Structure shapes dynamics and directionality in diverse brain networks: Mathematical principles and empirical confirmation in three species

Scientific Reports

Volumn 7, Issue , 2017, Pages

  Moon, Joon Young ^a   Kim, Junhyeok ^b   Ko, Tae Wook ^c   Kim, Minkyung ^b   Iturria Medina, Yasser ^d   Choi, Jee Hyun ^e   Lee, Joseph ^a   Mashour, George A ^a   Lee, UnCheol ^a  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^b Pohang University of Science and Technology (POSTECH)   (South Korea)

^c National Institute for Mathematical Sciences   (South Korea)

^d MCGILL UNIVERSITY   (Canada)

^e Korea Institute of Science and Technology   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANATOMY AND HISTOLOGY; ANIMAL; BIOLOGICAL MODEL; BRAIN; COGNITION; ELECTROENCEPHALOGRAPHY; HUMAN; MACACA; MOUSE; NERVE CELL NETWORK; NERVE TRACT; PHYSIOLOGY; SPECIES DIFFERENCE;

ALGORITHMS; ANIMALS; BRAIN; COGNITION; ELECTROENCEPHALOGRAPHY; HUMANS; MACACA; MICE; MODELS, NEUROLOGICAL; NERVE NET; NEURAL PATHWAYS; SPECIES SPECIFICITY;

EID: 85021643111     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep46606     Document Type: Article

References (52)

1. Murray, J. D. et al. A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17, 1661-1663 (2014).
2. Chaudhuri, R., Knoblauch, K., Gariel, M.-A., Kennedy, H. & Wang, X.-J. A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex. Neuron 88, 419-431 (2015).
3. Honey, C. J., Kötter, R., Breakspear, M. & Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. USA. 104, 10240-10245 (2007).
4. Gollo, L. L., Zalesky, A., Hutchison, R. M., van den Heuvel, M. & Breakspear, M. Dwelling quietly in the rich club: brain network determinants of slow cortical fuctuations. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 370 (2015).
5. Honey, C. J. & Sporns, O. Dynamical consequences of lesions in cortical networks. Hum. Brain Mapp. 29, 802-9 (2008).
6. Mišić, B., Vakorin, V. A., Paus, T. & McIntosh, A. R. Functional embedding predicts the variability of neural activity. Front. Syst. Neurosci. 5, 90 (2011).
7. Vakorin, V. A., Mišić, B., Krakovska, O. & McIntosh, A. R. Empirical and theoretical aspects of generation and transfer of information in a neuromagnetic source network. Front. Syst. Neurosci. 5, 96 (2011).
8. Tewarie, P. et al. Structural degree predicts functional network connectivity: a multimodal resting-state fMRI and MEG study. Neuroimage 97, 296-307 (2014).
9. de Haan, W., Mott, K., van Straaten, E. C. W., Scheltens, P. & Stam, C. J. Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease. PLoS Comput. Biol. 8, e1002582 (2012).
10. Marinazzo, D., Wu, G., Pellicoro, M., Angelini, L. & Stramaglia, S. Information flow in networks and the law of diminishing marginal returns: evidence from modeling and human electroencephalographic recordings. PLoS One 7, e45026 (2012).
11. Moon, J.-Y., Lee, U., Blain-Moraes, S. & Mashour, G. a. General Relationship of Global Topology, Local Dynamics, and Directionality in Large-Scale Brain Networks. PLOS Comput. Biol. 11, e1004225 (2015).
12. Ko, T.-W. & Ermentrout, G. B. Partially locked states in coupled oscillators due to inhomogeneous coupling. Phys. Rev. E. Stat. Nonlin. Soft Matter Phys. 78, 16203 (2008).
13. Shima, S. I. & Kuramoto, Y. Rotating spiral waves with phase-randomized core in nonlocally coupled oscillators. Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys. 69, 1-9 (2004).
14. Restrepo, J. G., Ott, E. & Hunt, B. R. Onset of synchronization in large networks of coupled oscillators. Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys. 71, 1-12 (2005).
15. Abrahms, D. M. & Strogatz, S. H. Chimera states in a ring of nonlocally coupled oscillators. Int. J. Bifurc. Chaos 16, 21-37 (2006).
16. Gong, G. et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb. Cortex 19, 524-536 (2009).
17. van den Heuvel, M. P., Bullmore, E. T. & Sporns, O. Comparative Connectomics. Trends Cogn. Sci. 20, 345-61 (2016).
18. Cabral, J., Hugues, E., Sporns, O. & Deco, G. Role of local network oscillations in resting-state functional connectivity. Neuroimage 57, 130-139 (2011).
19. Kitzbichler, M. G. et al. Broadband Criticality of Human Brain Network Synchronization. PLoS Comput. Biol. 5, e1000314 (2009).
20. Finger, H. et al. Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path. PLOS Comput. Biol. 12, e1005025 (2016).
21. Schmidt, R., LaFleur, K. J. R., Reus, M. A. de, Berg, L. H. van den & Heuvel, M. P. van den. Kuramoto model simulation of neural hubs and dynamic synchrony in the human cerebral connectome. BMC Neurosci. 16 (2015).
22. Ton, R., Deco, G. & Daffertshofer, A. Structure-Function Discrepancy: Inhomogeneity and Delays in Synchronized Neural Networks. PLoS Comput. Biol. 10, e1003736 (2014).
23. Hoppensteadt, F. C. & Izhikevich, E. M. Weakly Connected Neural Networks. (Springer-Verlag, 1997).
24. Breakspear, M., Heitmann, S. & Daffgertshofer, A. Generative models of cortical oscillations: neurobiological implications of the kuramoto model. Front. Hum. Neurosci. 4, 190 (2010).
25. Kuramoto, Y. In Lecture Notes in Physics, International Symposium on Mathematical Problems in Theoretical Physics. 39. (ed. Arakai, H.) 420-422 (Springer-Verlag, 1975).
26. Strogatz, S. H. From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators. Phys. D Nonlinear Phenom. 143, 1-20 (2000).
27. Izhikevich, E. Phase models with explicit time delays. Phys. Rev. E 58, 905-908 (1998).
28. Stam, C. J. & van Straaten, E. C. W. Go with the flow: use of a directed phase lag index (dPLI) to characterize patterns of phase relations in a large-scale model of brain dynamics. Neuroimage 62, 1415-1428 (2012).
29. Young, M. P. The Organization of Neural Systems in the Primate Cerebral Cortex. Proc. R. Soc. B Biol. Sci. 252, 13-18 (1993).
30. Lewis, J. W. & Van Essen, D. C. Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey. J. Comp. Neurol. 428, 112-37 (2000).
31. Stam, C. J., Nolte, G. & Daffertshofer, A. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum. Brain Mapp. 28, 1178-1193 (2007).
32. Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008).
33. Hilgetag, C.-C., Burns, G. A. P. C., O'Neill, M. A., Scannell, J. W. & Young, M. P. Anatomical connectivity defines the organization of clusters of cortical areas in the macaque and the cat. Philos. Trans. R. Soc. B Biol. Sci. 355, 91-110 (2000).
34. Bota, M., Dong, H.-W. & Swanson, L. W. Combining collation and annotation efforts toward completion of the rat and mouse connectomes in BAMS. Front. Neuroinform. 6, 2 (2012).
35. Wu, D. et al. In vivo high-resolution diffusion tensor imaging of the mouse brain. Neuroimage 83, 18-26 (2013).
36. Iturria-Medina, Y. et al. V Latin American Congress on Biomedical Engineering CLAIB 2011 May 16-21, 2011, Habana, Cuba. 33, (Springer Berlin Heidelberg, 2013).
37. Liska, A., Galbusera, A., Schwarz, A. J. & Gozzi, A. Functional connectivity hubs of the mouse brain. Neuroimage 115, 281-291 (2015).
38. Sporns, O., Honey, C. J. & Kötter, R. Identification and classification of hubs in brain networks. PLoS One 2, e1049 (2007).
39. de Reus, M. A. & van den Heuvel, M. P. Rich club organization and intermodule communication in the cat connectome. J. Neurosci. 33, 12929-39 (2013).
40. Miranda-Dominguez, O. et al. Bridging the Gap between the Human and Macaque Connectome: A Quantitative Comparison of Global Interspecies Structure-Function Relationships and Network Topology. J. Neurosci. 34, 5552-5563 (2014).
41. Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 13, 336 (2012).
42. Mišić, B. et al. Network-Level Structure-Function Relationships in Human Neocortex. Cereb. Cortex 26, 3285-96 (2016).
43. Rabinovich, M. I., and Afraimovich, V. S., Bick, C. & Varona, P. Information flow dynamics in the brain. Phys. Life Rev. 9, 51-73 (2012).
44. Daffertshofer, A. & van Wijk, B. C. M. On the Influence of Amplitude on the Connectivity between Phases. Front. Neuroinform. 5, 6 (2011).
45. Hlinka, J. & Coombes, S. Using computational models to relate structural and functional brain connectivity. Eur. J. Neurosci. 36, 2137-45 (2012).
46. Granger, C. W. J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica 37, 424 (1969).
47. Staniek, M. & Lehnertz, K. Symbolic transfer entropy. Phys. Rev. Lett. 100, 158101 (2008).
48. Catanzaro, M., Boguñá, M. & Pastor-Satorras, R. Generation of uncorrelated random scale-free networks. Phys. Rev. E. Stat. Nonlin. Soft Matter Phys. 71, 27103 (2005).
49. Yanagawa, T., Chao, Z. C., Hasegawa, N. & Fujii, N. Large-scale information flow in conscious and unconscious states: an ECoG study in monkeys. PLoS One 8, e80845 (2013).
50. Nagasaka, Y., Shimoda, K. & Fujii, N. Multidimensional recording (MDR) and data sharing: an ecological open research and educational platform for neuroscience. PLoS One 6, e22561 (2011).
51. Choi, J. H. et al. High resolution electroencephalography in freely moving mice. J. Neurophysiol. 104, 1825-34 (2010).
52. Lee, M., Kim, D., Shin, H.-S., Sung, H.-G. & Choi, J. H. High-density EEG recordings of the freely moving mice using polyimidebased microelectrode. J. Vis. Exp. doi: 10.3791/2562 (2011).