Studying task-modulated functional connectivity using functional magnetic resonance imaging

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Somente assinantes

Resumo

Over the past decade, the main focus in functional magnetic resonance imaging (fMRI) studies on the structural and functional organization of the human brain has shifted from functional segregation, i. e., the functional specialization of individual brain structures, to functional integration, i. e., the collective activity of the brain systems. Currently, the most common type of fMRI study is the resting-state functional connectivity studies, due to the relative ease of obtaining and statistically analyzing data. At the same time, growing attention is paid to dynamic changes of functional connections during task performance. In this review, we briefly describe the nature of functional connectivity measured using fMRI, review existing task-modulated functional connectivity methods, and provide practical recommendations for choosing a statistical analysis method and planning the fMRI task design. In conclusion, we will discuss the significance and prospects of studying task-modulated functional connectivity for fundamental research into the systemic organization of the human brain in health and pathology.

Sobre autores

R. Masharipov

N.P. Bechtereva Institute of the Human Brain, RAS

Email: masharipov@ihb.spb.ru
Saint-­Petersburg, Russia

M. Didour

N.P. Bechtereva Institute of the Human Brain, RAS

Email: masharipov@ihb.spb.ru
Saint-­Petersburg, Russia

D. Cherednichenko

N.P. Bechtereva Institute of the Human Brain, RAS

Email: masharipov@ihb.spb.ru
Saint-­Petersburg, Russia

M. Kireev

N.P. Bechtereva Institute of the Human Brain, RAS

Autor responsável pela correspondência
Email: masharipov@ihb.spb.ru
Saint-­Petersburg, Russia

Bibliografia

  1. Анохин К.В. Когнитом: в поисках фундаментальной нейронаучной теории сознания. Журнал высшей нервной деятельности им И.П. Павлова. 2021. 71 (1): 39–71. https://doi.org/10.31857/s0044467721010032
  2. Бернштейн Н.А. Современные искания и физиологии нервного процесса. 1935. Под ред. И.М. Фейгенберга. И.Е. Сироткиной. М.: Смысл, 2003. 330 с.
  3. Бехтерев В.М. Проводящие пути спинного и головного мозга: Руководство к изучению внутренних связей мозга. Ч. 1. [Соч.] В.М. Бехтерев, 3-е изд., испр. и доп. СПб.: Издательство Риккера, 1898. 496 с.
  4. Бехтерева Н.П., Бондарчук А.Н. Об оптимизации этапов хирургического лечения гиперкинезов. Вопр. нейрохир. 1968. № 3. С. 39–44.
  5. Бехтерева Н.П., Камбарова Д.К., Поздеев В.К. Устойчивое патологическое состояние при болезнях мозга. Л.: Медицина, 1978. 240 с.
  6. Бехтерева Н.П. Здоровый и больной мозг человека. Академия наук CССР. Отделение физиологии. Л.: Наука, 1980. 208 с.
  7. Киреев М.В. Системная организация работы мозга при обеспечении целенаправленного поведения: дисс. … док. биол. наук Киреев Максим Владимирович. Санкт-Петербург, 2017. 304 с.
  8. Лурия А.Р. Высшие корковые функции человека и их нарушения при локальных поражениях мозга. Москва: Издательство Московского университета, 1962. 432 с.
  9. Ухтомский А.А. Избранные труды. Л.: “Наука”, Ленинградское отделение, 1978. С. 175.
  10. Anokhin P.K. Nodular Mechanism of Functional Systems as a Self-regulating Apparatus. Progress in Brain Research. 1968. 22: 230–251. https://doi.org/10.1016/s0079-6123(08)63509-8
  11. Anokhin P.K. Functional system as the basis for investigation of the embryonic development of functions. VI. Congr. Soviet Physiol., Tbilisi, U.S.S.R., 1937.
  12. Argyropoulou M.I., Xydis V.G., Astrakas L.G. Functional connectivity of the pediatric brain. Neuroradiology. 2024. 66: 2071–2082. https://doi.org/10.1007/s00234-024-03453-5
  13. Badillo S., Vincent T., Ciuciu P. Group-level impacts of within- and between-subject hemodynamic variability in fMRI. NeuroImage. 2013. 82: 433–448. https://doi.org/10.1016/j.neuroimage.2013.05.100
  14. Baumann A.W., Schäfer T.A., Ruge H. Instructional load induces functional connectivity changes linked to task automaticity and mnemonic preference. NeuroImage. 2023. 277: 120262. https://doi.org/10.1016/j.neuroimage.2023.120262
  15. Bechtereva N.P. The Neurophysiological Aspects OJ Human Mental Activity. Oxford Univ. Press, New York, 1978. 181 p.
  16. Biswal B.B., Mennes M., Zuo X.N. et al. Toward discovery science of human brain function. Proceedings of the National Academy of Sciences. 2010. 107: 4734–4739. https://doi.org/10.1073/pnas.091185510
  17. Biswal B.B., Yetkin F.Z., Haughton V.M., Hyde J.S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine. 1995. 34: 537–541. https://doi.org/10.1002/mrm.1910340409
  18. Bolt T., Nomi J.S., Rubinov M., Uddin L.Q. Correspondence between evoked and intrinsic functional brain network configurations. Human Brain Mapping. 2017. 38 (4): 1992–2007. https://doi.org/10.1002/hbm.23500
  19. Bonett D.G., Wright T.A. Sample size requirements for estimating Pearson, Kendall and Spearman correlations. Psychometrika. 2000. 65: 23–28. https://doi.org/10.1007/BF02294183
  20. Brosch M., Budinger E., Scheich, H. Stimulus-related gamma oscillations in primate auditory cortex. J. Neurophysiol. 2002. 87: 2715–2725. https://doi.org/10.1152/jn.2002.87.6.2715
  21. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience. 2009. 10 (3): 186–198. https://doi.org/10.1038/nrn2575
  22. Buxton R.B. Dynamic models of BOLD contrast. NeuroImage. 2012. 62 (2): 953–961. https://doi.org/10.1016/j.neuroimage.2012.01.012
  23. Cash R.F.H., Müller V.I., Fitzgerald P.B., Eickhoff S.B., Zalesky A. Altered brain activity in unipolar depression unveiled using connectomics. Nat. Mental Health. 2023. 1: 174–185. https://doi.org/10.1038/s44220-023-00038-8
  24. Chen G., Taylor P.A., Reynolds R.C., Leibenluft E., Pine D.S., Brotman M.A. et al. BOLD Response is more than just magnitude: Improving detection sensitivity through capturing hemodynamic profiles. NeuroImage. 2023. 277: 120224. https://doi.org/10.1016/j.neuroimage.2023.120224
  25. Ciric R., Rosen A.F.G., Erus G., Cieslak M., Adebimpe A., Cook P.A. et al. Mitigating head motion artifact in functional connectivity MRI. Nature Protocols. 2018. 13: 2801–2826. https://doi.org/10.1038/s41596-018-0065-y
  26. Cole M., Ito T., Schultz D., Mill R., Chen R., Cocuzza C. Task activations produce spurious but systematic inflation of task functional connectivity estimates. NeuroImage. 2019. 189: 1–18. https://doi.org/10.1016/j.neuroimage.2018.12.054
  27. Cole M.W., Ito T., Cocuzza C., Sanchez-Romero R. The functional relevance of Task-State functional connectivity. Journal of Neuroscience. 2021. 41 (12): 2684–2702. https://doi.org/10.1523/jneurosci.1713-20.2021
  28. Cole M.W., Reynolds J.R., Power J.D., Repovs G., Anticevic A., Braver T.S. Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience. 2013. 16 (9): 1348–1355. https://doi.org/10.1038/nn.3470
  29. Cole M., Bassett D.S., Power J.D., Braver T.S., Petersen S.E. Intrinsic and task-evoked network architectures of the human brain. Neuron. 2014. 83: 238–251. https://doi.org/10.1016/j.neuron.2014.05.014
  30. Corbetta M., Patel G., Shulman G.L. The reorienting system of the human brain: from environment to theory of mind. Neuron. 2008. 58 (3): 306–324. https://doi.org/10.1016/j.neuron.2008.04.017
  31. Cosío-Guirado R., Tapia-Medina M.G., Kaya C., Peró-Cebollero M., Villuendas-González E.R., Guàrdia-Olmos J. A comprehensive systematic review of fmri studies on brain connectivity in healthy children and adolescents: current insights and future directions. Developmental Cognitive Neuroscience. 2024. 69: 101438. https://doi.org/10.1016/j.dcn.2024.101438
  32. Coutanche M.N., Thompson-Schill S.L. Informational connectivity: identifying synchronized discriminability of multi-voxel patterns across the brain. Front. Hum. Neurosci. 2013. 7. https://doi.org/10.3389/fnhum.2013.00015
  33. Crick F., Koch C. A framework for consciousness. Nat Neurosci. 2003. 6: 119–126. https://doi.org/10.1038/nn0203-119
  34. Deco G., Jirsa V.K., McIntosh A.M., Sporns O., Kötter R. Key role of coupling, delay, and noise in resting brain fluctuations. Proc. Natl Acad. Sci. 2009. 106: 10302–10307. https://doi.org/10.1073/pnas.0901831106
  35. Di X., Biswal B.B. Toward task connectomics: examining whole-brain task modulated connectivity in different task domains. Cereb. Cortex. 2019. 29: 1572–1583. https://doi.org/10.1093/cercor/bhy055
  36. Di X., Reynolds R.C., Biswal B.B. Imperfect (de)convolution may introduce spurious psychophysiological interactions and how to avoid it. Human Brain Mapping. 2017. 38 (4): 1723–1740. https://doi.org/10.1002/hbm.23413
  37. Dobbs D. Fact or phrenology? Scientific American Mind. 2005. 16 (1): 24–31. http://www.jstor.org/stable/24997594
  38. Dodel S., Golestani N., Pallier C., El Kouby V., Bihan D.L., Poline J. Condition-dependent functional connectivity: syntax networks in bilinguals. Philosophical Transactions of the Royal Society B Biological Sciences. 2005. 360 (1457): 921–935. https://doi.org/10.1098/rstb.2005.1653
  39. Dominicus L., Van Rijn L., Van Der A.J., Van Der Spek R., Podzimek D., Begemann M. et al. fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review. NeuroImage Clinical. 2023. 40: 103515. https://doi.org/10.1016/j.nicl.2023.103515
  40. Dowdle L.T., Ghose G., Chen C.C., Ugurbil K., Yacoub E., Vizioli L. Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI. Prog. Neurobiol. 2021. 207: 102171. https://doi.org/10.1016/j.pneurobio.2021.102171
  41. Drew P.J., Mateo C., Turner K.L., Yu X., Kleinfeld D. Ultra-slow oscillations in FMRI and Resting-State connectivity: neuronal and vascular contributions and technical confounds. Neuron. 2020. 107 (5): 782–804. https://doi.org/10.1016/j.neuron.2020.07.020
  42. Edde M., Leroux G., Altena E., Chanraud S. Functional brain connectivity changes across the human life span: From fetal development to old age. Journal of Neuroscience Research. 2020. 99 (1): 236–262. https://doi.org/10.1002/jnr.24669
  43. Ekstrom A.D. Regional variation in neurovascular coupling and why we still lack a Rosetta Stone. Philosophical Transactions of the Royal Society B Biological Sciences. 2020. 376 (1815): 20190634. https://doi.org/10.1098/rstb.2019.0634
  44. Elam J.S., Glasser M.F., Harms M.P., Sotiropoulos S.N., Andersson J.L., Burgess G.C. et al. The Human Connectome Project: A retrospective. NeuroImage. 2021. 244: 118543. https://doi.org/10.1016/j.neuroimage.2021.118543
  45. Engel A., Fries P., Singer W. Dynamic predictions: oscillations and synchrony in top–down processing. Nat. Rev. Neurosci. 2001. 2: 704–716. https://doi.org/10.1038/35094565
  46. Farahani F.V., Karwowski W., Lighthall N.R. Application of graph Theory for identifying connectivity patterns in human brain networks: A systematic review. Frontiers in Neuroscience. 2019. 13. https://doi.org/10.3389/fnins.2019.00585
  47. Fedorenko E., Duncan J., Kanwisher N. Broad domain generality in focal regions of frontal and parietal cortex. Proceedings of the National Academy of Sciences. 2013. 110 (41): 16616–16621. https://doi.org/10.1073/pnas.1315235110
  48. Ferré P., Jarret J., Brambati S.M., Bellec P., Joanette Y. Task-Induced Functional connectivity of picture naming in Healthy aging: the impacts of age and task complexity. Neurobiology of Language. 2020. 1 (2): 161–184. https://doi.org/10.1162/nol_a_00007
  49. Ferrier D. The functions of the brain. London: Smith, Elder & Co, 1876. 323 p.
  50. Flourens P. Recherches expérimentales sur les proprétés et les fonctions du système nerveux dans les animaux vertèbres. Paris: Baillière, 1824. 516 p.
  51. Fornito A., Harrison B.J., Zalesky A., Simons J.S. Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proceedings of the National Academy of Sciences. 2012. 109: 12788–12793. https://doi.org/10.1073/pnas.1204185109
  52. Fornito A., Zalesky A., Breakspear M. Graph analysis of the human connectome: Promise, progress, and pitfalls. NeuroImage. 2013. 80: 426–444. https://doi.org/10.1016/j.neuroimage.2013.04.087
  53. Fornito A. Brain organization: From cells and circuits to systems and networks. In Brown G.G., Crosson B., Haaland K.Y. & King T.Z. (Eds.) APA handbook of neuropsychology: Neuroscience and neuromethods. American Psychological Association, 2023. P. 3–32.
  54. Fox P.T., Raichle M.E., Mintun M.A., Dence C. Nonoxidative glucose consumption during focal physiologic neural activity. Science. 1988. 241: 462–464. https://doi.org/10.1126/science.3260686
  55. Fox M.D., Corbetta M., Snyder A.Z., Vincent J.L., Raichle M.E. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences. 2006. 103 (26): 10046–10051. https://doi.org/10.1073/pnas.0604187103
  56. Fox M.D., Snyder A.Z., Vincent J.L., Corbetta M., Van Essen D.C., Raichle M.E. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences. 2005. 102 (27): 9673–9678. https://doi.org/10.1073/pnas.0504136102
  57. Frackowiak R.S.J., Jones T., Lenzi G.L., Heather J.D. Regional cerebral oxygen utilization and blood flow in normal man using oxygen-15 and positron emission tomography. Acta Neurologica Scandinavica. 1980. 62 (6): 336–344. https://doi.org/10.1111/j.1600-0404.1980.tb03046.x
  58. Franzmeier N., Hartmann J., Taylor A.N.W., Araque-Caballero M.Á., Simon-Vermot L., Kambeitz-Ilankovic L. et al. The left frontal cortex supports reserve in aging by enhancing functional network efficiency. Alz. Res. Therapy. 2018. 10 (1): 28. https://doi.org/10.1186/s13195-018-0358-y
  59. Fries P. Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu. Rev. Neurosci. 2009. 32: 209–224. https://doi.org/10.1146/annurev.neuro.051508.135603
  60. Fries P. Rhythms for cognition: communication through coherence. Neuron. 2015. 88: 220–235. https://doi.org/10.1016/j.neuron.2015.09.034
  61. Friston K.J. Beyond phrenology: What can neuroimaging tell us about distributed circuitry? Annual Review of Neuroscience. 2002. 25 (1): 221–250. https://doi.org/10.1146/annurev.neuro.25.112701.142846
  62. Friston K.J. Functional and Effective Connectivity: a review. Brain Connectivity. 2011. 1 (1): 13–36. https://doi.org/10.1089/brain.2011.0008
  63. Friston K.J., Buechel C., Fink G.R., Morris J., Rolls E., Dolan R.J. Psychophysiological and Modulatory Interactions in Neuroimaging. NeuroImage. 1997. 6: 218–229. https://doi.org/10.1006/nimg.1997.0291
  64. Friston K.J, Harrison L., Penny W. Dynamic causal modelling. NeuroImage. 2003. 19 (4): 1273–1302. https://doi.org/10.1016/s1053-8119(03)00202-7
  65. Fritsch G., Hitzig E. Uber die elektrische Erregbarkeit des Grosshirns. Archivfuer Anatomie. Physiologie und wissenschaftliche Medicin. 1870. 37: 300e332.
  66. Fuster J.M. The cognit: A network model of cortical representation. International Journal of Psychophysiology. 2006. 60 (2): 125–132. https://doi.org/10.1016/j.ijpsycho.2005.12.015
  67. Gall F.J., Spurzheim J.K. Anatomie et Physiologie du Système Nerveux en Général et du Cerveau en Particulier. Paris: Schoell, 1810–1819.
  68. Gerchen M.F. Kirsch P. Combining task-related activation and connectivity analysis of fMRI data reveals complex modulation of brain networks. Human Brain Mapping. 2017. 38 (11): 5726–5739. https://doi.org/10.1002/hbm.23762
  69. Gitelman D.R., Penny W.D., Ashburner J., Friston K.J. Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. NeuroImage. 2003. 19: 200–207. https://doi.org/10.1016/S1053-8119(03)00058-2
  70. Goldstein K. Die Lokalisation in der Grosshirnrinde. Handbuch der normalen und pathologischen Physiologie der Leibestlbungen. Knoll und Arnold. Leipzig, 1927.
  71. Gomez D.E.P., Polimeni J.R., Lewis L.D. The temporal specificity of BOLD fMRI is systematically related to anatomical and vascular features of the human brain. Imaging Neuroscience. 2024. https://doi.org/10.1162/imag_a_00399
  72. Gordon E.M., Laumann T.O., Gilmore A.W., Newbold D.J., Greene D.J., Berg J.J. et al. Precision functional mapping of individual human brains. Neuron. 2017. 95 (4): 791–807.e7. https://doi.org/10.1016/j.neuron.2017.07.011
  73. Greene A.S., Gao S., Noble S., Scheinost D., Constable R.T. How Tasks Change Whole-Brain Functional Organization to reveal Brain-Phenotype Relationships. Cell Reports. 2020. 32 (8): 108066. https://doi.org/10.1016/j.celrep.2020.108066
  74. Greicius M.D., Krasnow B., Reiss A.L., Menon V. Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences. 2002. 100 (1): 253–258. https://doi.org/10.1073/pnas.0135058100
  75. Guedj C., Vuilleumier P. Modulation of pulvinar connectivity with cortical areas in the control of selective visual attention. NeuroImage. 2023. 266: 119832. https://doi.org/10.1016/j.neuroimage.2022.119832
  76. Haller A. Elementa physiologiae corporis humani. Neapoli, Apud Vincentium Ursinum, 1757–1766.
  77. Handwerker D.A., Gonzalez-Castillo J., D’Esposito M., Bandettini P.A. The continuing challenge of understanding and modeling hemodynamic variation in fMRI. NeuroImage. 2012. 62 (2): 1017–1023. https://doi.org/10.1016/j.neuroimage.2012.02.015
  78. Hebb D.O. The Organization of Behavior: A Neuropsychological Theory. New York: Wiley and Sons, 1949. 335 p.
  79. Henson R.N., Olszowy W., Tsvetanov K.A., Yadav P.S., Zeidman P. Evaluating models of the ageing BOLD response. Human Brain Mapping. 2024. 45 (15). https://doi.org/10.1002/hbm.70043
  80. Horn A., Fox M.D. Opportunities of connectomic neuromodulation. NeuroImage. 2020. 221: 117180. https://doi.org/10.1016/j.neuroimage.2020.117180
  81. Hugdahl K., Raichle M.E., Mitra A., Specht K. On the existence of a generalized non-specific task-dependent network. Frontiers in Human Neuroscience. 2015. 9. https://doi.org/10.3389/fnhum.2015.00430
  82. Jiang R., Scheinost D., Zuo N., Wu J., Qi S., Liang Q. et al. A Neuroimaging Signature of Cognitive Aging from Whole-Brain Functional Connectivity. Advanced Science. 2022. 9 (24). https://doi.org/10.1002/advs.202201621
  83. Jiang R., Zuo N., Ford J.M., Qi S., Zhi D., Zhuo C. et al. Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships. NeuroImage. 2019. 207: 116370. https://doi.org/10.1016/j.neuroimage.2019.116370
  84. Kataeva G.V., Korotkov A.D., Kireev M.V., Medvedev S.V. Factor structure of regional cerebral blood flow and glucose metabolism rate as a tool to study the default mode of the brain. Human Physiology. 2013. 39 (1): 48–53. https://doi.org/10.1134/s0362119713010052
  85. Kelso J.S. Synergies: atoms of brain and behavior. Advances in Experimental Medicine and Biology. 2008. 629: 83–91. https://doi.org/10.1007/978-0-387-77064-2_5
  86. Kireev M., Slioussar N., Korotkov A.D., Chernigovskaya T.V., Medvedev S.V. Changes in functional connectivity within the fronto-temporal brain network induced by regular and irregular Russian verb production. Frontiers in Human Neuroscience. 2015. 9. https://doi.org/10.3389/fnhum.2015.00036
  87. Kleist K. Gehirnpathologie. Leipzig: Barth, 1934.
  88. Kriegeskorte N., Mur M., Bandettini P. Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience. 2008. 2. https://doi.org/10.3389/neuro.06.004.2008
  89. Kwong K.K., Belliveau J.W., Chesler D.A., Goldberg I.E., Weisskoff R.M., Poncelet B.P. et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences. 1992. 89 (12). 5675–5679. https://doi.org/10.1073/pnas.89.12.5675
  90. Lashley K.S. Brain Mechanisms and Intelligence. The University Press, 1929. 222 p.
  91. Lewis L.D., Setsompop K., Rosen B.R., Polimeni J.R. Fast fMRI can detect oscillatory neural activity in humans. Proceedings of the National Academy of Sciences. 2016. 113 (43). https://doi.org/10.1073/pnas.1608117113
  92. Li J.M., Acland B.T., Brenner A.S., Bentley W.J. Snyder L.H. Relationships between correlated spikes, oxygen and LFP in the resting-state primate. NeuroImage. 2022. 247: 118728. https://doi.org/10.1016/j.neuroimage.2021.118728
  93. Liu J., Newsome W.T. Local field potential in cortical area MT: stimulus tuning and behavioral correlations. J. Neurosci. 2006. 26: 7779–7790. https://doi.org/10.1523/JNEUROSCI.5052-05.2006
  94. Logothetis N.K., Pauls J., Augath M., Trinath T., Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001. 412: 150–157. https://doi.org/10.1038/35084005
  95. Luo Y., Schulz K.P., Alvarez T.L., Halperin J.M., Li X. Distinct topological properties of cue-evoked attention processing network in persisters and remitters of childhood ADHD. Cortex. 2018. 109: 234–244. https://doi.org/10.1016/j.cortex.2018.09.013
  96. Luria A.R. Higher cortical functions in man. London: Tavistock, 1966. 529 p.
  97. Mascali D., Moraschi M., DiNuzzo M., Tommasin S., Fratini M., Gili T. et al. Evaluation of denoising strategies for task-based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks. Human Brain Mapping. 2021. 42: 1805–1828. https://doi.org/10.1002/hbm.25332
  98. Masharipov R., Knyazeva I., Korotkov A., Cherednichenko D., Kireev M. Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics. Commun. Biol. 2024. 7: 1402. https://doi.org/10.1038/s42003-024-07088-3
  99. Masson H.L., Pillet I., Boets B., De Beeck H.O. Task-dependent changes in functional connectivity during the observation of social and non-social touch interaction. Cortex. 2020. 125: 73–89. https://doi.org/10.1016/j.cortex.2019.12.011
  100. Mateo C., Knutsen P.M., Tsai P.S., Shih A.Y., Kleinfeld D. Entrainment of arteriole vasomotor fluctuations by neural activity is a basis of blood-oxygenation-level-dependent “Resting-State” connectivity. Neuron. 2017. 96: 936–948.e3. https://doi.org/10.1016/j.neuron.2017.10.012
  101. McLaren D.G., Ries M.L., Xu G., Johnson S.C. A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches. NeuroImage. 2012. 61: 1277–1286. https://doi.org/10.1016/j.neuroimage.2012.03.068
  102. Medvedev S.V., Korotkov A.D., Kireev M.V. Hidden nodes of the brain systems. Human Physiology. 2019. 45 (5): 552–556. https://doi.org/10.1134/s0362119719050104
  103. Medvedev S.V., Masharipov R.S., Korotkov A.D., Kireev M.V. Characteristics of the Involvement of Hidden Nodes in the Activity of Human Brain Systems Revealed on fMRI Data. Hum. Physiol. 2023. 49: 1–11. https://doi.org/10.1134/S0362119722700141
  104. Melloni L., Mudrik L., Pitts M., Bendtz K., Ferrante O., Gorska U. et al. An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLoS ONE. 2023. 18 (2): e0268577. https://doi.org/10.1371/journal.pone.0268577
  105. Mill R.D., Ito T., Cole M.W. From connectome to cognition: The search for mechanism in human functional brain networks. NeuroImage. 2017. 160: 124–139. https://doi.org/10.1016/j.neuroimage.2017.01.060
  106. Moreira J.F.G., McLaughlin K.A., Silvers J.A. Characterizing the network architecture of emotion regulation neurodevelopment. Cerebral Cortex. 2021. 31 (9): 4140–4150. https://doi.org/10.1093/cercor/bhab074
  107. Mumford J.A., Turner B.L., Ashby F.G., Poldrack R.A. Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage. 2012. 59: 2636–2643. https://doi.org/10.1016/j.neuroimage.2011.08.076
  108. Munk Н. Zur Physiologie der Grosshirnrinde. Berliner Physiologische Gesellschaft. Marz, 1877.
  109. Niessing J. Ebisch B., Schmidt K.E., Niessing M., Singer W., Galuske R.A.W. Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science. 2005. 309: 948–951. https://doi.org/10.1126/science.1110948
  110. Nieto-Castañón A. Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Hilbert Press, 2020. 113 p. https://doi.org/10.56441/hilbertpress.2207.6598
  111. Nir Y., Fisch L., Mukamel R., Gelbard-Sagiv H., Arieli A., Fried I., Malach R. Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations. Curr. Biol. 2007. 17: 1275–1285. https://doi.org/10.1016/j.cub.2007.06.066
  112. O’Reilly J.X., Woolrich M.W., Behrens T.E., Smith S.M., Johansen-Berg H. Tools of the trade: psychophysiological interactions and functional connectivity. Social Cognitive and Affective Neuroscience. 2012. 7 (5): 604–609. https://doi.org/10.1093/scan/nss055
  113. Ogawa S., Lee T.M., Kay A.R., Tank D.W. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences. 1990. 87 (24): 9868–9872. https://doi.org/10.1073/pnas.87.24.9868
  114. Ogawa S., Tank D.W., Menon R., Ellermann J.M., Kim S.G., Merkle H., Ugurbil K. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences. 1992. 89 (13): 5951–5955. https://doi.org/10.1073/pnas.89.13.5951
  115. Palmigiano A., Geisel T., Wolf F., Battaglia D. Flexible information routing by transient synchrony. Nat. Neurosci. 2017.20: 1014–1022. https://doi.org/10.1038/nn.4569
  116. Paz-Alonso P.M., Oliver M., Lerma-Usabiaga G., Caballero-Gaudes C., Quiñones I., Suárez-Coalla P. et al. Neural correlates of phonological, orthographic and semantic reading processing in dyslexia. NeuroImage Clinical. 2018. 20: 433–447. https://doi.org/10.1016/j.nicl.2018.08.018
  117. Polimeni J.R., Lewis L.D. Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response. Progress in Neurobiology. 2021. 207: 102174. https://doi.org/10.1016/j.pneurobio.2021.102174
  118. Prince J.S. Charest I., Kurzawski J.W., Pyles J.A., Tarr M.J., Kay K.N. Improving the accuracy of single-trial fMRI response estimates using GLMsingle. eLife. 2022. 11. https://doi.org/10.7554/eLife.77599
  119. Raichle M.E., MacLeod A.M., Snyder A.Z., Powers W.J., Gusnard D.A., Shulman G.L. A default mode of brain function. Proceedings of the National Academy of Sciences. 2001. 98 (2): 676–682. https://doi.org/10.1073/pnas.98.2.676
  120. Raichle M.E. Two views of brain function. Trends. Cogn. Sci. 2010. 14: 180–190. https://doi.org/10.1016/j.tics.2010.01.008
  121. Regehr W.G. Short-term presynaptic plasticity. Cold Spring Harb. Perspect. Biol. 2012. 4: a005702. https://doi.org/10.1101/cshperspect.a005702
  122. Ren P., Anderson A.J., McDermott K., Baran T.M., Lin F. Cognitive fatigue and cortical-striatal network in old age. Aging. 2019. 11 (8): 2312–2326. https://doi.org/10.18632/aging.101915
  123. Rissman J., Gazzaley A., D’Esposito M. Measuring functional connectivity during distinct stages of a cognitive task. NeuroImage. 2004. 23: 752–763. https://doi.org/10.1016/j.neuroimage.2004.06.035
  124. Roger E., De Almeida L.R., Loevenbruck H., Perrone-Bertolotti M., Cousin E., Schwartz J. et al. Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability. NeuroImage. 2022. 263: 119672. https://doi.org/10.1016/j.neuroimage.2022.119672
  125. Rokem A., Kay K. Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. Gigascience. 2020. 9 (12): giaa133. https://doi.org/10.1093/gigascience/giaa133
  126. Saggar M., Uddin L.Q. Pushing the boundaries of psychiatric neuroimaging to ground diagnosis in biology. eNeuro. 2019. 6 (6): ENEURO.0384-19.2019. https://doi.org/10.1523/eneuro.0384-19.2019
  127. Saggar M., Sporns O., Gonzalez-Castillo J., Bandettini P.A., Carlsson G., Glover G., Reiss A.L. Towards a new approach to reveal dynamical organization of the brain using topological data analysis. Nature Communications. 2018. 9 (1). https://doi.org/10.1038/s41467-018-03664-4
  128. Santoro A., Battiston F., Lucas M., Petri G., Amico E. Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior. Nat Commun. 2024. 15: 10244. https://doi.org/10.1038/s41467-024-54472-y
  129. Schmitz T.W., Correia M.M., Ferreira C.S., Prescot A.P., Anderson M.C. Hippocampal GABA enables inhibitory control over unwanted thoughts. Nat Commun. 2017. 8: 1311. https://doi.org/10.1038/s41467-017-00956-z
  130. Schoffelen J.M., Oostenveld R., Fries P. Neuronal coherence as a mechanism of effective corticospinal interaction. Science. 2005. 308: 111–113. https://doi.org/10.1126/science.1107027
  131. Schölvinck M.L., Maier A., Ye F.Q., Duyn J.H., Leopold D.A. Neural basis of global resting-state fMRI activity. Proc. Natl Acad. Sci. 2010. 107: 10238–10243. https://doi.org/10.1073/pnas.0913110107
  132. Sechenov I.M. The elements of thought, final version. 1901. In: Sechenov I.M. Selected Works, Amsterdam, E.J. Bonset, 1968.
  133. Seeley W.W. The salience network: a neural system for perceiving and responding to homeostatic demands. Journal of Neuroscience. 2019. 39 (50): 9878–9882. https://doi.org/10.1523/jneurosci.1138–17.2019
  134. Seeley W.W., Menon V., Schatzberg A.F., Keller J., Glover G.H., Kenna H. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience. 2007. 27 (9): 2349–2356. https://doi.org/10.1523/jneurosci.5587-06.2007
  135. Seth A.K., Bayne, T. Theories of consciousness. Nat. Rev. Neurosci. 2022. 23: 439–452. https://doi.org/10.1038/s41583-022-00587-4
  136. Shmuel A., Leopold D.A. Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest. Hum. Brain Mapp. 2008. 29: 751–761. https://doi.org/10.1002/hbm.20580
  137. Shulman G.L., Fiez J.A., Corbetta M., Buckner R.L., Miezin F.M., Raichle M.E., Petersen S.E. Common Blood Flow Changes across Visual Tasks: II. Decreases in Cerebral Cortex. Journal of Cognitive Neuroscience. 1997. 9 (5): 648–663. https://doi.org/10.1162/jocn.1997.9.5.648
  138. Sizemore A.E., Phillips-Cremins J.E., Ghrist R., Bassett D.S. The importance of the whole: Topological data analysis for the network neuroscientist. Network Neuroscience. 2018. 3 (3): 656–673. https://doi.org/10.1162/netn_a_00073
  139. Slioussar N., Korotkov A., Cherednichenko D., Chernigovskaya T., Kireev M. Exploring the nature of morphological regularity: an fMRI study on Russian. Language Cognition and Neuroscience. 2023. 39 (1): 24–39. https://doi.org/10.1080/23273798.2023.2237138
  140. Smith S.M., Fox P.T., Miller K.L., Glahn D.C., Fox P.M., Mackay C.E. et al. Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences. 2009. 106 (31): 13040–13045. https://doi.org/10.1073/pnas.0905267106
  141. Smucny J., Lesh T.A., Zarubin V.C., Niendam T.A., Ragland J.D., Tully L.M., Carter C.S. One-Year stability of frontoparietal cognitive control network connectivity in recent onset schizophrenia: A Task-Related 3T FMRI study. Schizophrenia Bulletin. 2019. 46 (5): 1249–1258. https://doi.org/10.1093/schbul/sbz122
  142. Sporns O., Tononi G., Kötter R. The Human Connectome: A Structural Description of the Human Brain. PLOS Computational Biology. 2005. 1: e42. https://doi.org/10.1371/journal.pcbi.0010042
  143. Stephan K.E., Friston K.J. Analyzing effective connectivity with functional magnetic resonance imaging. WIREs Cognitive Science. 2010. 1 (3): 446–459. https://doi.org/10.1002/wcs.58
  144. Vinck M., Uran C., Spyropoulos G., Onorato I., Broggini A.C., Schneider M., Canales-Johnson A. Principles of large-scale neural interactions. Neuron. 2023. 111: 987–1002. https://doi.org/10.1016/j.neuron.2023.03.015
  145. Vygotsky L. Thought and language. Boston MA: The MIT Press, 1970. 168 p.
  146. Wang H., Fan L., Song M., Liu B., Wu D., Jiang R. et al. Functional connectivity predicts individual development of inhibitory control during adolescence. Cereb. Cortex. 2021. 31: 2686–2700. https://doi.org/10.1093/cercor/bhaa383
  147. Weiss Р. Ergebnisse der Biologie. Berlin: Verlag von Julius Springer, 1928. 582 p.
  148. Wen Z., Seo J., Pace-Schott E.F., Milad M.R. Abnormal dynamic functional connectivity during fear extinction learning in PTSD and anxiety disorders. Molecular Psychiatry. 2022. 27 (4): 2216–2224. https://doi.org/10.1038/s41380-022-01462-5
  149. Wilson H.R., Cowan J.D. Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. 1972. 12: 1–24. https://doi.org/10.1016/s0006-3495(72)86068-5
  150. Yang H., Di X., Gong Q., Sweeney J., Biswal B. Investigating inhibition deficit in schizophrenia using task-modulated brain networks. Brain Structure and Function. 2020. 225 (5): 1601–1613. https://doi.org/10.1007/s00429-020-02078-7
  151. Yu R., Han B., Wu X., Wei G., Zhang J., Ding M., Wen X. Dual-functional network regulation underlies the central executive system in working memory. Neuroscience. 2023. 524: 158–180. https://doi.org/10.1016/j.neuroscience.2023.05.025
  152. Yu Y., Herman P., Rothman D.L., Agarwal D., Hyder F. Evaluating the gray and white matter energy budgets of human brain function. J. Cereb. Blood Flow Metab. 2018. 38: 1339–1353. https://doi.org/10.1177/0271678x17708691
  153. Zhang J., Kucyi A., Raya J., Nielsen A.N., Nomi J.S., Damoiseaux J.S. et al. What have we really learned from functional connectivity in clinical populations? NeuroImage. 2021. 242: 118466. https://doi.org/10.1016/j.neuroimage.2021.118466
  154. Zhao W., Makowski C., Hagler D.J., Garavan H.P., Thompson W.K., Greene D.J. et al. Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity. NeuroImage. 2023. 270: 119946. https://doi.org/10.1016/j.neuroimage.2023.119946
  155. Zheltyakova M., Korotkov A., Masharipov R., Myznikov A., Didur M., Cherednichenko D., et al. Social interaction with an anonymous opponent requires increased involvement of the theory of mind neural system: an FMRI study. Frontiers in Behavioral Neuroscience. 2022. 16. https://doi.org/10.3389/fnbeh.2022.807599

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML

Declaração de direitos autorais © Russian Academy of Sciences, 2025

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».