Topological data analysis suggests human brain network reconfiguration during the transition from resting state to cognitive load

Abstract

BACKGROUND: Neural networks of the brain continually adapt to changing environmental demands. The network approach in neuroscience, which focuses on the analysis of structural and functional network characteristics related to cognitive functions, is a highly promising avenue for understanding the psychophysiological mechanisms underlying the adaptive dynamics of cognitive processes.

AIM: We aimed to explore how the topological features of functional connectomes in the human brain are linked to different cognitive demands. The focus was on understanding the dynamic changes in brain networks during working memory tasks to identify network characteristics inherent to working memory.

METHODS: We examined the topological characteristics of functional brain networks in the resting state and cognitive load provided by the execution of the Sternberg Item Recognition Paradigm based on electroencephalographic data. Electroencephalogram traces from 67 healthy adults were processed to estimate functional connectivity using the coherence method. We propose that the topological properties of functional networks in the human brain are distinct between cognitive load and resting state, with higher integration in the networks during cognitive load.

RESULTS: The topological features of functional connectomes depend on the current state of cognitive processing and change with task-induced cognitive load variation. Moreover, functional connectivity during working memory tasks showed a faster emergence of homology group generators, supporting the idea of a relationship between the initial stages of working memory execution and an increase in faster network integration, with connector hubs playing a crucial role.

CONCLUSION: Collected evidence suggest that cognitive states, particularly those related to working memory, are associated with distinct topological properties of functional brain networks, highlighting the importance of network dynamics in cognitive processing.

Keywords

cognitive neuroscience / functional neuroimaging / brain electrical activity mapping / connectome mapping / working memory

References

[1] Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393(6684):440–442. doi: 10.1038/30918

[2] Watts D.J., Strogatz S.H. Collective dynamics of ‘small-world’ networks // Nature. 1998. Vol. 393, N 6684. P. 440–442. doi: 10.1038/30918

[3] Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol. 2007;3(2):e17. doi: 10.1371/journal.pcbi.0030017

[4] Achard S., Bullmore E. Efficiency and cost of economical brain functional networks // PLoS Comput Biol. 2007. Vol. 3, N 2. P. e17. doi: 10.1371/journal.pcbi.0030017

[5] He Y, Wang J, Wang L, et al. Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS One. 2009;4(4):e5226. doi: 10.1371/journal.pone.0005226

[6] He Y., Wang J., Wang L., et al. Uncovering intrinsic modular organization of spontaneous brain activity in humans // PLoS One. 2009. Vol. 4, N 4. P. e5226. doi: 10.1371/journal.pone.0005226

[7] Sporns O, Honey CJ, Kötter R. Identification and classification of hubs in brain networks. PLoS One. 2007;2(10):e1049. doi: 10.1371/journal.pone.0001049

[8] Sporns O., Honey C.J., Kötter R. Identification and Classification of Hubs in Brain Networks // PLoS One. 2007. Vol. 2, N 10. P. e1049. doi: 10.1371/journal.pone.0001049

[9] Cole MW, Reynolds JR, Power JD, et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci. 2013;16(9):1348–1355. doi: 10.1038/nn.3470

[10] Cole M.W., Reynolds J.R., Power J.D., et al. Multi-task connectivity reveals flexible hubs for adaptive task control // Nat Neurosci. 2013. Vol. 16, N 9. P. 1348–1355. doi: 10.1038/nn.3470

[11] Seeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27(9):2349–2356. doi: 10.1523/JNEUROSCI.5587-06.2007

[12] Seeley W.W., Menon V., Schatzberg A.F., et al. Dissociable intrinsic connectivity networks for salience processing and executive control // J Neurosci. 2007. Vol. 27, N 9. P. 2349–2356. doi: 10.1523/JNEUROSCI.5587-06.2007

[13] Byrge L, Sporns O, Smith LB. Developmental process emerges from extended brain–body–behavior networks. Trends Cogn Sci. 2014;18(8):395–403. doi: 10.1016/j.tics.2014.04.010

[14] Byrge L., Sporns O., Smith L.B. Developmental process emerges from extended brain–body–behavior networks // Trends Cogn Sci. 2014. Vol. 18, N 8. P. 395–403. doi: 10.1016/j.tics.2014.04.010

[15] Hagmann P, Kurant M, Gigandet X, et al. Mapping human whole-brain structural networks with diffusion MRI. PLoS One. 2007;2(7):e597. doi: 10.1371/journal.pone.0000597

[16] Hagmann P., Kurant M., Gigandet X., et al. Mapping human whole-brain structural networks with diffusion MRI // PLoS One. 2007. Vol. 2, N 7. P. e597. doi: 10.1371/journal.pone.0000597

[17] Park HJ, Friston K. Structural and functional brain networks: from connections to cognition. Science. 2013;342(6158):1238411. doi:10.1126/science.1238411

[18] Park H.J., Friston K. Structural and functional brain networks: from connections to cognition // Science. 2013. Vol. 342, N 6158. P. 1238411. doi: 10.1126/science.1238411

[19] Chen J, Müller VI, Dukart J, et al. Intrinsic connectivity patterns of task-defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to molecular architecture. Biol Psychiatry. 2021;89(3):308–319. doi: 10.1016/j.biopsych.2020.09.024

[20] Chen J., Müller V.I., Dukart J., et al. Intrinsic connectivity patterns of task-defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to molecular architecture // Biol Psychiatry. 2021. Vol. 89, N 3. P. 308–319. doi: 10.1016/j.biopsych.2020.09.024

[21] Yeo BT, Krienen FM, Sepulcre J, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(3):1125–1165. doi: 10.1152/jn.00338.2011

[22] Yeo B.T., Krienen F.M., Sepulcre J., et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity // J Neurophysiol. 2011. Vol. 106, N 3. P. 1125–1165. doi: 10.1152/jn.00338.2011

[23] McAuley JJ, Costa L da F, Caetano TS. The rich-club phenomenon across complex network hierarchies. Appl Phys Lett. 2007;91(8):084103. doi: 10.1063/1.2773951

[24] McAuley J.J., Costa L. da F., Caetano T.S. The rich-club phenomenon across complex network hierarchies // Appl Phys Lett. 2007. Vol. 91, N 8. P. 084103. doi: 10.1063/1.2773951

[25] Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012;13(5):336–349. doi: 10.1038/nrn3214

[26] Bullmore E., Sporns O. The economy of brain network organization // Nat Rev Neurosci. 2012. Vol. 13, N 5. P. 336–349. doi: 10.1038/nrn3214

[27] Gu S, Pasqualetti F, Cieslak M, et al. Controllability of structural brain networks. Nat Commun. 2015;6:8414. doi: 10.1038/ncomms9414

[28] Gu S., Pasqualetti F., Cieslak M., et al. Controllability of structural brain networks // Nat Commun. 2015. Vol. 6. P. 8414. doi: 10.1038/ncomms9414

[29] Marvel CL, Desmond JE. Functional topography of the cerebellum in verbal working memory. Neuropsychol Rev. 2010;20(3):271–279. doi: 10.1007/s11065-010-9137-7

[30] Marvel C.L., Desmond J.E. Functional topography of the cerebellum in verbal working memory // Neuropsychol Rev. 2010. Vol. 20, N 3. P. 271–279. doi: 10.1007/s11065-010-9137-7

[31] Baars BJ, Gage NM. Cognition, brain, and consciousness: introduction to cognitive neuroscience. Academic Press; 2010.

[32] Baars B.J., Gage N.M. Cognition, brain, and consciousness: introduction to cognitive neuroscience. Academic Press, 2010.

[33] Vatansever D, Manktelow AE, Sahakian BJ, et al. Angular default mode network connectivity across working memory load. Hum Brain Mapp. 2017;38(1):41–52. doi: 10.1002/hbm.23341

[34] Vatansever D., Manktelow A.E., Sahakian B.J., et al. Angular default mode network connectivity across working memory load // Hum Brain Mapp. 2017. Vol. 38, N 1. P. 41–52. doi: 10.1002/hbm.23341

[35] Avery EW, Yoo K, Rosenberg MD, et al. Distributed patterns of functional connectivity predict working memory performance in novel healthy and memory-impaired individuals. J Cogn Neurosci. 2020;32(2):241–255. doi: 10.1162/jocn_a_01487

[36] Avery E.W., Yoo K., Rosenberg M.D., et al. Distributed patterns of functional connectivity predict working memory performance in novel healthy and memory-impaired individuals // J Cogn Neurosci. 2020. Vol. 32, N 2. P. 241–255. doi: 10.1162/jocn_a_01487

[37] Greene AS, Gao S, Scheinost D, Constable RT. Task-induced brain state manipulation improves prediction of individual traits. Nat Commun. 2018;9(1):2807. doi: 10.1038/s41467-018-04920-3

[38] Greene A.S., Gao S., Scheinost D., Constable R.T. Task-induced brain state manipulation improves prediction of individual traits // Nat Commun. 2018. Vol. 9, N 1. P. 2807. doi: 10.1038/s41467-018-04920-3

[39] Finc K, Bonna K, He X, et al. Dynamic reconfiguration of functional brain networks during working memory training. Nat Commun. 2020;11(1):2435. Corrected and republished from: Nat Commun. 2020;11(1):3891. doi: 10.1038/s41467-020-15631-z

[40] Finc K., Bonna K., He X., et al. Dynamic reconfiguration of functional brain networks during working memory training: 1 // Nat Commun. 2020. Vol. 11, N 1. P. 2807. doi: 10.1038/s41467-018-04920-3

[41] Sternberg S. High-speed scanning in human memory. Science. 1966;153(3736):652–654. doi: 10.1126/science.153.3736.652

[42] Sternberg S. High-speed scanning in human memory // Science. 1966. Vol. 153, N 3736. P. 652–654. doi: 10.1126/science.153.3736.652

[43] Jas M, Engemann DA, Bekhti Y, et al. Autoreject: automated artifact rejection for MEG and EEG data. Neuroimage. 2017;159:417–429. doi: 10.1016/j.neuroimage.2017.06.030

[44] Jas M., Engemann D.A., Bekhti Y., et al. Autoreject: automated artifact rejection for MEG and EEG data // Neuromage. 2017. Vol. 159. P. 417–429. doi: 10.1016/j.neuroimage.2017.06.030

[45] Dale AM, Liu AK, Fischl BR, et al. Dynamic statistical parametric mapping. Neuron. 2000;26(1):55–67. doi: 10.1016/S0896-6273(00)81138-1

[46] Dale A.M., Liu A.K., Fischl B.R., et al. Dynamic statistical parametric mapping // Neuron. 2000. Vol. 26, N 1. P. 55–67. doi: 10.1016/S0896-6273(00)81138-1

[47] Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53(1):1–15. doi: 10.1016/j.neuroimage.2010.06.010

[48] Destrieux C., Fischl B., Dale A., Halgren E., et al. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature // Neuroimage. 2010. Vol. 53, N 1. P. 1–15. doi: 10.1016/j.neuroimage.2010.06.010

[49] Nunez PL, Silberstein RB, Shi Z, et al. EEG coherency II: experimental comparisons of multiple measures. Clin Neurophysiol. 1999;110(3):469–486. doi: 10.1016/s1388-2457(98)00043-1

[50] Nunez P.L., Silberstein R.B., Shi Z., et al. EEG coherency II: experimental comparisons of multiple measures // Clin Neurophysiol. 1999. Vol. 110, N 3. P. 469–486. doi: 10.1016/s1388-2457(98)00043-1

[51] Buchanan CR, Bastin ME, Ritchie SJ, et al. The effect of network thresholding and weighting on structural brain networks in the UK Biobank. Neuroimage. 2020;211:116443. doi: 10.1016/j.neuroimage.2019.116443

[52] Buchanan C.R., Bastin M.E., Ritchie S.J., et al. The effect of network thresholding and weighting on structural brain networks in the UK Biobank // Neuroimage. 2020. Vol. 211. P. 116443. doi: 10.1016/j.neuroimage.2019.116443

[53] Civier O, Smith RE, Yeh CH, et al. Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI? Neuroimage. 2019;194:68–81. doi: 10.1016/j.neuroimage.2019.02.039

[54] Civier O., Smith R.E., Yeh C.H., et al. Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI? // Neuroimage. 2019. Vol. 194. P. 68–81. doi: 10.1016/j.neuroimage.2019.02.039

[55] Zakharov I, Tabueva A, Adamovich T, et al. Alpha band resting-state EEG connectivity is associated with non-verbal intelligence. Front Hum Neurosci. 2020;14:10. doi: 10.3389/fnhum.2020.00010

[56] Zakharov I., Tabueva A., Adamovich T., et al. Alpha band resting-state EEG connectivity is associated with non-verbal intelligence // Front Hum Neurosci. 2020. Vol. 14. P. 10. doi: 10.3389/fnhum.2020.00010

[57] Fefferman C, Mitter S, Narayanan H. Testing the manifold hypothesis. J Amer Math Soc. 2016;29(4):983–1049. doi: 10.1090/jams/852

[58] Fefferman C., Mitter S., Narayanan H. Testing the manifold hypothesis // J Amer Math Soc. 2016. Vol. 29, N 4. P. 983–1049. doi: 10.1090/jams/852

[59] Onuchin A, Kachan O. Individual topology structure of eye movement trajectories. In: Kryzhanovsky B, Dunin-Barkowski W, Redko V, Tiumentsev Y, editors. Advances in Neural Computation, Machine Learning, and Cognitive Research VI. Vol 1064. Studies in computational intelligence. Springer International Publishing; 2023. P. 45–55. doi: 10.1007/978-3-031-19032-2_5

[60] Onuchin A., Kachan O. Individual topology structure of eye movement trajectories. In: Kryzhanovsky B., Dunin-Barkowski W., Redko V., Tiumentsev Y., editors. Advances in Neural Computation, Machine Learning, and Cognitive Research VI. Vol. 1064. Studies in computational intelligence. Springer International Publishing, 2023. P. 45–55.

[61] Kachan O, Onuchin A. Topological data analysis of eye movements. Proceedings of the science conference 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI); 2021 Apr 13–16; P. 1398–1401. doi: 10.1109/ISBI48211.2021.9433898

[62] Kachan O., Onuchin A. Topological data analysis of eye movementsю Proceedings of the science conference 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI); 2021 Apr 13–16; P. 1398–1401. doi: 10.1109/ISBI48211.2021.9433898

[63] Barannikov S, Trofimov I, Balabin N, Burnaev E. Representation topology divergence: a method for comparing neural network representations. 2023. Available from: http://arxiv.org/abs/2201.00058

[64] Barannikov S., Trofimov I., Balabin N., Burnaev E. Representation topology divergence: a method for comparing neural network representations. 2023. Доступ по ссылке: http://arxiv.org/abs/2201.00058

[65] Maria C, Boissonnat JD, Glisse M, Yvinec M. The gudhi library: simplicial complexes and persistent homology. In: Hong H, Yap C, editors. Mathematical Software — ICMS 2014. Vol. 8592. Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2014. P. 167–174. doi: 10.1007/978-3-662-44199-2_28

[66] Maria C., Boissonnat J.D., Glisse M., Yvinec M. The gudhi library: simplicial complexes and persistent homology. In: Hong H., Yap C., editors. Mathematical Software — ICMS 2014. Vol. 8592. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2014. P. 167–174. doi: 10.1007/978-3-662-44199-2_28

[67] Giusti C, Pastalkova E, Curto C, Itskov V. Clique topology reveals intrinsic geometric structure in neural correlations. Proc Natl Acad Sci U S A. 2015;112(44):13455–13460. doi: 10.1073/pnas.1506407112

[68] Giusti C., Pastalkova E., Curto C., Itskov V. Clique topology reveals intrinsic geometric structure in neural correlations // Proc Natl Acad Sci U S A. 2015. Vol. 112, N 44. P. 13455–13460. doi: 10.1073/pnas.1506407112

[69] Bacigalupo F, Luck SJ. Lateralized suppression of alpha-band EEG activity as a mechanism of target processing. J Neurosci. 2019;39(5): 900–917. doi: 10.1523/JNEUROSCI.0183-18.2018

[70] Bacigalupo F., Luck S.J. Lateralized suppression of alpha-band EEG activity as a mechanism of target processing // J Neurosci. 2019. Vol. 39, N 5. P. 900–917. doi: 10.1523/JNEUROSCI.0183-18.2018

[71] Küchenhoff S, Sorg C, Schneider SC, et al. Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks. Eur J Neurosci. 2021;53(10):3362–3377. doi: 10.1111/ejn.15206

[72] Küchenhoff S., Sorg C., Schneider S.C., et al. Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks // Eur J Neurosci. 2021. Vol. 53, N 10. P. 3362–3377. doi: 10.1111/ejn.15206

[73] Baars BJ. A cognitive theory of consciousness. Cambridge University Press; 1988.

[74] Baars B.J. A cognitive theory of consciousness. Cambridge University Press; 1988.

[75] Shanahan M. Embodiment and the inner lifecognition and consciousness in the space of possible minds. Oxford University Press; 2010. doi: 10.1093/acprof:oso/9780199226559.001.0001

[76] Shanahan M. Embodiment and the inner lifecognition and consciousness in the space of possible minds. Oxford University Press; 2010. doi: 10.1093/acprof:oso/9780199226559.001.000

[77] Dehaene S, Changeux JP. Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness. PLoS Biol. 2005;3(5):e141. doi: 10.1371/journal.pbio.0030141

[78] Dehaene S., Changeux J.P. Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness // PLoS Biol. 2005. Vol. 3, N 5. P. e141. doi: 10.1371/journal.pbio.0030141

[79] Gómez-Gardeñes J, Zamora-López G, Moreno Y, Arenas A. From modular to centralized organization of synchronization in functional areas of the cat cerebral cortex. PLoS One. 2010;5(8):e12313. doi: 10.1371/journal.pone.0012313

[80] Gómez-Gardeñes J., Zamora-López G., Moreno Y., Arenas A. From modular to centralized organization of synchronization in functional areas of the cat cerebral cortex // PLoS One. 2010. Vol. 5, N 8. P. e12313. doi: 10.1371/journal.pone.0012313

[81] Laird AR, Fox PM, Eickhoff SB, et al. Behavioral interpretations of intrinsic connectivity networks. J Cogn Neurosci. 2011;23(12):4022–4037. doi: 10.1162/jocn_a_00077

[82] Laird A.R., Fox P.M., Eickhoff S.B., et al. Behavioral interpretations of intrinsic connectivity networks // J Cogn Neurosci. 2011. Vol. 23, N 12. P. 4022–4037. doi: 10.1162/jocn_a_00077

[83] Toppi J, Astolfi L, Risetti M, et al. Different topological properties of eeg-derived networks describe working memory phases as revealed by graph theoretical analysis. Front Hum Neurosci. 2018;11:637. doi: 10.3389/fnhum.2017.00637

[84] Toppi J., Astolfi L., Risetti M., et al. Different topological properties of EEG-derived networks describe working memory phases as revealed by graph theoretical analysis // Front Hum Neurosci. 2018. Vol. 11. P. 637. doi: 10.3389/fnhum.2017.00637

[85] Eriksson J, Vogel EK, Lansner A, et al. Neurocognitive architecture of working memory. Neuron. 2015;88(1):33–46. doi: 10.1016/j.neuron.2015.09.020

[86] Eriksson J., Vogel E.K., Lansner A., et al. Neurocognitive architecture of working memory // Neuron. 2015. Vol. 88, N 1. P. 33–46. doi: 10.1016/j.neuron.2015.09.020

RIGHTS & PERMISSIONS

Eco-Vector