Brain Imaging and Behavior

, Volume 12, Issue 2, pp 467–476 | Cite as

Negative functional brain networks

  • Fabrizio Parente
  • Marianna Frascarelli
  • Alessia Mirigliani
  • Fabio Di Fabio
  • Massimo Biondi
  • Alfredo Colosimo
Original Research


The anticorrelations in fMRI measurements are still not well characterized, but some new evidences point to a possible physiological role. We explored the topology of functional brain networks characterized by negative edgess and their possible alterations in schizophrenia, using functional images of 8 healthy subjects and 8 schizophrenic patients in a resting state condition. In order to minimize the insertion of artifactual negative correlations, the preprocessing of images was carried out by the CompCorr procedure, and the results compared with the Global Signal Regression (GSR) procedure. The degree distribution, the centrality, the efficiency and the rich-club behavior were used to characterize the functional brain network with negative links of healthy controls in comparison with schizophrenic patients. The results show that functional brain networks with both positive and negative values have a truncated power-law degree distribution. Moreover, although functional brain networks characterized by negative values have not small-world topology, they show a specific disassortative configuration: the more connected nodes tend to have fewer connections between them. This feature is lost using the GSR procedure. Finally, the comparison with schizophrenic patients showed a decreased (local and global) efficiency associated to a decreased connectivity among central nodes. As a conclusion, functional brain networks characterized by negative values, despite lacking a well defined topology, show specific features, different from random, and indicate an implication in the alterations associated to schizophrenia.


Resting state Functional connectivity Anticorrelated networks Network analysis Schizophrenia 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11682_2017_9715_MOESM1_ESM.pdf (628 kb)
ESM 1 (PDF 628 kb)


  1. Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human functional brain network with highly connected association cortical hubs. The Journal of Neuroscience, 26(1), 63–72.CrossRefPubMedGoogle Scholar
  2. Anticevic, A., Gancsos, M., Murray, J. D., Repovs, G., Driesen, N. R., Ennis, D. J., Niciu, M. J., Morgan, P. T., Surti, T. S., Bloch, M. H., Ramani, R., Smith, M. A., Wang, X. J., Krystal, J. H., & Corlett, P. R. (2012). Nmda receptor function in large-scale anticorrelated neural systems with implications for cognition and schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 109(41), 16720–16725.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component based noised correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage, 37(1), 90–101.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Chai, X. J., Castanon, A. N., Ongur, D., & Whitfield-Gabrieli, S. (2012). Anti-correlations in resting state networks without global signal regression. NeuroImage, 59(2), 1420–1428.CrossRefPubMedGoogle Scholar
  5. Chai, X. J., Ofen, N., Gabrieli, J. D., & Whitfield-Gabrieli, S. (2014). Selective development of anticorrelated networks in the intrinsic functional organization of the human brain. Journal of Cognitive Neuroscience, 26(3), 501–513.CrossRefPubMedGoogle Scholar
  6. Chang, C., & Glover, G. H. (2009). Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. NeuroImage, 447(4), 1448–1459.CrossRefGoogle Scholar
  7. Chen, G., Chen, G., Xie, C., & Li, S. J. (2011). Negative functional connectivity and its dependence on the shortest path length of positive network in the resting-state human brain. Brain Connectivity, 1(3), 195–206.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Di, X., & Biswal, B. B. (2013). Modulatory interactions of resting-state brain functional connectivity. PloS One, 8(8), e71163.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA, 102(27), 9673–9678.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009). The global signal and observed anticorrelated resting state brain networks. Journal of Neurophysiology, 101(6), 3270–3328.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Fransson, P. (2005). Spontaneous low-frequency bold signal fluctuations: An fmri investigation ofthe resting-state default mode of brain function hypothesis. Hum. Brain Mapp, 26, 15–29.CrossRefGoogle Scholar
  12. Friston, K. J. (2011). Functional and effective connectivity: A review. Brain Connectivity, 1, 13–36.CrossRefPubMedGoogle Scholar
  13. Glickman, M. E., Rao, S. R., & Schultz, M. R. (2014). False discovery rate control is a recommended alternative to bonferroni-type adjustments in health studies. Journal of Clinical Epidemiology, 67(8), 850–857.CrossRefPubMedGoogle Scholar
  14. Gopinath, K., Krishnamurthy, V., Cabanban, R., & Crosson, B. A. (2015). Hubs of anticorrelation in high-resolution resting-state functional connectivity network architecture. Brain Connectivity, 5(5), 267–275.CrossRefPubMedGoogle Scholar
  15. Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2005). 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, 100(1), 253–258.CrossRefGoogle Scholar
  16. van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. The Journal of Neuroscience, 31(44), 15775–15786.CrossRefPubMedGoogle Scholar
  17. van den Heuvel, M. P., Sporn, O., Collin, G., Scheewe, T., Mandl, R. C., Cahn, W., Goni, J., Hulshoff Pol, H. E., & Kahn, R. S. (2013). Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychitry., 70(8), 783–792.CrossRefGoogle Scholar
  18. Jack, A. I., Dawson, A. J., Begany, K. L., Leckie, R. L., Barry, K. P., Ciccia, A. H., & Snyder, A. Z. (2013). Fmri reveals reciprocal inhibition between social and physical cognitive domains. NeuroImage, 66, 385–401.CrossRefPubMedGoogle Scholar
  19. Josipovic, Z., Dinstein, I., Weber, J., & Heeger, D. J. (2012). Influence of meditation on anti-correlated networks in the brain. Frontiers in Human Neuroscience, 5, 183.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Keller, C. J., Bickel, S., Honey, C. J., Groppe, D. M., Entz, L., Craddock, R. C., Lado, F. A., Kelly, C., Milham, M., & Mehta, A. D. (2013). Neurophysiological investigation of spontaneous correlated and anticorrelated fluctuations of the bold signal. The Journal of Neuroscience, 33(15), 6333–6342.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Keller, J. B., Hedden, T., Thompson, T. W., Anteraper, S. A., Gabrieli, J. D., & Whitfield-Gabrieli, S. (2015). Resting-state anticorrelations between medial and lateral prefrontal cortex: Association with working memory, aging, and individual differences. Cortex, 64, 271–280.CrossRefPubMedGoogle Scholar
  22. Lynall, M. E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Muller, U., & Bullmore, E. (2010). Functional connectivity and brain networks in schizophrenia. The Journal of Neuroscience, 30(28), 9477–9487.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science, 296(5569), 910–913.CrossRefPubMedGoogle Scholar
  24. Murphy, R., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009). The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? NeuroImage, 44(1), 893–905.CrossRefPubMedGoogle Scholar
  25. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. Neuroimage, 59(3), 2142–2154.CrossRefPubMedGoogle Scholar
  26. Power, J. D., Mitra, A., Laumann TO, Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage, 84, 320–341.CrossRefPubMedGoogle Scholar
  27. Rubinov, M., & Sporn, O. (2011). Weight-conserving characterization of complex functional brain networks. NeuroImage, 56(4), 2068–2079.CrossRefPubMedGoogle Scholar
  28. Rubinov, M., & Sporns, O. (2009). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 53(3), 1059–1069.Google Scholar
  29. Schwarz, A. J., & McGonigle, J. (2011). Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data. NeuroImage, 55(3), 1132–1146.CrossRefPubMedGoogle Scholar
  30. Tian, L., Jiang, T., Liu, Y., Yu, C., Wang, K., Zhou, Y., Song, M., & Li, K. (2007). The relationship within and between the extrinsic and intrin- sic systems indicated by resting state correlational patterns of sensory cortices. NeuroImage, 36(3), 684–690.CrossRefPubMedGoogle Scholar
  31. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. NeuroImage, 15(1), 273–289.CrossRefPubMedGoogle Scholar
  32. Uddin, L. Q., Kelly, A. M., Biswal, B. B., Xavier Castellanos, F., & Milham, M. P. (2009). Functional connectivity of default mode network components: Correlation, anticorrelation, and causality. Human Brain Mapping, 30(2), 625–637.CrossRefPubMedGoogle Scholar
  33. Whitfield-Gabrieli, S., Thermenos, H. W., Milanovic, S., Tsuang, M. T., Faraone, S. V., McCarley, R. W., Shenton, M. E., Green, A. I., Nieto- Castanon, A., La Violette, P., Wojcik, J., Gabrieli, J. D., & Seidman, L. J. (2009). Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 106(4), 1279–1284.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Wong, C. W., Olafsson, V., Tal, O., & Liu, T. T. (2012). Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state func- tional mri. NeuroImage, 63, 356–364.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Wotruba, D., Michels, L., Buechler, R., Metzler, S., Theodoridou, A., Gerstenberg, M., Walitza, S., Kollias, S., Rossler, W., & Heekeren, K. (2014). Aberrant coupling within and across the default mode, task-positive, and salience network in subjects at risk for psychosis. Schizophrenia Bulletin, 40(5), 1095–1104.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Anatomy, Histology, Forensic Medicine and OrthopaedicsSapienza University of RomeRomeItaly
  2. 2.Department of Neurology and PsychiatrySapienza University of RomeRomeItaly

Personalised recommendations