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Gyral-sulcal contrast in intrinsic functional brain networks across task performances

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A Correction to this article was published on 04 January 2021

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Abstract

Functional mechanism of the brain and its relationship with the brain structural substrate have been an interest for multiple disciplines for centuries. Recently, gyri and sulci, two basic cortical folding patterns, have been demonstrated to act different functional roles. Specifically, a variety of functional MRI (fMRI) studies have consistently suggested that gyri represent a global functional center while sulci serve as a local functional unit under either resting state or task stimulus, which are further supported by brain structural analysis reporting that gyri have thicker cortex and denser long-distance axonal fibers. However, the consistency of such gyral-sulcal functional difference across different task stimuli, as well as its association with task conditions, remains to be explored. To this end, we used intrinsic networks as the testbed for cross-task comparison, and adopted a computational framework of dictionary learning and sparse representation of whole-brain fMRI signals to systematically examine the potential gyral-sulcal difference in signal representation residual (SRR) which reflected the degree of global functional communication. Using all seven task-based fMRI datasets in Human Connectome Project Q1 release, we found that within the intrinsic functional networks, the fMRI SRR was significantly smaller on gyral regions than on sulcal regions across different task stimuli, indicating that gyral regions were more involved in global functions of the brain and interregional communications. Moreover, the magnitudes of such gyral-sulcal difference varied across task conditions and intrinsic networks. Our work adds novel explanation and insight to the existing knowledge of functional differences between gyri and sulci.

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References

  • Andersson, J. L., R, Jenkinson, M., & Smith, S. (2010). Non-linear registration, aka spatial normalisation. FMRIB technical report TR07JA2.

  • Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., & Feldt, C. (2013). Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage, 80, 169–189.

    Article  PubMed  Google Scholar 

  • Barrett, L. F., & Satpute, A. B. (2013). Large-scale brain networks in affective and social neuroscience: towards an integrative functional architecture of the brain. Current Opinion in Neurobiology, 23(3), 361–372.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Beaty, R. E., Benedek, M., Kaufman, S. B., & Silvia, P. J. (2015). Default and executive network coupling supports creative idea production. Scientific Reports, 5, 10964.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290.

    Article  PubMed  Google Scholar 

  • Budde, M. D., & Annese, J. (2013). Quantification of anisotropy and fiber orientation in human brain histological sections. Frontiers in Integrative Neuroscience, 7, 3.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186.

    Article  PubMed  CAS  Google Scholar 

  • Cahalane, D. J., Charvet, C. J., & Finlay, B. L. (2012). Systematic, balancing gradients in neuron density and number across the primate isocortex. Frontiers in neuroanatomy, 6.

  • Chen, H., Zhang, T., Guo, L., Li, K., Yu, X., Li, L., Hu, X., Han, J., Hu, X., & Liu, T. (2012). Coevolution of gyral folding and structural connection patterns in primate brains. Cerebral Cortex, 23(5), 1208–1217.

    Article  PubMed  PubMed Central  Google Scholar 

  • Collins, C. E., Airey, D. C., Young, N. A., Leitch, D. B., & Kaas, J. H. (2010). Neuron densities vary across and within cortical areas in primates. Proceedings of the National Academy of Sciences, 107(36), 15927–15932.

  • Connolly, C. J. (1950). External Morphology of the Primate Brain. Springfield: CC Thomas.

    Google Scholar 

  • Creutzfeldt, O. (1995). Cortex cerebri. New York: Oxford University Press.

    Google Scholar 

  • Deng, F., Jiang, X., Zhu, D., Zhang, T., Li, K., Guo, L., & Liu, T. (2014). A functional model of cortical gyri and sulci. Brain Structure and Function, 219(4), 1473–1491.

    Article  PubMed  Google Scholar 

  • Di, X., Gohel, S., Kim, E. H., & Biswal, B. B. (2013). Task vs. rest—different network configurations between the coactivation and the resting-state brain networks. Frontiers in Human Neuroscience, 7, 493.

    PubMed  PubMed Central  Google Scholar 

  • Dombrowski, S. M., Hilgetag, C. C., & Barbas, H. (2001). Quantitative architecture distinguishes prefrontal cortical systems in the rhesus monkey. Cerebral Cortex, 11(10), 975–988.

    Article  PubMed  CAS  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), 172–179.

    Article  PubMed  Google Scholar 

  • Fedorenko, E., Duncan, J., & Kanwisher, N. (2013). Broad domain generality in focal regions of frontal and parietal cortex. Proceedings of the National Academy of Sciences, 110(41), 16616–16621.

  • 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.

  • Fischl, B., Rajendran, N., Busa, E., Augustinack, J., Hinds, O., Yeo, B. T., Mohlberg, H., Amunts, K., & Zilles, K. (2008). Cortical folding patterns and predicting cytoarchitecture. Cerebral Cortex, 18(8), 1973–1980.

    Article  PubMed  Google Scholar 

  • Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2006). Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences, 103(26), 10046–10051.

  • Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8(9), 700.

    Article  PubMed  CAS  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, 102(27), 9673–9678.

  • Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., Van Essen, D. C., & Jenkinson, M. WU-Minn HCP Consortium (2013). The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage, 80, 105–124.

  • Glasser, M. F., & Van Essen, D. C. (2011). Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI. Journal of Neuroscience, 31(32), 11597–11616.

    Article  PubMed  CAS  Google Scholar 

  • 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), 1536–1552.

    Article  PubMed  Google Scholar 

  • Götz, M., & Huttner, W. B. (2005). Developmental cell biology: The cell biology of neurogenesis. Nature Reviews Molecular Cell Biology, 6(10), 777–788.

    Article  PubMed  CAS  Google Scholar 

  • Gratton, C., Sun, H., & Petersen, S. E. (2018). Control networks and hubs. Psychophysiology, 55(3), e13032.

  • 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, 100(1), 253–258.

  • Harris, K. D., & Mrsic-Flogel, T. D. (2013). Cortical connectivity and sensory coding. Nature, 503(7474), 51–58.

    Article  PubMed  CAS  Google Scholar 

  • Hilgetag, C. C., & Barbas, H. (2005). Developmental mechanics of the primate Cerebral Cortex. Anatomy and Embryology, 210(5–6), 411–417.

    Article  PubMed  Google Scholar 

  • Jbabdi, S., & Johansen-Berg, H. (2011). Tractography: where do we go from here? Brain connectivity Brain Connectivity, 1(3), 169–183.

  • Jenkinson, M., Bannister, P., Brady, J. M., & Smith, S. M. (2002). Improved optimisation for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825–841.

    Article  PubMed  Google Scholar 

  • Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). Fsl. Neuroimage, 62, 782–790.

    Article  PubMed  Google Scholar 

  • Jiang, X., Li, X., Lv, J., Zhang, T., Zhang, S., Guo, L., & Liu, T. (2015). Sparse representation of HCP grayordinate data reveals novel functional architecture of Cerebral Cortex. Human Brain Mapping, 36(12), 5301–5319.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jiang, X., Li, X., Lv, J., Zhao, S., Zhang, S., Zhang, W., Zhang, T., Han, J., Guo, L., & Liu, T. (2018a). Temporal dynamics assessment of spatial overlap pattern of functional brain networks reveals novel functional architecture of Cerebral Cortex. IEEE Transactions on Biomedical Engineering, 65(6), 1183–1192.

    Article  PubMed  Google Scholar 

  • Jiang, X., Zhao, L., Liu, H., Guo, L., Kendrick, K. M., & Liu, T. (2018b). A Cortical Folding Pattern-Guided Model of Intrinsic Functional Brain Networks in Emotion Processing. Frontiers in Neuroscience, 12, 575.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S., & Hudspeth, A. J. (2000). Principles of neural science (Vol. 4). New York: McGraw-hill.

    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. Journal of Neuroscience the Official Journal of the Society for Neuroscience, 31(22), 8259–8270.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • la Fougère, C., Grant, S., Kostikov, A., Schirrmacher, R., Gravel, P., Schipper, H. M., Reader, A., Evans, A., & Thiel, A. J. N. (2011). Where in-vivo imaging meets cytoarchitectonics: the relationship between cortical thickness and neuronal density measured with high-resolution [18F] flumazenil-PET. Neuroimage, 56(3), 951–960.

  • Lam, Y. W., & Sherman, S. M. (2009). Functional organization of the somatosensory cortical layer 6 feedback to the thalamus. Cerebral Cortex, 20(1), 13–24.

    Article  PubMed Central  Google Scholar 

  • Lindquist, K. A., & Barrett, L. F. (2012). A functional architecture of the human brain: emerging insights from the science of emotion. Trends in Cognitive Sciences, 16(11), 533–540.

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu, H., Jiang, X., Zhang, T., Ren, Y., Hu, X., Guo, L., Han, J., & Liu, T. (2017). Elucidating functional differences between cortical gyri and sulci via sparse representation HCP grayordinate fMRI data. Brain Research, 1672, 81–90.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Liu, H., Zhang, S., Jiang, X., Zhang, T., Huang, H., Ge, F., Zhao, L., Li, X., Hu, X., Han, J., Guo, L., & Liu, T. (2018). The Cerebral Cortex is Bisectionally Segregated into Two Fundamentally Different Functional Units of Gyri and Sulci. Cerebral Cortex, 29(10), 4238–4252.

    Article  PubMed Central  Google Scholar 

  • Lv, J., Jiang, X., Li, X., Zhu, D., Chen, H., Zhang, T., Zhang, S., Hu, X., Han, J., Huang, H., Zhang, J., Guo, L., & Liu, T. (2015a). Sparse representation of whole-brain fMRI signals for identification of functional networks. Medical Image Analysis, 20(1), 112–134.

    Article  PubMed  Google Scholar 

  • Lv, J., Jiang, X., Li, X., Zhu, D., Zhang, S., Zhao, S., Chen, H., Zhang, T., Hu, X., Han, J., Ye, J., Guo, L., & Liu, T. (2015b). Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function. IEEE Transactions on Biomedical Engineering, 62(4), 1120–1131.

    Article  PubMed  Google Scholar 

  • Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2010). Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11(Jan), 19–60.

    Google Scholar 

  • Mountcastle, V. B. (1997). The columnar organization of the neocortex. Brain, 120(4), 701–722.

    Article  PubMed  Google Scholar 

  • Nazeri, A., Chakravarty, M. M., Rajji, T. K., Felsky, D., Rotenberg, D. J., Mason, M., Xu, L. N., Lobaugh, N. J., Mulsant, B. H., & Voineskos, A. N. (2015). Superficial white matter as a novel substrate of age-related cognitive decline. Neurobiology of Aging, 36(6), 2094–2106.

    Article  PubMed  Google Scholar 

  • Nie, J., Guo, L., Li, K., Wang, Y., Chen, G., Li, L., Chen, H., Deng, F., Jiang, X., Zhang, T., Huang, L., Faraco, C., Zhang, D., Guo, C., Yap, P.-T., Hu, X., Li, G., Lv, J., Yuan, Y., Zhu, D., Han, J., Sabatinelli, D., Zhao, Q., Miller, L. S., Xu, B., Shen, P., Platt, S., Shen, D., Hu, X., & Liu, T. (2011). Axonal Fiber Terminations Concentrate on Gyri. Cerebral Cortex, 22(12), 2831–2839.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pandya, D. N., & Kuypers, H. G. J. M. (1969). Cortico-cortical connections in the rhesus monkey. Brain Research, 13(1), 13–36.

    Article  PubMed  CAS  Google Scholar 

  • Park, H.-J., & Friston, K. (2013). Structural and functional brain networks: from connections to cognition. Science, 342(6158), 1238411.

    Article  PubMed  CAS  Google Scholar 

  • Passingham, R. E., Stephan, K. E., & Kötter, R. (2002). The anatomical basis of functional localization in the cortex. Nature Reviews Neuroscience, 3(8), 606–616.

    Article  PubMed  CAS  Google Scholar 

  • Perin, R., Berger, T. K., & Markram, H. (2011). A synaptic organizing principle for cortical neuronal groups. Proceedings of the National Academy of Sciences, 108(13), 5419–5424.

  • Pessoa, L. (2012). Beyond brain regions: Network perspective of cognition–emotion interactions. Behavioral and Brain Sciences, 35(3), 158–159.

    Article  PubMed  Google Scholar 

  • Phillips, O. R., Clark, K. A., Luders, E., Azhir, R., Joshi, S. H., Woods, R. P., Mazziotta, J. C., Toga, A. W., & Narr, K. L. (2013). Superficial white matter: effects of age, sex, and hemisphere. Brain connectivity, 3(2), 146–159.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  CAS  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.

  • Rakic, P. (1984). Experimental modification of gyral patterns. Cerebral dominance: the biological foundations, 179.

  • Reveley, C., Seth, A. K., Pierpaoli, C., Silva, A. C., Yu, D., Saunders, R. C., Leopold, D. A., & Frank, Q. Y. (2015). Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography. Proceedings of the National Academy of Sciences, 112(21), E2820-E2828.

  • Richman, D. P., Stewart, R. M., Hutchinson, J. W., & Caviness, V. S. (1975). Mechanical model of brain convolutional development. Science, 189(4196), 18–21.

    Article  PubMed  CAS  Google Scholar 

  • Schilling, K., Gao, Y., Janve, V., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2018). Confirmation of a gyral bias in diffusion MRI fiber tractography. Human Brain Mapping, 39(3), 1449–1466.

    Article  PubMed  Google Scholar 

  • Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27(9), 2349–2356.

    Article  PubMed  CAS  Google Scholar 

  • Sepulcre, J., Liu, H., Talukdar, T., Martincorena, I., Yeo, B. T., & Buckner, R. L. (2010). The organization of local and distant functional connectivity in the human brain. PLoS computational biology, 6(6), e1000808.

  • Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences, 106(31), 13040–13045.

  • Spreng, R. N., Sepulcre, J., Turner, G. R., Stevens, W. D., & Schacter, D. L. (2013). Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain. Journal of cognitive neuroscience, 25(1), 74–86.

    Article  PubMed  Google Scholar 

  • Stahl, R., Walcher, T., Romero, C. D. J., Pilz, G. A., Cappello, S., Irmler, M., Sanz-Aquela, J. M., Beckers, J., Blum, R., & Borrell, V. (2013). Trnp1 regulates expansion and folding of the mammalian Cerebral Cortex by control of radial glial fate. Cell, 153(3), 535–549.

    Article  PubMed  CAS  Google Scholar 

  • Thomson, A. M., & Lamy, C. (2007). Functional maps of neocortical local circuitry. Frontiers in Neuroscience, 1(1), 19–42.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Triantafyllou, C., Hoge, R. D., Krueger, G., Wiggins, C. J., Potthast, A., Wiggins, G. C., & Wald, L. L. (2005). Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. Neuroimage, 26(1), 243–250.

    Article  PubMed  CAS  Google Scholar 

  • Uehara, T., Yamasaki, T., Okamoto, T., Koike, T., Kan, S., Miyauchi, S., Kira, J., & Tobimatsu, S. (2014). Efficiency of a “small-world” brain network depends on consciousness level: a resting-state FMRI study. Cerebral Cortex, 24(6), 1529.

    Article  PubMed  Google Scholar 

  • Van Essen, D. C. (1997). A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature, 385(6614), 313.

    Article  PubMed  Google Scholar 

  • Van Essen, D. C., & Maunsell, J. H. R. (1980). Two-dimensional maps of the Cerebral Cortex. Journal of Comparative Neurology, 191(2), 255–281.

    Article  PubMed  Google Scholar 

  • Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., & Consortium, W.-M. H. (2013). The WU-Minn human connectome project: an overview. Neuroimage, 80, 62–79.

    Article  PubMed  Google Scholar 

  • Wagstyl, K., Ronan, L., Goodyer, I. M., & Fletcher, P. C. (2015). Cortical thickness gradients in structural hierarchies. Neuroimage, 111, 241–250.

    Article  PubMed  Google Scholar 

  • Weiner, K. S., & Grill-Spector, K. (2010). Sparsely-distributed organization of face and limb activations in human ventral temporal cortex. Neuroimage, 52(4), 1559–1573.

    Article  PubMed  Google Scholar 

  • Welvaert, M., & Rosseel, Y. (2013). On the definition of signal-to-noise ratio and contrast-to-noise ratio for fMRI data. PloS one, 8(11), e77089.

  • Xu, G., Knutsen, A. K., Dikranian, K., Kroenke, C. D., Bayly, P. V., & Taber, L. A. (2010). Axons pull on the brain, but tension does not drive cortical folding. Journal of biomechanical engineering, 132(7), 071013.

    Article  PubMed  Google Scholar 

  • Yeh, F. C., Verstynen, T. D., Wang, Y., Fernández-Miranda, J. C., & Tseng, W. Y. I. (2013). Deterministic diffusion fiber tracking improved by quantitative anisotropy. PloS One, 8(11), e80713.

  • Yeh, F. C., Wedeen, V. J., & Tseng, W. Y. I. (2010). Generalized q-sampling imaging. IEEE Transactions on Medical Imaging, 29(9), 1626–1635.

    Article  PubMed  Google Scholar 

  • Zeng, T., Chen, H., Fakhry, A., Hu, X., Liu, T., & Ji, S. (2015). Allen mouse brain atlases reveal different neural connection and gene expression patterns in cerebellum gyri and sulci. Brain Structure & Function, 220(5), 2691–2703.

    Article  CAS  Google Scholar 

  • Zhang, T., Chen, H., Guo, L., Li, K., Li, L., Zhang, S., Shen, D., Hu, X., & Liu, T. (2014). Characterization of U-shape streamline fibers: Methods and applications. Medical Image Analysis, 18(5), 795–807.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang, W., Lv, J., Li, X., Zhu, D., Jiang, X., Zhang, S., Zhao, Y., Ye, J., Hu, D., & Liu, T. (2018). Experimental comparisons of sparse dictionary learning and independent component analysis for brain network inference from fMRI data. IEEE Transactions on Biomedical Engineering, 66(1), 289–299.

    Article  PubMed  Google Scholar 

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Acknowledgements

We would like to thank the Human Connectome Project for sharing the datasets used in this work. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Funding

This study was funded by National Natural Science Foundation of China (U1801265, 31971288, 31671005, 61703073, 61976045, 61773315 and 61936007), the Special Fund for Basic Scientific Research of Central Colleges (ZYGX2017KYQD165), National Institutes of Health (DA033393, AG042599) and National Science Foundation (IIS-1149260, CBET-1302089, BCS-1439051 and DBI-1564736).

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Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, China

    Lin Zhao, Tuo Zhang & Lei Guo

  2. Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA

    Lin Zhao & Tianming Liu

  3. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    Xi Jiang

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Author contributions included conception and study design (LZ, TZ and TL), data collection, preprocessing and processing (LZ), statistical analysis (LZ, TZ), interpretation of results (LZ, TZ, XJ), drafting the manuscript work or revising it critically for important intellectual content (LZ, TZ, LG, TL and XJ) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).

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Zhao, L., Zhang, T., Guo, L. et al. Gyral-sulcal contrast in intrinsic functional brain networks across task performances. Brain Imaging and Behavior 15, 1483–1498 (2021). https://doi.org/10.1007/s11682-020-00347-x

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