publication . Other literature type . Preprint . Article . 2014

Graph analysis of functional brain networks: practical issues in translational neuroscience

Mario Chavez; Jonas Richiardi;
Open Access
  • Published: 05 Oct 2014
  • Publisher: The Royal Society
Abstract
International audience; The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms o...
Subjects
free text keywords: Quantitative Biology - Neurons and Cognition, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SCCO.NEUR]Cognitive science/Neuroscience, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Articles, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, ddc:616.8
Funded by
EC| LASAGNE
Project
LASAGNE
multi-LAyer SpAtiotemporal Generalized NEtworks
  • Funder: European Commission (EC)
  • Project Code: 318132
  • Funding stream: FP7 | SP1 | ICT
,
EC| MIND
Project
MIND
Modelling and Inference on brain Networks for Diagnosis
  • Funder: European Commission (EC)
  • Project Code: 299500
  • Funding stream: FP7 | SP3 | PEOPLE
Communities
FET FP7FET Proactive: Dynamics of Multi-Level Complex Systems (DyM-CS)
FET FP7FET Proactive: multi-LAyer SpAtiotemporal Generalized NEtworks
EGI FederationEGI Countries: Switzerland
189 references, page 1 of 13

[1] Friston, K. J. 2011 Functional and effective connectivity: a review. Brain connectivity 1, 13-36. ISSN 2158-0022. (doi:10.1089/brain.2011.0008). PMID: 22432952.

[2] Fox, M. D. & Greicius, M. 2010 Clinical applications of resting state functional connectivity. Frontiers in Systems Neuroscience 4, 19. (doi:10.3389/fnsys.2010.00019).

[3] Stephan, K., Hilgetag, C.-C., Burns, G., O'Neill, M., Young, M. & Kötter, R. 2000 Computational analysis of functional connectivity between areas of primate cerebral cortex. Philosophical Transactions of the Royal Society B: Biological Sciences 355, 111-126. ISSN 09628436. [OpenAIRE]

[4] Mazzocchi, F. 2008 Complexity in biology. exceeding the limits of reductionism and determinism using complexity theory. EMBO Reports 9, 10-14. ISSN 1469-221X. (doi:10.1038/sj.embor. 7401147). PMID: 18174892 PMCID: PMC2246621. [OpenAIRE]

[5] Varela, F., Lachaux, J.-P., Rodriguez, E. & Martinerie, J. 2001 The brainweb: Phase synchronization and large-scale integration. Nature Reviews Neuroscience 2, 229-239.

[6] Carter, A. R., Shulman, G. L. & Corbetta, M. 2012 Why use a connectivity-based approach to study stroke and recovery of function? NeuroImage 62, 2271-2280. ISSN 1095-9572. (doi:10.1016/j. neuroimage.2012.02.070). PMID: 22414990 PMCID: PMC3733251.

[7] Bassett, D. S. & Bullmore, E. T. 2006 Small world brain networks. Neuroscientist 12, 512-523.

[8] Achard, S. & Bullmore, E. 2007 Efficiency and cost of economical human brain functional networks. PLoS Computational Biology 3, 0174-0183. [OpenAIRE]

[9] Rubinov, M. & Sporns, O. 2010 Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52, 1059-1069.

[10] Fornito, A., Zalesky, A. & Breakspear, M. 2013 Graph analysis of the human connectome: Promise, progress, and pitfalls. NeuroImage 80, 426-444. ISSN 1053-8119. (doi:10.1016/j.neuroimage.2013. 04.087). [OpenAIRE]

[11] Stam, C. J. & van Straaten, E. C. W. 2012 The organization of physiological brain networks. Clinical Neurophysiology 123, 1067-1087. ISSN 1388-2457. (doi:10.1016/j.clinph.2012.01.011). [OpenAIRE]

[12] Zalesky, A., Fornito, A. & Bullmore, E. T. 2010 Network-based statistic: Identifying differences in brain networks. NeuroImage 53, 1197-1207. ISSN 1053-8119. (doi:10.1016/j.neuroimage.2010.06. 041). [OpenAIRE]

[13] He, Y. & Evans, A. 2010 Graph theoretical modeling of brain connectivity. Current Opinion in Neurology 23, 341-350.

[14] Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D. 2006 Complex networks: Structure and dynamics. Physics Reports 424, 175-308. ISSN 0370-1573. (doi:10.1016/j.physrep.2005.10. 009).

[15] Bishop, C. M. 1995 Neural networks for pattern recognition. Oxford university press.

189 references, page 1 of 13
Abstract
International audience; The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms o...
Subjects
free text keywords: Quantitative Biology - Neurons and Cognition, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SCCO.NEUR]Cognitive science/Neuroscience, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Articles, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, ddc:616.8
Funded by
EC| LASAGNE
Project
LASAGNE
multi-LAyer SpAtiotemporal Generalized NEtworks
  • Funder: European Commission (EC)
  • Project Code: 318132
  • Funding stream: FP7 | SP1 | ICT
,
EC| MIND
Project
MIND
Modelling and Inference on brain Networks for Diagnosis
  • Funder: European Commission (EC)
  • Project Code: 299500
  • Funding stream: FP7 | SP3 | PEOPLE
Communities
FET FP7FET Proactive: Dynamics of Multi-Level Complex Systems (DyM-CS)
FET FP7FET Proactive: multi-LAyer SpAtiotemporal Generalized NEtworks
EGI FederationEGI Countries: Switzerland
189 references, page 1 of 13

[1] Friston, K. J. 2011 Functional and effective connectivity: a review. Brain connectivity 1, 13-36. ISSN 2158-0022. (doi:10.1089/brain.2011.0008). PMID: 22432952.

[2] Fox, M. D. & Greicius, M. 2010 Clinical applications of resting state functional connectivity. Frontiers in Systems Neuroscience 4, 19. (doi:10.3389/fnsys.2010.00019).

[3] Stephan, K., Hilgetag, C.-C., Burns, G., O'Neill, M., Young, M. & Kötter, R. 2000 Computational analysis of functional connectivity between areas of primate cerebral cortex. Philosophical Transactions of the Royal Society B: Biological Sciences 355, 111-126. ISSN 09628436. [OpenAIRE]

[4] Mazzocchi, F. 2008 Complexity in biology. exceeding the limits of reductionism and determinism using complexity theory. EMBO Reports 9, 10-14. ISSN 1469-221X. (doi:10.1038/sj.embor. 7401147). PMID: 18174892 PMCID: PMC2246621. [OpenAIRE]

[5] Varela, F., Lachaux, J.-P., Rodriguez, E. & Martinerie, J. 2001 The brainweb: Phase synchronization and large-scale integration. Nature Reviews Neuroscience 2, 229-239.

[6] Carter, A. R., Shulman, G. L. & Corbetta, M. 2012 Why use a connectivity-based approach to study stroke and recovery of function? NeuroImage 62, 2271-2280. ISSN 1095-9572. (doi:10.1016/j. neuroimage.2012.02.070). PMID: 22414990 PMCID: PMC3733251.

[7] Bassett, D. S. & Bullmore, E. T. 2006 Small world brain networks. Neuroscientist 12, 512-523.

[8] Achard, S. & Bullmore, E. 2007 Efficiency and cost of economical human brain functional networks. PLoS Computational Biology 3, 0174-0183. [OpenAIRE]

[9] Rubinov, M. & Sporns, O. 2010 Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52, 1059-1069.

[10] Fornito, A., Zalesky, A. & Breakspear, M. 2013 Graph analysis of the human connectome: Promise, progress, and pitfalls. NeuroImage 80, 426-444. ISSN 1053-8119. (doi:10.1016/j.neuroimage.2013. 04.087). [OpenAIRE]

[11] Stam, C. J. & van Straaten, E. C. W. 2012 The organization of physiological brain networks. Clinical Neurophysiology 123, 1067-1087. ISSN 1388-2457. (doi:10.1016/j.clinph.2012.01.011). [OpenAIRE]

[12] Zalesky, A., Fornito, A. & Bullmore, E. T. 2010 Network-based statistic: Identifying differences in brain networks. NeuroImage 53, 1197-1207. ISSN 1053-8119. (doi:10.1016/j.neuroimage.2010.06. 041). [OpenAIRE]

[13] He, Y. & Evans, A. 2010 Graph theoretical modeling of brain connectivity. Current Opinion in Neurology 23, 341-350.

[14] Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D. 2006 Complex networks: Structure and dynamics. Physics Reports 424, 175-308. ISSN 0370-1573. (doi:10.1016/j.physrep.2005.10. 009).

[15] Bishop, C. M. 1995 Neural networks for pattern recognition. Oxford university press.

189 references, page 1 of 13
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