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2 - Network Neuroscience Methods for Studying Intelligence

from Part I - Fundamental Issues

Published online by Cambridge University Press:  11 June 2021

By
Kirsten Hilger  and
Olaf Sporns
Edited by
Aron K. Barbey ,
Sherif Karama  and
Richard J. Haier
Aron K. Barbey
Affiliation:
University of Illinois, Urbana-Champaign
Sherif Karama
Affiliation:
McGill University, Montréal
Richard J. Haier
Affiliation:
University of California, Irvine
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Summary

The human brain is a complex network consisting of numerous functionally specialized brain regions and their inter-regional connections. In recent years, much research has focused on identifying principles of the anatomical and functional organization of brain networks (Bullmore & Sporns, 2009; Sporns, 2014) and their relation to spontaneous (resting-state; Buckner, Krienen, & Yeo, 2013; Fox et al., 2005) or task-related brain activity (Cole, Bassett, Power, Braver, & Petersen, 2014). Numerous studies have identified relationships between variations in network elements or features and individual differences in behavior and cognition. In the context of this monograph, studies of general cognitive ability (often indexed as general intelligence) are of special interest. In this chapter we survey some of the methodological aspects surrounding studies of human brain networks using noninvasive large-scale imaging and electrophysiological techniques and discuss the application of such network approaches in studies of human intelligence.

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Chapter
Information
The Cambridge Handbook of Intelligence and Cognitive Neuroscience , pp. 26 - 43
Publisher: Cambridge University Press
Print publication year: 2021

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References

Amico, E., Arenas, A., & Goñi, J. (2019) Centralized and distributed cognitive task processing in the human connectome. Network Neuroscience, 3(2), 455–474.CrossRefGoogle ScholarPubMed
Anokhin, A. P., Lutzenberger, W., & Birbaumer, N. (1999). Spatiotemporal organization of brain dynamics and intelligence: An EEG study in adolescents. International Journal of Psychophysiology, 33(3), 259–273.Google Scholar
Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018). Communication dynamics in complex brain networks. Nature Reviews Neuroscience, 19(1), 17–33.Google Scholar
Barabási, A. L. (2016). Network science. Cambridge University Press.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 1–13.CrossRefGoogle ScholarPubMed
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, 10–27.Google Scholar
Basten, U., Stelzel, C., & Fiebach, C. J. (2013). Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network. Intelligence, 41(5), 517–528.Google Scholar
Betzel, R. F., & Bassett, D. S. (2017a). Generative models for network neuroscience: Prospects and promise. Journal of the Royal Society Interface, 14(136), 20170623.Google Scholar
Betzel, R. F., & Bassett, D. S. (2017b). Multi-scale brain networks. Neuroimage, 160, 73–83.Google Scholar
Bielczyk, N. Z., Uithol, S., van Mourik, T., Anderson, P., Glennon, J. C., & Buitelaar, J. K. (2019). Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches. Network Neuroscience, 3(2), 237–273.CrossRefGoogle ScholarPubMed
Birn, R. M., Molloy, E. K., Patriat, R., Parker, T., Meier, T. B., Kirk, G. R., … Prabhakaran, V. (2013). The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage, 83, 550–558.Google Scholar
Buckner, R. L., Krienen, F. M., & Yeo, B. T. (2013). Opportunities and limitations of intrinsic functional connectivity MRI. Nature Neuroscience, 16(7), 832–837.Google Scholar
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.Google Scholar
Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13(5), 336–349.Google Scholar
Cheung, M., Chan, A. S., Han, Y. M., & Sze, S. L. (2014). Brain activity during resting state in relation to academic performance. Journal of Psychophysiology, 28(2), 47–53.Google Scholar
Chiang, M.-C., Barysheva, M., Shattuck, D. W., Lee, A. D., Madsen, S. K., Avedissian, C., … Thompson, P. M. (2009). Genetics of brain fiber architecture and intellectual performance. Journal of Neuroscience, 29(7), 2212–2224.CrossRefGoogle ScholarPubMed
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.Google Scholar
Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 8988–8999.Google Scholar
Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting system of the human brain: From environment to theory of mind. Neuron, 58(3), 306–324.Google Scholar
Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215.CrossRefGoogle ScholarPubMed
Craddock, R. C., Jbabdi, S., Yan, C. G., Vogelstein, J. T., Castellanos, F. X., Di Martino, A., … Milham, M. P. (2013). Imaging human connectomes at the macroscale. Nature Methods, 10(6), 524–539.Google Scholar
Crossley, N. A., Mechelli, A., Vértes, P. E., Winton-Brown, T. T., Patel, A. X., Ginestet, C. E., … Bullmore, E. T. (2013). Cognitive relevance of the community structure of the human brain functional coactivation network. Proceedings of the National Academy of Sciences USA, 110(28), 11583–11588.Google Scholar
Damiani, D., Pereira, L. K., Damiani, D., & Nascimento, A. M. (2017). Intelligence neurocircuitry: Cortical and subcortical structures. Journal of Morphological Sciences, 34(3), 123–129.Google Scholar
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences USA, 103(37), 13848–13853.Google Scholar
Dosenbach, N. U. F., Fair, D. A, Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A. T., … Petersen, S. E. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences USA, 104(26), 11073–11078.Google Scholar
Duan, F., Watanabe, K., Yoshimura, Y., Kikuchi, M., Minabe, Y., & Aihara, K. (2014). Relationship between brain network pattern and cognitive performance of children revealed by MEG signals during free viewing of video. Brain and Cognition, 86, 10–16.Google Scholar
Dubois, J., Galdi, P., Paul, L. K., Adolphs, R., Engineering, B., Angeles, L., … Dubois, J. (2018). A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philosophical Transactions of the Royal Society of London B Biological Sciences, 373(1756), 20170284.Google Scholar
Dunst, B., Benedek, M., Koschutnig, K., Jauk, E., & Neubauer, A. C. (2014). Sex differences in the IQ-white matter microstructure relationship: A DTI study. Brain and Cognition, 91, 71–78.Google Scholar
Ewald, A., Avarvand, F. S., & Nolte, G. (2013). Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: A simulation study. Biomedizinische Technik, 58(2), 165–178.Google Scholar
Ferguson, M. A., Anderson, J. S., & Spreng, R. N. (2017). Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture. Network Neuroscience, 1(2), 192–207.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), 1664–1671.CrossRefGoogle ScholarPubMed
Fornito, A., Zalesky, A., & Bullmore, E. (2016). Fundamentals of brain network analysis. Cambridge, MA: Academic Press.Google Scholar
Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1–44.Google Scholar
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. Proceedings of the National Academy of Sciences USA, 102(27), 9673–9678.Google Scholar
Fukushima, M., Betzel, R. F., He, Y., de Reus, M. A., van den Heuvel, M. P., Zuo, X. N., & Sporns, O. (2018). Fluctuations between high- and low-modularity topology in time-resolved functional connectivity. NeuroImage, 180(Pt. B), 406–416.Google Scholar
Girn, M., Mills, C., & Christo, K. (2019). Linking brain network reconfiguration and intelligence: Are we there yet? Trends in Neuroscience and Education, 15, 62–70.Google Scholar
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., … Smith, S. M. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178.Google Scholar
Gollo, L. L., Roberts, J. A., Cropley, V. L., Di Biase, M. A., Pantelis, C., Zalesky, A., & Breakspear, M. (2018). Fragility and volatility of structural hubs in the human connectome. Nature Neuroscience, 21(8), 1107–1116.Google Scholar
Gonzalez-Castillo, J., Hoy, C. W., Handwerker, D. A., Robinson, M. E., Buchanan, L. C., Saad, Z. S., & Bandettini, P. A. (2015). Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns. Proceedings of the National Academy of Sciences USA, 112(28), 8762–8767.Google Scholar
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016). Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebral Cortex, 26(1), 288–303.CrossRefGoogle ScholarPubMed
Greene, A. S., Gao, S., Scheinost, D., & Costable, T. (2018). Task-induced brain states manipulation improves prediction of individual traits. Nature Communications, 9(1), 2807.Google Scholar
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences USA, 100(1), 253–258.Google Scholar
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009). Resting-state functional connectivity reflects structural connectivity in the default mode network. Cerebral Cortex, 19(1), 72–78.Google Scholar
Haász, J., Westlye, E. T., Fjær, S., Espeseth, T., Lundervold, A., & Lundervold, A. J. (2013). General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults. NeuroImage, 83, 372–383.Google Scholar
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), e159.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.Google Scholar
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, 10–25.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
Hilger, K., Fukushima, M., Sporns, O., & Fiebach, C. J. (2020). Temporal stability of functional brain modules associated with human intelligence. Human Brain Mapping, 41(2), 362–372.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 USA, 104(24), 10240–10245.Google Scholar
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences USA, 106(6), 2035–2040.Google Scholar
Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S., & Menon, R. S. (2013). Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Human Brain Mapping, 34(9), 2154–2177.CrossRefGoogle ScholarPubMed
Jahidin, A. H., Taib, M. N., Tahir, N. M., Megat Ali, M. S. A., & Lias, S. (2013). Asymmetry pattern of resting EEG for different IQ levels. Procedia – Social and Behavioral Sciences, 97, 246–251.CrossRefGoogle Scholar
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C., & Behrens, T. E. (2015). Measuring macroscopic brain connections in vivo. Nature Neuroscience, 18(11), 1546.Google Scholar
Jeub, L. G., Sporns, O., & Fortunato, S. (2018). Multiresolution consensus clustering in networks. Scientific Reports, 8(1), 3259.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), 135–154.Google Scholar
Kievit, R. A., Davis, S. W., Griffiths, J. D., Correia, M. M., & Henson, R. N. A. (2016). A watershed model of individual differences in fluid intelligence. Neuropsychologia, 91, 186–198.Google Scholar
Kievit, R. A., van Rooijen, H., Wicherts, J. M., Waldorp, L. J., Kan, K. J., Scholte, H. S., & Borsboom, D. (2012). Intelligence and the brain: A model-based approach. Cognitive Neuroscience, 3(2), 89–97.Google Scholar
Kim, D.-J., Davis, E. P., Sandman, C. A., Sporns, O., O’Donnell, B. F., Buss, C., & Hetrick, W. P. (2015). Children’s intellectual ability is associated with structural network integrity. NeuroImage, 124(Pt. A), 550–556.Google Scholar
Koenis, M. M. G., Brouwer, R. M., van den Heuvel, M. P., Mandl, R. C. W., van Soelen, I. L. C., Kahn, R. S., … Hulshoff Pol, H. E. (2015). Development of the brain’s structural network efficiency in early adolescence: A longitudinal DTI twin study. Human Brain Mapping, 36(12), 4938–4953.Google Scholar
Kruschwitz, J. D., Waller, L., Daedelow, L. S., Walter, H., & Veer, I. M. (2018). General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set. Neuroimage, 171, 323–331.CrossRefGoogle 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), 1393–1406.Google Scholar
Langer, N., Pedroni, A., & Jäncke, L. (2013). The problem of thresholding in small-world network analysis. PLoS One, 8(1), e53199.Google Scholar
Langeslag, S. J. E., Schmidt, M., Ghassabian, A., Jaddoe, V. W., Hofman, A., van der Lugt, A., … White, T. J. H. (2013). Functional connectivity between parietal and frontal brain regions and intelligence in young children: The generation R study. Human Brain Mapping, 34(12), 3299–3307.Google Scholar
Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.Google Scholar
Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M. Y., … Schlaggar, B. L. (2015). Functional system and areal organization of a highly sampled individual human brain. Neuron, 87(3), 657–670.Google Scholar
Lee, T. W., Wu, Y. Te, Yu, Y. W. Y., Wu, H. C., & Chen, T. J. (2012). A smarter brain is associated with stronger neural interaction in healthy young females: A resting EEG coherence study. Intelligence, 40(1), 38–48.Google Scholar
Lerch, J. P., Worsley, K., Shaw, W. P., Greenstein, D. K., Lenroot, R.K., Giedd, J., & Evans, A. C. (2006). Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage, 31(3), 993–1003.Google Scholar
Li, Y. H., Liu, Y., Li, J., Qin, W., Li, K. C., Yu, C. S., & Jiang, T. Z. (2009). Brain anatomical network and intelligence. Plos Computational Biology, 5(5), e1000395.Google Scholar
Ma, J., Kang, H. J., Kim, J. Y., Jeong, H. S., Im, J. J., Namgung, E., … Yoon, S. (2017). Network attributes underlying intellectual giftedness in the developing brain. Scientific Reports, 7(1), 11321.Google Scholar
Maier-Hein, K. H., Neher, P. F., Houde, J. C., Côté, M. A., Garyfallidis, E., Zhong, J., … Reddick, W. E. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications, 8(1), 1349.Google Scholar
Malpas, C. B., Genc, S., Saling, M. M., Velakoulis, D., Desmond, P. M., & Brien, T. J. O. (2016). MRI correlates of general intelligence in neurotypical adults. Journal of Clinical Neuroscience, 24, 128–134.Google Scholar
Navas-Sánchez, F. J., Alemán-Gómez, Y., Sánchez-Gonzalez, J., Guzmán-De-Villoria, J. A, Franco, C., Robles, O., … Desco, M. (2013). White matter microstructure correlates of mathematical giftedness and intelligence quotient. Human Brain Mapping, 35(6), 2619–2631.CrossRefGoogle ScholarPubMed
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33(7), 1004–1023.Google Scholar
Neubauer, A. C., Wammerl, M., Benedek, M., Jauk, E., & Jausovec, N. (2017). The influence of transcranial alternating current on fluid intelligence. A fMRI study. Personality and Individual Differences, 118, 50–55.Google Scholar
Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.Google Scholar
Pahor, A., & Jaušovec, N. (2014). Theta–gamma cross-frequency coupling relates to the level of human intelligence. Intelligence, 46, 283–290.Google Scholar
Pamplona, G. S. P., Santos Neto, G. S., Rosset, S. R. E., Rogers, B. P., & Salmon, C. E. G. (2015). Analyzing the association between functional connectivity of the brain and intellectual performance. Frontiers in Human Neuroscience, 9, 61.Google Scholar
Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014). Evaluation and statistical inference for human connectomes. Nature Methods, 11(10), 1058–1063.CrossRefGoogle ScholarPubMed
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … Petersen, S. E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678.Google Scholar
Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage, 105, 536–551.Google Scholar
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. Proceedings of the National Academy of Sciences, 98(2), 676–682.Google Scholar
Rubinov, M. (2016). Constraints and spandrels of interareal connectomes. Nature Communications, 7(1), 13812.CrossRefGoogle ScholarPubMed
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059–1069.Google Scholar
Rubinov, M., & Sporns, O. (2011). Weight-conserving characterization of complex functional brain networks. Neuroimage, 56(4), 2068–2079.Google Scholar
Ryyppö, E., Glerean, E., Brattico, E., Saramäki, J., & Korhonen, O. (2018). Regions of interest as nodes of dynamic functional brain networks. Network Neuroscience, 2(4), 513–535.Google Scholar
Santarnecchi, E., Muller, T., Rossi, S., Sarkar, A., Polizzotto, N. R., Rossi, A., & Cohen Kadosh, R. (2016). Individual differences and specificity of prefrontal gamma frequency-tACS on fluid intelligence capabilities. Cortex, 75, 33–43.Google Scholar
Santarnecchi, E., Rossi, S., & Rossi, A. (2015). The smarter, the stronger: Intelligence level correlates with brain resilience to systematic insults. Cortex, 64, 293–309.Google Scholar
Schmithorst, V. J. (2009). Developmental sex differences in the relation of neuroanatomical connectivity to intelligence. Intelligence, 37(2), 164–173. Schultz, X. D. H., & Cole, X. W. (2016). Higher intelligence is associated with less ask-related brain network reconfiguration. Journal of Neuroscience, 36(33), 8551–8561.Google Scholar
Sherman, L. E., Rudie, J. D., Pfeifer, J. H., Masten, C. L., McNealy, K., & Dapretto, M. (2014). Development of the default mode and central executive networks across early adolescence: A longitudinal study. Developmental Cognitive Neuroscience, 10, 148–159.Google Scholar
Shinn, M., Romero-Garcia, R., Seidlitz, J., Váša, F., Vértes, P. E., & Bullmore, E. (2017). Versatility of nodal affiliation to communities. Scientific Reports, 7(1), 4273.CrossRefGoogle ScholarPubMed
Smit, D. J. A, Stam, C. J., Posthuma, D., Boomsma, D. I., & De Geus, E. J. C. (2008). Heritability of “small-world” networks in the brain: A graph theoretical analysis of resting-state EEG functional connectivity. Human Brain Mapping, 29(12), 1368–1378.Google Scholar
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., … Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences USA, 106(31), 13040–13045.Google Scholar
Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E., Glasser, M. F., … Miller, K. L. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, 18(11), 1565–1567.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), 1168–1176.Google Scholar
Sporns, O. (2014). Contributions and challenges for network models in cognitive neuroscience. Nature Neuroscience, 17(5), 652–660.CrossRefGoogle Scholar
Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annual Review of Psychology, 67, 613–640.CrossRefGoogle ScholarPubMed
Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 28(11), 1178–1193.Google Scholar
Tang, C. Y., Eaves, E. L., Ng, J. C., Carpenter, D. M., Mai, X., Schroeder, D. H., … Haier, R. J. (2010). Brain networks for working memory and factors of intelligence assessed in males and females with fMRI and DTI. Intelligence, 38(3), 293–303.Google Scholar
Tavor, I., Jones, O. P., Mars, R. B., Smith, S. M., Behrens, T. E., & Jbabdi, S. (2016). Task-free MRI predicts individual differences in brain activity during task performance. Science, 352(6282), 216–220.Google Scholar
Telesford, Q. K., Lynall, M. E., Vettel, J., Miller, M. B., Grafton, S. T., & Bassett, D. S. (2016). Detection of functional brain network reconfiguration during task-driven cognitive states. NeuroImage, 142, 198–210.Google Scholar
Thomas, C., Frank, Q. Y., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014). Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proceedings of the National Academy of Sciences USA, 111(46), 16574–16579.Google Scholar
Vaiana, M., & Muldoon, S. F. (2018). Multilayer brain networks. Journal of Nonlinear Science, 1–23.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. Journal of Neuroscience, 29(23), 7619–7624.Google Scholar
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393(6684), 440–442.Google Scholar
Wolf, D., Fischer, F. U., Fesenbeckh, J., Yakushev, I., Lelieveld, I. M., Scheurich, A., … Fellgiebel, A. (2014). Structural integrity of the corpus callosum predicts long-term transfer of fluid intelligence-related training gains in normal aging. Human Brain Mapping, 35(1), 309–318.Google Scholar
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122.Google Scholar
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., … Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165.Google Scholar
Yeo, R. A., Ryman, S. G., van den Heuvel, M. P., de Reus, M. A., Jung, R. E., Pommy, J., … Calhoun, V. D. (2016). Graph metrics of structural brain networks in individuals with schizophrenia and healthy controls: Group differences, relationships with intelligence, and genetics. Journal of the International Neuropsychological Society, 22(2), 240–249.Google Scholar
Yu, C. S., Li, J., Liu, Y., Qin, W., Li, Y. H., Shu, N., … Li, K. C. (2008). White matter tract integrity and intelligence in patients with mental retardation and healthy adults. Neuroimage, 40(4), 1533–1541.Google Scholar
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014). Time-resolved resting-state brain networks. Proceedings of the National Academy of Sciences, 111(28), 10341–10346.Google Scholar
Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010). Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage, 50(3), 970–983.Google Scholar
Zalesky, A., Fornito, A., Seal, M. L., Cocchi, L., Westin, C., Bullmore, E. T., … Pantelis, C. (2011). Disrupted axonal fiber connectivity in schizophrenia. Biological Psychiatry, 69(1), 80–89.Google Scholar
Zuo, X. N., & Xing, X. X. (2014). Test–retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective. Neuroscience & Biobehavioral Reviews, 45, 100–118.Google Scholar

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