. 305–317, 2002. View at Publisher · View at Google Scholar · View at Scopus
  86. A. Mezer, Y. Yovel, O. Pasternak, T. Gorfine, and Y. Assaf, “Cluster analysis of resting-state fMRI time series,” NeuroImage, vol. 45, no. 4, pp. 1117–1125, 2009. View at Publisher · View at Google Scholar · View at Scopus
  87. M. van den Heuvel, R. Mandl, and H. H. Pol, “Normalized cut group clustering of resting-state fMRI data,” PLoS ONE, vol. 3, no. 4, Article ID e2001, 2008. View at Publisher · View at Google Scholar · View at Scopus
  88. R. Salvador, J. Suckling, M. R. Coleman, J. D. Pickard, D. Menon, and E. Bullmore, “Neurophysiological architecture of functional magnetic resonance images of human brain,” Cerebral Cortex, vol. 15, no. 9, pp. 1332–2342, 2005. View at Publisher · View at Google Scholar · View at Scopus
  89. A. L. Cohen, D. A. Fair, N. U. F. Dosenbach et al., “Defining functional areas in individual human brains using resting functional connectivity MRI,” NeuroImage, vol. 41, no. 1, pp. 45–57, 2008. View at Publisher · View at Google Scholar · View at Scopus
  90. M. P. van den Heuvel, C. J. Stam, M. Boersma, and H. E. Hulshoff Pol, “Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain,” NeuroImage, vol. 43, no. 3, pp. 528–539, 2008. View at Publisher · View at Google Scholar · View at Scopus
  91. R. Schachtner, G. Pöppel, and E. W. Lang, “Bayesian extensions of non-negative matrix factorization,” in Proceedings of the 2nd International Workshop on Cognitive Information Processing (CIP '10), pp. 57–62, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  92. A. T. Cemgil, “Bayesian inference in non-negative matrix factorization models,” Computational Intelligence and Neuroscience, vol. 2009, Article ID 785152, 17 pages, 2009. View at Publisher · View at Google Scholar
  93. K. A. Norman, S. M. Polyn, G. J. Detre, and J. V. Haxby, “Beyond mind-reading: multi-voxel pattern analysis of fMRI data,” Trends in Cognitive Sciences, vol. 10, no. 9, pp. 424–430, 2006. View at Publisher · View at Google Scholar · View at Scopus
  94. J. D. Haynes and G. Rees, “Decoding mental states from brain activity in humans,” Nature Reviews Neuroscience, vol. 7, no. 7, pp. 523–534, 2006. View at Publisher · View at Google Scholar · View at Scopus
  95. F. Pereira, T. Mitchell, and M. Botvinick, “Machine learning classifiers and fMRI: a tutorial overview,” NeuroImage, vol. 45, no. 1, pp. S199–209, 2009. View at Publisher · View at Google Scholar · View at Scopus
  96. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. View at Google Scholar
  97. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  98. S. E. Joel, B. S. Caffo, P. C. M. van Zijl, and J. J. Pekar, “On the relationship between seed-based and ica-based measures of functional connectivity,” Magnetic Resonance in Medicine, vol. 66, no. 3, pp. 644–657. View at Publisher · View at Google Scholar
  99. A. A. Elseoud, H. Littow, J. Remes et al., “Group-ICA model order highlights patterns of functional brain connectivity,” Frontiers in Systems Neuroscience, vol. 5, p. 73, 2011. View at Google Scholar · View at Scopus
  100. J. Li, Z. J. Wang, and M. J. McKeown, “Controlling the false discovery rate in modeling brain functional connectivity,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '08), pp. 2105–2108, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  101. B. P. Rogers and J. C. Gore, “Empirical comparison of sources of variation for FMRI connectivity analysis,” PLoS ONE, vol. 3, no. 11, Article ID e3708, 2008. View at Publisher · View at Google Scholar · View at Scopus
  102. G. Varoquaux, A. Gramfort, J. -B. Poline, and B. Thirion, “Brain covariance selection: better individual functional connectivity models using population prior,” 2010. View at Google Scholar
  103. S. Erla, L. Faes, E. Tranquillini, D. Orrico, and G. Nollo, “Multivariate autoregressive model with instantaneous effects to improve brain connectivity estimation,” in Proceedings of the 7th NFSI & ICBEM 2009 Conference, 2009.
  104. S. Haufe, R. Tomioka, G. Nolte, K. R. Müller, and M. Kawanabe, “Modeling sparse connectivity between underlying brain sources for EEG/MEG,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 8, pp. 1954–1963, 2010. View at Publisher · View at Google Scholar · View at Scopus
  105. S. Palva, S. Monto, and J. M. Palva, “Graph properties of synchronized cortical networks during visual working memory maintenance,” NeuroImage, vol. 49, no. 4, pp. 3257–3268, 2010. View at Publisher · View at Google Scholar · View at Scopus
  106. G. Deco, V. K. Jirsa, and A. R. McIntosh, “Emerging concepts for the dynamical organization of resting-state activity in the brain,” Nature Reviews Neuroscience, vol. 12, no. 1, pp. 43–56, 2011. View at Publisher · View at Google Scholar · View at Scopus
  107. G. Deco and M. Corbetta, “The dynamical balance of the brain at rest,” Neuroscientist, vol. 17, no. 1, pp. 107–123, 2011. View at Publisher · View at Google Scholar · View at Scopus
  108. D. A. Fair, A. L. Cohen, J. D. Power et al., “Functional brain networks develop from a “local to distributed” organization,” PLoS Computational Biology, vol. 5, no. 5, Article ID e1000381, 2009. View at Publisher · View at Google Scholar · View at Scopus
  109. A. C. Vogel, J. D. Power, S. E. Petersen, and B. L. Schlaggar, “Development of the brain's functional network architecture,” Neuropsychology Review, vol. 20, no. 4, pp. 362–375, 2010. View at Publisher · View at Google Scholar · View at Scopus
  110. B. J. Shannon, M. E. Raichle, A. Z. Snyder et al., “Premotor functional connectivity predicts impulsivity in juvenile offenders,” Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 27, pp. 11241–11245, 2011. View at Publisher · View at Google Scholar · View at Scopus
  111. C. D. Smyser, A. Z. Snyder, and J. J. Neil, “Functional connectivity MRI in infants: exploration of the functional organization of the developing brain,” NeuroImage, vol. 56, no. 3, pp. 1437–1452, 2011. View at Publisher · View at Google Scholar · View at Scopus
  112. K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” NeuroImage, vol. 19, no. 4, pp. 1273–1302, 2003. View at Publisher · View at Google Scholar · View at Scopus
  113. R. Goebel, A. Roebroeck, D. S. Kim, and E. Formisano, “Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping,” Magnetic Resonance Imaging, vol. 21, no. 10, pp. 1251–1261, 2003. View at Publisher · View at Google Scholar · View at Scopus
  114. O. David, “fMRI connectivity, meaning and empiricism. Comments on: roebroeck et al. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution,” NeuroImage, vol. 58, no. 2, pp. 306–309, 2009. View at Publisher · View at Google Scholar · View at Scopus
  115. A. Roebroeck, E. Formisano, and R. Goebel, “The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution,” NeuroImage, vol. 58, no. 2, pp. 296–302, 2009. View at Publisher · View at Google Scholar · View at Scopus
  116. A. Roebroeck, E. Formisano, and R. Goebel, “Reply to Friston and David. After comments on: the identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution,” NeuroImage, vol. 58, no. 2, pp. 310–311, 2009. View at Publisher · View at Google Scholar · View at Scopus
  117. Z. Zhou, M. Ding, Y. Chen, P. Wright, Z. Lu, and Y. Liu, “Detecting directional influence in fMRI connectivity analysis using PCA based Granger causality,” Brain Research, vol. 1289, pp. 22–29, 2009. View at Publisher · View at Google Scholar · View at Scopus
  118. J. C. Rajapakse, Y. Wang, X. Zheng, and J. Zhou, “Probabilistic framework for brain connectivity from functional MR images,” IEEE Transactions on Medical Imaging, vol. 27, no. 6, pp. 825–833, 2008. View at Publisher · View at Google Scholar · View at Scopus
  119. J. C. Rajapakse and J. Zhou, “Learning effective brain connectivity with dynamic Bayesian networks,” NeuroImage, vol. 37, no. 3, pp. 749–760, 2007. View at Publisher · View at Google Scholar · View at Scopus
  120. A. Roebroeck, A. K. Seth, and P. Valdes-Sosa, “Causal time series analysis of functional magnetic resonance imaging data,” Journal of Machine Learning Research, vol. 12, pp. 65–94, 2011. View at Google Scholar
  121. D. Battaglia, A. Witt, F. Wolf, and T. Geisel, “Dynamic effective connectivity of inter-areal brain circuits,” PLoS Computational Biology, vol. 8, no. 3, Article ID e1002438, 2012. View at Google Scholar
  122. S. M. Smith, K. L. Miller, G. Salimi-Khorshidi et al., “Network modelling methods for FMRI,” NeuroImage, vol. 54, no. 2, pp. 875–891, 2011. View at Publisher · View at Google Scholar · View at Scopus
  123. O. Sporns, “The human connectome: a complex network,” Annals of the New York Academy of Sciences, vol. 1224, no. 1, pp. 109–125, 2011. View at Publisher · View at Google Scholar · View at Scopus
  124. R. Diestel, Chapter Graph Theory, Springer, New York, NY, USA, 2005.
  125. E. Bullmore, A. Barnes, D. S. Bassett et al., “Generic aspects of complexity in brain imaging data and other biological systems,” NeuroImage, vol. 47, no. 3, pp. 1125–1134, 2009. View at Publisher · View at Google Scholar · View at Scopus
  126. O. Sporns, C. J. Honey, and R. Kötter, “Identification and classification of hubs in brain networks,” PLoS ONE, vol. 2, no. 10, Article ID e1049, 2007. View at Publisher · View at Google Scholar · View at Scopus
  127. R. Albert, H. Jeong, and A. L. Barabási, “Error and attack tolerance of complex networks,” Nature, vol. 406, no. 6794, pp. 378–382, 2000. View at Publisher · View at Google Scholar · View at Scopus
  128. C. J. Stam, W. De Haan, A. Daffertshofer et al., “Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease,” Brain, vol. 132, no. 1, pp. 213–224, 2009. View at Publisher · View at Google Scholar · View at Scopus
  129. C. J. Honey and O. Sporns, “Dynamical consequences of lesions in cortical networks,” Human Brain Mapping, vol. 29, no. 7, pp. 802–809, 2008. View at Publisher · View at Google Scholar · View at Scopus
  130. D. J. Watts and S. H. Strogatz, “Collective dynamics of 'small-world9 networks,” Nature, vol. 393, no. 6684, pp. 440–442, 1998. View at Google Scholar · View at Scopus
  131. M. E. J. Newman, “Modularity and community structure in networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 23, pp. 8577–8582, 2006. View at Publisher · View at Google Scholar · View at Scopus
  132. E. Ravasz and L. Barabasi, “Hierarchical organization in complex networks,” Physical Review E, vol. 67, Article ID 026112, 2003. View at Google Scholar
  133. A. L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999. View at Publisher · View at Google Scholar · View at Scopus
  134. V. Latora and M. Marchiori, “Efficient behavior of small-world networks,” Physical Review Letters, vol. 87, no. 19, Article ID 198701, pp. 198701/1–198701/4, 2001. View at Google Scholar · View at Scopus
  135. D. S. Bassett and E. Bullmore, “Small-world brain networks,” Neuroscientist, vol. 12, no. 6, pp. 512–523, 2006. View at Publisher · View at Google Scholar · View at Scopus
  136. O. Sporns and J. D. Zwi, “The small world of the cerebral cortex,” Neuroinformatics, vol. 2, no. 2, pp. 145–162, 2004. View at Publisher · View at Google Scholar · View at Scopus
  137. A. A. Ioannides, “Dynamic functional connectivity,” Current Opinion in Neurobiology, vol. 17, no. 2, pp. 161–170, 2007. View at Publisher · View at Google Scholar
  138. G. S. Wig, B. L. Schlaggar, and S. E. Petersen, “Concepts and principles in the analysis of brain networks,” Annals of the New York Academy of Sciences, vol. 1224, no. 1, pp. 126–146, 2011. View at Publisher · View at Google Scholar · View at Scopus
  139. C. J. Stam, “Characterization of anatomical and functional connectivity in the brain: a complex networks perspective.,” International Journal of Psychophysiology, vol. 77, no. 3, pp. 186–194, 2010. View at Google Scholar · View at Scopus
  140. M. Guye, F. Bartolomei, and J. P. Ranjeva, “Imaging structural and functional connectivity: towards a unified definition of human brain organization,” Current Opinion in Neurology, vol. 21, no. 4, pp. 393–403, 2008. View at Google Scholar · View at Scopus
  141. T. Medkour, A. T. Walden, and A. Burgess, “Graphical modelling for brain connectivity via partial coherence,” Journal of Neuroscience Methods, vol. 180, no. 2, pp. 374–383, 2009. View at Publisher · View at Google Scholar · View at Scopus
  142. Y. He and A. Evans, “Graph theoretical modeling of brain connectivity,” Current Opinion in Neurology, vol. 23, no. 4, pp. 341–350, 2010. View at Publisher · View at Google Scholar · View at Scopus
  143. X. Miao, X. Wu, R. Li, K. Chen, and L. Yao, “Altered connectivity pattern of hubs in default-mode network with alzheimer’s disease: an granger causality modeling approach,” PLoS One, vol. 6, no. 10, Article ID e25546, 2011. View at Publisher · View at Google Scholar
  144. O. Sporns, G. Tononi, and G. M. Edelman, “Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices,” Cerebral Cortex, vol. 10, no. 2, pp. 127–141, 2000. View at Google Scholar · View at Scopus
  145. P. Hagmann, L. Cammoun, X. Gigandet et al., “Mapping the structural core of human cerebral cortex.,” PLoS Biology, vol. 6, no. 7, p. e159, 2008. View at Google Scholar · View at Scopus
  146. Y. He, Z. J. Chen, and A. C. Evans, “Small-world anatomical networks in the human brain revealed by cortical thickness from MRI,” Cerebral Cortex, vol. 17, no. 10, pp. 2407–2419, 2007. View at Publisher · View at Google Scholar · View at Scopus
  147. R. L. Buckner, J. Sepulcre, T. Talukdar et al., “Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease,” Journal of Neuroscience, vol. 29, no. 6, pp. 1860–1873, 2009. View at Publisher · View at Google Scholar · View at Scopus
  148. V. M. Eguiluz, D. R. Chialvo, G. A. Cecchi, M. Baliki, and V. Apkariana, “Scale-free brain functional networks,” Physical Review Letters, vol. 94, Article ID 018102, 2005. View at Publisher · View at Google Scholar
  149. C. J. Stam and J. C. Reijneveld, “Graph theoretical analysis of complex networks in the brain,” Nonlinear Biomedical Physics, vol. 1, p. 3, 2007. View at Publisher · View at Google Scholar · View at Scopus
  150. S. Achard, R. Salvador, B. Whitcher, J. Suckling, and E. Bullmore, “A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs,” Journal of Neuroscience, vol. 26, no. 1, pp. 63–72, 2006. View at Publisher · View at Google Scholar · View at Scopus
  151. S. Achard and E. Bullmore, “Efficiency and cost of economical brain functional networks,” PLoS Computational Biology, vol. 3, no. 2, pp. 0174–0183, 2007. View at Publisher · View at Google Scholar · View at Scopus
  152. Y. He, J. Wang, L. Wang et al., “Uncovering intrinsic modular organization of spontaneous brain activity in humans,” PLoS ONE, vol. 4, no. 4, Article ID e5226, 2009. View at Publisher · View at Google Scholar · View at Scopus
  153. D. Meunier, S. Achard, A. Morcom, and E. Bullmore, “Age-related changes in modular organization of human brain functional networks,” NeuroImage, vol. 44, no. 3, pp. 715–723, 2009. View at Publisher · View at Google Scholar · View at Scopus
  154. S. L. Simpsona, M. N. Moussab, and P. J. Laurientic, “An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks,” NeuroImage, vol. 60, no. 2, pp. 1117–1126, 2012. View at Google Scholar
  155. L. Deuker, E. T. Bullmore, M. Smith et al., “Reproducibility of graph metrics of human brain functional networks,” NeuroImage, vol. 47, no. 4, pp. 1460–1468, 2009. View at Publisher · View at Google Scholar · View at Scopus
  156. Q. K. Telesford, A. R. Morgan, S. Hayasaka et al., “Reproducibility of graph metrics in fMRI networks,” Frontiers in Neuroinformatics, vol. 4, p. 117, 2010. View at Google Scholar
  157. J. H. Wang, X. N. Zuo, S. Gohel, M. P. Milham, B. B. Biswal, and Y. He, “Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data,” PLoS ONE, vol. 6, no. 7, Article ID e21976, 2011. View at Publisher · View at Google Scholar · View at Scopus
  158. U. Braun, M. M. Plichta, C. Esslinger et al., “Test-retest reliability of resting-state connectivity network characteristics using fmri and graph theoretical measures,” Neuroimage, vol. 59, no. 2, pp. 1404–1412, 2012. View at Google Scholar
  159. J. Wang, L. Wang, Y. Zang et al., “Parcellation-dependent small-world brain functional networks: a resting-state fmri study,” Human Brain Mapping, vol. 30, no. 5, pp. 1511–1523, 2009. View at Publisher · View at Google Scholar · View at Scopus
  160. L. Wang, C. Zhu, Y. He et al., “Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder,” Human Brain Mapping, vol. 30, no. 2, pp. 638–649, 2009. View at Publisher · View at Google Scholar · View at Scopus
  161. A. Fornito, A. Zalesky, and E. T. Bullmore, “Network scaling effects in graph analytic studies of human resting-state fMRI data,” Frontiers in Systems Neuroscience, vol. 4, p. 22, 2010. View at Publisher · View at Google Scholar · View at Scopus
  162. S. Hayasaka and P. J. Laurienti, “Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data,” NeuroImage, vol. 50, no. 2, pp. 499–508, 2010. View at Publisher · View at Google Scholar · View at Scopus
  163. A. Zalesky, A. Fornito, I. H. Harding et al., “Whole-brain anatomical networks: does the choice of nodes matter?” NeuroImage, vol. 50, no. 3, pp. 970–983, 2010. View at Publisher · View at Google Scholar · View at Scopus
  164. M. Rubinov and O. Sporns, “Weight-conserving characterization of complex functional brain networks,” NeuroImage, vol. 56, no. 4, pp. 2068–2079, 2011. View at Publisher · View at Google Scholar · View at Scopus
  165. A. Zalesky, A. Fornito, and E. T. Bullmore, “Network-based statistic: identifying differences in brain networks,” NeuroImage, vol. 53, no. 4, pp. 1197–1207, 2010. View at Publisher · View at Google Scholar · View at Scopus
  166. J. S. Damoiseaux and M. D. Greicius, “Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity,” Brain Structure and Function, pp. 1–9, 2009. View at Publisher · View at Google Scholar · View at Scopus