Brain Imaging and Behavior

, Volume 12, Issue 6, pp 1742–1758 | Cite as

The relationship between voxel-based metrics of resting state functional connectivity and cognitive performance in cognitively healthy elderly adults

  • Haobo Zhang
  • Perminder S. SachdevEmail author
  • Anbupalam Thalamuthu
  • Yong He
  • Mingrui Xia
  • Nicole A. Kochan
  • John D. Crawford
  • Julian N. Trollor
  • Henry Brodaty
  • Wei WenEmail author


In previous studies, resting-state functional connectivity (FC) metrics of specific brain regions or networks based on prior hypotheses have been correlated with cognitive performance. Without constraining our analyses to specific regions or networks, we employed whole-brain voxel-based weighted degree (WD), a measure of local FC strength, to be correlated with three commonly used neuropsychological assessments of language, executive function and memory retrieval in both positive and negative directions in 67 cognitively healthy elderly adults. We also divided voxel-based WD into short-ranged and long-ranged WDs to evaluate the influence of FC distance on the WD-cognition relationship, and performed three validation tests. Our results showed that for language and executive function tests, positive WD correlates were located in the frontal and temporal cortices, and negative WD correlates in the precuneus and occipital cortices; for memory retrieval, positive WD correlates were located in the inferior temporal cortices, and negative WD correlates in the anterior cingulate cortices and supplementary motor areas. An FC-distance-dependent effect was also observed, with the short-ranged WD correlates of language and executive function tests located in the medial brain regions and the long-ranged WD correlates in the lateral regions. Our findings suggest that inter-individual differences in FC at rest are predictive of cognitive ability in the elderly adults. Moreover, the distinct patterns of positive and negative WD correlates of cognitive performance recapitulate the dichotomy between task-activated and task-deactivated neural systems, implying that a competition between distinct neural systems on functional network topology may have cognitive relevance.


Resting state Functional connectivity Weighted degree Voxel-based Neuropsychological tests Elderly adults 



We are grateful to all participants in the Sydney Memory and Ageing Study (MAS) and the MAS Research Team. The authors would also like to thank Dr. Sophia Dean and Ms. Angie Russell for proofreading and preparing the manuscript for submission.


This study was funded by the National Health and Medical Research Council of Australia Program Grant (ID 350833) and Project Grant (ID 510175), as well as an Australian Research Council Discovery Grant (ID DP0774213).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by the Human Research Ethics Committee (HREC) of the University of New South Wales (UNSW Sydney).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11682_2018_9843_MOESM1_ESM.docx (5.7 mb)
Supplementary material 1 (DOCX 5808 KB)


  1. Abrahams, S., Goldstein, L. H., Simmons, A., Brammer, M. J., Williams, S. C., Giampietro, V. P., et al. (2003). Functional magnetic resonance imaging of verbal fluency and confrontation naming using compressed image acquisition to permit overt responses. Human Brain Mapping, 20(1), 29–40. Scholar
  2. Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. [Comparative Study Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. The Journal of Neuroscience, 26(1), 63–72. Scholar
  3. Aertsen, A. M., Gerstein, G. L., Habib, M. K., & Palm, G. (1989). Dynamics of neuronal firing correlation: modulation of “effective connectivity”. Journal of Neurophysiology, 61(5), 900–917.CrossRefGoogle Scholar
  4. Alexander-Bloch, A. F., Vertes, P. E., Stidd, R., Lalonde, F., Clasen, L., Rapoport, J., et al. (2013). The anatomical distance of functional connections predicts brain network topology in health and schizophrenia. Cerebral Cortex, 23(1), 127–138. Scholar
  5. Alvarez, J. A., & Emory, E. (2006). Executive function and the frontal lobes: a meta-analytic review. Neuropsychology Review, 16(1), 17–42. Scholar
  6. American Psychiatric Association (1995). Diagnostic and Statistical Manual of Mental Disorders, 4th edition, International Version (DSM-IV). Washington DC: American Psychiatric Association.Google Scholar
  7. Anderson, T. M., Sachdev, P. S., Brodaty, H., Trollor, J. N., & Andrews, G. (2007). Effects of sociodemographic and health variables on mini-mental state exam scores in older Australians. The American Journal of Geriatric Psychiatry, 15(6), 467–476. Scholar
  8. Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., Lustig, C., Head, D., Raichle, M. E., et al. (2007). Disruption of large-scale brain systems in advanced aging. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. Neuron, 56(5), 924–935. Scholar
  9. Archer, J. A., Lee, A., Qiu, A., & Chen, S. H. (2016). A comprehensive analysis of connectivity and aging over the adult life span. Brain Connectivity, 6(2), 169–185. Scholar
  10. Azulay, H., Striem, E., & Amedi, A. (2009). Negative BOLD in sensory cortices during verbal memory: a component in generating internal representations? Brain Topography, 21(3–4), 221–231. Scholar
  11. Baria, A. T., Mansour, A., Huang, L., Baliki, M. N., Cecchi, G. A., Mesulam, M. M., et al. (2013). Linking human brain local activity fluctuations to structural and functional network architectures. Neuroimage, 73, 144–155. Scholar
  12. Benton, A. L. (1967). Problems of test construction in the field of aphasia. Cortex, 3, 32–58.CrossRefGoogle Scholar
  13. Billingsley, R. L., Simos, P. G., Castillo, E. M., Sarkari, S., Breier, J. I., Pataraia, E., et al. (2004). Spatio-temporal cortical dynamics of phonemic and semantic fluency. Journal of Clinical and Experimental Neuropsychology, 26(8), 1031–1043. Scholar
  14. Birn, R. M., Kenworthy, L., Case, L., Caravella, R., Jones, T. B., Bandettini, P. A., et al. (2010). Neural systems supporting lexical search guided by letter and semantic category cues: a self-paced overt response fMRI study of verbal fluency. Neuroimage, 49(1), 1099–1107. Scholar
  15. Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network: anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38. Scholar
  16. Buckner, R. L., Krienen, F. M., & Yeo, B. T. (2013). Opportunities and limitations of intrinsic functional connectivity MRI. Nature Neuroscience, 16(7), 832–837. Scholar
  17. Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., et al. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. [Comparative Study Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. The Journal of Neuroscience, 29(6), 1860–1873. Scholar
  18. Buckner, R. L., & Vincent, J. L. (2007). Unrest at rest: default activity and spontaneous network correlations. Neuroimage, 37(4), 1091–1096. discussion 1097–1099.CrossRefPubMedGoogle Scholar
  19. Chen, G., Ward, B. D., Xie, C., Li, W., Wu, Z., Jones, J. L., et al. (2011). Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. [Research Support, N.I.H., Extramural]. Radiology, 259(1), 213–221. Scholar
  20. Chen, N. K., Chou, Y. H., Song, A. W., & Madden, D. J. (2009). Measurement of spontaneous signal fluctuations in fMRI: adult age differences in intrinsic functional connectivity. Brain Structure and Function, 213(6), 571–585. Scholar
  21. Chou, Y. H., Chen, N. K., & Madden, D. J. (2013). Functional brain connectivity and cognition: effects of adult age and task demands. [Research Support, N.I.H., Extramural]. Neurobiology of Aging, 34(8), 1925–1934. Scholar
  22. Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014). Intrinsic and task-evoked network architectures of the human brain. Neuron, 83(1), 238–251. Scholar
  23. Costafreda, S. G., Fu, C. H., Lee, L., Everitt, B., Brammer, M. J., & David, A. S. (2006). A systematic review and quantitative appraisal of fMRI studies of verbal fluency: role of the left inferior frontal gyrus. Human Brain Mapping, 27(10), 799–810. Scholar
  24. Coutinho, J. F., Fernandesl, S. V., Soares, J. M., Maia, L., Goncalves, O. F., & Sampaio, A. (2016). Default mode network dissociation in depressive and anxiety states. Brain Imaging and Behavior, 10(1), 147–157. Scholar
  25. Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.CrossRefGoogle Scholar
  26. Crossley, N. A., Mechelli, A., Vertes, P. E., Winton-Brown, T. T., Patel, A. X., Ginestet, C. E., et al. (2013). Cognitive relevance of the community structure of the human brain functional coactivation network. Proceedings of the National Academy of Sciences of the United States of America, 110(28), 11583–11588. Scholar
  27. Dai, Z., Yan, C., Li, K., Wang, Z., Wang, J., Cao, M., et al. (2015). Identifying and mapping connectivity patterns of brain network hubs in Alzheimer’s disease. Cerebral Cortex, 25(10), 3723–3742. Scholar
  28. Damoiseaux, J. S., Beckmann, C. F., Arigita, E. J., Barkhof, F., Scheltens, P., Stam, C. J., et al. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cerebral Cortex, 18(8), 1856–1864. Scholar
  29. Davachi, L., Mitchell, J. P., & Wagner, A. D. (2003). Multiple routes to memory: distinct medial temporal lobe processes build item and source memories. Proceedings of the National Academy of Sciences of the United States of America, 100(4), 2157–2162. Scholar
  30. Dosenbach, N. U., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A., et al. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences of the United States of America, 104(26), 11073–11078. Scholar
  31. Du, H. X., Liao, X. H., Lin, Q. X., Li, G. S., Chi, Y. Z., Liu, X., et al. (2015). Test-retest reliability of graph metrics in high-resolution functional connectomics: a resting-state functional MRI study. CNS Neuroscience and Therapeutics, 21(10), 802–816. Scholar
  32. Duchek, J. M., Balota, D. A., Thomas, J. B., Snyder, A. Z., Rich, P., Benzinger, T. L., et al. (2013). Relationship between Stroop performance and resting state functional connectivity in cognitively normal older adults. Neuropsychology, 27(5), 516–528. Scholar
  33. Ferreira, L. K., & Busatto, G. F. (2013). Resting-state functional connectivity in normal brain aging. Neuroscience and Biobehavioral Reviews, 37(3), 384–400. Scholar
  34. Fisher, R. A. (1921). On the ‘probable error’ of a coefficient of correlation deduced from a small sample. Metron, 1, 3–32.Google Scholar
  35. Fornito, A., Zalesky, A., & Bullmore, E. T. (2010). Network scaling effects in graph analytic studies of human resting-state FMRI data. Frontiers in Systems Neuroscience, 4, 22. Scholar
  36. Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. [Research Support, N.I.H., Extramural Review]. Nature Reviews Neuroscience, 8(9), 700–711. Scholar
  37. Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. [Comparative Study Research Support, N.I.H., Extramural Research Support Gov’t, P.H.S.]. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673–9678, Scholar
  38. Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009). The global signal and observed anticorrelated resting state brain networks. Journal of Neurophysiology, 101(6), 3270–3283. Scholar
  39. Fransson, P. (2005). Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Human Brain Mapping, 26(1), 15–29. Scholar
  40. Friston, K. J., Ashburner, J., Kiebel, S. J., Nichols, T. E., & Penny, W. D. (2007). Statistical parametric mapping: The analysis of functional brain images. London: Academic Press.CrossRefGoogle Scholar
  41. Gourovitch, M. L., Kirkby, B. S., Goldberg, T. E., Weinberger, D. R., Gold, J. M., Esposito, G., et al. (2000). A comparison of rCBF patterns during letter and semantic fluency. Neuropsychology, 14(3), 353–360.CrossRefGoogle Scholar
  42. Gross, C. G. (1994). How inferior temporal cortex became a visual area. Cerebral Cortex, 4(5), 455–469.CrossRefGoogle Scholar
  43. Gross, C. G. (2008). Single neuron studies of inferior temporal cortex. Neuropsychologia, 46(3), 841–852. Scholar
  44. Hairston, W. D., Hodges, D. A., Casanova, R., Hayasaka, S., Kraft, R., Maldjian, J. A., et al. (2008). Closing the mind’s eye: deactivation of visual cortex related to auditory task difficulty. Neuroreport, 19(2), 151–154. Scholar
  45. Hallquist, M. N., Hwang, K., & Luna, B. (2013). The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage, 82, 208–225. Scholar
  46. Hampson, M., Driesen, N., Roth, J. K., Gore, J. C., & Constable, R. T. (2010). Functional connectivity between task-positive and task-negative brain areas and its relation to working memory performance. Magnetic Resonance Imaging, 28(8), 1051–1057. Scholar
  47. Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., & Constable, R. T. (2006). Brain connectivity related to working memory performance. The Journal of Neuroscience, 26(51), 13338–13343. Scholar
  48. Hayasaka, S., & Laurienti, P. J. (2010). Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data. [Comparative Study Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. Neuroimage, 50(2), 499–508. Scholar
  49. He, H., & Liu, T. T. (2012). A geometric view of global signal confounds in resting-state functional MRI. Neuroimage, 59(3), 2339–2348. Scholar
  50. He, Y., Chen, Z. J., & Evans, A. C. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. Cerebral Cortex, 17(10), 2407–2419. Scholar
  51. Kelly, A. M., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008). Competition between functional brain networks mediates behavioral variability. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. Neuroimage, 39(1), 527–537. Scholar
  52. Kim, H., Daselaar, S. M., & Cabeza, R. (2010). Overlapping brain activity between episodic memory encoding and retrieval: roles of the task-positive and task-negative networks. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. Neuroimage, 49(1), 1045–1054. Scholar
  53. Kim, J. H., Lee, J. M., Jo, H. J., Kim, S. H., Lee, J. H., Kim, S. T., et al. (2010). Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: functional connectivity-based parcellation method. Neuroimage, 49(3), 2375–2386. Scholar
  54. Kirchhoff, B. A., Wagner, A. D., Maril, A., & Stern, C. E. (2000). Prefrontal-temporal circuitry for episodic encoding and subsequent memory. The Journal of Neuroscience, 20(16), 6173–6180.CrossRefGoogle Scholar
  55. Koyama, M. S., Di Martino, A., Zuo, X. N., Kelly, C., Mennes, M., Jutagir, D. R., et al. (2011). Resting-state functional connectivity indexes reading competence in children and adults. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. The Journal of Neuroscience, 31(23), 8617–8624. Scholar
  56. Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137(Pt 1), 12–32. Scholar
  57. Lezak, M. D., Howieson, D. B., Loring, D. W., Hannay, H. J., & Fischer, J. S. (2004). Neuropsychological assessment (4th edn.). New York: Oxford University Press.Google Scholar
  58. Liang, X., Zou, Q., He, Y., & Yang, Y. (2013). Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. [Randomized Controlled Trial Research Support, N.I.H., Intramural Research Support, Non-U.S. Gov’t]. Proceedings of the National Academy of Sciences of the United States of America, 110(5), 1929–1934. Scholar
  59. Liao, X. H., Xia, M. R., Xu, T., Dai, Z. J., Cao, X. Y., Niu, H. J., et al. (2013). Functional brain hubs and their test-retest reliability: a multiband resting-state functional MRI study. Neuroimage, 83, 969–982. Scholar
  60. Lin, P., Yang, Y., Jovicich, J., De Pisapia, N., Wang, X., Zuo, C. S., et al. (2016). Static and dynamic posterior cingulate cortex nodal topology of default mode network predicts attention task performance. Brain Imaging and Behavior, 10(1), 212–225. Scholar
  61. Margulies, D. S., Bottger, J., Long, X., Lv, Y., Kelly, C., Schafer, A., et al. (2010). Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity. MAGMA, 23(5–6), 289–307. Scholar
  62. Megias, A., Navas, J. F., Petrova, D., Candido, A., Maldonado, A., Garcia-Retamero, R., et al. (2015). Neural mechanisms underlying urgent and evaluative behaviors: an fMRI study on the interaction of automatic and controlled processes. Human Brain Mapping, 36(8), 2853–2864. Scholar
  63. Mennes, M., Kelly, C., Zuo, X. N., Di Martino, A., Biswal, B. B., Castellanos, F. X., et al. (2010). Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage, 50(4), 1690–1701. Scholar
  64. Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009). The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage, 44(3), 893–905. Scholar
  65. Poline, J. B., Worsley, K. J., Evans, A. C., & Friston, K. J. (1997). Combining spatial extent and peak intensity to test for activations in functional imaging. Neuroimage, 5(2), 83–96. Scholar
  66. Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013). Evidence for hubs in human functional brain networks. Neuron, 79(4), 798–813. Scholar
  67. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. [Research Support, Non-U.S. Gov’t Research Support, U. S. Gov’t, P.H.S.]. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676–682, Scholar
  68. Rey, A. (1964). L’examen clinique en psychologie. Paris: Presses Universitaires de France.Google Scholar
  69. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059–1069. Scholar
  70. Sachdev, P. S., Brodaty, H., Reppermund, S., Kochan, N. A., Trollor, J. N., Draper, B., et al. (2010). The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70–90 years. International Psychogeriatrics, 22(8), 1248–1264. Scholar
  71. Salvador, R., Suckling, J., Coleman, M. R., Pickard, J. D., Menon, D., & Bullmore, E. (2005). Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex, 15(9), 1332–1342. Scholar
  72. Sambataro, F., Murty, V. P., Callicott, J. H., Tan, H. Y., Das, S., Weinberger, D. R., et al. (2010). Age-related alterations in default mode network: impact on working memory performance. Neurobiology of Aging, 31(5), 839–852. Scholar
  73. Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., et al. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. [Research Support. N.I.H., Extramural Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S.]. The Journal of Neuroscience, 27(9), 2349–2356. Scholar
  74. Seo, E. H., Lee, D. Y., Lee, J. M., Park, J. S., Sohn, B. K., Lee, D. S., et al. (2013). Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer’s disease. [Research Support, Non-U.S. Gov’t]. PLoS One, 8(1), e53922. Scholar
  75. Sepulcre, J., Liu, H., Talukdar, T., Martincorena, I., Yeo, B. T., & Buckner, R. L. (2010). The organization of local and distant functional connectivity in the human brain. PLoS Computational Biology, 6(6), e1000808. Scholar
  76. Sestieri, C., Corbetta, M., Romani, G. L., & Shulman, G. L. (2011). Episodic memory retrieval, parietal cortex, and the default mode network: functional and topographic analyses. The Journal of Neuroscience, 31(12), 4407–4420. Scholar
  77. Shaw, E. E., Schultz, A. P., Sperling, R. A., & Hedden, T. (2015). Functional connectivity in multiple cortical networks is associated with performance across cognitive domains in older adults. Brain Connectivity, 5(8), 505–516. Scholar
  78. Smallwood, J., Gorgolewski, K. J., Golchert, J., Ruby, F. J., Engen, H., Baird, B., et al. (2013). The default modes of reading: modulation of posterior cingulate and medial prefrontal cortex connectivity associated with comprehension and task focus while reading. Frontiers in Human Neuroscience, 7, 734. Scholar
  79. Song, X. W., Dong, Z. Y., Long, X. Y., Li, S. F., Zuo, X. N., Zhu, C. Z., et al. (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One, 6(9), e25031. Scholar
  80. Spoormaker, V. I., Schroter, M. S., Gleiser, P. M., Andrade, K. C., Dresler, M., Wehrle, R., et al. (2010). Development of a large-scale functional brain network during human non-rapid eye movement sleep. The Journal of Neuroscience, 30(34), 11379–11387. Scholar
  81. Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. (2004). Organization, development and function of complex brain networks. [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S. Research Support, U.S. Gov’t, P.H.S. Review]. Trends in Cognitive Sciences, 8(9), 418–425. Scholar
  82. Spreen, O., & Benton, A. L. (1969). Neurosensory center comprehensive examination for aphasia: Manual of instructions (NCCEA). Victoria: University of Victoria.Google Scholar
  83. Squire, L. R., & Zola-Morgan, S. (1991). The medial temporal lobe memory system. Science, 253(5026), 1380–1386.CrossRefGoogle Scholar
  84. Stam, C. J., de Haan, W., Daffertshofer, A., Jones, B. F., Manshanden, I., van Cappellen van Walsum, A. M., et al. (2009). Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. [Research Support, Non-U.S. Gov’t]. Brain, 132(Pt 1), 213–224. Scholar
  85. Tomasi, D., & Volkow, N. D. (2011). Functional connectivity hubs in the human brain. [Research Support, N.I.H., Extramural]. Neuroimage, 57(3), 908–917. Scholar
  86. Vaidya, C. J., & Gordon, E. M. (2013). Phenotypic variability in resting-state functional connectivity: current status. Brain Connectivity, 3(2), 99–120. Scholar
  87. van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696. Scholar
  88. van den Heuvel, M. P., Stam, C. J., Boersma, M., & Hulshoff Pol, H. E. (2008). Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage, 43(3), 528–539. Scholar
  89. Vertes, P. E., Alexander-Bloch, A. F., Gogtay, N., Giedd, J. N., Rapoport, J. L., & Bullmore, E. T. (2012). Simple models of human brain functional networks. Proceedings of the National Academy of Sciences of the United States of America, 109(15), 5868–5873. Scholar
  90. Vincent, J. L., Kahn, I., Snyder, A. Z., Raichle, M. E., & Buckner, R. L. (2008). Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. Journal of Neurophysiology, 100(6), 3328–3342. Scholar
  91. Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015). GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Frontiers in Human Neuroscience, 9, 386. Scholar
  92. Wang, L., Laviolette, P., O’Keefe, K., Putcha, D., Bakkour, A., Van Dijk, K. R., et al. (2010). Intrinsic connectivity between the hippocampus and posteromedial cortex predicts memory performance in cognitively intact older individuals. Neuroimage, 51(2), 910–917. Scholar
  93. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge UP.CrossRefGoogle Scholar
  94. 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(4), 1408–1416. Scholar
  95. Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L. O., et al. (2004). Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256(3), 240–246. Scholar
  96. Xia, M., Wang, J., & He, Y. (2013). BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One, 8(7), e68910. Scholar
  97. Yan, C. G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., et al. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. Neuroimage, 76, 183–201. Scholar
  98. Yan, C. G., & Zang, Y. F. (2010). DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13.Google Scholar
  99. Yang, Z., Chang, C., Xu, T., Jiang, L., Handwerker, D. A., Castellanos, F. X., et al. (2014). Connectivity trajectory across lifespan differentiates the precuneus from the default network. Neuroimage, 89, 45–56. Scholar
  100. Zalesky, A., Fornito, A., & Bullmore, E. (2012). On the use of correlation as a measure of network connectivity. Neuroimage, 60(4), 2096–2106. Scholar
  101. Zhang, S., Ide, J. S., & Li, C. S. (2012). Resting-state functional connectivity of the medial superior frontal cortex. Cerebral Cortex, 22(1), 99–111. Scholar
  102. Zuo, X. N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F. X., Sporns, O., et al. (2012). Network centrality in the human functional connectome. [Research Support, Non-U.S. Gov’t]. Cerebral Cortex, 22(8), 1862–1875. Scholar
  103. Zuo, X. N., Xu, T., Jiang, L., Yang, Z., Cao, X. Y., He, Y., et al. (2013). Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. Neuroimage, 65, 374–386. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Haobo Zhang
    • 1
    • 2
    • 3
  • Perminder S. Sachdev
    • 3
    • 4
    • 8
    Email author
  • Anbupalam Thalamuthu
    • 3
  • Yong He
    • 5
  • Mingrui Xia
    • 5
  • Nicole A. Kochan
    • 3
    • 4
  • John D. Crawford
    • 3
  • Julian N. Trollor
    • 3
    • 6
  • Henry Brodaty
    • 3
    • 7
    • 8
  • Wei Wen
    • 3
    • 4
    Email author
  1. 1.College of Psychology and SociologyShenzhen UniversityShenzhenChina
  2. 2.Shenzhen Key Laboratory of Affective and Social Cognitive ScienceShenzhen UniversityShenzhenChina
  3. 3.Centre for Healthy Brain Ageing, School of PsychiatryUNSWSydneyAustralia
  4. 4.Neuropsychiatric Institute, NPI, Euroa CentrePrince of Wales HospitalRandwickAustralia
  5. 5.State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
  6. 6.Department of Developmental Disability Neuropsychiatry, School of PsychiatryUNSW AustraliaSydneyAustralia
  7. 7.Academic Department for Old Age PsychiatryPrince of Wales HospitalRandwickAustralia
  8. 8.Dementia Collaborative Research Centre, School of PsychiatryUNSW AustraliaSydneyAustralia

Personalised recommendations