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Intrinsic non-hub connectivity predicts human inter-temporal decision-making

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Abstract

Inter-temporal decision-making is ubiquitous in daily life and has been considered as a critical characteristic associated with an individual’s success. Such decisions require us to tradeoff between short-term and long-term benefits. Prior studies have indicated that inter-temporal decision involves various brain regions that tend to occupy the central hubs. However, it is unclear whether the functional connectivities among hub as well as non-hub regions can predict discounting behaviors. Here, we combined with graph-theoretical algorithm and multivariate pattern analysis to explore whether voxel-wise functional connectivity strength in the whole brain could predict discounting rates (indexed as logk, based on the adaptive delay-discounting task) in a relatively large sample (n = 429) of young adults. Results revealed that short- and long-distance as well as all-range non-hub functional connectivity strength in the limbic system (i.e., medial orbitofrontal cortex and parahippocampus) were inversely associated with discounting rates. Furthermore, these results were robust and did not appear to be due to potential confounding factors. Above weight-based degree metric is commonly indicative of the communication pattern of local and global parallel information processing, and it therefore provides novel insights into the neural mechanisms underlying inter-temporal decision-making from the perspective of human brain topological organizations.

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References

  • Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. The Journal of Neuroscience, 16(1), 63–72.

    Google Scholar 

  • Alessi, S., & Petry, N. M. (2003). Pathological gambling severity is associated with impulsivity in a delay discounting procedure. Behavioural Processes, 64(3), 345–354.

    PubMed  Google Scholar 

  • Beasley, T. M., Erickson, S., & Allison, D. B. (2009). Rank-based inverse normal transformations are increasingly used, but are they merited? Behavior Genetics, 39(5), 580–595.

    PubMed  PubMed Central  Google Scholar 

  • Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52(2), 336–372.

    Google Scholar 

  • Berns, G. S., Laibson, D., & Loewenstein, G. (2007). Intertemporal choice–toward an integrative framework. Trends in Cognitive Sciences, 11(11), 482–488.

    PubMed  Google Scholar 

  • Bickel, W. K., Odum, A. L., & Madden, G. J. (1999). Impulsivity and cigarette smoking: Delay discounting in current, never, and ex-smokers. Psychopharmacology, 146(4), 447–454.

    CAS  PubMed  Google Scholar 

  • Bludau, S., Eickhoff, S. B., Mohlberg, H., Caspers, S., Laird, A. R., Fox, P. T., Schleicher, A., Zilles, K., & Amunts, K. (2014). Cytoarchitecture, probability maps and functions of the human frontal pole. Neuroimage, 93, 260–275.

    PubMed  Google Scholar 

  • Bondy, J. A., & Murty, U. S. R. (1976). Graph theory with applications (Vol. 290): Macmillan London.

  • Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009). Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer's disease. Journal of Neuroscience, 29(6), 1860–1873.

    CAS  PubMed  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.

    CAS  PubMed  Google Scholar 

  • Burgess, P. W., Scott, S. K., & Frith, C. D. (2003). The role of the rostral frontal cortex (area 10) in prospective memory: A lateral versus medial dissociation. Neuropsychologia, 41(8), 906–918.

    PubMed  Google Scholar 

  • Cai, H., Chen, J., Liu, S., Zhu, J., & Yu, Y. (2020). Brain functional connectome-based prediction of individual decision impulsivity. cortex, 125, 288–298.

    PubMed  Google Scholar 

  • Chen, Z., Guo, Y., & Feng, T. (2017). Delay discounting is predicted by scale-free dynamics of default mode network and salience network. Neuroscience, 362, 219–227.

    PubMed  Google Scholar 

  • Chen, Z., Guo, Y., Zhang, S., & Feng, T. (2019a). Pattern classification differentiates decision of intertemporal choices using multi-voxel pattern analysis. cortex, 111, 183–195.

    PubMed  Google Scholar 

  • Chen, Z., Hu, X., Chen, Q., & Feng, T. (2019b). Altered structural and functional brain network overall organization predict human intertemporal decision-making. Human Brain Mapping, 40(1), 306–328.

    PubMed  Google Scholar 

  • Clithero, J. A., Carter, R. M., & Huettel, S. A. (2009). Local pattern classification differentiates processes of economic valuation. Neuroimage, 45(4), 1329–1338.

    PubMed  Google Scholar 

  • Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16(9), 1348–1355.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Cox, D. D., & Savoy, R. L. (2003). Functional magnetic resonance imaging (fMRI) "brain reading": Detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage, 19(2), 261–270.

    PubMed  Google Scholar 

  • Damoiseaux, J., Rombouts, S., Barkhof, F., Scheltens, P., Stam, C., Smith, S. M., et al. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences, 103(37), 13848–13853.

    CAS  Google Scholar 

  • de Reus, M. A., & Van den Heuvel, M. P. (2013). The parcellation-based connectome: Limitations and extensions. Neuroimage, 80, 397–404.

    PubMed  Google Scholar 

  • Dombrovski, A. Y., Siegle, G. J., Szanto, K., Clark, L., Reynolds, C., & Aizenstein, H. (2012). The temptation of suicide: Striatal gray matter, discounting of delayed rewards, and suicide attempts in late-life depression. Psychological Medicine, 42(6), 1203–1215.

    CAS  PubMed  Google Scholar 

  • Douw, L., Baayen, H., Bosma, I., Klein, M., Vandertop, P., Heimans, J., Stam, K., de Munck, J., & Reijneveld, J. (2008). Treatment-related changes in functional connectivity in brain tumor patients: A magnetoencephalography study. Experimental Neurology, 212(2), 285–290.

    PubMed  Google Scholar 

  • Drucker, H., Burges, C. J., Kaufman, L., Smola, A. J., & Vapnik, V. (1997). Support vector regression machines. Paper presented at the Advances in neural information processing systems.

  • Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences, 201602413.

  • 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.

    CAS  Google Scholar 

  • Franzmeier, N., Düzel, E., Jessen, F., Buerger, K., Levin, J., Duering, M., Dichgans, M., Haass, C., Suárez-Calvet, M., Fagan, A. M., Paumier, K., Benzinger, T., Masters, C. L., Morris, J. C., Perneczky, R., Janowitz, D., Catak, C., Wolfsgruber, S., Wagner, M., Teipel, S., Kilimann, I., Ramirez, A., Rossor, M., Jucker, M., Chhatwal, J., Spottke, A., Boecker, H., Brosseron, F., Falkai, P., Fliessbach, K., Heneka, M. T., Laske, C., Nestor, P., Peters, O., Fuentes, M., Menne, F., Priller, J., Spruth, E. J., Franke, C., Schneider, A., Kofler, B., Westerteicher, C., Speck, O., Wiltfang, J., Bartels, C., Araque Caballero, M. Á., Metzger, C., Bittner, D., Weiner, M., Lee, J. H., Salloway, S., Danek, A., Goate, A., Schofield, P. R., Bateman, R. J., & Ewers, M. (2018). Left frontal hub connectivity delays cognitive impairment in autosomal-dominant and sporadic Alzheimer’s disease. Brain, 141(4), 1186–1200.

    PubMed  PubMed Central  Google Scholar 

  • Freeman, L. C., Borgatti, S. P., & White, D. R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13(2), 141–154.

    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.

    PubMed  PubMed Central  Google Scholar 

  • Haxby, J. V. (2012). Multivariate pattern analysis of fMRI: The early beginnings. Neuroimage, 62(2), 852–855.

    PubMed  Google Scholar 

  • Haxby, J. V., Connolly, A. C., & Guntupalli, J. S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual Review of Neuroscience, 37, 435–456.

    CAS  PubMed  Google Scholar 

  • He, Q., Xue, G., Chen, C., Chen, C., Lu, Z.-L., & Dong, Q. (2013). Decoding the neuroanatomical basis of reading ability: A multivoxel morphometric study. Journal of Neuroscience, 33(31), 12835–12843.

    CAS  PubMed  Google Scholar 

  • He, Y., Chen, Z., & Evans, A. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 17(10), 2407–2419.

    PubMed  Google Scholar 

  • Hu, S., Ide, J. S., Zhang, S., Sinha, R., & Chiang-shan, R. L. (2015). Conflict anticipation in alcohol dependence—A model-based fMRI study of stop signal task. Neuroimage: Clinical, 8, 39–50.

    Google Scholar 

  • Iturria-Medina, Y., Sotero, R. C., Canales-Rodríguez, E. J., Alemán-Gómez, Y., & Melie-García, L. (2008). Studying the human brain anatomical network via diffusion-weighted MRI and graph theory. Neuroimage, 40(3), 1064–1076.

    PubMed  Google Scholar 

  • Jimura, K., & Poldrack, R. A. (2012). Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia, 50(4), 544–552.

    PubMed  Google Scholar 

  • Johnson, M. W., & Bickel, W. K. (2002). Within-subject comparison of real and hypothetical money rewards in delay discounting. Journal of the Experimental Analysis of Behavior, 77(2), 129–146.

    PubMed  PubMed Central  Google Scholar 

  • Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences, 103(10), 3863–3868.

    CAS  Google Scholar 

  • Kringelbach, M. L. (2005). The human orbitofrontal cortex: Linking reward to hedonic experience. Nature Reviews Neuroscience, 6(9), 691–702.

    CAS  PubMed  Google Scholar 

  • Lempert, K. M., & Phelps, E. A. (2016). The malleability of intertemporal choice. Trends in Cognitive Sciences, 20(1), 64–74.

    PubMed  Google Scholar 

  • Li, N., Ma, N., Liu, Y., He, X.-S., Sun, D.-L., Fu, X.-M., Zhang, X., Han, S., & Zhang, D. R. (2013). Resting-state functional connectivity predicts impulsivity in economic decision-making. Journal of Neuroscience, 33(11), 4886–4895.

    CAS  PubMed  Google Scholar 

  • Liang, X., Zou, Q., He, Y., & Yang, Y. (2013). Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proceedings of the National Academy of Sciences, 110(5), 1929–1934.

    CAS  Google Scholar 

  • Liu, H., Qin, W., Li, W., Fan, L., Wang, J., Jiang, T., & Yu, C. (2013). Connectivity-based parcellation of the human frontal pole with diffusion tensor imaging. Journal of Neuroscience, 33(16), 6782–6790.

    CAS  PubMed  Google Scholar 

  • Liu, J., Xia, M., Dai, Z., Wang, X., Liao, X., Bi, Y., et al. (2017). Intrinsic brain hub connectivity underlies individual differences in spatial working memory. Cerebral Cortex, 27(12), 5496–5508.

    PubMed  Google Scholar 

  • Liu, Y., Yu, C., Zhang, X., Liu, J., Duan, Y., Alexander-Bloch, A. F., Liu, B., Jiang, T., & Bullmore, E. (2014). Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease. Cerebral Cortex, 24(6), 1422–1435.

    PubMed  Google Scholar 

  • Lv, C., Wang, Q., Chen, C., Qiu, J., Xue, G., & He, Q. (2019). The regional homogeneity patterns of the dorsal medial prefrontal cortex predict individual differences in decision impulsivity. Neuroimage, 200, 556–561.

    PubMed  Google Scholar 

  • Lv, C., Wang, Q., Chen, C., Xue, G., & He, Q. (2020). Activation patterns of the dorsal medial prefrontal cortex and frontal pole predict individual differences in decision impulsivity. Brain Imaging and Behavior In press.

  • Mariano, T., Bannerman, D., McHugh, S., Preston, T., Rudebeck, P., Rudebeck, S., et al. (2009). Impulsive choice in hippocampal but not orbitofrontal cortex-lesioned rats on a nonspatial decision-making maze task. European Journal of Neuroscience, 30(3), 472–484.

    CAS  Google Scholar 

  • McClure, S. M., Ericson, K. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2007). Time discounting for primary rewards. Journal of Neuroscience, 27(21), 5796–5804.

    CAS  PubMed  Google Scholar 

  • McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306(5695), 503–507.

    CAS  Google Scholar 

  • McNamee, D., Rangel, A., & O'doherty, J. P. (2013). Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex. Nature Neuroscience, 16(4), 479–485.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mobini, S., Body, S., Ho, M.-Y., Bradshaw, C., Szabadi, E., Deakin, J., & Anderson, I. M. (2002). Effects of lesions of the orbitofrontal cortex on sensitivity to delayed and probabilistic reinforcement. Psychopharmacology, 160(3), 290–298.

    CAS  PubMed  Google Scholar 

  • Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430.

    PubMed  Google Scholar 

  • Oldham, S., & Fornito, A. (2019). The development of brain network hubs. Developmental Cognitive Neuroscience, 36, 100607.

    PubMed  Google Scholar 

  • Öngür, D., Ferry, A. T., & Price, J. L. (2003). Architectonic subdivision of the human orbital and medial prefrontal cortex. Journal of Comparative Neurology, 460(3), 425–449.

    Google Scholar 

  • Paloyelis, Y., Asherson, P., Mehta, M. A., Faraone, S. V., & Kuntsi, J. (2010). DAT1 and COMT effects on delay discounting and trait impulsivity in male adolescents with attention deficit/hyperactivity disorder and healthy controls. Neuropsychopharmacology, 35(12), 2414–2426.

    PubMed  PubMed Central  Google Scholar 

  • Peters, J., & Büchel, C. (2010). Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions. Neuron, 66(1), 138–148.

    CAS  PubMed  Google Scholar 

  • Peters, J., & Büchel, C. (2011). The neural mechanisms of inter-temporal decision-making: Understanding variability. Trends in Cognitive Sciences, 15(5), 227–239.

    PubMed  Google Scholar 

  • Ramnani, N., & Owen, A. M. (2004). Anterior prefrontal cortex: Insights into function from anatomy and neuroimaging. Nature Reviews Neuroscience, 5(3), 184–194.

    CAS  PubMed  Google Scholar 

  • Rolls, E. T. (2004). The functions of the orbitofrontal cortex. Brain and Cognition, 55(1), 11–29.

    PubMed  Google Scholar 

  • Sasse, L. K., Peters, J., Büchel, C., & Brassen, S. (2015). Effects of prospective thinking on intertemporal choice: The role of familiarity. Human Brain Mapping, 36(10), 4210–4221.

    PubMed  PubMed Central  Google Scholar 

  • Sellitto, M., Ciaramelli, E., & di Pellegrino, G. (2010). Myopic discounting of future rewards after medial orbitofrontal damage in humans. Journal of Neuroscience, 30(49), 16429–16436.

    CAS  PubMed  Google Scholar 

  • Shin, D.-J., Jung, W. H., He, Y., Wang, J., Shim, G., Byun, M. S., Jang, J. H., Kim, S. N., Lee, T. Y., Park, H. Y., & Kwon, J. S. (2014). The effects of pharmacological treatment on functional brain connectome in obsessive-compulsive disorder. Biological Psychiatry, 75(8), 606–614.

    CAS  PubMed  Google Scholar 

  • Shukla, D. K., Keehn, B., Smylie, D. M., & Müller, R.-A. (2011). Microstructural abnormalities of short-distance white matter tracts in autism spectrum disorder. Neuropsychologia, 49(5), 1378–1382.

    PubMed  PubMed Central  Google Scholar 

  • Sporns, O. (2011). The human connectome: A complex network. Annals of the New York Academy of Sciences, 1224(1), 109–125.

    PubMed  Google Scholar 

  • Sporns, O., Honey, C. J., & Kotter, R. (2007). Identification and classification of hubs in brain networks. PLoS One, 2(10), e1049.

    PubMed  PubMed Central  Google Scholar 

  • Tomasi, D., & Volkow, N. D. (2010). Functional connectivity density mapping. Proceedings of the National Academy of Sciences, 107(21), 9885–9890.

    CAS  Google Scholar 

  • van den Bos, W., Rodriguez, C. A., Schweitzer, J. B., & McClure, S. M. (2014). Connectivity strength of dissociable striatal tracts predict individual differences in temporal discounting. Journal of Neuroscience, 34(31), 10298–10310.

    PubMed  Google Scholar 

  • Van Den Bos, W., Rodriguez, C. A., Schweitzer, J. B., & McClure, S. M. (2015a). Adolescent impatience decreases with increased frontostriatal connectivity. Proceedings of the National Academy of Sciences, 112(29), E3765–E3774.

    Google Scholar 

  • Van Den Bos, W., Rodriguez, C. A., Schweitzer, J. B., & McClure, S. M. (2015b). Adolescent impatience decreases with increased frontostriatal connectivity. Proceedings of the National Academy of Sciences, 201423095.

  • van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696.

    PubMed  Google Scholar 

  • Wang, Q., Chen, C., Cai, Y., Li, S., Zhao, X., Zheng, L., Zhang, H., Liu, J., Chen, C., & Xue, G. (2016). Dissociated neural substrates underlying impulsive choice and impulsive action. Neuroimage, 134, 540–549.

    PubMed  Google Scholar 

  • Wang, Q., Luo, S., Monterosso, J., Zhang, J., Fang, X., Dong, Q., & Xue, G. (2014a). Distributed value representation in the medial prefrontal cortex during intertemporal choices. Journal of Neuroscience, 34(22), 7522–7530.

    CAS  PubMed  Google Scholar 

  • Wang, X., Xia, M., Lai, Y., Dai, Z., Guo, Q., Cheng, Z., et al. (2014b). Disrupted resting-state functional connectivity in minimally treated chronic shiziphrenia. Schizophrenia Research, 156(2014), 150–156.

    PubMed  Google Scholar 

  • Yu, R. (2012). Regional white matter volumes correlate with delay discounting. PLoS One, 7(2), e32595.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zha, R., Bu, J., Wei, Z., Han, L., Zhang, P., Ren, J., Li, J. A., Wang, Y., Yang, L., Vollstädt-Klein, S., & Zhang, X. (2019). Transforming brain signals related to value evaluation and self-control into behavioral choices. Human Brain Mapping, 40(4), 1049–1061.

    PubMed  Google Scholar 

  • Zhang, W., Li, S., Wang, X., Gong, Y., Yao, L., Xiao, Y., Liu, J., Keedy, S. K., Gong, Q., Sweeney, J. A., & Lui, S. (2018). Abnormal dynamic functional connectivity between speech and auditory areas in schizophrenia patients with auditory hallucinations. Neuroimage: Clinical, 19, 918–924.

    Google Scholar 

  • Zuo, X.-N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F. X., Sporns, O., & Milham, M. P. (2011). Network centrality in the human functional connectome. Cerebral Cortex, 22(8), 1862–1875.

    PubMed  Google Scholar 

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Acknowledgments

This work was supported by research grants from the Humanities and Social Science Fund Project of the Ministry of Education (20YJC190018), National Natural Science Foundation of China (31972906), and Entrepreneurship and Innovation Program for Chongqing Overseas Returned Scholars (cx2017049).

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Wang, Q., Zhu, Y., Wang, Y. et al. Intrinsic non-hub connectivity predicts human inter-temporal decision-making. Brain Imaging and Behavior 15, 2005–2016 (2021). https://doi.org/10.1007/s11682-020-00395-3

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