Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-25T23:16:39.421Z Has data issue: false hasContentIssue false

13 - An Integrated, Dynamic Functional Connectome Underlies Intelligence

from Part III - Neuroimaging Methods and Findings

Published online by Cambridge University Press:  11 June 2021

Aron K. Barbey
Affiliation:
University of Illinois, Urbana-Champaign
Sherif Karama
Affiliation:
McGill University, Montréal
Richard J. Haier
Affiliation:
University of California, Irvine
Get access

Summary

Intelligence is an elusive concept. For well over a century, what exactly intelligence is and how best to measure it has been debated (see Sternberg & Kaufman, 2011). In one predominant factorization of the components of intelligence it is separated into fluid and crystalized categories, with fluid intelligence measuring one’s reasoning and problem-solving ability, and crystallized intelligence measuring lifetime knowledge (Cattell, 1971). Influential theories of intelligence, particularly fluid intelligence, have proposed that aspects of cognitive control, most notably working memory, are the drivers of intelligent behavior (Conway, Getz, Macnamara, & Engel de Abreu, 2011; Conway, Kane, & Engle, 2003; Kane & Engle, 2002; Kovacs & Conway, 2016). More specifically, it is thought that the control aspect of working memory, the central executive proposed by Baddeley and Hitch (1974), is the basis for the types of cognitive processes tapped by intelligence assessments (Conway et al., 2003; Kane & Engle, 2002). It has further been proposed that the control process underlying intelligence may not be a single process, but instead a cluster of domain-general control processes, including attentional control, interference resolution, updating of relevant information, and others (Conway et al., 2011; Kovacs & Conway, 2016). Here, we focus on what we have learned about how intelligence emerges from brain function, taking the perspective that cognitive control ability and intelligence are supported by similar brain mechanisms, namely integration, efficiency, and plasticity. These mechanisms are best investigated using brain network methodology. From a network neuroscience perspective, integration refers to interactions across distinct brain networks; efficiency refers to the speed at which information can be transferred across the brain; and plasticity refers to the ability of brain networks to reconfigure, or rearrange, into an organization that is optimal for the current context. Therefore, we review relevant literature relating brain network function to both intelligence and cognitive control, as well as literature relating intelligence to cognitive control. Given the strong link between fluid intelligence, in particular, and cognitive control, we focus mainly on literature probing fluid intelligence in this chapter.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., & Sporns, O. (2009). Modeling the impact of lesions in the human brain. PLoS Computational Biology, 5(6), e1000408.Google Scholar
Baddeley, A. D., & Hitch, G. (1974). Working memory. In Bower, G. H. (ed.) Psychology of learning and motivation, 8th ed. (pp. 4789). New York: Academic Press.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 820.Google Scholar
Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135(Pt 4), 11541164.Google Scholar
Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. The Neuroscientist, 12(6), 512523.Google Scholar
Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R. (2009). Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences of the United States of America, 106(28), 1174711752.Google Scholar
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 1027.Google Scholar
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016). Optimally controlling the human connectome: The role of network topology. Scientific Reports, 6, 30770.Google Scholar
Bohlken, M. M., Brouwer, R. M., Mandl, R. C. W., Hedman, A. M., van den Heuvel, M. P., van Haren, N. E. M., … Hulshoff Pol, H. E. (2016). Topology of genetic associations between regional gray matter volume and intellectual ability: Evidence for a high capacity network. Neuroimage, 124(Pt A), 10441053.CrossRefGoogle ScholarPubMed
Breakspear, M. (2017). Dynamic models of large-scale brain activity. Nature Neuroscience, 20(3), 340352.CrossRefGoogle ScholarPubMed
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186198.Google Scholar
Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13(5), 336349.Google Scholar
Cabral, J., Kringelbach, M. L., & Deco, G. (2017). Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms. Neuroimage, 160, 8496.Google Scholar
Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014). The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262274.CrossRefGoogle ScholarPubMed
Cao, M., Wang, Z., & He, Y. (2015). Connectomics in psychiatric research: Advances and applications. Neuropsychiatric Disease and Treatment, 11, 28012810.Google Scholar
Cattell, R. B. (1971). Abilities: Their structure, growth and action. Boston, MA: Houghton Mifflin.Google Scholar
Cattell, R. B., & Horn, J. D. (1978). A check on the theory of fluid and crystallized intelligence with description of new subtest designs. Journal of Educational Measurement, 15(3), 139164.Google Scholar
Chen, T., Cai, W., Ryali, S., Supekar, K., & Menon, V. (2016). Distinct global brain dynamics and spatiotemporal organization of the salience network. PLoS Biology, 14(6), e1002469.CrossRefGoogle ScholarPubMed
Chen, J. E., Chang, C., Greicius, M. D., & Glover, G. H. (2015). Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics. Neuroimage, 111, 476488.CrossRefGoogle ScholarPubMed
Chen, Y., Spagna, A., Wu, T., Kim, T. H., Wu, Q., Chen, C., … Fan, J. (2019). Testing a cognitive control model of human intelligence. Scientific Reports, 9, 2898.Google Scholar
Cohen, J. R. (2018). The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity. Neuroimage, 180(Pt B), 515525.Google Scholar
Cohen, J. R., & D’Esposito, M. (2016). The segregation and integration of distinct brain networks and their relationship to cognition. The Journal of Neuroscience, 36(48), 1208312094.Google Scholar
Cohen, J. R., Gallen, C. L., Jacobs, E. G., Lee, T. G., & D’Esposito, M. (2014). Quantifying the reconfiguration of intrinsic networks during working memory. PLoS One, 9(9), e106636.CrossRefGoogle ScholarPubMed
Cole, M. W., Ito, T., Bassett, D. S., & Schultz, D. H. (2016). Activity flow over resting-state networks shapes cognitive task activations. Nature Neuroscience, 19(12), 17181726.CrossRefGoogle ScholarPubMed
Cole, M. W., Ito, T., & Braver, T. S. (2015). Lateral prefrontal cortex contributes to fluid intelligence through multinetwork connectivity. Brain Connectivity, 5(8), 497504.Google Scholar
Cole, M. W., Laurent, P., & Stocco, A. (2013). Rapid instructed task learning: A new window into the human brain’s unique capacity for flexible cognitive control. Cognitive, Affective & Behavioral Neuroscience, 13(1), 122.Google Scholar
Cole, M. W., Pathak, S., & Schneider, W. (2010). Identifying the brain’s most globally connected regions. Neuroimage, 49(4), 31323148.CrossRefGoogle ScholarPubMed
Cole, M. W., Yarkoni, T., Repovš, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. The Journal of Neuroscience, 32(26), 89888999.Google Scholar
Conway, A. R. A., Getz, S. J., Macnamara, B., & Engel de Abreu, P. M. J. (2011). Working memory and intelligence. In Sternberg, R. J., & Kaufman, S. B. (eds.), The Cambridge handbook of intelligence (pp. 394418). New York: Cambridge University Press.Google Scholar
Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences, 7(12), 547552.Google Scholar
Deco, G., & Corbetta, M. (2011). The dynamical balance of the brain at rest. The Neuroscientist, 17(1), 107123.Google Scholar
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2013). Resting brains never rest: Computational insights into potential cognitive architectures. Trends in Neurosciences, 36(5), 268274.Google Scholar
Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O., & Kötter, R. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. Proceedings of the National Academy of Sciences of the United States of America, 106(25), 1030210307.Google Scholar
Deco, G., & Kringelbach, M. L. (2014). Great expectations: Using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron, 84(5), 892905.Google Scholar
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015). Rethinking segregation and integration: Contributions of whole-brain modelling. Nature Reviews Neuroscience, 16(7), 430439.Google Scholar
Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. Proceedings of the National Academy of Sciences of the United States of America, 95(24), 1452914534.Google Scholar
Dosenbach, N. U. F., Fair, D. A., Cohen, A. L., Schlaggar, B. L., & Petersen, S. E. (2008). A dual-networks architecture of top-down control. Trends in Cognitive Sciences, 12(3), 99105.Google Scholar
Dubin, M. (2017). Imaging TMS: Antidepressant mechanisms and treatment optimization. International Review of Psychiatry, 29(2), 8997.Google Scholar
Dubois, J., Galdi, P., Paul, L. K., & Adolphs, R. (2018). A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philosophical Transactions of the Royal Society B: Biological Sciences, 373, 20170284.Google Scholar
Duncan, J. (2001). An adaptive coding model of neural function in prefrontal cortex. Nature Reviews Neuroscience, 2(11), 820829.Google Scholar
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172179.CrossRefGoogle ScholarPubMed
Ekman, M., Derrfuss, J., Tittgemeyer, M., & Fiebach, C. J. (2012). Predicting errors from reconfiguration patterns in human brain networks. Proceedings of the National Academy of Sciences of the United States of America, 109(41), 1671416719.Google Scholar
Elton, A., & Gao, W. (2014). Divergent task-dependent functional connectivity of executive control and salience networks. Cortex, 51, 5666.CrossRefGoogle ScholarPubMed
Elton, A., & Gao, W. (2015). Task-related modulation of functional connectivity variability and its behavioral correlations. Human Brain Mapping, 36(8), 32603272.Google Scholar
Euler, M. J. (2018). Intelligence and uncertainty: Implications of hierarchical predictive processing for the neuroscience of cognitive ability. Neuroscience and Biobehavioral Reviews, 94, 93112.Google Scholar
Finc, K., Bonna, K., Lewandowska, M., Wolak, T., Nikadon, J., Dreszer, J., … Kühn, S. (2017). Transition of the functional brain network related to increasing cognitive demands. Human Brain Mapping, 38(7), 36593674.Google Scholar
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., … Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 16641671.Google Scholar
Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience, 16(3), 159172.Google Scholar
Fox, M. D., Buckner, R. L., Liu, H., Chakravarty, M. M., Lozano, A. M., & Pascual-Leone, A. (2014). Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proceedings of the National Academy of Sciences of the United States of America, 111(41), E4367E4375.Google Scholar
Friedman, N. P., & Miyake, A. (2017). Unity and diversity of executive functions: Individual differences as a window on cognitive structure. Cortex, 86, 186204.Google Scholar
Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., Defries, J. C., & Hewitt, J. K. (2006). Not all executive functions are related to intelligence. Psychological Science, 17(2), 172179.Google Scholar
Gallen, C. L., & D’Esposito, M. (2019). Modular brain network organization: A biomarker of cognitive plasticity. Trends in Cognitive Sciences, 23(4), 293304.Google Scholar
Gallen, C. L., Turner, G. R., Adnan, A., & D’Esposito, M. (2016). Reconfiguration of brain network architecture to support executive control in aging. Neurobiology of Aging, 44, 4252.Google Scholar
Garlick, D. (2002). Understanding the nature of the general factor of intelligence: The role of individual differences in neural plasticity as an explanatory mechanism. Psychological Review, 109(1), 116136.Google Scholar
Girn, M., Mills, C., & Christoff, K. (2019). Linking brain network reconfiguration and intelligence: Are we there yet? Trends in Neuroscience and Education, 15, 6270.Google Scholar
Gläscher, J., Rudrauf, D., Colom, R., Paul, L. K., Tranel, D., Damasio, H., & Adolphs, R. (2010). Distributed neural system for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 47054709.Google Scholar
Godwin, D., Barry, R. L., & Marois, R. (2015). Breakdown of the brain’s functional network modularity with awareness. Proceedings of the National Academy of Sciences of the United States of America, 112(12), 37993804.Google Scholar
Gonzalez-Castillo, J., & Bandettini, P. A. (2018). Task-based dynamic functional connectivity: Recent findings and open questions. Neuroimage, 180(Pt B), 526533.CrossRefGoogle ScholarPubMed
Goodkind, M., Eickhoff, S. B., Oathes, D. J., Jiang, Y., Chang, A., Jones-Hagata, L. B., … Etkin, A. (2015). Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry, 72(4), 305315.CrossRefGoogle ScholarPubMed
Gordon, E. M., Stollstorff, M., & Vaidya, C. J. (2012). Using spatial multiple regression to identify intrinsic connectivity networks involved in working memory performance. Human Brain Mapping, 33(7), 15361552.Google Scholar
Goschke, T. (2014). Dysfunctions of decision-making and cognitive control as transdiagnostic mechanisms of mental disorders: Advances, gaps, and needs in current research. International Journal of Methods in Psychiatric Research, 23(Suppl 1), 4157.Google Scholar
Gratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., … Petersen, S. E. (2018). Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron, 98(2), 439452.e5.Google Scholar
Gratton, C., Lee, T. G., Nomura, E. M., & D’Esposito, M. (2013). The effect of theta-burst TMS on cognitive control networks measured with resting state fMRI. Frontiers in Systems Neuroscience, 7, 124.CrossRefGoogle ScholarPubMed
Gratton, C., Nomura, E. M., Perez, F., & D’Esposito, M. (2012). Focal brain lesions to critical locations cause widespread disruption of the modular organization of the brain. Journal of Cognitive Neuroscience, 24(6), 12751285.Google Scholar
Gratton, C., Sun, H., & Petersen, S. E. (2018). Control networks and hubs. Psychophysiology, 55(3), e13032.Google Scholar
Greene, A. S., Gao, S., Scheinost, D., & Constable, R. T. (2018). Task-induced brain state manipulation improves prediction of individual traits. Nature Communications, 9(1), 2807.Google Scholar
Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., … Bassett, D. S. (2015). Controllability of structural brain networks. Nature Communications, 6, 8414.Google Scholar
Guimerà, R., Mossa, S., Turtschi, A., & Amaral, L. A. N. (2005). The worldwide air transportation network: Anomalous centrality, community structure, and cities’ global roles. Proceedings of the National Academy of Sciences of the United States of America, 102(22), 77947799.Google Scholar
Haier, R. J., Siegel, B. V., Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., … Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12, 199217.Google Scholar
Hart, M. G., Ypma, R. J. F., Romero-Garcia, R., Price, S. J., & Suckling, J. (2016). Graph theory analysis of complex brain networks: New concepts in brain mapping applied to neurosurgery. Journal of Neurosurgery, 124(6), 16651678.Google Scholar
Hearne, L. J., Mattingley, J. B., & Cocchi, L. (2016). Functional brain networks related to individual differences in human intelligence at rest. Scientific Reports, 6, 32328.CrossRefGoogle ScholarPubMed
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017a). Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 1025.Google Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017b). Intelligence is associated with the modular structure of intrinsic brain networks. Scientific Reports, 7(1), 16088.Google Scholar
Honey, C. J., Kötter, R., Breakspear, M., & Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences of the United States of America, 104(24), 1024010245.Google Scholar
Hutchison, R. M., & Morton, J. B. (2015). Tracking the brain’s functional coupling dynamics over development. The Journal of Neuroscience, 35(17), 68496859.Google Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135154, discussion 154–187.Google Scholar
Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin & Review, 9(4), 637671.Google Scholar
Kenett, Y. N., Medaglia, J. D., Beaty, R. E., Chen, Q., Betzel, R. F., Thompson-Schill, S. L., & Qiu, J. (2018). Driving the brain towards creativity and intelligence: A network control theory analysis. Neuropsychologia, 118(Pt A), 7990.Google Scholar
Kitzbichler, M. G., Henson, R. N. A., Smith, M. L., Nathan, P. J., & Bullmore, E. T. (2011). Cognitive effort drives workspace configuration of human brain functional networks. The Journal of Neuroscience, 31(22), 82598270.Google Scholar
Kovacs, K., & Conway, A. R. A. (2016). Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27(3), 151177.Google Scholar
Kucyi, A., Tambini, A., Sadaghiani, S., Keilholz, S., & Cohen, J. R. (2018). Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity. Network Neuroscience, 2(4), 397417.Google Scholar
Langer, N., Pedroni, A., Gianotti, L. R. R., Hänggi, J., Knoch, D., & Jäncke, L. (2012). Functional brain network efficiency predicts intelligence. Human Brain Mapping, 33(6), 13931406.Google Scholar
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5(5), e1000395.Google Scholar
Liang, X., Zou, Q., He, Y., & Yang, Y. (2016). Topologically reorganized connectivity architecture of default-mode, executive-control, and salience networks across working memory task loads. Cerebral Cortex, 26(4), 15011511.CrossRefGoogle ScholarPubMed
Liu, H., Yu, H., Li, Y., Qin, W., Xu, L., Yu, C., & Liang, M. (2017). An energy-efficient intrinsic functional organization of human working memory: A resting-state functional connectivity study. Behavioural Brain Research, 316, 6673.Google Scholar
Malpas, C. B., Genc, S., Saling, M. M., Velakoulis, D., Desmond, P. M., & O’Brien, T. J. (2016). MRI correlates of general intelligence in neurotypical adults. Journal of Clinical Neuroscience, 24, 128134.Google Scholar
McTeague, L. M., Goodkind, M. S., & Etkin, A. (2016). Transdiagnostic impairment of cognitive control in mental illness. Journal of Psychiatric Research, 83, 3746.Google Scholar
McTeague, L. M., Huemer, J., Carreon, D. M., Jiang, Y., Eickhoff, S. B., & Etkin, A. (2017). Identification of common neural circuit disruptions in cognitive control across psychiatric disorders. American Journal of Psychiatry, 174(7), 676685.Google Scholar
Mercado, E. III. (2008). Neural and cognitive plasticity: From maps to minds. Psychological Bulletin, 134(1), 109137.Google Scholar
Mesulam, M.-M. (1990). Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Annals of Neurology, 28(5), 597613.Google Scholar
Meunier, D., Lambiotte, R., & Bullmore, E. T. (2010). Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience, 4, 200.Google Scholar
Mill, R. D., Ito, T., & Cole, M. W. (2017). From connectome to cognition: The search for mechanism in human functional brain networks. Neuroimage, 160, 124139.Google Scholar
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167202.Google Scholar
Mitra, A., Snyder, A. Z., Blazey, T., & Raichle, M. E. (2015). Lag threads organize the brain’s intrinsic activity. Proceedings of the National Academy of Sciences of the United States of America, 112(17), E2235E2244.Google Scholar
Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.Google Scholar
O’Reilly, R. C., Herd, S. A., & Pauli, W. M. (2010). Computational models of cognitive control. Current Opinion in Neurobiology, 20(2), 257261.Google Scholar
Opitz, A., Fox, M. D., Craddock, R. C., Colcombe, S., & Milham, M. P. (2016). An integrated framework for targeting functional networks via transcranial magnetic stimulation. Neuroimage, 127, 8696.Google Scholar
Power, J. D., & Petersen, S. E. (2013). Control-related systems in the human brain. Current Opinion in Neurobiology, 23(2), 223228.CrossRefGoogle ScholarPubMed
Raven, J. (2000). The Raven’s progressive matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 148.Google Scholar
Sadaghiani, S., Poline, J. B., Kleinschmidt, A., & D’Esposito, M. (2015). Ongoing dynamics in large-scale functional connectivity predict perception. Proceedings of the National Academy of Sciences of the United States of America, 112(27), 84638468.Google Scholar
Santarnecchi, E., Emmendorfer, A., Tadayon, S., Rossi, S., Rossi, A., Pascual-Leone, A., & Honeywell SHARP Team Authors. (2017). Network connectivity correlates of variability in fluid intelligence performance. Intelligence, 65, 3547.Google Scholar
Schultz, D. H., & Cole, M. W. (2016). Higher intelligence is associated with less task-related brain network reconfiguration. The Journal of Neuroscience, 36(33), 85518561.Google Scholar
Shanmugan, S., Wolf, D. H., Calkins, M. E., Moore, T. M., Ruparel, K., Hopson, R. D., … Satterthwaite, T. D. (2016). Common and dissociable mechanisms of executive system dysfunction across psychiatric disorders in youth. American Journal of Psychiatry, 173(5), 517526.Google Scholar
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., … Poldrack, R. A. (2016). The dynamics of functional brain networks: Integrated network states during cognitive task performance. Neuron, 92(2), 544554.Google Scholar
Shine, J. M., & Poldrack, R. A. (2018). Principles of dynamic network reconfiguration across diverse brain states. Neuroimage, 180(Pt B), 396405.CrossRefGoogle ScholarPubMed
Snyder, H. R., Miyake, A., & Hankin, B. L. (2015). Advancing understanding of executive function impairments and psychopathology: Bridging the gap between clinical and cognitive approaches. Frontiers in Psychology, 6, 328.Google Scholar
Song, M., Zhou, Y., Li, J., Liu, Y., Tian, L., Yu, C., & Jiang, T. (2008). Brain spontaneous functional connectivity and intelligence. Neuroimage, 41(3), 11681176.Google Scholar
Spadone, S., Della Penna, S., Sestieri, C., Betti, V., Tosoni, A., Perrucci, M. G., … Corbetta, M. (2015). Dynamic reorganization of human resting-state networks during visuospatial attention. Proceedings of the National Academy of Sciences of the United States of America, 112(26), 81128117.Google Scholar
Sporns, O. (2010). Networks of the brain. Cambridge, MA: MIT Press.Google Scholar
Sporns, O. (2013). Network attributes for segregation and integration in the human brain. Current Opinion in Neurobiology, 23(2), 162171.Google Scholar
Stanley, M. L., Dagenbach, D., Lyday, R. G., Burdette, J. H., & Laurienti, P. J. (2014). Changes in global and regional modularity associated with increasing working memory load. Frontiers in Human Neuroscience, 8, 954.Google Scholar
Sternberg, R. J., & Kaufman, S. B. (eds.) (2011). The Cambridge handbook of intelligence. New York: Cambridge University Press.Google Scholar
Thompson, G. J., Magnuson, M. E., Merritt, M. D., Schwarb, H., Pan, W.-J., McKinley, A., … Keilholz, S. D. (2013). Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually. Human Brain Mapping, 34(12), 32803298.Google Scholar
van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. The Journal of Neuroscience, 31(44), 1577515786.Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. The Journal of Neuroscience, 29(23), 76197624.Google Scholar
Vatansever, D., Menon, D. K., Manktelow, A. E., Sahakian, B. J., & Stamatakis, E. A. (2015). Default mode dynamics for global functional integration. The Journal of Neuroscience, 35(46), 1525415262.Google Scholar
Wang, C., Ong, J. L., Patanaik, A., Zhou, J., & Chee, M. W. L. (2016). Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states. Proceedings of the National Academy of Sciences of the United States of America, 113(34), 96539658.Google Scholar
Wang, L., Song, M., Jiang, T., Zhang, Y., & Yu, C. (2011). Regional homogeneity of the resting-state brain activity correlates with individual intelligence. Neuroscience Letters, 488(3), 275278.Google Scholar
Wechsler, D. (2008). Wechsler Adult Intelligence Scale – Fourth edition (WAIS-IV). San Antonio, TX: Pearson.Google Scholar
Wechsler, D. (2011). Wechsler Abbreviated Scale of Intelligence – Second edition (WASI-II). San Antonio, TX: Pearson.Google Scholar
Xia, M., & He, Y. (2011). Magnetic resonance imaging and graph theoretical analysis of complex brain networks in neuropsychiatric disorders. Brain Connectivity, 1(5), 349365.Google Scholar
Xiao, L., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2019). Alternating diffusion map based fusion of multimodal brain connectivity networks for IQ prediction. IEEE Transactions on Biomedical Engineering, 68(8), 21402151.Google Scholar
Yin, S., Wang, T., Pan, W., Liu, Y., & Chen, A. (2015). Task-switching cost and intrinsic functional connectivity in the human brain: Toward understanding individual differences in cognitive flexibility. PLoS One, 10(12), e0145826.CrossRefGoogle ScholarPubMed
Zippo, A. G., Della Rosa, P. A., Castiglioni, I., & Biella, G. E. M. (2018). Alternating dynamics of segregation and integration in human EEG functional networks during working-memory task. Neuroscience, 371, 191206.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×