The envirome and the connectome: Exploring the structural noise in the human brain associated with socioeconomic deprivation

Frontiers in Human Neuroscience

Volumn , Issue NOV, 2013, Pages

  Krishnadas, Rajeev ^a   Kim, Jongrae ^a   McLean, John ^a   David Batty, G ^b,c   McLean, Jennifer S ^d   Millar, Keith ^a   Packard, Chris J ^e   Cavanagh, Jonathan ^a  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b MEDICAL RESEARCH COUNCIL   (United Kingdom)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

^d Glasgow Centre for Population Health   (United Kingdom)

^e Glasgow Clinical Research Facility   (United Kingdom)

Author keywords

Cortical thickness; Graph theory; Gray nodes; Modularity; Neighborhood deprivation; Socioeconomic status

Indexed keywords

ADULT; ARTICLE; BRAIN CORTEX; BRAIN FUNCTION; BRAIN REGION; CONNECTOME; CORTICAL THICKNESS (BRAIN); ENVIROME; FEMALE; GRAY MATTER; HUMAN; HUMAN EXPERIMENT; INFORMATION PROCESSING; MALE; MIDDLE AGED; NEIGHBORHOOD; NERVE CELL NETWORK; NEUROANATOMY; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; SOCIAL STATUS;

EID: 84888119256     PISSN: 16625161     EISSN: None     Source Type: Journal    
DOI: 10.3389/fnhum.2013.00722     Document Type: Article

References (68)

1. Achard, S., and Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Comput. Biol. 3:e17. doi: 10.1371/journal.pcbi.0030017
2. Alexander-Bloch, A., Giedd, J. N., and Bullmore, E. (2013). Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14, 322-336. doi: 10.1038/nrn3465
3. Bassett, D. S., Bullmore, E., Verchinski, B. A., Mattay, V. S., Weinberger, D. R., and Meyer-Lindenberg, A. (2008). Hierarchical organization of human cortical networks in health and schizophrenia. J. Neurosci. 28, 9239-9248. doi: 10.1523/JNEUROSCI.1929-08.2008
4. Bressler, S. L., and Menon, V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14, 277-290. doi: 10.1016/j.tics.2010.04.004
5. 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
6. Bullmore, E., and Sporns, O. (2012). The economy of brain network organization. Nat. Rev. Neurosci. 13, 336-349. doi: 10.1038/nrn3214
7. Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J., and Evans, A. C. (2008). Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb. Cortex 18, 2374-2381. doi: 10.1093/cercor/bhn003
8. Chen, Z. J., He, Y., Rosa-Neto, P., Gong, G., and Evans, A. C. (2011). Age-related alterations in the modular organization of structural cortical network by using cortical thickness from MRI. Neuroimage 56, 235-245. doi: 10.1016/j.neuroimage.2011.01.010
9. Dale, A. M., Fischl, B., and Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179-194. doi: 10.1006/nimg.1998.0395
10. Deans, K. A., Bezlyak, V., Ford, I., Batty, G. D., Burns, H., Cavanagh, J., et al. (2009). Differences in atherosclerosis according to area level socioeconomic deprivation: cross sectional, population based study. BMJ 339:b4170. doi: 10.1136/bmj.b4170
11. Desikan, R. S., Segonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968-980. doi: 10.1016/j.neuroimage.2006.01.021
12. Destrieux, C., Fischl, B., Dale, A., and Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1-15. doi: 10.1016/j.neuroimage.2010.06.010
13. Diez Roux, A. V., and Mair, C. (2010). Neighborhoods and health. Ann. N.Y. Acad. Sci. 1186, 125-145. doi: 10.1111/j.1749-6632.2009.05333.x
14. Echtermeyer, C., Han, C. E., Rotarska-Jagiela, A., Mohr, H., Uhlhaas, P. J., and Kaiser, M. (2011). Integrating temporal and spatial scales: human structural network motifs across age and region of interest size. Front. Neuroinform. 5:10. doi: 10.3389/fninf.2011.00010
15. Fischl, B., and Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U.S.A. 97, 11050-11055. doi: 10.1073/pnas.200033797
16. Fischl, B., Sereno, M. I., and Dale, A. M. (1999). Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195-207. doi: 10.1006/nimg.1998.0396
17. Genovese, C. R., Lazar, N. A., and Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870-878. doi: 10.1006/nimg.2001.1037
18. Gianaros, P. J., Horenstein, J. A., Cohen, S., Matthews, K. A., Brown, S. M., Flory, J. D., et al. (2007). Perigenual anterior cingulate morphology covaries with perceived social standing. Soc. Cogn. Affect. Neurosci. 2, 161-173. doi: 10.1093/scan/nsm013
19. Gianaros, P. J., Horenstein, J. A., Hariri, A. R., Sheu, L. K., Manuck, S. B., Matthews, K. A., et al. (2008). Potential neural embedding of parental social standing. Soc. Cogn. Affect. Neurosci. 3, 91-96. doi: 10.1093/scan/nsn003
20. Gianaros, P. J., Manuck, S. B., Sheu, L. K., Kuan, D. C., Votruba-Drzal, E., Craig, A. E., et al. (2011). Parental education predicts corticostriatal functionality in adulthood. Cereb. Cortex21, 896-910. doi: 10.1093/cercor/bhq160
21. Hackman, D. A., Farah, M. J., and Meaney, M. J. (2010). Socioeconomic status and the brain: mechanistic insights from human and animal research. Nat. Rev. Neurosci. 11, 651-659. doi: 10.1038/nrn2897
22. Hanggi, J., Wotruba, D., and Jancke, L. (2011). Globally altered structural brain network topology in grapheme-color synesthesia. J. Neurosci. 31, 5816-5828. doi: 10.1523/JNEUROSCI.0964-10.2011
23. Hanson, J. L., Chandra, A., Wolfe, B., and Pollak, S. D. (2011). Association between income and the hippocampus. PLoS ONE 6:e18712. doi: 10.1371/journal.pone.0018712
24. Hartwell, L. H., Hopfield, J. J., Leibler, S., and Murray, A. W. (1999). From molecular to modular cell biology. Nature 402, C47-C52. doi: 10.1038/35011540
25. He, Y., Chen, Z. J., and Evans, A. C. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17, 2407-2419. doi: 10.1093/cercor/bhl149
26. He, Y., Dagher, A., Chen, Z., Charil, A., Zijdenbos, A., Worsley, K., et al. (2009). Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain 132, 3366-3379. doi: 10.1093/brain/awp089
27. Hintze, A., and Adami, C. (2008). Evolution of complex modular biological networks. PLoS Comput. Biol. 4:e23. doi: 10.1371/journal.pcbi.0040023
28. Hogstrom, L. J., Westlye, L. T., Walhovd, K. B., and Fjell, A. M. (2012). The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Cereb. Cortex 23, 2521-2530. doi: 10.1093/cercor/bhs231
29. Im, K., Lee, J.-M., Yoon, U., Shin, Y.-W., Hong, S. B., Kim, I. Y., et al. (2006). Fractal dimension in human cortical surface: multiple regression analysis with cortical thickness, sulcal depth, and folding area. Hum. Brain Mapp. 27, 994-1003. doi: 10.1002/hbm.20238
30. Jednorog, K., Altarelli, I., Monzalvo, K., Fluss, J., Dubois, J., Billard, C., et al. (2012). The influence of socioeconomic status on children's brain structure. PLoS ONE 7:e42486. doi: 10.1371/journal.pone.0042486
31. Kaiser, M. (2007). Brain architecture: a design for natural computation. Philos. Trans. A Math. Phys. Eng. Sci. 365, 3033-3045. doi: 10.1098/rsta.2007.0007
32. Knox, S., Welsh, P., Bezlyak, V., McConnachie, A., Boulton, E., Deans, K. A., et al. (2012). 25-Hydroxyvitamin D is lower in deprived groups, but is not associated with carotid intima media thickness or plaques: results from pSoBid. Atherosclerosis 223, 437-441. doi: 10.1016/j.atherosclerosis.2012.05.001
33. Krishnadas, R., McLean, J., Batty, G. D., Burns, H., Deans, K., Ford, I., et al. (2013). Cardio-metabolic risk factors and cortical thickness in a neurologically healthy male population: results from the psychological, social and biological determinants of ill health (pSoBid) study. Neuroimage Clin. 2, 646-657. doi: 10.1016/j.nicl.2013.04.012
34. Kuhn, S., Schubert, F., and Gallinat, J. (2010). Reduced thickness of medial orbitofrontal cortex in smokers. Biol. Psychiatry 68, 1061-1065. doi: 10.1016/j.biopsych.2010.08.004
35. Lazar, A., Abel, D., and Vicsek, T. (2010). Modularity measure of networks with overlapping communities. Europhys. Lett. 90:18001. doi: 10.1209/0295-5075/90/18001
36. Lerch, J. P., Worsley, K., Shaw, W. P., Greenstein, D. K., Lenroot, R. K., Giedd, J., et al. (2006). Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage 31, 993-1003. doi: 10.1016/j.neuroimage.2006.01.042
37. Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., et al. (2009). Brain anatomical network and intelligence. PLoS Comput. Biol. 5:e1000395. doi: 10.1371/journal.pcbi.1000395
38. Lopez-Larson, M. P., Bogorodzki, P., Rogowska, J., McGlade, E., King, J. B., Terry, J., et al. (2011). Altered prefrontal and insular cortical thickness in adolescent marijuana users. Behav. Brain Res. 220, 164-172. doi: 10.1016/j.bbr.2011.02.001
39. McGuinness, D., McGlynn, L. M., Johnson, P. C., Macintyre, A., Batty, G. D., Burns, H., et al. (2012). Socio-economic status is associated with epigenetic differences in the pSoBid cohort. Int. J. Epidemiol. 41, 151-160. doi: 10.1093/ije/dyr215
40. McLean, J., Krishnadas, R., Batty, G. D., Burns, H., Deans, K. A., Ford, I., et al. (2012). Early life socioeconomic status, chronic physiological stress and hippocampal N-acetyl aspartate concentrations. Behav. Brain Res. 235, 225-230. doi: 10.1016/j.bbr.2012.08.013
41. Miller, G., and Chapman, J. (2001). Misunderstanding analysis of covariance. J. Abnorm. Psychol. 110, 40-48. doi: 10.1037/0021-843X.110.1.40
42. Momenan, R., Steckler, L. E., Saad, Z. S., Van Rafelghem, S., Kerich, M. J., and Hommer, D. W. (2012). Effects of alcohol dependence on cortical thickness as determined by magnetic resonance imaging. Psychiatry Res. Neuroimaging 204, 101-111. doi: 10.1016/j.pscychresns.2012.05.003
43. Newman, M. E. (2006). Modularity and community structure in networks. Proc. Natl. Acad. Sci. U.S.A. 103, 8577-8582. doi: 10.1073/pnas.0601602103
44. Newman, M. E., and Girvan, M. (2004). Finding and evaluating community structure in networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 69:026113. doi: 10.1103/PhysRevE.69.026113
45. Nicosia, V., Mangioni, G., Carchiolo, V., and Malger, M. (2009). Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech. Theory Exp. 2009, P03024. doi: 10.1088/1742-5468/2009/03/P03024
46. Panizzon, M. S., Fennema-Notestine, C., Eyler, L. T., Jernigan, T. L., Prom-Wormley, E., Neale, M., et al. (2009). Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex 19, 2728-2735. doi: 10.1093/cercor/bhp026
47. Raj, A., Mueller, S. G., Young, K., Laxer, K. D., and Weiner, M. (2010). Network-level analysis of cortical thickness of the epileptic brain. Neuroimage 52, 1302-1313. doi: 10.1016/j.neuroimage.2010.05.045
48. Rakic, P. (2007). The radial edifice of cortical architecture: from neuronal silhouettes to genetic engineering. Brain Res. Rev. 55, 204-219. doi: 10.1016/j.brainresrev.2007.02.010
49. Rakic, P. (2009). Evolution of the neocortex: a perspective from developmental biology. Nat. Rev. Neurosci. 10, 724-735. doi: 10.1038/nrn2719
50. Rathbone, R., Counsell, S. J., Kapellou, O., Dyet, L., Kennea, N., Hajnal, J., et al. (2011). Perinatal cortical growth and childhood neurocognitive abilities. Neurology 77, 1510-1517. doi: 10.1212/WNL.0b013e318233b215
51. Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., et al. (2011). How does your cortex grow? J. Neurosci. 31, 7174-7177. doi: 10.1523/JNEUROSCI.0054-11.2011
52. Romero-Garcia, R., Atienza, M., Clemmensen, L. H., and Cantero, J. L. (2012). Effects of network resolution on topological properties of human neocortex. Neuroimage 59, 3522-3532. doi: 10.1016/j.neuroimage.2011.10.086
53. Salinas, J., Mills, E. D., Conrad, A. L., Koscik, T., Andreasen, N. C., and Nopoulos, P. (2012). Sex differences in parietal lobe structure and development. Gend. Med. 9, 44-55. doi: 10.1016/j.genm.2012.01.003
54. Sanabria-Diaz, G., Melie-García, L., Iturria-Medina, Y., Alemán-Gómez, Y., Hernández-González, G., Valdés-Urrutia, L., et al. (2010). Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks. Neuroimage 50, 1497-1510. doi: 10.1016/j.neuroimage.2010.01.028
55. Shaw, P., Malek, M., Watson, B., Sharp, W., Evans, A., and Greenstein, D. (2012). Development of cortical surface area and gyrification in attention-deficit/hyperactivity disorder. Biol. Psychiatry 72, 191-197. doi: 10.1016/j.biopsych.2012.01.031
56. Sporns, O. (2011). The human connectome: a complex network. Ann. N.Y. Acad. Sci. 1224, 109-125. doi: 10.1111/j.1749-6632.2010.05888.x
57. Srireddy, P., Agnihotri, A., Park, J., Taylor, J., Connolly, M., and Krishnadas, R. (2012). Ethnicity, deprivation and psychosis: the Glasgow experience. Epidemiol. Psychiatr. Sci. 21, 311-316. doi: 10.1017/S2045796012000352
58. Stafford, M., and Marmot, M. (2003). Neighbourhood deprivation and health: does it affect us all equally? Int. J. Epidemiol. 32, 357-366. doi: 10.1093/ije/dyg084
59. Vaessen, M. J., Braakman, H. M., Heerink, J. S., Jansen, J. F., Debeij-Van Hall, M. H., Hofman, P. A., et al. (2012). Abnormal modular organization of functional networks in cognitively impaired children with frontal lobe epilepsy. Cereb. Cortex 23, 1997-2006. doi: 10.1093/cercor/bhs186
60. Van Den Heuvel, M. P., Stam, C. J., Kahn, R. S., and Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. J. Neurosci. 29, 7619-7624. doi: 10.1523/JNEUROSCI.1443-09.2009
61. Van Wijk, B., Stam, C. J., and Daffershofer, A. (2010). Comparing brain networks of different size and connectivity density using graph theory. PLoS ONE 5:e13701. doi: 10.1371/journal.pone.0013701
62. Velupillai, Y. N., Packard, C. J., Batty, G. D., Bezlyak, V., Burns, H., Cavanagh, J., et al. (2008). Psychological, social and biological determinants of ill health (pSoBid): study protocol of a population-based study. BMC Public Health 8:126. doi: 10.1186/1471-2458-8-126
63. Wang, X., Li, L., and Cheng, Y. (2012). "An improved newman algorithm for mining overlapping modules from protein-protein interaction networks, " in Bio-Inspired Computing and Applications, eds D. Huang, Y. Gan, P. Premaratne and K. Han (Berlin-Heidelberg: Springer Link), 442-447.
64. Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., et al. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage 53, 1135-1146. doi: 10.1016/j.neuroimage.2009.12.028
65. Worsley, K. J., Chen, J. I., Lerch, J., and Evans, A. C. (2005). Comparing functional connectivity via thresholding correlations and singular value decomposition. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 913-920. doi: 10.1098/rstb.2005.1637
66. Yang, Y., Raine, A., Joshi, A. A., Joshi, S., Chang, Y. T., Schug, R. A., et al. (2012). Frontal information flow and connectivity in psychopathy. Br. J. Psychiatry 201, 408-409. doi: 10.1192/bjp.bp.111.107128
67. Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yucel, M., Pantelis, C., et al. (2010). Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50, 970-983. doi: 10.1016/j.neuroimage.2009.12.027
68. Zhao, Y., Lee, H., Kim, J., and Cho, K. (2011). "Low-degree nodes having strong local effects but weak global effects could be drug targets, " in The 12th International Conference on Systems Biology. (Heidelberg; Mannheim).