Insights into brain architectures from the homological scaffolds of functional connectivity networks

Frontiers in Systems Neuroscience

Volumn 10, Issue NOV, 2016, Pages

  Lord, Louis David ^a   Expert, Paul ^b   Fernandes, Henrique M ^a,c   Petri, Giovanni ^d   Van Hartevelt, Tim J ^a,c   Vaccarino, Francesco ^d,e   Deco, Gustavo ^f   Turkheimer, Federico ^b   Kringelbach, Morten L ^a,c  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c AARHUS UNIVERSITY   (Denmark)

^d ISI FOUNDATION   (Italy)

^e POLITECNICO DI TORINO   (Italy)

^f UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

fMRI; Functional connectivity; Homological scaffold; Integration and segregation; Persistent homology

Indexed keywords

ADULT; ARTICLE; BRAIN ARCHITECTURE; CONNECTOME; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; HUMAN EXPERIMENT; IMAGE RECONSTRUCTION; MALE; MEDICAL PARAMETERS; NEUROIMAGING; NORMAL HUMAN; TISSUE SCAFFOLD;

EID: 84995529978     PISSN: 16625137     EISSN: None     Source Type: Journal    
DOI: 10.3389/fnsys.2016.00085     Document Type: Article

References (43)

1. Alexander-Bloch, A. F., Gogtay, N., Meunier, D., Birn, R., Clasen, L., Lalonde, F., et al. (2010). Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Front. Syst. Neurosci. 4: 147. doi: 10.3389/fnsys.2010.00147
2. Bassett, D. S., Nelson, B. G., Mueller, B. A., Camchong, J., and Lim, K. O. (2012). Altered resting state complexity in schizophrenia. Neuroimage 59, 2196-2207. doi: 10.1016/j.neuroimage.2011.10.002
3. Bauer, U., and Lesnick, M. (2014). "Induced matchings of barcodes and the algebraic stability of persistence, " in Proceedings of the Thirtieth Annual Symposium on Computational Geometry SOCG’14 (New York, NY: ACM), 355: 355-355: 364.
4. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008-P10013. doi: 10.1088/1742-5468/2008/10/P10008
5. Brandes, U. (2001). A faster algorithm for betweenness centrality*. J. Math. Sociol. 25, 163-177. doi: 10.1080/0022250X.2001.9990249
6. Bullmore, E., and Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-198. doi: 10.1038/nrn2575
7. Bullmore, E., and Sporns, O. (2012). The economy of brain network organization. Nat. Rev. Neurosci. 13, 336-349. doi: 10.1038/nrn3214
8. Buzsáki, G., and Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science 304, 1926-1929. doi: 10.1126/science.1099745
9. Chazal, F., de Silva, V., Glisse, M., and Oudot, S. (2012). The structure and stability of persistence modules. ArXiv e-prints.
10. Cohen-Steiner, D., Edelsbrunner, H., and Harer, J. (2007). Stability of persistence diagrams. Discrete Comput. Geom. 37, 103-120. doi: 10.1007/s00454-006-1 276-5
11. Cole, M. W., Yarkoni, T., Repovš, G., Anticevic, A., and Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. J. Neurosci. 32, 8988-8999. doi: 10.1523/JNEUROSCI.0536-1 2.2012
12. Csermely, P. (2004). Strong links are important, but weak links stabilize them. Trends Biochem. Sci. 29, 331-334. doi: 10.1016/j.tibs.2004.05.004
13. Deco, G., and Kringelbach, M. L. (2014). Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 84, 892-905. doi: 10.1016/j.neuron.2014.08.034
14. Deco, G., Tononi, G., Boly, M., and Kringelbach, M. L. (2015). Rethinking segregation and integration: contributions of whole-brain modelling. Nat. Rev. Neurosci. 16, 430-439. doi: 10.1038/nrn3963
15. Dey, T. K., Hirani, A. N., and Krishnamoorthy, B. (2011a). Optimal homologous cycles, total unimodularity, and linear programming. SIAM J. Comput. 40, 1026-1044. doi: 10.1137/100800245
16. Dey, T. K., Sun, J., and Wang, Y. (2011b). Approximating cycles in a shortest basis of the first homology group from point data. Inverse Probl. 27: 124004. doi: 10.1088/0266-5611/27/12/124004
17. Doyle, A. C. (1998). The Hound of the Baskervilles. Oxford, UK: Oxford University Press.
18. Garrison, K. A., Scheinost, D., Finn, E. S., Shen, X., and Constable, R. T. (2015). The (in) stability of functional brain network measures across thresholds. Neuroimage 118, 651-661. doi: 10.1016/j.neuroimage.2015.05.046
19. Giusti, C., Ghrist, R., and Bassett, D. S. (2016). Two’s company, three (or more) is a simplex: algebraic-topological tools for understanding higher-order structure in neural data. arXiv preprint arXiv: 1601.01704.
20. Granovetter, M. S. (1973). The strength of weak ties. Am. J. Sociol. 78, 1360-1380. doi: 10.1086/225469
21. Hansen, E. C., Battaglia, D., Spiegler, A., Deco, G., and Jirsa, V. K. (2015). Functional connectivity dynamics: modeling the switching behavior of the resting state. Neuroimage 105, 525-535. doi: 10.1016/j.neuroimage.2014.11.001
22. Kringelbach, M. L., McIntosh, A. R., Ritter, P., Jirsa, V. K., and Deco, G. (2015). The rediscovery of slowness: exploring the timing of cognition. Trends Cogn. Sci. 19, 616-628. doi: 10.1016/j.tics.2015.07.011
23. Lord, L.-D., Allen, P., Expert, P., Howes, O., Broome, M., Lambiotte, R., et al. (2012). Functional brain networks before the onset of psychosis: a prospective fMRI study with graph theoretical analysis. Neuroimage 1, 91-98. doi: 10.1016/j.nicl.2012.09.008
24. Lord, L.-D., Allen, P., Expert, P., Howes, O., Lambiotte, R., McGuire, P., et al. (2011). Characterization of the anterior cingulate’s role in the atrisk mental state using graph theory. Neuroimage 56, 1531-1539. doi: 10.1016/j.neuroimage.2011.02.012
25. Lynall, M.-E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Muller, U., et al. (2010). Functional connectivity and brain networks in schizophrenia. J. Neurosci. 30, 9477-9487. doi: 10.1523/JNEUROSCI.0333-10.2010
26. Munkres, J. R. (1984). Elements of Algebraic Topology, Vol. 2. Menlo Park: Addison-Wesley Reading.
27. Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., et al. (2007). Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. U.S.A. 104, 7332-7336. doi: 10.1073/pnas.0610245104
28. Pajevic, S., and Plenz, D. (2012). The organization of strong links in complex networks. Nat. Phys. 8, 429-436. doi: 10.1038/nphys2257
29. Pandit, A. S., Expert, P., Lambiotte, R., Bonnelle, V., Leech, R., Turkheimer, F. E., et al. (2013). Traumatic brain injury impairs small-world topology. Neurology 80, 1826-1833. doi: 10.1212/WNL.0b013e3182929f38
30. Petri, G., Expert, P., Turkheimer, F., Carhart-Harris, R., Nutt, D., Hellyer, P., et al. (2014). Homological scaffolds of brain functional networks. J. R. Soc. Interface 11: 20140873. doi: 10.1098/rsif.2014.0873
31. Petri, G., Scolamiero, M., Donato, I., and Vaccarino, F. (2013). Topological strata of weighted complex networks. PLoS ONE 8: e66506. doi: 10.1371/journal.pone.0066506
32. Power, J. D., Cohen, A. L., Nelson, S.M., Wig, G. S., Barnes, K. A., Church, J. A., et al. (2011). Functional network organization of the human brain. Neuron 72, 665-678. doi: 10.1016/j.neuron.2011.09.006
33. Rubinov, M., and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003
34. Schneidman, E., Berry, M. J. II, Segev, R., and Bialek, W. (2006). Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440, 1007-1012. doi: 10.1038/nature04701
35. Schwarz, A. J., and McGonigle, J. (2011). Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data. Neuroimage 55, 1132-1146. doi: 10.1016/j.neuroimage.2010.12.047
36. Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208-S219. doi: 10.1016/j.neuroimage.2004.07.051
37. Sporns, O. (2013). Network attributes for segregation and integration in the human brain. Curr. Opin. Neurobiol. 23, 162-171. doi: 10.1016/j.conb.2012.11.015
38. Tausz, A., Vejdemo-Johansson, M., and Adams, H. (2011). "JavaPlex: a research software package for persistent (Co) homology, " Available online at: http://javaplex.github.io/
39. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273-289. doi: 10.1006/nimg.2001.0978
40. van den Heuvel, M. P., and Sporns, O. (2011). Rich-club organization of the human connectome. J. Neurosci. 31, 15775-15786. doi: 10.1523/JNEUROSCI.3539-11.2011
41. Váša, F., Shanahan, M., Hellyer, P. J., Scott, G., Cabral, J., and Leech, R. (2015). Effects of lesions on synchrony and metastability in cortical networks. Neuroimage 118, 456-467. doi: 10.1016/j.neuroimage.2015.05.042
42. Wang, K., Jiang, T., Yu, C., Tian, L., Li, J., Liu, Y., et al. (2008). Spontaneous activity associated with primary visual cortex: a resting-state fMRI study. Cereb. Cortex 18, 697-704. doi: 10.1093/cercor/bhm105
43. Zomorodian, A., and Carlsson, G. (2005). Computing persistent homology. Discrete Comput. Geometry 33, 249-274. doi: 10.1007/s00454-004-1146-y