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Multimodal data revealed different neurobiological correlates of intelligence between males and females

  • Rongtao Jiang
  • Vince D. Calhoun
  • Yue Cui
  • Shile Qi
  • Chuanjun Zhuo
  • Jin Li
  • Rex Jung
  • Jian Yang
  • Yuhui Du
  • Tianzi Jiang
  • Jing SuiEmail author
Original Research

Abstract

Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

Keywords

Individualized prediction Intelligence quotient Connectome-based predictive modeling Multimodal Gender difference 

Notes

Acknowledgments

This project was supported by China Natural Science Foundation (No. 61773380), Brain Science and Brain-inspired Technology Plan of Beijing City (Z181100001518005), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant No. XDB32040100), the National Institute of Health (1R01EB005846, 1R56MH117107, 1R01MH094524, P20GM103472, P30GM122734), the National Science Foundation (1539067), and National Key R&D Program of China (2017YFC0112000).

Compliance with ethical standards

Conflict of interest

The authors report no financial relationships with commercial interests.

Informed consent

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

Ethical approval

The current study was approved by the Ethics Committee of Institute of Automation, Chinese Academy of Sciences.

Supplementary material

11682_2019_146_MOESM1_ESM.docx (7.2 mb)
ESM 1 (DOCX 7.21 mb)

References

  1. Abutalebi, J., Della Rosa, P. A., Gonzaga, A. K., Keim, R., Costa, A., & Perani, D. (2013). The role of the left putamen in multilingual language production. Brain and Language, 125(3), 307–315.  https://doi.org/10.1016/j.bandl.2012.03.009.Google Scholar
  2. Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner, W., Segall, J. M., Silva, R. F., Havlicek, M., Rachakonda, S., Fries, J., Kalyanam, R., Michael, A. M., Caprihan, A., Turner, J. A., Eichele, T., Adelsheim, S., Bryan, A. D., Bustillo, J., Clark, V. P., Feldstein Ewing, S. W., Filbey, F., Ford, C. C., Hutchison, K., Jung, R. E., Kiehl, K. A., Kodituwakku, P., Komesu, Y. M., Mayer, A. R., Pearlson, G. D., Phillips, J. P., Sadek, J. R., Stevens, M., Teuscher, U., Thoma, R. J., & Calhoun, V. D. (2011). A baseline for the multivariate comparison of resting-state networks. Frontiers in Systems Neuroscience, 5, 2.  https://doi.org/10.3389/fnsys.2011.00002.Google Scholar
  3. Aminoff, E. M., Kveraga, K., & Bar, M. (2013). The role of the parahippocampal cortex in cognition. Trends in Cognitive Sciences, 17(8), 379–390.  https://doi.org/10.1016/j.tics.2013.06.009.Google Scholar
  4. Bar, M., Gronau, N., & Aminoff, E. (2006). The Parahippocampal cortex mediates spatial and nonspatial associations. Cerebral Cortex, 17(7), 1493–1503.  https://doi.org/10.1093/cercor/bhl078 Google Scholar
  5. Baron-Cohen, S., Knickmeyer, R. C., & Belmonte, M. K. (2005). Sex differences in the brain: Implications for explaining autism. Science, 310(5749), 819–823.  https://doi.org/10.1126/science.1115455.Google Scholar
  6. Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends in Cognitive Sciences, 20(2), 87–95.  https://doi.org/10.1016/j.tics.2015.10.004.Google Scholar
  7. Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., Fink, A., Qiu, J., Kwapil, T. R., Kane, M. J., & Silvia, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences of the United States of America, 115, 1087–1092.  https://doi.org/10.1073/pnas.1713532115.Google Scholar
  8. Becker, L., Kutz, D., & Voelcker-Rehage, C. (2016). Exercise-induced changes in basal ganglia volume and their relation to cognitive performance. J Neurol Neuromed, 1(5), 19-24.  https://doi.org/10.29245/2572.942X/2016/5.1044.
  9. Bell, E. C., Willson, M. C., Wilman, A. H., Dave, S., & Silverstone, P. H. (2006). Males and females differ in brain activation during cognitive tasks. Neuroimage, 30(2), 529–538.  https://doi.org/10.1016/j.neuroimage.2005.09.049.Google Scholar
  10. Bonner, M. F., & Price, A. R. (2013). Where is the anterior temporal lobe and what does it do? The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 33(10), 4213–4215.  https://doi.org/10.1523/JNEUROSCI.0041-13.2013.Google Scholar
  11. Burgess, N., Maguire, E. A., Spiers, H. J., & O'Keefe, J. (2001). A temporoparietal and prefrontal network for retrieving the spatial context of lifelike events. Neuroimage, 14(2), 439–453.  https://doi.org/10.1006/nimg.2001.0806.Google Scholar
  12. Bzdok, D., Hartwigsen, G., Reid, A., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2016). Left inferior parietal lobe engagement in social cognition and language. Neuroscience and Biobehavioral Reviews, 68, 319–334.  https://doi.org/10.1016/j.neubiorev.2016.02.024.Google Scholar
  13. Cahill, L., Haier, R. J., White, N. S., Fallon, J., Kilpatrick, L., Lawrence, C., Potkin, S. G., & Alkire, M. T. (2001). Sex-related difference in amygdala activity during emotionally influenced memory storage. Neurobiology of Learning and Memory, 75(1), 1–9.  https://doi.org/10.1006/nlme.2000.3999.Google Scholar
  14. Chen, S. H., & Desmond, J. E. (2005). Cerebrocerebellar networks during articulatory rehearsal and verbal working memory tasks. Neuroimage, 24(2), 332–338.  https://doi.org/10.1016/j.neuroimage.2004.08.032.Google Scholar
  15. 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(41), 10323–10329.  https://doi.org/10.1523/JNEUROSCI.3259-08.2008.Google Scholar
  16. Clements, A. M., Rimrodt, S. L., Abel, J. R., Blankner, J. G., Mostofsky, S. H., Pekar, J. J., Denckla, M. B., & Cutting, L. E. (2006). Sex differences in cerebral laterality of language and visuospatial processing. Brain and Language, 98(2), 150–158.  https://doi.org/10.1016/j.bandl.2006.04.007.Google Scholar
  17. Colom, R., Karama, S., Jung, R. E., & Haier, R. J. (2010). Human intelligence and brain networks. Dialogues in Clinical Neuroscience, 12(4), 489–501.Google Scholar
  18. Cui, Y., Liu, B., Zhou, Y., Fan, L., Li, J., Zhang, Y., Wu, H., Hou, B., Wang, C., Zheng, F., Qiu, C., Rao, L. L., Ning, Y., Li, S., & Jiang, T. (2016). Genetic effects on fine-grained human cortical regionalization. Cerebral Cortex, 26(9), 3732–3743.  https://doi.org/10.1093/cercor/bhv176.Google Scholar
  19. Dai, X. Y., Ryan, J. J., Paolo, A. M., & Harrington, R. G. (1990). Factor-Analysis of the Mainland Chinese Version of the Wechsler Adult Intelligence Scale (Wais-Rc) in a Brain-Damaged Sample. International Journal of Neuroscience, 55(2–4), 107–111.  https://doi.org/10.3109/00207459008985956.Google Scholar
  20. Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews. Neuroscience, 11(3), 201–211.  https://doi.org/10.1038/nrn2793.Google Scholar
  21. Dezfouli, A., & Balleine, B. W. (2012). Habits, action sequences and reinforcement learning. The European Journal of Neuroscience, 35(7), 1036–1051.  https://doi.org/10.1111/j.1460-9568.2012.08050.x.Google Scholar
  22. Dosenbach, N. U., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., et al. (2010). Prediction of individual brain maturity using fMRI. Science, 329(5997), 1358–1361.  https://doi.org/10.1126/science.1194144.Google Scholar
  23. Fah, L. Y. (2009). Logical thinking abilities among form 4 students in the interior division of Sabah, Malaysia. Journal of Science and Mathematics Education in Southeast Asia, 32(2), 161–187.Google Scholar
  24. Fair, D. A., Dosenbach, N. U. F., Church, J. A., Cohen, A. L., Brahmbhatt, S., Miezin, F. M., Barch, D. M., Raichle, M. E., Petersen, S. E., & Schlaggar, B. L. (2007). Development of distinct control networks through segregation and integration. Proceedings of the National Academy of Sciences, 104(33), 13507–13512.  https://doi.org/10.1073/pnas.0705843104.Google Scholar
  25. Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Chu, C., Xie, S., Laird, A. R., Fox, P. T., Eickhoff, S. B., Yu, C., & Jiang, T. (2016). The human Brainnetome atlas: A new brain atlas based on connectional architecture. Cerebral Cortex, 26(8), 3508–3526.  https://doi.org/10.1093/cercor/bhw157.Google Scholar
  26. Feng, C., Yuan, J., Geng, H., Gu, R., Zhou, H., Wu, X., & Luo, Y. (2018). Individualized prediction of trait narcissism from whole-brain resting-state functional connectivity. Human Brain Mapping, 39, 3701–3712.  https://doi.org/10.1002/hbm.24205.Google Scholar
  27. Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664–1671.  https://doi.org/10.1038/nn.4135.Google Scholar
  28. Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences, 97(20), 11050–11055.  https://doi.org/10.1073/pnas.200033797 Google Scholar
  29. Frederikse, M. E., Lu, A., Aylward, E., Barta, P., & Pearlson, G. (1999). Sex differences in the inferior parietal lobule. Cerebral Cortex, 9(8), 896–901.  https://doi.org/10.1093/cercor/9.8.896.Google Scholar
  30. Gabrieli, J. D., Ghosh, S. S., & Whitfield-Gabrieli, S. (2015). Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron, 85(1), 11–26.  https://doi.org/10.1016/j.neuron.2014.10.047.Google Scholar
  31. Genc, E., Fraenz, C., Schluter, C., Friedrich, P., Hossiep, R., Voelkle, M. C., et al. (2018). Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature Communications, 9(1), 1905.  https://doi.org/10.1038/s41467-01804268-8.Google Scholar
  32. Glascher, 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), 4705–4709.  https://doi.org/10.1073/pnas.0910397107.Google Scholar
  33. Goh, S., Bansal, R., Xu, D., Hao, X., Liu, J., & Peterson, B. S. (2011). Neuroanatomical correlates of intellectual ability across the life span. Developmental Cognitive Neuroscience, 1(3), 305–312.  https://doi.org/10.1016/j.dcn.2011.03.001.Google Scholar
  34. Goriounova, N. A., & Mansvelder, H. D. J. F. i. H. N. (2019). Genes, Cells and Brain Areas of Intelligence, 13.  https://doi.org/10.3389/fnhum.2019.00044.
  35. Grazioplene, R. G., S, G. R., Gray, J. R., Rustichini, A., Jung, R. E., & DeYoung, C. G. (2015). Subcortical intelligence: Caudate volume predicts IQ in healthy adults. Human Brain Mapping, 36(4), 1407–1416.  https://doi.org/10.1002/hbm.22710.Google Scholar
  36. 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.  https://doi.org/10.1038/s41467-018-04920-3.Google Scholar
  37. Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2005). The neuroanatomy of general intelligence: Sex matters. Neuroimage, 25(1), 320–327.  https://doi.org/10.1016/j.neuroimage.2004.11.019.Google Scholar
  38. Halpern, D. F., Benbow, C. P., Geary, D. C., Gur, R. C., Hyde, J. S., & Gernsbacher, M. A. (2007). The science of sex differences in science and mathematics. Psychological Science in the Public Interest, 8(1), 1–51.  https://doi.org/10.1111/j.1529-1006.2007.00032.x.Google Scholar
  39. Hartwigsen, G., Golombek, T., & Obleser, J. (2015). Repetitive transcranial magnetic stimulation over left angular gyrus modulates the predictability gain in degraded speech comprehension. Cortex, 68, 100–110.  https://doi.org/10.1016/j.cortex.2014.08.027.Google Scholar
  40. Hill, A. C., Laird, A. R., & Robinson, J. L. (2014). Gender differences in working memory networks: A BrainMap meta-analysis. Biological Psychology, 102, 18–29.  https://doi.org/10.1016/j.biopsycho.2014.06.008.Google Scholar
  41. Hsu, W. T., Rosenberg, M. D., Scheinost, D., Constable, R. T., & Chun, M. M. (2018). Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Social Cognitive and Affective Neuroscience, 13(2), 224–232.  https://doi.org/10.1093/scan/nsy002.Google Scholar
  42. Huttenlocher, P. R. (1990). Morphometric study of human cerebral cortex development. Neuropsychologia, 28(6), 517–527.  https://doi.org/10.1016/0028-3932(90)90031-I.Google Scholar
  43. Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Hakonarson, H., Gur, R. E., Gur, R. C., & Verma, R. (2014). Sex differences in the structural connectome of the human brain. Proceedings of the National Academy of Sciences of the United States of America, 111(2), 823–828.  https://doi.org/10.1073/pnas.1316909110.Google Scholar
  44. Irwing, P., & Lynn, R. (2006). Intelligence: Is there a sex difference in IQ scores? Nature, 442(7098), E1–E1; discussion E2.  https://doi.org/10.1038/nature04966.Google Scholar
  45. Jangraw, D. C., Gonzalez-Castillo, J., Handwerker, D. A., Ghane, M., Rosenberg, M. D., Panwar, P., & Bandettini, P. A. (2018). A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task. Neuroimage, 166, 99–109.  https://doi.org/10.1016/j.neuroimage.2017.10.019.Google Scholar
  46. Janzen, G., Wagensveld, B., & van Turennout, M. (2007). Neural representation of navigational relevance is rapidly induced and long lasting. Cerebral Cortex, 17(4), 975–981.  https://doi.org/10.1093/cercor/bhl008.Google Scholar
  47. Jensen, A. R. (1998). The g factor: The science of mental ability.  https://doi.org/10.1007/BF02685991.
  48. Jiang, R. T., Qi, S. L., Du, Y. H., Yan, W. Z., Calhoun, V. D., Jiang, T. Z., et al. (2017). Predicting Individualized Intelligence Quotient Scores Using Brainnetome-Atlas Based Functional Connectivity. 2017 Ieee 27th International Workshop on Machine Learning for Signal Processing.  https://doi.org/10.1109/MLSP.2017.8168150.
  49. Jiang, R., Calhoun, V. D., Zuo, N., Lin, D., Li, J., Fan, L., Qi, S., Sun, H., Fu, Z., Song, M., Jiang, T., & Sui, J. (2018). Connectome-based individualized prediction of temperament trait scores. Neuroimage, 183, 366–374.  https://doi.org/10.1016/j.neuroimage.2018.08.038.Google Scholar
  50. Jin, L., Bing, L., Chuansheng, C., Yue, C., Liqing, S., Yun, Z., et al. (2015). RAB2A Polymorphism impacts prefrontal morphology, functional connectivity, and working memory. 36(11), 4372–4382.  https://doi.org/10.1002/hbm.22924.
  51. 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(2), 135–154; discussion 154-187.  https://doi.org/10.1017/S0140525X07001185.Google Scholar
  52. Jung, R. E., Mead, B. S., Carrasco, J., & Flores, R. A. (2013). The structure of creative cognition in the human brain. Frontiers in Human Neuroscience, 7, 330.  https://doi.org/10.3389/fnhum.2013.00330.Google Scholar
  53. 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, 79–90.  https://doi.org/10.1016/j.neuropsychologia.2018.01.001.Google Scholar
  54. Kimura, D. (1996). Sex, sexual orientation and sex hormones influence human cognitive function. Current Opinion in Neurobiology, 6(2), 259-263.  https://doi.org/10.1016/S0959-4388(96)80081-X.
  55. Langeslag, S. J., Schmidt, M., Ghassabian, A., Jaddoe, V. W., Hofman, A., van der Lugt, A., et al. (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.  https://doi.org/10.1002/hbm.22143.Google Scholar
  56. Levy, I., Hasson, U., Avidan, G., Hendler, T., & Malach, R. (2001). Center-periphery organization of human object areas. Nature Neuroscience, 4(5), 533–539.  https://doi.org/10.1038/87490.Google Scholar
  57. Liu, B., Li, J., Zhang, X., Tao, Y., Cui, Y., Jiang, T., et al. (2016). Polygenic risk for schizophrenia influences cortical Gyrification in 2 independent general populations. Schizophrenia Bulletin, 43(3), 673–680.  https://doi.org/10.1093/schbul/sbw051.Google Scholar
  58. Liu, Z., Zhang, J., Xie, X., Rolls, E. T., Sun, J., Zhang, K., Jiao, Z., Chen, Q., Zhang, J., Qiu, J., & Feng, J. (2018). Neural and genetic determinants of creativity. Neuroimage, 174, 164–176.  https://doi.org/10.1016/j.neuroimage.2018.02.067.Google Scholar
  59. Manto, M., Bower, J. M., Conforto, A. B., Delgado-Garcia, J. M., da Guarda, S. N., Gerwig, M., et al. (2012). Consensus paper: Roles of the cerebellum in motor control--the diversity of ideas on cerebellar involvement in movement. Cerebellum, 11(2), 457–487.  https://doi.org/10.1007/s12311-011-0331-9. Google Scholar
  60. Mariën, P., Ackermann, H., Adamaszek, M., Barwood, C. H. S., Beaton, A., Desmond, J., de Witte, E., Fawcett, A. J., Hertrich, I., Küper, M., Leggio, M., Marvel, C., Molinari, M., Murdoch, B. E., Nicolson, R. I., Schmahmann, J. D., Stoodley, C. J., Thürling, M., Timmann, D., Wouters, E., & Ziegler, W. (2014). Consensus paper: Language and the cerebellum: An ongoing enigma. Cerebellum (London, England), 13(3), 386–410.  https://doi.org/10.1007/s12311-013-0540-5. Google Scholar
  61. Meng, X., Jiang, R., Lin, D., Bustillo, J., Jones, T., Chen, J., Yu, Q., du, Y., Zhang, Y., Jiang, T., Sui, J., & Calhoun, V. D. (2017). Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data. Neuroimage, 145(Pt B), 218–229.  https://doi.org/10.1016/j.neuroimage.2016.05.026.Google Scholar
  62. Murdoch, B. E. (2010). The cerebellum and language: Historical perspective and review. Cortex, 46(7), 858–868.  https://doi.org/10.1016/j.cortex.2009.07.018.Google Scholar
  63. Narr, K. L., Toga, A. W., Szeszko, P., Thompson, P. M., Woods, R. P., Robinson, D., Sevy, S., Wang, Y. P., Schrock, K., & Bilder, R. M. (2005). Cortical thinning in cingulate and occipital cortices in first episode schizophrenia. Biological Psychiatry, 58(1), 32–40.  https://doi.org/10.1016/j.biopsych.2005.03.043.Google Scholar
  64. 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(9), 2163–2171.  https://doi.org/10.1093/cercor/bhl125.Google Scholar
  65. Nejad, A. B., Jiang, J., Zhisheng, K., Salleh, S. R., Manning, V., Graham, S., et al. (2009). IQ-related fMRI differences during cognitive set shifting. Cerebral Cortex, 20(3), 641–649.  https://doi.org/10.1093/cercor/bhp130.Google Scholar
  66. Pezoulas, V. C., Zervakis, M., Michelogiannis, S., & Klados, M. A. (2017). Resting-state functional connectivity and network analysis of cerebellum with respect to crystallized IQ and gender. Frontiers in Human Neuroscience, 11, 189.  https://doi.org/10.3389/fnhum.2017.00189.Google Scholar
  67. Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., & Petersen, S. E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678.  https://doi.org/10.1016/j.neuron.2011.09.006.Google Scholar
  68. Qi, S., Yang, X., Zhao, L., Calhoun, V. D., Perrone-Bizzozero, N., Liu, S., Jiang, R., Jiang, T., Sui, J., & Ma, X. (2018). MicroRNA132 associated multimodal neuroimaging patterns in unmedicated major depressive disorder. Brain, 141, 916–926.  https://doi.org/10.1093/brain/awx366.Google Scholar
  69. Rashid, B., Damaraju, E., Pearlson, G. D., & Calhoun, V. D. (2014). Dynamic connectivity states estimated from resting fMRI identify differences among schizophrenia, bipolar disorder, and healthy control subjects. Frontiers in Human Neuroscience, 8, 897.  https://doi.org/10.3389/fnhum.2014.00897.Google Scholar
  70. Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience, 19(1), 165–171.  https://doi.org/10.1038/nn.4179.Google Scholar
  71. Ryman, S. G., Yeo, R. A., Witkiewitz, K., Vakhtin, A. A., van den Heuvel, M., de Reus, M., Flores, R. A., Wertz, C. R., & Jung, R. E. (2016). Fronto-parietal gray matter and white matter efficiency differentially predict intelligence in males and females. Human Brain Mapping, 37(11), 4006–4016.  https://doi.org/10.1002/hbm.23291.Google Scholar
  72. Schmithorst, V. J., & Holland, S. K. (2006). Functional MRI evidence for disparate developmental processes underlying intelligence in boys and girls. Neuroimage, 31(3), 1366–1379.  https://doi.org/10.1016/j.neuroimage.2006.01.010.Google Scholar
  73. Schmithorst, V. J., & Holland, S. K. (2007). Sex differences in the development of neuroanatomical functional connectivity underlying intelligence found using Bayesian connectivity analysis. Neuroimage, 35(1), 406–419.  https://doi.org/10.1016/j.neuroimage.2006.11.046.Google Scholar
  74. Schnack, H. G., van Haren, N. E., Brouwer, R. M., Evans, A., Durston, S., Boomsma, D. I., et al. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral Cortex, 25(6), 1608–1617.  https://doi.org/10.1093/cercor/bht357.Google Scholar
  75. Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., & Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. [protocol]. Nature Protocols, 12(3), 506–518.  https://doi.org/10.1038/nprot.2016.178.Google Scholar
  76. 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.  https://doi.org/10.1016/j.neuroimage.2008.02.036.Google Scholar
  77. Stoodley, C. J., & Schmahmann, J. D. (2009). Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies. Neuroimage, 44(2), 489–501.  https://doi.org/10.1016/j.neuroimage.2008.08.039.Google Scholar
  78. Sui, J., Adali, T., Yu, Q., Chen, J., & Calhoun, V. D. (2012). A review of multivariate methods for multimodal fusion of brain imaging data. Journal of Neuroscience Methods, 204(1), 68–81.  https://doi.org/10.1016/j.jneumeth.2011.10.031.Google Scholar
  79. Sui, J., Pearlson, G. D., Du, Y., Yu, Q., Jones, T. R., Chen, J., et al. (2015). In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia. Biological Psychiatry, 78(11), 794–804.  https://doi.org/10.1016/j.biopsych.2015.02.017.Google Scholar
  80. Sui, J., Qi, S., van Erp, T. G. M., Bustillo, J., Jiang, R., Lin, D., Turner, J. A., Damaraju, E., Mayer, A. R., Cui, Y., Fu, Z., du, Y., Chen, J., Potkin, S. G., Preda, A., Mathalon, D. H., Ford, J. M., Voyvodic, J., Mueller, B. A., Belger, A., McEwen, S. C., O’Leary, D. S., McMahon, A., Jiang, T., & Calhoun, V. D. (2018). Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nature Communications, 9(1), 3028.  https://doi.org/10.1038/s41467-018-05432-w.Google Scholar
  81. Tomasi, D., & Volkow, N. D. (2012). Laterality patterns of brain functional connectivity: Gender effects. Cerebral Cortex, 22(6), 1455–1462.  https://doi.org/10.1093/cercor/bhr230.Google Scholar
  82. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273–289.  https://doi.org/10.1006/nimg.2001.0978.Google Scholar
  83. Vakhtin, A. A., Ryman, S. G., Flores, R. A., & Jung, R. E. (2014). Functional brain networks contributing to the Parieto-frontal integration theory of intelligence. Neuroimage, 103, 349–354.  https://doi.org/10.1016/j.neuroimage.2014.09.055.Google Scholar
  84. van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. The Journal of Neuroscience, 31(44), 15775–15786.  https://doi.org/10.1523/JNEUROSCI.3539-11.2011.Google Scholar
  85. Wechsler, D. (1981). WAIS-R manual: Wechsler adult intelligence scale-revised: Psychological Corporation.Google Scholar
  86. Yan, C., Gong, G., Wang, J., Wang, D., Liu, D., Zhu, C., Chen, Z. J., Evans, A., Zang, Y., & He, Y. (2011). Sex- and brain size-related small-world structural cortical networks in young adults: A DTI tractography study. Cerebral Cortex, 21(2), 449–458.  https://doi.org/10.1093/cercor/bhq111.Google Scholar
  87. Yip, S. W., Scheinost, D., Potenza, M. N., & Carroll, K. M. (2019). Connectome-based prediction of cocaine abstinence. American Journal of Psychiatry, 176(2), 156–164.  https://doi.org/10.1176/appi.ajp.2018.17101147.Google Scholar
  88. Zhang, X., Yu, J.-T., Li, J., Wang, C., Tan, L., Liu, B., & Jiang, T. (2015). Bridging integrator 1 (BIN1) genotype effects on working memory, hippocampal volume, and functional connectivity in young healthy individuals. Neuropsychopharmacology, 40(7), 1794-1803.  https://doi.org/10.1038/npp.2015.30 .
  89. Zhi, D., Calhoun, V. D., Lv, L., Ma, X., Ke, Q., Fu, Z., du, Y., Yang, Y., Yang, X., Pan, M., Qi, S., Jiang, R., Yu, Q., & Sui, J. (2018). Aberrant dynamic functional network connectivity and graph properties in major depressive disorder. Frontiers in Psychiatry, 9, 339.  https://doi.org/10.3389/fpsyt.2018.00339.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Rongtao Jiang
    • 1
    • 2
  • Vince D. Calhoun
    • 3
  • Yue Cui
    • 1
    • 2
  • Shile Qi
    • 3
  • Chuanjun Zhuo
    • 4
  • Jin Li
    • 1
    • 2
  • Rex Jung
    • 5
  • Jian Yang
    • 6
  • Yuhui Du
    • 3
  • Tianzi Jiang
    • 1
    • 2
    • 7
    • 8
  • Jing Sui
    • 1
    • 2
    • 8
    Email author
  1. 1.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaUSA
  4. 4.Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Tianjin Mental Health CenterNankai University Affiliated Anding HospitalTianjinChina
  5. 5.Department of Psychiatry and NeurosciencesUniversity of New MexicoAlbuquerqueUSA
  6. 6.Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and ElectronicsBeijing Institute of TechnologyBeijingChina
  7. 7.University of Electronic Science and Technology of ChinaChengduChina
  8. 8.Chinese Academy of Sciences Center for Excellence in Brain ScienceInstitute of AutomationBeijingChina

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