Abstract
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.
References
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. https://doi.org/10.1002/hbm.10126.
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. https://doi.org/10.1523/JNEUROSCI.3874-05.2006.
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.
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. https://doi.org/10.1093/cercor/bhr388.
Alvarez, J. A., & Emory, E. (2006). Executive function and the frontal lobes: a meta-analytic review. Neuropsychology Review, 16(1), 17–42. https://doi.org/10.1007/s11065-006-9002-x.
American Psychiatric Association (1995). Diagnostic and Statistical Manual of Mental Disorders, 4th edition, International Version (DSM-IV). Washington DC: American Psychiatric Association.
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. https://doi.org/10.1097/JGP.0b013e3180547053.
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. https://doi.org/10.1016/j.neuron.2007.10.038.
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. https://doi.org/10.1089/brain.2015.0345.
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. https://doi.org/10.1007/s10548-009-0089-2.
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. https://doi.org/10.1016/j.neuroimage.2013.01.072.
Benton, A. L. (1967). Problems of test construction in the field of aphasia. Cortex, 3, 32–58.
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. https://doi.org/10.1080/13803390490515333.
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. https://doi.org/10.1016/j.neuroimage.2009.07.036.
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. https://doi.org/10.1196/annals.1440.011.
Buckner, R. L., Krienen, F. M., & Yeo, B. T. (2013). Opportunities and limitations of intrinsic functional connectivity MRI. Nature Neuroscience, 16(7), 832–837. https://doi.org/10.1038/nn.3423.
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. https://doi.org/10.1523/JNEUROSCI.5062-08.2009.
Buckner, R. L., & Vincent, J. L. (2007). Unrest at rest: default activity and spontaneous network correlations. Neuroimage, 37(4), 1091–1096. https://doi.org/10.1016/j.neuroimage.2007.01.010. discussion 1097–1099.
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. https://doi.org/10.1148/radiol.10100734.
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. https://doi.org/10.1007/s00429-009-0218-4.
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. https://doi.org/10.1016/j.neurobiolaging.2013.02.012.
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. https://doi.org/10.1016/j.neuron.2014.05.014.
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. https://doi.org/10.1002/hbm.20221.
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. https://doi.org/10.1007/s11682-015-9375-7.
Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.
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. https://doi.org/10.1073/pnas.1220826110.
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. https://doi.org/10.1093/cercor/bhu246.
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. https://doi.org/10.1093/cercor/bhm207.
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. https://doi.org/10.1073/pnas.0337195100.
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. https://doi.org/10.1073/pnas.0704320104.
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. https://doi.org/10.1111/cns.12431.
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. https://doi.org/10.1037/a0033402.
Ferreira, L. K., & Busatto, G. F. (2013). Resting-state functional connectivity in normal brain aging. Neuroscience and Biobehavioral Reviews, 37(3), 384–400. https://doi.org/10.1016/j.neubiorev.2013.01.017.
Fisher, R. A. (1921). On the ‘probable error’ of a coefficient of correlation deduced from a small sample. Metron, 1, 3–32.
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. https://doi.org/10.3389/fnsys.2010.00022.
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. https://doi.org/10.1038/nrn2201.
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, https://doi.org/10.1073/pnas.0504136102.
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. https://doi.org/10.1152/jn.90777.2008.
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. https://doi.org/10.1002/hbm.20113.
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.
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.
Gross, C. G. (1994). How inferior temporal cortex became a visual area. Cerebral Cortex, 4(5), 455–469.
Gross, C. G. (2008). Single neuron studies of inferior temporal cortex. Neuropsychologia, 46(3), 841–852. https://doi.org/10.1016/j.neuropsychologia.2007.11.009.
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. https://doi.org/10.1097/WNR.0b013e3282f42509.
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. https://doi.org/10.1016/j.neuroimage.2013.05.116.
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. https://doi.org/10.1016/j.mri.2010.03.021.
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. https://doi.org/10.1523/JNEUROSCI.3408-06.2006.
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. https://doi.org/10.1016/j.neuroimage.2009.12.051.
He, H., & Liu, T. T. (2012). A geometric view of global signal confounds in resting-state functional MRI. Neuroimage, 59(3), 2339–2348. https://doi.org/10.1016/j.neuroimage.2011.09.018.
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. https://doi.org/10.1093/cercor/bhl149.
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. https://doi.org/10.1016/j.neuroimage.2007.08.008.
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. https://doi.org/10.1016/j.neuroimage.2009.07.058.
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. https://doi.org/10.1016/j.neuroimage.2009.10.016.
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.
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. https://doi.org/10.1523/JNEUROSCI.4865-10.2011.
Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137(Pt 1), 12–32. https://doi.org/10.1093/brain/awt162.
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.
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. https://doi.org/10.1073/pnas.1214900110.
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. https://doi.org/10.1016/j.neuroimage.2013.07.058.
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. https://doi.org/10.1007/s11682-015-9384-6.
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. https://doi.org/10.1007/s10334-010-0228-5.
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. https://doi.org/10.1002/hbm.22812.
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. https://doi.org/10.1016/j.neuroimage.2010.01.002.
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. https://doi.org/10.1016/j.neuroimage.2008.09.036.
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. https://doi.org/10.1006/nimg.1996.0248.
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. https://doi.org/10.1016/j.neuron.2013.07.035.
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, https://doi.org/10.1073/pnas.98.2.676.
Rey, A. (1964). L’examen clinique en psychologie. Paris: Presses Universitaires de France.
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003.
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. https://doi.org/10.1017/S1041610210001067.
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. https://doi.org/10.1093/cercor/bhi016.
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. https://doi.org/10.1016/j.neurobiolaging.2008.05.022.
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. https://doi.org/10.1523/JNEUROSCI.5587-06.2007.
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. https://doi.org/10.1371/journal.pone.0053922.
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. https://doi.org/10.1371/journal.pcbi.1000808.
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. https://doi.org/10.1523/JNEUROSCI.3335-10.2011.
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. https://doi.org/10.1089/brain.2014.0327.
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. https://doi.org/10.3389/fnhum.2013.00734.
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. https://doi.org/10.1371/journal.pone.0025031.
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. https://doi.org/10.1523/JNEUROSCI.2015-10.2010.
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. https://doi.org/10.1016/j.tics.2004.07.008.
Spreen, O., & Benton, A. L. (1969). Neurosensory center comprehensive examination for aphasia: Manual of instructions (NCCEA). Victoria: University of Victoria.
Squire, L. R., & Zola-Morgan, S. (1991). The medial temporal lobe memory system. Science, 253(5026), 1380–1386.
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. https://doi.org/10.1093/brain/awn262.
Tomasi, D., & Volkow, N. D. (2011). Functional connectivity hubs in the human brain. [Research Support, N.I.H., Extramural]. Neuroimage, 57(3), 908–917. https://doi.org/10.1016/j.neuroimage.2011.05.024.
Vaidya, C. J., & Gordon, E. M. (2013). Phenotypic variability in resting-state functional connectivity: current status. Brain Connectivity, 3(2), 99–120. https://doi.org/10.1089/brain.2012.0110.
van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696. https://doi.org/10.1016/j.tics.2013.09.012.
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. https://doi.org/10.1016/j.neuroimage.2008.08.010.
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. https://doi.org/10.1073/pnas.1111738109.
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. https://doi.org/10.1152/jn.90355.2008.
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. https://doi.org/10.3389/fnhum.2015.00386.
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. https://doi.org/10.1016/j.neuroimage.2010.02.046.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge UP.
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. https://doi.org/10.1016/j.neuroimage.2009.05.005.
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. https://doi.org/10.1111/j.1365-2796.2004.01380.x.
Xia, M., Wang, J., & He, Y. (2013). BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One, 8(7), e68910. https://doi.org/10.1371/journal.pone.0068910.
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. https://doi.org/10.1016/j.neuroimage.2013.03.004.
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.
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. https://doi.org/10.1016/j.neuroimage.2013.10.039.
Zalesky, A., Fornito, A., & Bullmore, E. (2012). On the use of correlation as a measure of network connectivity. Neuroimage, 60(4), 2096–2106. https://doi.org/10.1016/j.neuroimage.2012.02.001.
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. https://doi.org/10.1093/cercor/bhr088.
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. https://doi.org/10.1093/cercor/bhr269.
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. https://doi.org/10.1016/j.neuroimage.2012.10.017.
Acknowledgements
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.
Funding
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).
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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).
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Zhang, H., Sachdev, P.S., Thalamuthu, A. et al. The relationship between voxel-based metrics of resting state functional connectivity and cognitive performance in cognitively healthy elderly adults. Brain Imaging and Behavior 12, 1742–1758 (2018). https://doi.org/10.1007/s11682-018-9843-y
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DOI: https://doi.org/10.1007/s11682-018-9843-y