Brain Structure and Function

, Volume 222, Issue 3, pp 1131–1151 | Cite as

A seed-based cross-modal comparison of brain connectivity measures

  • Andrew T. ReidEmail author
  • Felix Hoffstaedter
  • Gaolang Gong
  • Angela R. Laird
  • Peter Fox
  • Alan C. Evans
  • Katrin Amunts
  • Simon B. Eickhoff
Original Article


Human neuroimaging methods have provided a number of means by which the connectivity structure of the human brain can be inferred. For instance, correlations in blood-oxygen-level-dependent (BOLD) signal time series are commonly used to make inferences about “functional connectivity.” Correlations across samples in structural morphometric measures, such as voxel-based morphometry (VBM) or cortical thickness (CT), have also been used to estimate connectivity, putatively through mutually trophic effects on connected brain areas. In this study, we have compared seed-based connectivity estimates obtained from four common correlational approaches: resting-state functional connectivity (RS-fMRI), meta-analytic connectivity modeling (MACM), VBM correlations, and CT correlations. We found that the two functional approaches (RS-fMRI and MACM) had the best agreement. While the two structural approaches (CT and VBM) had better-than-random convergence, they were no more similar to each other than to the functional approaches. The degree of correspondence between modalities varied considerably across seed regions, and also depended on the threshold applied to the connectivity distribution. These results demonstrate some degrees of similarity between connectivity inferred from structural and functional covariances, particularly for the most robust functionally connected regions (e.g., the default mode network). However, they also caution that these measures likely capture very different aspects of brain structure and function.


Multimodal comparison Cortical thickness VBM Resting-state fMRI MACM 


Compliance with ethical standards


This study was supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/4-1; EI 816/6-1 and LA 3071/3-1.), the National Institute of Mental Health (R01-MH074457) and the European EFT program (Human Brain Project).


  1. Aboitiz F, Scheibel AB, Zaidel E (1992) Morphometry of the Sylvian fissure and the corpus callosum, with emphasis on sex differences. Brain 115:1521–1541PubMedCrossRefGoogle Scholar
  2. Alexander-Bloch A, Raznahan A, Bullmore E, Giedd J (2013) The convergence of maturational change and structural covariance in human cortical networks. J Neurosci 33:2889–2899PubMedPubMedCentralCrossRefGoogle Scholar
  3. Amft M, Bzdok D, Laird AR, Fox PT, Schilbach L, Eickhoff SB (2015) Definition and characterization of an extended social-affective default network. Brain Struct Funct 220(2):1031–1049PubMedCrossRefGoogle Scholar
  4. Anticevic A, Dierker DL, Gillespie SK, Repovs G, Csernansky JG, Van Essen DC, Barch DM (2008) Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia. Neuroimage 41:835–848PubMedPubMedCentralCrossRefGoogle Scholar
  5. Ashburner J, Friston KJ (2000) Voxel-based morphometry–the methods. Neuroimage 11:805–821PubMedCrossRefGoogle Scholar
  6. Baaré WF, Hulshoff Pol HE, Boomsma DI, Posthuma D, de Geus EJ, Schnack HG, van Haren NE, van Oel CJ, Kahn RS (2001) Quantitative genetic modeling of variation in human brain morphology. Cereb Cortex (New York, NY: 1991) 11:816–824Google Scholar
  7. Beckmann M, Johansen-Berg H, Rushworth MFS (2009) Connectivity-based parcellation of human cingulate cortex and its relation to functional specialization. J Neurosci: Off J Soc Neurosci 29:1175–1190CrossRefGoogle Scholar
  8. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1):144–155PubMedCrossRefGoogle Scholar
  9. Behzadi Y, Restom K, Liau J, Liu TT (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37:90–101PubMedPubMedCentralCrossRefGoogle Scholar
  10. Bi G, Poo M (2001) Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu Rev Neurosci 24:139–166PubMedCrossRefGoogle Scholar
  11. Birn RM, Smith MA, Jones TB, Bandettini PA (2008) The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40:644–654PubMedCrossRefGoogle Scholar
  12. Bojak I, Oostendorp TF, Reid AT, Kotter R (2010) Connecting mean field models of neural activity to EEG and fMRI data. Brain Topogr 23:139–149PubMedCrossRefGoogle Scholar
  13. Bzdok D, Schilbach L, Vogeley K, Schneider K, Laird AR, Langner R, Eickhoff SB (2012) Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy. Brain Struct Funct 217:783–796PubMedPubMedCentralCrossRefGoogle Scholar
  14. Chai XJ, Castanon AN, Ongur D, Whitfield-Gabrieli S (2012) Anticorrelations in resting state networks without global signal regression. Neuroimage 59:1420–1428PubMedCrossRefGoogle Scholar
  15. Chouinard-Decorte F, McKay DR, Reid AT, Khundrakpam B, Zhao L, Karama S, Rioux P, Sprooten E, Knowles E, Kent JW, Curran JE, Göring HH, Dyer TD, Olvera RL, Kochunov P, Duggirala R, Fox PT, Almasy L, Blangero J, Bellec P, Evans AC, Glahn DC (2014) Heritable changes in regional cortical thickness with age. Brain Imaging Behav 8:208–216PubMedPubMedCentralCrossRefGoogle Scholar
  16. Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH, Quigley MA, Meyerand ME (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am J Neuroradiol 22:1326–1333PubMedGoogle Scholar
  17. Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, Bullmore ET (2014) The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain: J Neurol 137:2382–2395Google Scholar
  18. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179–194PubMedCrossRefGoogle Scholar
  19. Desai R, Liebenthal E, Possing ET, Waldron E, Binder JR (2005) Volumetric vs. surface-based alignment for localization of auditory cortex activation. Neuroimage 26:1019–1029PubMedCrossRefGoogle Scholar
  20. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A (2004) Neuroplasticity: changes in grey matter induced by training. Nature 427:311–312PubMedCrossRefGoogle Scholar
  21. Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT (2009) Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum Brain Mapp 30:2907–2926PubMedPubMedCentralCrossRefGoogle Scholar
  22. Eickhoff SB, Jbabdi S, Caspers S, Laird AR, Fox PT, Zilles K, Behrens TE (2010) Anatomical and functional connectivity of cytoarchitectonic areas within the human parietal operculum. J Neurosci 30:6409–6421PubMedPubMedCentralCrossRefGoogle Scholar
  23. Eickhoff SB, Bzdok D, Laird AR, Kurth F, Fox PT (2012) Activation likelihood estimation meta-analysis revisited. Neuroimage 59(3):2349–2361PubMedCrossRefGoogle Scholar
  24. Elston GN (2002) Cortical heterogeneity: implications for visual processing and polysensory integration. J Neurocytol 31:317–335PubMedCrossRefGoogle Scholar
  25. Engert F, Bonhoeffer T (1999) Dendritic spine changes associated with hippocampal long-term synaptic plasticity. Nature 399:66–70PubMedCrossRefGoogle Scholar
  26. Evans AC (2013) Networks of anatomical covariance. Neuroimage 80:489–504PubMedCrossRefGoogle Scholar
  27. Fioravanti V, Benuzzi F, Codeluppi L, Contardi S, Cavallieri F, Nichelli P, Valzania F (2015) MRI correlates of Parkinson’s disease progression: a voxel based morphometry study, MRI correlates of Parkinson’s disease progression: a voxel based morphometry study. Parkinson’s Dis 2015:e378032Google Scholar
  28. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711PubMedCrossRefGoogle Scholar
  29. Fox PT, Lancaster JL, Laird AR, Eickhoff SB (2014) Meta-analysis in human neuroimaging: Computational modeling of large-scale databases. Annu Rev Neurosci 37:409–434PubMedPubMedCentralCrossRefGoogle Scholar
  30. Frost MA, Goebel R (2012) Measuring structural-functional correspondence: spatial variability of specialised brain regions after macro-anatomical alignment. Neuroimage 59:1369–1381PubMedCrossRefGoogle Scholar
  31. Glasser MF, Van Essen DC (2011) Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J Neurosci 31:11597–11616PubMedPubMedCentralCrossRefGoogle Scholar
  32. Goodkind M, Eickhoff SB, Oathes DJ, Jiang Y, Chang A, Jones-Hagata LB, Ortega BN, Zaiko YV, Roach EL, Korgaonkar MS, Grieve SM, Galatzer-Levy I, Fox PT, Etkin A (2015) JAMA Psychiatry 72(4):305–315PubMedPubMedCentralCrossRefGoogle Scholar
  33. Goulas A, Bastiani M, Bezgin G, Uylings HB, Roebroeck A, Stiers P (2014) Comparative analysis of the macroscale structural connectivity in the macaque and human brain. PLoS Comput Biol 10:e1003529PubMedPubMedCentralCrossRefGoogle Scholar
  34. Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci 100:253–258PubMedCrossRefGoogle Scholar
  35. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6:e159PubMedPubMedCentralCrossRefGoogle Scholar
  36. He Y, Chen Z, Evans A (2008) Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease. J Neurosci 28:4756–4766PubMedCrossRefGoogle Scholar
  37. Hinkley LB, Marco EJ, Findlay AM, Honma S, Jeremy RJ, Strominger Z, Bukshpun P, Wakahiro M, Brown WS, Paul LK, Barkovich AJ, Mukherjee P, Nagarajan SS, Sherr EH (2012) The role of corpus callosum development in functional connectivity and cognitive processing. PLoS One 7:e39804PubMedPubMedCentralCrossRefGoogle Scholar
  38. Hoffstaedter F, Grefkes C, Zilles K, Eickhoff SB (2013) The “what” and “when” of self-initiated movements. Cereb Cortex 23:520–530PubMedCrossRefGoogle Scholar
  39. Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P (2009) Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci USA 106:2035–2040PubMedPubMedCentralCrossRefGoogle Scholar
  40. Jaccard P (1912) The distribution of flora in the alpine zone. New Phytol 11:37–50CrossRefGoogle Scholar
  41. Khundrakpam BS, Reid A, Brauer J, Carbonell F, Lewis J, Ameis S, Karama S, Lee J, Chen Z, Das S, Evans AC, Brain Development Cooperative Group (2013) Developmental Changes in Organization of Structural Brain Networks. Cerebral Cortex 23:2072–2085PubMedCrossRefGoogle Scholar
  42. Kim JS, Singh V, Lee JK, Lerch J, Ad-Dab’bagh Y, MacDonald D, Lee JM, Kim SI, Evans AC (2005) Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage 27:210–221PubMedCrossRefGoogle Scholar
  43. Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB (2010) A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct Funct 214:519–534PubMedPubMedCentralCrossRefGoogle Scholar
  44. Laat KF de, Reid AT, Grim DC, Evans AC, Kötter R, van Norden AGW, de Leeuw F-E (2012) Cortical thickness is associated with gait disturbances in cerebral small vessel disease. Neuroimage 59:1478–1484.
  45. Laird AR, Eickhoff SB, Li K, Robin DA, Glahn DC, Fox PT (2009a) Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J Neurosci 29:14496–14505PubMedPubMedCentralCrossRefGoogle Scholar
  46. Laird AR, Lancaster JL, Fox PT (2009b) Lost in localization? The focus is meta-analysis. Neuroimage 48:18–20PubMedPubMedCentralCrossRefGoogle Scholar
  47. Laird AR, Eickhoff SB, Fox PM, Uecker AM, Ray KL, Saenz JJ, McKay DR, Bzdok D, Laird RW, Robinson JL, Turner JA, Turkeltaub PE, Lancaster JL, Fox PT (2011) The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data. BMC Res Notes 4:349PubMedPubMedCentralCrossRefGoogle Scholar
  48. Laird AR, Eickhoff SB, Rottschy C, Bzdok D, Ray KL, Fox PT (2013) Networks of task co-activations. NeuroImage 80:505–514PubMedPubMedCentralCrossRefGoogle Scholar
  49. Langner R, Eickhoff SB (2013) Sustaining attention to simple tasks: a meta-analytic review of the neural mechanisms of vigilant attention. Psychol Bull 139(4):870–900PubMedCrossRefGoogle Scholar
  50. Lerch JP, Pruessner JC, Zijdenbos A, Hampel H, Teipel SJ, Evans AC (2005) Focal decline of cortical thickness in Alzheimer’s disease identified by computational neuroanatomy. Cereb Cortex (New York, NY: 1991) 15:995–1001Google Scholar
  51. Lerch JP, Worsley K, Shaw WP, Greenstein DK, Lenroot RK, Giedd J, Evans AC (2006) Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage 31:993–1003PubMedCrossRefGoogle Scholar
  52. Li SC, Brehmer Y, Shing YL, Werkle-Bergner M, Lindenberger U (2006) Neuromodulation of associative and organizational plasticity across the life span: empirical evidence and neurocomputational modeling. Neurosci Biobehav Rev 30:775–790PubMedCrossRefGoogle Scholar
  53. Li P, Legault J, Litcofsky KA (2014) Neuroplasticity as a function of second language learning: anatomical changes in the human brain. Cortex 58:301–324PubMedCrossRefGoogle Scholar
  54. Lyttelton O, Boucher M, Robbins S, Evans A (2007) An unbiased iterative group registration template for cortical surface analysis. Neuroimage 34:1535–1544PubMedCrossRefGoogle Scholar
  55. Lyttelton OC, Karama S, Ad-Dab’bagh Y, Zatorre RJ, Carbonell F, Worsley K, Evans AC (2009) Positional and surface area asymmetry of the human cerebral cortex. Neuroimage 46:895–903PubMedCrossRefGoogle Scholar
  56. MacDonald D, Kabani N, Avis D, Evans AC (2000) Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage 12:340–356PubMedCrossRefGoogle Scholar
  57. Maguire EA, Gadian DG, Johnsrude IS, Good CD, Ashburner J, Frackowiak RS, Frith CD (2000) Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci USA 97:4398–4403PubMedPubMedCentralCrossRefGoogle Scholar
  58. Malikovic A, Amunts K, Schleicher A, Mohlberg H, Eickhoff SB, Wilms M, Palomero-Gallagher N, Armstrong E, Zilles K (2007) Cytoarchitectonic analysis of the human extrastriate cortex in the region of V5/MT+: a probabilistic, stereotaxic map of area hOc5. Cereb Cortex 17(3):562–574PubMedCrossRefGoogle Scholar
  59. Margulies DS, Vincent JL, Kelly C, Lohmann G, Uddin LQ, Biswal BB, Villringer A, Castellanos FX, Milham MP, Petrides M (2009) Precuneus shares intrinsic functional architecture in humans and monkeys. Proc Natl Acad Sci USA 106:20069–20074PubMedPubMedCentralCrossRefGoogle Scholar
  60. Markov NT, Misery P, Falchier A, Lamy C, Vezoli J, Quilodran R, Gariel MA, Giroud P, Ercsey-Ravasz M, Pilaz LJ, Huissoud C, Barone P, Dehay C, Toroczkai Z, Van Essen DC, Kennedy H, Knoblauch K (2011) Weight consistency specifies regularities of macaque cortical networks. Cereb Cortex (New York, NY: 1991) 21:1254–1272Google Scholar
  61. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Paus T, Simpson G, Pike B, Holmes C, Collins L, Thompson P, MacDonald D, Iacoboni M, Schormann T, Amunts K, Palomero-Gallagher N, Geyer S, Parsons L, Narr K, Kabani N, Le Goualher G, Boomsma D, Cannon T, Kawashima R, Mazoyer B (2001) A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 356:1293–1322PubMedPubMedCentralCrossRefGoogle Scholar
  62. Mechelli A, Friston KJ, Frackowiak RS, Price CJ (2005) Structural covariance in the human cortex. J Neurosci 25:8303–8310PubMedCrossRefGoogle Scholar
  63. Messé A, Hutt MT, König P, Hilgetag CC (2015) A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks. Sci Rep 5:7870PubMedPubMedCentralCrossRefGoogle Scholar
  64. Miranda-Dominguez O, Mills BD, Grayson D, Woodall A, Grant KA, Kroenke CD, Fair DA (2014) Bridging the gap between the human and macaque connectome: a quantitative comparison of global interspecies structure-function relationships and network topology. J Neurosci 34:5552–5563PubMedPubMedCentralCrossRefGoogle Scholar
  65. Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44:893–905PubMedCrossRefGoogle Scholar
  66. Narsude M, Gallichan D, van der Zwaag W, Gruetter R, Marques JP (2016) Three-dimensional echo planar imaging with controlled aliasing: A sequence for high temporal resolution functional MRI. Magn Reson Med 75(6):2350–2361PubMedCrossRefGoogle Scholar
  67. Nooner KB, Colcombe SJ, Tobe RH, Mennes M, Benedict MM, Moreno AL, Panek LJ, Brown S, Zavitz ST, Li Q, Sikka S, Gutman D, Bangaru S, Schlachter RT, Kamiel SM, Anwar AR, Hinz CM, Kaplan MS, Rachlin AB, Adelsberg S, Cheung B, Khanuja R, Yan C, Craddock CC, Calhoun V, Courtney W, King M, Wood D, Cox CL, Kelly AM, Di Martino A, Petkova E, Reiss PT, Duan N, Thomsen D, Biswal B, Coffey B, Hoptman MJ, Javitt DC, Pomara N, Sidtis JJ, Koplewicz HS, Castellanos FX, Leventhal BL, Milham MP (2012) The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front Neurosci 6:152PubMedPubMedCentralCrossRefGoogle Scholar
  68. O’Kusky J, Strauss E, Kosaka B, Wada J, Li D, Druhan M, Petrie J (1988) The corpus callosum is larger with right-hemisphere cerebral speech dominance. Ann Neurol 24:379–383PubMedCrossRefGoogle Scholar
  69. Panizzon MS, Fennema-Notestine C, Eyler LT, Jernigan TL, Prom-Wormley E, Neale M, Jacobson K, Lyons MJ, Grant MD, Franz CE, Xian H, Tsuang M, Fischl B, Seidman L, Dale A, Kremen WS (2009) Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex 19:2728–2735PubMedPubMedCentralCrossRefGoogle Scholar
  70. Poldrack RA (2006) Can cognitive processes be inferred from neuroimaging data? Trends Cogn Sci (Regul Ed) 10:59–63CrossRefGoogle Scholar
  71. Poldrack RA, Kittur A, Kalar D, Miller E, Seppa C, Gil Y, Parker DS, Sabb FW, Bilder RM (2011) The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front Neuroinform 5:17PubMedPubMedCentralCrossRefGoogle Scholar
  72. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59:2142–2154PubMedCrossRefGoogle Scholar
  73. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84:320–341PubMedCrossRefGoogle Scholar
  74. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc Natl Acad Sci 98:676–682PubMedPubMedCentralCrossRefGoogle Scholar
  75. Rajagopalan V, Pioro EP (2015) Disparate voxel based morphometry (VBM) results between SPM and FSL softwares in ALS patients with frontotemporal dementia: which VBM results to consider? BMC Neurol 15:32PubMedPubMedCentralCrossRefGoogle Scholar
  76. Reid AT, Evans AC (2013) Structural networks in Alzheimer’s disease. Eur Neuropsychopharmacol: J Eur Coll Neuropsychopharmacol 23:63–77CrossRefGoogle Scholar
  77. Reid AT, van Norden AG, de Laat KF, van Oudheusden LJ, Zwiers MP, Evans AC, de Leeuw F-E, Kötter R (2010) Patterns of cortical degeneration in an elderly cohort with cerebral small vessel disease. Hum Brain Mapp 31:1983–1992PubMedCrossRefGoogle Scholar
  78. Reid AT, Bzdok D, Genon S, Langner R, Müller VI, Eickhoff CR, Hoffstaedter F, Cieslik E-C, Fox PT, Laird AR, Amunts K, Caspers S, Eickhoff SB (2015a) ANIMA: a data-sharing initiative for neuroimaging meta-analyses. Neuroimage. 124(B):1245–1253Google Scholar
  79. Reid AT, Lewis J, Bezgin G, Khundrakpam B, Eickhoff SB, McIntosh AR, Bellec P, Evans AC (2015b) A cross-modal, cross-species comparison of connectivity measures in the primate brain. Neuroimage 125:311–331PubMedCrossRefGoogle Scholar
  80. Roland PE, Hilgetag CC, Deco G (2014) Cortico-cortical communication dynamics. Front Syst Neurosci 8:19PubMedPubMedCentralGoogle Scholar
  81. Roski C, Caspers S, Lux S, Hoffstaedter F, Bergs R, Amunts K, Eickhoff SB (2014) Activation shift in elderly subjects across functional systems: an fMRI study. Brain Struct Funct 219:707–718PubMedCrossRefGoogle Scholar
  82. Rottschy C, Langner R, Dogan I, Reetz K, Laird AR, Schulz JB, Fox PT, Eickhoff SB (2012) Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 60:830–846PubMedCrossRefGoogle Scholar
  83. Saleem KS, Miller B, Price JL (2014) Subdivisions and connectional networks of the lateral prefrontal cortex in the macaque monkey. J Comp Neurol 522:1641–1690PubMedCrossRefGoogle Scholar
  84. Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE, Wolf DH (2013) An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64:240–256PubMedCrossRefGoogle Scholar
  85. Schilbach L, Bzdok D, Timmermans B, Fox PT, Laird AR, Vogeley K, Eickhoff SB (2012) Introspective minds: using ALE meta-analyses to study commonalities in the neural correlates of emotional processing, social & unconstrained cognition. PLoS One 7:e30920PubMedPubMedCentralCrossRefGoogle Scholar
  86. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97PubMedCrossRefGoogle Scholar
  87. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, Filippini N, Watkins KE, Toro R, Laird AR, Beckmann CF (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci USA 106:13040–13045PubMedPubMedCentralCrossRefGoogle Scholar
  88. Swadlow HA, Waxman SG (2012) Axonal conduction delays. Scholarpedia 7:1451CrossRefGoogle Scholar
  89. Tohka J, Zijdenbos A, Evans A (2004) Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 23:84–97PubMedCrossRefGoogle Scholar
  90. Trachtenberg JT, Chen BE, Knott GW, Feng G, Sanes JR, Welker E, Svoboda K (2002) Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature 420:788–794PubMedCrossRefGoogle Scholar
  91. Tuladhar AM, Reid AT, Shumskaya E, de Laat KF, van Norden AGW, van Dijk EJ, Norris DG, de Leeuw F-E (2015) Relationship between white matter hyperintensities, cortical thickness, and cognition. Stroke; J Cereb Circ 46:425–432CrossRefGoogle Scholar
  92. Turkeltaub PE, Eickhoff SB, Laird AR, Fox M, Wiener M, Fox P (2012) Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses. Hum Brain Mapp 33:1–13PubMedCrossRefGoogle Scholar
  93. Tyszka JM, Kennedy DP, Adolphs R, Paul LK (2011) Intact bilateral resting-state networks in the absence of the corpus callosum. J Neurosci 31:15154–15162PubMedPubMedCentralCrossRefGoogle Scholar
  94. Uddin LQ, Mooshagian E, Zaidel E, Scheres A, Margulies DS, Kelly AM, Shehzad Z, Adelstein JS, Castellanos FX, Biswal BB, Milham MP (2008) Residual functional connectivity in the split-brain revealed with resting-state functional MRI. Neuroreport 19:703–709PubMedPubMedCentralCrossRefGoogle Scholar
  95. van den Heuvel MP, Sporns O (2011) Rich-Club organization of the human connectome. J Neurosci 31:15775–15786PubMedCrossRefGoogle Scholar
  96. van Wijk BCM, Stam CJ, Daffertshofer A (2010) Comparing brain networks of different size and connectivity density using graph theory. PLoS One 5(10):e13701PubMedPubMedCentralCrossRefGoogle Scholar
  97. Weissenbacher A, Kasess C, Gerstl F, Lanzenberger R, Moser E, Windischberger C (2009) Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. Neuroimage 47:1408–1416PubMedCrossRefGoogle Scholar
  98. Winkler AM, Kochunov P, Blangero J, Almasy L, Zilles K, Fox PT, Duggirala R, Glahn DC (2010) Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage 53:1135–1146PubMedCrossRefGoogle Scholar
  99. Xia J, Miu J, Ding H, Wang X, Chen H, Wang J, Wu J, Zhao J, Huang H, Tian W (2013) Changes of brain gray matter structure in Parkinson’s disease patients with dementia. Neural Regen Res 8:1276–1285PubMedPubMedCentralGoogle Scholar
  100. Xu J, Moeller S, Auerbach EJ, Strupp J, Smith SM, Feinberg DA, Yacoub E, Uğurbil K (2013) Evaluation of slice accelerations using multiband echo planar imaging at 3 T. Neuroimage 83:991–1001PubMedCrossRefGoogle Scholar
  101. Yazgan MY, Wexler BE, Kinsbourne M, Peterson B, Leckman JF (1995) Functional significance of individual variations in callosal area. Neuropsychologia 33:769–779PubMedCrossRefGoogle Scholar
  102. Zheng D, Purves D (1995) Effects of increased neural activity on brain growth. Proc Natl Acad Sci USA 92:1802–1806PubMedPubMedCentralCrossRefGoogle Scholar
  103. Zhou J, Gennatas ED, Kramer JH, Miller BL, Seeley WW (2012) Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome. Neuron 73:1216–1227PubMedPubMedCentralCrossRefGoogle Scholar
  104. Zijdenbos AP, Forghani R, Evans AC (2002) Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 21:1280–1291PubMedCrossRefGoogle Scholar
  105. Zilles K, Schleicher A, Langemann C, Amunts K, Morosan P, Palomero-Gallagher N, Schormann T, Mohlberg H, Burgel U, Steinmetz H, Schlaug G, Roland PE (1997) Quantitative analysis of sulci in the human cerebral cortex: development, regional heterogeneity, gender difference, asymmetry, intersubject variability and cortical architecture. Hum Brain Mapp 5:218–221PubMedCrossRefGoogle Scholar
  106. zu Eulenburg P, Caspers S, Roski C, Eickhoff SB (2012) Meta-analytical definition and functional connectivity of the human vestibular cortex. Neuroimage 60:162–169PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Andrew T. Reid
    • 1
    • 9
    Email author
  • Felix Hoffstaedter
    • 1
    • 2
  • Gaolang Gong
    • 3
  • Angela R. Laird
    • 4
  • Peter Fox
    • 5
    • 6
  • Alan C. Evans
    • 7
  • Katrin Amunts
    • 1
    • 8
  • Simon B. Eickhoff
    • 1
    • 2
  1. 1.Institute for Neuroscience and Medicine (INM-1)Jülich Research CenterJülichGermany
  2. 2.Department of Clinical Neuroscience and MedicineHeinrich Heine UniversityDüsseldorfGermany
  3. 3.School of Brain and Cognitive SciencesNational Key Laboratory of Cognitive Neuroscience and LearningBeijingChina
  4. 4.Department of PhysicsFlorida International UniversityMiamiUSA
  5. 5.University of Texas Health Sciences Center at San AntonioSan AntonioUSA
  6. 6.South Texas Veterans Health Care SystemSan AntonioUSA
  7. 7.McConnell Brain Imaging Center, Montreal Neurological InstituteMcGill UniversityMontrealCanada
  8. 8.C. & O. Vogt Institute for Brain ResearchHeinrich Heine UniversityDüsseldorfGermany
  9. 9.Donders Institute for Brain, Cognition and BehaviorRadboud University NijmegenNijmegenThe Netherlands

Personalised recommendations