Structural architecture and brain network efficiency link polygenic scores to intelligence.

[en] Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

Disciplines :

Neurosciences & behavior

Author, co-author :

Genç, Erhan

Metzen, Dorothea

Fraenz, Christoph

Schlüter, Caroline

Voelkle, Manuel C.

Arning, Larissa

Streit, Fabian

Nguyen, Huu Phuc

Güntürkün, Onur

Ocklenburg, Sebastian

Kumsta, Robert ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)

External co-authors :

yes

Language :

English

Title :

Structural architecture and brain network efficiency link polygenic scores to intelligence.

Publication date :

2023

Journal title :

Human Brain Mapping

ISSN :

1097-0193

Publisher :

John Wiley & Sons, Hoboken, United States - New York

Peer reviewed :

Peer Reviewed verified by ORBi

Commentary :

Available on ORBilu :

since 05 April 2023

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Bibliography

Abdellaoui, A., & Verweij, K. J. H. (2021). Dissecting polygenic signals from genome-wide association studies on human behaviour. Nature Human Behaviour, 5, 686–694. https://doi.org/10.1038/s41562-021-01110-y
Ammerman, B. A., Serang, S., Jacobucci, R., Burke, T. A., Alloy, L. B., & McCloskey, M. S. (2018). Exploratory analysis of mediators of the relationship between childhood maltreatment and suicidal behavior. Journal of Adolescence, 69, 103–112. https://doi.org/10.1016/j.adolescence.2018.09.004
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22, 8–20. https://doi.org/10.1016/j.tics.2017.10.001
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, 1154–1164. https://doi.org/10.1093/brain/aws021
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. https://doi.org/10.1016/j.intell.2015.04.009
Beauducel, A., Brocke, B., & Liepmann, D. (2001). Perspectives on fluid and crystallized intelligence: Facets for verbal, numerical, and figural intelligence. Personality and Individual Differences, 30, 977–994. https://doi.org/10.1016/S0191-8869(00)00087-8
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage, 34, 144–155. https://doi.org/10.1016/j.neuroimage.2006.09.018
Behrens, T. E. J., Woolrich, M. W., Jenkinson, M., Johansen-Berg, H., Nunes, R. G., Clare, S., Matthews, P. M., Brady, J. M., & Smith, S. M. (2003). Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine, 50, 1077–1088. https://doi.org/10.1002/mrm.10609
Calvin, C. M., Batty, G. D., Der, G., Brett, C. E., Taylor, A., Pattie, A., Čukić, I., & Deary, I. J. (2017). Childhood intelligence in relation to major causes of death in 68 year follow-up: Prospective population study. BMJ, 357, 1–14. https://doi.org/10.1136/bmj.j2708
Cheng, S., Wu, C., Qi, X., Liu, L., Ma, M., Zhang, L., Cheng, B., Liang, C., Li, P., Kafle, O. P., Wen, Y., & Zhang, F. (2020). A large-scale genetic correlation scan between intelligence and brain imaging phenotypes. Cerebral Cortex, 30, 4197–4203. https://doi.org/10.1093/cercor/bhaa043
Choi, S. W., Mak, T. S.-H., & O'Reilly, P. F. (2020). Tutorial: A guide to performing polygenic risk score analyses. Nature Protocols, 15, 2759–2772. https://doi.org/10.1038/s41596-020-0353-1
Choi, Y. Y., Shamosh, N. A., Cho, S. H., DeYoung, C. G., Lee, M. J., Lee, J.-M., Kim, S. I., Cho, Z.-H., Kim, K., Gray, J. R., & Lee, K. H. (2008). Multiple bases of human intelligence revealed by cortical thickness and neural activation. The Journal of Neuroscience, 28, 10323–10329. https://doi.org/10.1523/JNEUROSCI.3259-08.2008
Cox, S. R., Ritchie, S. J., Fawns-Ritchie, C., Tucker-Drob, E. M., & Deary, I. J. (2019). Structural brain imaging correlates of general intelligence in UK biobank. Intelligence, 76, 101376. https://doi.org/10.1016/j.intell.2019.101376
Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage, 9, 179–194. https://doi.org/10.1006/nimg.1998.0395
Deary, I. J. (2014). The stability of intelligence from childhood to old age. Current Directions in Psychological Science, 23, 239–245. https://doi.org/10.1177/0963721414536905
Deary, I. J., Cox, S. R., & Hill, W. D. (2021). Genetic variation, brain, and intelligence differences. Molecular Psychiatry, 27, 335–353. https://doi.org/10.1038/s41380-021-01027-y
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews. Neuroscience, 11, 201–211. https://doi.org/10.1038/nrn2793
Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35, 13–21. https://doi.org/10.1016/j.intell.2006.02.001
Dijkstra, E. W. (1959). A note on two problems in Connexion with graphs. Numerische Mathematik, 1, 269–271. https://doi.org/10.1007/BF01386390
Dima, D., & Breen, G. (2015). Polygenic risk scores in imaging genetics: Usefulness and applications. Journal of Psychopharmacology, 29, 867–871. https://doi.org/10.1177/0269881115584470
Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. PLoS Genetics, 9, e1003348. https://doi.org/10.1371/journal.pgen.1003348
Elliott, M. L., Belsky, D. W., Anderson, K., Corcoran, D. L., Ge, T., Knodt, A., Prinz, J. A., Sugden, K., Williams, B., Ireland, D., Poulton, R., Caspi, A., Holmes, A., Moffitt, T., & Hariri, A. R. (2019). A polygenic score for higher educational attainment is associated with larger brains. Cerebral Cortex, 29, 3496–3504. https://doi.org/10.1093/cercor/bhy219
Erdodi, L. A., Abeare, C. A., Lichtenstein, J. D., Tyson, B. T., Kucharski, B., Zuccato, B. G., & Roth, R. M. (2017). Wechsler adult intelligence scale-fourth edition (WAIS-IV) processing speed scores as measures of noncredible responding: The third generation of embedded performance validity indicators. Psychological Assessment, 29, 148–157. https://doi.org/10.1037/pas0000319
Everts, R., Lidzba, K., Wilke, M., Kiefer, C., Mordasini, M., Schroth, G., Perrig, W., & Steinlin, M. (2009). Strengthening of laterality of verbal and visuospatial functions during childhood and adolescence. Human Brain Mapping, 30, 473–483. https://doi.org/10.1002/hbm.20523
Fatahi, Z., Ghorbani, A., Ismail Zibaii, M., & Haghparast, A. (2020). Neural synchronization between the anterior cingulate and orbitofrontal cortices during effort-based decision making. Neurobiology of Learning and Memory, 175, 107320. https://doi.org/10.1016/j.nlm.2020.107320
Feng, J., Chen, C., Cai, Y., Ye, Z., Feng, K., Liu, J., Zhang, L., Yang, Q., Li, A., Sheng, J., Zhu, B., Yu, Z., Chen, C., Dong, Q., & Xue, G. (2020). Partitioning heritability analyses unveil the genetic architecture of human brain multidimensional functional connectivity patterns. Human Brain Mapping, 41, 3305–3317. https://doi.org/10.1002/hbm.25018
Fischer, F. U., Wolf, D., Scheurich, A., & Fellgiebel, A. (2014). Association of structural global brain network properties with intelligence in normal aging. PLoS One, 9, e86258. https://doi.org/10.1371/journal.pone.0086258
Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. NeuroImage, 9, 195–207. https://doi.org/10.1006/nimg.1998.0396
Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., Busa, E., Seidman, L. J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., & Dale, A. M. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14, 11–22. https://doi.org/10.1093/cercor/bhg087
Fisher, R. A. (1921). On the “probable error” of a coefficient of correlation deduced from a small sample. Metron, 1, 3–32.
Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews. Neuroscience, 8, 700–711. https://doi.org/10.1038/nrn2201
Fraenz, C., Schlüter, C., Friedrich, P., Jung, R. E., Güntürkün, O., & Genç, E. (2021). Interindividual differences in matrix reasoning are linked to functional connectivity between brain regions nominated by Parieto-frontal integration theory. Intelligence, 87, 101545. https://doi.org/10.1016/j.intell.2021.101545
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33, 1–22. https://doi.org/10.18637/jss.v033.i01
Ge, T., Chen, C.-Y., Doyle, A. E., Vettermann, R., Tuominen, L. J., Holt, D. J., Sabuncu, M. R., & Smoller, J. W. (2019). The shared genetic basis of educational attainment and Ccerebral cortical morphology. Cerebral Cortex, 29, 3471–3481. https://doi.org/10.1093/cercor/bhy216
Genç, E., Fraenz, C., Schlüter, C., Friedrich, P., Hossiep, R., Voelkle, M. C., Ling, J. M., Güntürkün, O., & Jung, R. E. (2018). Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature Communications, 9, 1905. https://doi.org/10.1038/s41467-018-04268-8
Genç, E., Fraenz, C., Schlüter, C., Friedrich, P., Voelkle, M. C., Hossiep, R., & Güntürkün, O. (2019). The neural architecture of general knowledge. European Journal of Personality, 33, 589–605. https://doi.org/10.1002/per.2217
Genç, E., Schlüter, C., Fraenz, C., Arning, L., Metzen, D., Nguyen, H. P., Voelkle, M. C., Streit, F., Güntürkün, O., Kumsta, R., & Ocklenburg, S. (2021). Polygenic scores for cognitive abilities and their association with different aspects of general intelligence-a deep phenotyping approach. Molecular Neurobiology, 58, 4145–4156. https://doi.org/10.1007/s12035-021-02398-7
Gläscher, J., Tranel, D., Paul, L. K., Rudrauf, D., Rorden, C., Hornaday, A., Grabowski, T., Damasio, H., & Adolphs, R. (2009). Lesion mapping of cognitive abilities linked to intelligence. Neuron, 61, 681–691. https://doi.org/10.1016/j.neuron.2009.01.026
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., Smith, S. M., & van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536, 171–178. https://doi.org/10.1038/nature18933
Góngora, D., Vega-Hernández, M., Jahanshahi, M., Valdés-Sosa, P. A., & Bringas-Vega, M. L. (2020). Crystallized and fluid intelligence are predicted by microstructure of specific white-matter tracts. Human Brain Mapping, 41, 906–916. https://doi.org/10.1002/hbm.24848
Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., Shatokhina, N., Zsembik, L. C. P., Thomopoulos, S. I., Zhu, A. H., Strike, L. T., Agartz, I., Alhusaini, S., Almeida, M. A. A., Alnæs, D., … Medland, S. E. (2020). The genetic architecture of the human cerebral cortex. Science, 367, eaay6690. https://doi.org/10.1126/science.aay6690
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (p. 692). The Guilford Press.
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. https://doi.org/10.1016/j.intell.2016.11.001
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017b). Intelligence is associated with the modular structure of intrinsic brain networks. Scientific Reports, 7, 16088. https://doi.org/10.1038/s41598-017-15795-7
Ivković, M., Kuceyeski, A., & Raj, A. (2012). Statistics of weighted brain networks reveal hierarchical organization and Gaussian degree distribution. PLoS One, 7, e35029. https://doi.org/10.1371/journal.pone.0035029
Jansen, P. R., Muetzel, R. L., Polderman, T. J. C., Jaddoe, V. W., Verhulst, F. C., van der Lugt, A., Tiemeier, H., Posthuma, D., & White, T. (2019). Polygenic scores for neuropsychiatric traits and white matter microstructure in the pediatric population. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4, 243–250. https://doi.org/10.1016/j.bpsc.2018.07.010
Jung, R. E., & Haier, R. J. (2007). The Parieto-frontal integration theory (P-FIT) of intelligence: Converging neuroimaging evidence. The Behavioral and Brain Sciences, 30, 135–154. discussion 154–87. https://doi.org/10.1017/S0140525X07001185
Karbowski, J. (2007). Global and regional brain metabolic scaling and its functional consequences. BMC Biology, 5, 18. https://doi.org/10.1186/1741-7007-5-18
Kim, D.-J., Davis, E. P., Sandman, C. A., Sporns, O., O'Donnell, B. F., Buss, C., & Hetrick, W. P. (2016). Children's intellectual ability is associated with structural network integrity. NeuroImage, 124, 550–556. https://doi.org/10.1016/j.neuroimage.2015.09.012
Knol, M., Heshmatollah, A., Cremers, L., Kamran Ikram, M., Uitterlinden, A., van Dujin, C., Niessen, W., Vernooij, M., Afran Ikram, M., & Adams, H. H. H. (2019). Genetic variation underlying cognition and its relation with neurological outcomes and brain imaging. Aging, 11, 1440–1456. https://doi.org/10.18632/aging.101844
Kong, A., Thorleifsson, G., Frigge, M. L., Vilhjalmsson, B. J., Young, A. I., Thorgeirsson, T. E., Benonisdottir, S., Oddsson, A., Halldorsson, B. V., Masson, G., Gudbjartsson, D. F., Helgason, A., Bjornsdottir, G., Thorsteinsdottir, U., & Stefansson, K. (2018). The nature of nurture: Effects of parental genotypes. Science, 359, 424–428. https://doi.org/10.1126/science.aan6877
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. https://doi.org/10.1016/j.neuroimage.2018.01.018
Lee, J. J., McGue, M., Iacono, W. G., Michael, A. M., & Chabris, C. F. (2019). The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling. Intelligence, 75, 48–58. https://doi.org/10.1016/j.intell.2019.01.011
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., Bowers, P., Sidorenko, J., Karlsson Linnér, R., Fontana, M. A., Kundu, T., Lee, C., Li, H., Li, R., Royer, R., Timshel, P. N., Walters, R. K., Willoughby, E. A., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50, 1112–1121. https://doi.org/10.1038/s41588-018-0147-3
Lett, T. A., Vogel, B. O., Ripke, S., Wackerhagen, C., Erk, S., Awasthi, S., Trubetskoy, V., Brandl, E. J., Mohnke, S., Veer, I. M., Nöthen, M. M., Rietschel, M., Degenhardt, F., Romanczuk-Seiferth, N., Witt, S. H., Banaschewski, T., Bokde, A. L. W., Büchel, C., Quinlan, E. B., … Walter, H. (2020). Cortical surfaces mediate the relationship between polygenic scores for intelligence and general intelligence. Cerebral Cortex, 30, 2707–2718. https://doi.org/10.1093/cercor/bhz270
Leuba, G., & Kraftsik, R. (1994). Changes in volume, surface estimate, three-dimensional shape and total number of neurons of the human primary visual cortex from midgestation until old age. Anatomy and Embryology, 190, 351–366. https://doi.org/10.1007/BF00187293
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5, e1000395. https://doi.org/10.1371/journal.pcbi.1000395
Liepmann, D., Beauducel, A., Brocke, B., & Amthauer, R. (2007). Intelligenz-Struktur-Test 2000 R (IST 2000 R). Manual (2. erweiterte und überarbeitete Aufl.). Hogrefe, Göttingen.
Loughnan, R. J., Palmer, C. E., Thompson, W. K., Dale, A. M., Jernigan, T. L., & Fan, C. C. (2021). Gene-experience correlation during cognitive development: Evidence from the Adolescent Brain Cognitive Development (ABCD) Study. Preprint. https://doi.org/10.1101/637512
Ma, J., Kang, H. J., Kim, J. Y., Jeong, H. S., Im, J. J., Namgung, E., Kim, M. J., Lee, S., Kim, T. D., Oh, J. K., Chung, Y.-A., Lyoo, I. K., Lim, S. M., & Yoon, S. (2017). Network attributes underlying intellectual giftedness in the developing brain. Scientific Reports, 7, 11321. https://doi.org/10.1038/s41598-017-11593-3
Ma, Q., Rolls, E. T., Huang, C.-C., Cheng, W., & Feng, J. (2022). Extensive cortical functional connectivity of the human hippocampal memory system. Cortex, 147, 83–101. https://doi.org/10.1016/j.cortex.2021.11.014
McDaniel, M. (2005). Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence, 33, 337–346. https://doi.org/10.1016/j.intell.2004.11.005
Mitchell, B. L., Cuéllar-Partida, G., Grasby, K. L., Campos, A. I., Strike, L. T., Hwang, L.-D., Okbay, A., Thompson, P. M., Medland, S. E., Martin, N. G., Wright, M. J., & Rentería, M. E. (2020). Educational attainment polygenic scores are associated with cortical total surface area and regions important for language and memory. NeuroImage, 212, 116691. https://doi.org/10.1016/j.neuroimage.2020.116691
Moodie, J. E., Ritchie, S. J., Cox, S. R., Harris, M. A., Muñoz Maniega, S., Valdés Hernández, M. C., Pattie, A., Corley, J., Bastin, M. E., Starr, J. M., Wardlaw, J. M., & Deary, I. J. (2020). Fluctuating asymmetry in brain structure and general intelligence in 73-year-olds. Intelligence, 78, 101407. https://doi.org/10.1016/j.intell.2019.101407
Narr, K. L., Woods, R. P., Thompson, P. M., Szeszko, P., Robinson, D., Dimtcheva, T., Gurbani, M., Toga, A. W., & Bilder, R. M. (2007). Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cerebral Cortex, 17, 2163–2171. https://doi.org/10.1093/cercor/bhl125
Ntolka, E., & Papadatou-Pastou, M. (2018). Right-handers have negligibly higher IQ scores than left-handers: Systematic review and meta-analyses. Neuroscience and Biobehavioral Reviews, 84, 376–393. https://doi.org/10.1016/j.neubiorev.2017.08.007
O'Boyle, M. W., Cunnington, R., Silk, T. J., Vaughan, D., Jackson, G., Syngeniotis, A., & Egan, G. F. (2005). Mathematically gifted male adolescents activate a unique brain network during mental rotation. Cognitive Brain Research, 25, 583–587. https://doi.org/10.1016/j.cogbrainres.2005.08.004
Pakkenberg, B., & Gundersen, H. J. (1997). Neocortical neuron number in humans: Effect of sex and age. The Journal of Comparative Neurology, 384, 312–320.
Papadatou-Pastou, M., & Tomprou, D.-M. (2015). Intelligence and handedness: Meta-analyses of studies on intellectually disabled, typically developing, and gifted individuals. Neuroscience and Biobehavioral Reviews, 56, 151–165. https://doi.org/10.1016/j.neubiorev.2015.06.017
Park, H. J., & Friston, K. (2013). Structural and functional brain networks: From connections to cognition. Science, 342, 1238411. https://doi.org/10.1126/science.1238411
Pietschnig, J., Penke, L., Wicherts, J. M., Zeiler, M., & Voracek, M. (2015). Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? Neuroscience and Biobehavioral Reviews, 57, 411–432. https://doi.org/10.1016/j.neubiorev.2015.09.017
Pineda-Pardo, J. A., Martínez, K., Román, F. J., & Colom, R. (2016). Structural efficiency within a parieto-frontal network and cognitive differences. Intelligence, 54, 105–116. https://doi.org/10.1016/j.intell.2015.12.002
Plomin, R., & von Stumm, S. (2018). The new genetics of intelligence. Nature Reviews. Genetics, 19, 148–159. https://doi.org/10.1038/nrg.2017.104
Power, J. D., & Petersen, S. E. (2013). Control-related systems in the human brain. Current Opinion in Neurobiology, 23, 223–228. https://doi.org/10.1016/j.conb.2012.12.009
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage, 112, 267–277. https://doi.org/10.1016/j.neuroimage.2015.02.064
Ramsden, S., Richardson, F. M., Josse, G., Thomas, M. S. C., Ellis, C., Shakeshaft, C., Seghier, M. L., & Price, C. J. (2011). Verbal and non-verbal intelligence changes in the teenage brain. Nature, 479, 113–116. https://doi.org/10.1038/nature10514
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Santarnecchi, E., Emmendorfer, A., & Pascual-Leone, A. (2017). Dissecting the parieto-frontal correlates of fluid intelligence: A comprehensive ALE meta-analysis study. Intelligence, 63, 9–28. https://doi.org/10.1016/j.intell.2017.04.008
Santarnecchi, E., Tatti, E., Rossi, S., Serino, V., & Rossi, A. (2015). Intelligence-related differences in the asymmetry of spontaneous cerebral activity. Human Brain Mapping, 36, 3586–3602. https://doi.org/10.1002/hbm.22864
Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage Publications.
Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., Leeuw, C. A. d., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. R. I., Grasby, K. L., Hammerschlag, A. R., Kaminski, J. A., Karlsson, R., Krapohl, E., Lam, M., Nygaard, M., Reynolds, C. A., Trampush, J. W., … Posthuma, D. (2018). Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 50, 912–919. https://doi.org/10.1038/s41588-018-0152-6
Serang, S., & Jacobucci, R. (2020). Exploratory mediation analysis of dichotomous outcomes via regularization. Multivariate Behavioral Research, 55, 69–86. https://doi.org/10.1080/00273171.2019.1608145
Serang, S., Jacobucci, R., Brimhall, K. C., & Grimm, K. J. (2017). Exploratory mediation analysis via regularization. Structural Equation Modeling: A Multidisciplinary Journal, 24, 733–744. https://doi.org/10.1080/10705511.2017.1311775
Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8, 418–425. https://doi.org/10.1016/j.tics.2004.07.008
Syväoja, H. J., Kankaanpää, A., Joensuu, L., Kallio, J., Hakonen, H., Hillman, C. H., & Tammelin, T. H. (2019). The longitudinal associations of fitness and motor skills with academic achievement. Medicine and Science in Sports and Exercise, 51, 2050–2057. https://doi.org/10.1249/MSS.0000000000002031
Trecroci, A., Duca, M., Cavaggioni, L., Rossi, A., Scurati, R., Longo, S., Merati, G., Alberti, G., & Formenti, D. (2021). Relationship between cognitive functions and sport-specific physical performance in youth volleyball players. Brain Sciences, 11, 227. https://doi.org/10.3390/brainsci11020227
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, 7619–7624. https://doi.org/10.1523/JNEUROSCI.1443-09.2009
Wallis, J. D., & Kennerley, S. W. (2011). Contrasting reward signals in the orbitofrontal cortex and anterior cingulate cortex. Annals of the new York Academy of Sciences, 1239, 33–42. https://doi.org/10.1111/j.1749-6632.2011.06277.x
Wen, W., Zhu, W., He, Y., Kochan, N. A., Reppermund, S., Slavin, M. J., Brodaty, H., Crawford, J., Xia, A., & Sachdev, P. (2011). Discrete neuroanatomical networks are associated with specific cognitive abilities in old age. The Journal of Neuroscience, 31, 1204–1212. https://doi.org/10.1523/JNEUROSCI.4085-10.2011
Wiseman, S. J., Booth, T., Ritchie, S. J., Cox, S. R., Muñoz Maniega, S., Del Valdés Hernández, M. C., Dickie, D. A., Royle, N. A., Starr, J. M., Deary, I. J., Wardlaw, J. M., & Bastin, M. E. (2018). Cognitive abilities, brain white matter hyperintensity volume, and structural network connectivity in older age. Human Brain Mapping, 39, 622–632. https://doi.org/10.1002/hbm.23857
Yeo, R. A., Ryman, S. G., Pommy, J., Thoma, R. J., & Jung, R. E. (2016). General cognitive ability and fluctuating asymmetry of brain surface area. Intelligence, 56, 93–98. https://doi.org/10.1016/j.intell.2016.03.002
Zhao, B., Li, T., Yang, Y., Wang, X., Luo, T., Shan, Y., Zhu, Z., Xiong, D., Hauberg, M. E., Bendl, J., Fullard, J. F., Roussos, P., Li, Y., Stein, J. L., & Zhu, H. (2021). Common genetic variation influencing human white matter microstructure. Science, 372, eabf3736. https://doi.org/10.1126/science.abf3736
Zhao, B., Zhang, J., Ibrahim, J. G., Luo, T., Santelli, R. C., Li, Y., Li, T., Shan, Y., Zhu, Z., Zhou, F., Liao, H., Nichols, T. E., & Zhu, H. (2021). Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706). Molecular Psychiatry, 26, 3943–3955. https://doi.org/10.1038/s41380-019-0569-z
Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37, 197–206. https://doi.org/10.1086/651257
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society B, 64, 301–320.