Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-23T01:02:03.908Z Has data issue: false hasContentIssue false
  • English
  • Français

Modularity in network neuroscience and neural reuse

Published online by Cambridge University Press:  30 June 2016

Matthew L. Stanley  and
Felipe De Brigard
Matthew L. Stanley
Affiliation:
Center for Cognitive Neuroscience, Duke University, Durham, NC 27708. [email protected]
Felipe De Brigard
Affiliation:
Center for Cognitive Neuroscience, Duke University, Durham, NC 27708. [email protected] Duke Institute for Brain Sciences, Duke University, Durham, NC 27708. [email protected] Department of Philosophy, Duke University, Durham, NC 27708.
Get access Rights & Permissions [Opens in a new window]

Abstract

Neural reuse allegedly stands in stark contrast against a modular view of the brain. However, the development of unique modularity algorithms in network science has provided the means to identify functionally cooperating, specialized subsystems in a way that remains consistent with the neural reuse view and offers a set of rigorous tools to fully engage in Anderson's (2014) research program.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 39 , 2016 , e133
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, M. L. (2014) After phrenology: Neural reuse and the interactive brain. MIT Press.Google Scholar
Arenas, A., Fernandez, A. & Gomez, S. (2008) Analysis of the structure of complex networks at different resolution levels. New Journal of Physics 10:053039. doi: 10.1088/1367-2630/10/5/053039.Google Scholar
Barabasi, A.-L. & Oltvai, Z. (2004) Network biology: Understanding the cell's functional organization. Nature Reviews Genetics, 5:101–13. doi: 10.1038/nrg1272.Google Scholar
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M. & Grafton, S. T. (2011) Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences of the United States of America 108:7641–46. doi: 10.1073/pnas.1018985108.Google Scholar
Bullmore, E. T. & Sporns, O. (2009) Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10:186–98. doi: 10.1038/nrn2618.Google Scholar
Cao, H., Plichta, M. M., Schäfer, A., Haddad, L., Grimm, O., Schneider, M. & Tost, H. (2014) Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing and resting state. NeuroImage 84:888–900. doi: 10.1016/j.neuroimage.2013.09.013.Google Scholar
Meunier, D., Fonlupt, P., Saive, A.-L., Plailly, J., Ravel, N. & Royet, J.-P. (2014) Modular structure of functional networks in olfactory memory. NeuroImage 95C:264–75. doi: 10.1016/j.neuroimage.2014.03.041.Google Scholar
Meunier, D., Lambiotte, R., Fornito, A., Ersche, K. D. & Bullmore, E. T. (2009) Hierarchical modularity in human functional brain networks. Frontiers in Neuroinformatics 3:37. doi: 10.3389/neuro.11.037.2009.CrossRefGoogle Scholar
Moussa, M. N., Vechlekar, C. D., Burdette, J. H., Steen, M. R., Hugenschmidt, C. E. & Laurienti, P. J. (2011) Changes in cognitive state alter human functional brain networks. Frontiers in Human Neuroscience 5:83. doi: 10.3389/fnhum.2011.00083.Google Scholar
Moussa, M. N., Wesley, M. J., Porrino, L. J., Hayasaka, S., Bechara, A., Burdette, J. H. & Laurienti, P. J. (2014) Age-related differences in advantageous decision making are associated with distinct differences in functional community structure. Brain Connectivity 4:193–202. doi: 10.1089/brain.2013.0184.Google Scholar
Newman, M. E. J. (2006) Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103:8577–82. doi: 10.1073/pnas.0601602103.Google Scholar
Rubinov, M. & Sporns, O. (2010) Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52:1059–69. doi: 10.1016/j.neuroimage.2009.10.003.Google Scholar
Sales-Pardo, M., Guimera, R., Moreira, A. A. & Amaral, L. A. N. (2007) Extracting the hierarchical organization of complex systems. Proceedings of the National Academy of Sciences of the United States of America 104(39):15224–9. doi: 10.1073/pnas.0703740104.Google Scholar
Stanley, M. L., Dagenbach, D., Lyday, R. G., Burdette, J. H. & Laurienti, P. J. (2014) Changes in global and regional modularity associated with increasing working memory load. Frontiers in Human Neuroscience 8:954. doi: 10.3389/fnhum.2014.00954.Google Scholar
Stanley, M. L., Moussa, M. N., Paolini, B. M., Lyday, R. G., Burdette, J. H. & Laurienti, P. J. (2013) Defining nodes in complex brain networks. Frontiers in Computational Neuroscience 7:169. doi: 10.3389/fncom.2013.00169.CrossRefGoogle ScholarPubMed
Telesford, Q. K., Simpson, S. L., Burdette, J. H., Hayasaka, S. & Laurienti, P. J. (2011) The brain as a complex system: Using network science as a tool for understanding the brain. Brain Connectivity 1:295–308. doi: 10.1089/brain.2011.0055.Google Scholar
Valencia, M., Pastor, M. A., Fernández-Seara, M. A., Artieda, J., Martinerie, J. & Chavez, M. (2009) Complex modular structure of large-scale brain networks. Chaos 19:023119. doi: 10.1063/1.3129783.CrossRefGoogle ScholarPubMed