Comparative analysis of working memory tasks in fMRI and MEG studies

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Abstract

Context and relevance. To date, the study of brain correlates of working memory (WM) is associated with a number of theoretical as well as methodological difficulties. Firstly, substantially variable tasks are used to assess WM. Secondly, each neuroimaging method has its own characteristics and limitations. Objective. The aim of this paper was to systematize the tasks used to study the brain correlates of WM, as well as to analyze these paradigms in terms of the possibility and feasibility of their parallel use in fMRI and MEG studies, taking into account the specific requirements of both methods. Methods and materials. A literature search in the PubMed database identified 1,505 empirical studies published from 1995 to 2023 in which brain correlates of WM were studied using fMRI and/or MEG. The vast majority of them (1,398) used fMRI; 103 used MEG; 4 studies used both methods. Results. The analysis showed that the most frequently used tasks are the n-back task and the delayed match-to-sample task, including the Sternberg task. The considered tasks can use both verbal (e.g., letters, numbers, words, etc.) and non-verbal stimuli; they can be presented in different modalities (visual, auditory, and even tactile or vibrotactile). Conclusions. The features of these tasks and the possibility of their implementation in studies using fMRI and MEG are described.

General Information

Keywords: working memory, n-back task, Sternberg task, fMRI, MEG

Journal rubric: Methodology of Psychological Research

Article type: scientific article

DOI: https://doi.org/10.17759/exppsy.2025180112

Funding. The reported study was funded by Russian Science Foundation, project number 23-78-00008, https://rscf.ru/project/23-78-00008/ «Refined understanding of neural underpinnings of working memory in adult and ageing population through the combined use of fMRI and MEG data».

Received: 25.11.2024

Accepted:

For citation: Pechenkova E.V., Korolkova O.A., Panikratova Y.R., Pchelintseva M.E., Sinitsyn V.E. Comparative analysis of working memory tasks in fMRI and MEG studies. Eksperimental'naâ psihologiâ = Experimental Psychology (Russia), 2025. Vol. 18, no. 1, pp. 181–199. DOI: 10.17759/exppsy.2025180112. (In Russ., аbstr. in Engl.)

References

  1. Величковский, Б.Б. (2014). Тестирование рабочей памяти: от простого к сложному и снова к простому. Теоретическая и экспериментальная психология, 7(2), 133—142.
    Velichkovsky, B.B. (2014). Testing working memory: from simple to complex and back to simple. Theoretical and experimental psychology, 7(2), 133—142. (In Russ.).
  2. Ahveninen, J., Seidman, L.J., Chang, W.-T., Hämäläinen, M., Huang, S. (2017). Suppression of irrelevant sounds during auditory working memory. NeuroImage, 161, 1—8. https://doi.org/10.1016/j.neuroimage.2017.08.040
  3. Almodóvar-Payá, C., Guardiola-Ripoll, M., Giralt-López, M., Gallego, C., Salgado-Pineda, P., Miret, S., Salvador, R., Muñoz, M.J., Lázaro, L., Guerrero-Pedraza, A., Parellada, M., Carrión, M.I., Cuesta, M.J., Maristany, T., Sarró, S., Fañanás, L., Callado, L.F., Arias, B., Pomarol-Clotet, E., Fatjó-Vilas, M. (2022). NRN1 Gene as a Potential Marker of Early-Onset Schizophrenia: Evidence from Genetic and Neuroimaging Approaches. International Journal of Molecular Sciences, 23(13), 7456. https://doi.org/10.3390/ijms23137456
  4. Archer, J.A., Lee, A., Qiu, A., Chen, S.-H.A. (2018). Working memory, age and education: A lifespan fMRI study. PLOS ONE, 13(3), e0194878. https://doi.org/10.1371/journal.pone.0194878
  5. Assem, M., Blank, I.A., Mineroff, Z., Ademoğlu, A., Fedorenko, E. (2020). Activity in the fronto-parietal multiple-demand network is robustly associated with individual differences in working memory and fluid intelligence. Cortex, 131, 1—16. https://doi.org/10.1016/j.cortex.2020.06.013
  6. Baddeley, A., Hitch, G., Allen, R. (2020). A Multicomponent Model of Working Memory. In R. Logie, V. Camos, N. Cowan (Eds.), Working Memory: The state of the science (pp. 10—43). Oxford: Oxford University Press. https://doi.org/10.1093/oso/9780198842286.003.0002
  7. Barrouillet, P., Camos, V. (2020). The Time-Based Resource-Sharing Model of Working Memory. In R. Logie, V. Camos, N. Cowan (Eds.), Working Memory: The state of the science (pp. 85—115). Oxford: Oxford University Press. https://doi.org/10.1093/oso/9780198842286.003.0004
  8. Bauer, E., Sammer, G., Toepper, M. (2015). Trying to Put the Puzzle Together: Age and Performance Level Modulate the Neural Response to Increasing Task Load within Left Rostral Prefrontal Cortex. BioMed Research International, 2015, 1—11. https://doi.org/10.1155/2015/415458
  9. Bomyea, J., Stout, D.M., Simmons, A.N. (2019). Attenuated prefrontal and temporal neural activity during working memory as a potential biomarker of suicidal ideation in veterans with PTSD. Journal of Affective Disorders, 257, 607—614. https://doi.org/10.1016/j.jad.2019.07.050
  10. Bomyea, J., Taylor, C.T., Spadoni, A.D., Simmons, A.N. (2018). Neural mechanisms of interference control in working memory capacity. Human Brain Mapping, 39(2), 772—782. https://doi.org/10.1002/hbm.23881
  11. Brissenden, J.A., Tobyne, S.M., Osher, D.E., Levin, E.J., Halko, M.A., Somers, D.C. (2018). Topographic Cortico-cerebellar Networks Revealed by Visual Attention and Working Memory. Current Biology, 28(21), 3364—3372. https://doi.org/10.1016/j.cub.2018.08.059
  12. Brown, C.A., Jiang, Y., Smith, C.D., Gold, B.T. (2018). Age and Alzheimer’s pathology disrupt default mode network functioning via alterations in white matter microstructure but not hyperintensities. Cortex, 104, 58—74. https://doi.org/10.1016/j.cortex.2018.04.006
  13. Chai, W.J., Abd Hamid, A.I., Abdullah, J.M. (2018). Working Memory From the Psychological and Neurosciences Perspectives: A Review. Frontiers in Psychology, 9, 401. https://doi.org/10.3389/fpsyg.2018.00401
  14. Chuderski, A. (2014). The relational integration task explains fluid reasoning above and beyond other working memory tasks. Memory & Cognition, 42(3), 448—463. https://doi.org/10.3758/s13421-013-0366-x
  15. Clark, C.M., Lawlor-Savage, L., Goghari, V.M. (2017). Functional brain activation associated with working memory training and transfer. Behavioural Brain Research, 334, 34—49. https://doi.org/10.1016/j.bbr.2017.07.030
  16. Cowan, N., Morey, C.C., Naveh-Benjamin, M. (2020). An Embedded-Processes Approach to Working Memory. In R. Logie, V. Camos, N. Cowan (Eds.), Working Memory: The state of the science (pp. 44—84). Oxford: Oxford University Press. https://doi.org/10.1093/oso/9780198842286.003.0003
  17. Crowell, C.A., Davis, S.W., Beynel, L., Deng, L., Lakhlani, D., Hilbig, S.A., Palmer, H., Brito, A., Peterchev, A.V., Luber, B., Lisanby, S.H., Appelbaum, L.G., Cabeza, R. (2020). Older adults benefit from more widespread brain network integration during working memory. NeuroImage, 218, 116959. https://doi.org/10.1016/j.neuroimage.2020.116959
  18. Daneman, M., Carpenter, P.A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19(4), 450—466.
  19. Daume, J., Graetz, S., Gruber, T., Engel, A.K., Friese, U. (2017). Cognitive control during audiovisual working memory engages frontotemporal theta-band interactions. Scientific Reports, 7(1), 12585. https://doi.org/10.1038/s41598-017-12511-3
  20. Ducharme-Laliberté, G., Mellah, S., Boller, B., Belleville, S. (2022). More flexible brain activation underlies cognitive reserve in older adults. Neurobiology of Aging, 113(1), 63—72. https://doi.org/10.1016/j.neurobiolaging.2022.02.001
  21. Ecker, U.K.H., Oberauer, K., Lewandowsky, S. (2014). Working memory updating involves item-specific removal. Journal of Memory and Language, 74, 1—15. https://doi.org/10.1016/j.jml.2014.03.006
  22. Filbey, F.M., Slack, K.J., Sunderland, T.P., Cohen, R.M. (2006). Functional magnetic resonance imaging and magnetoencephalography differences associated with APOEε4 in young healthy adults. NeuroReport, 17(15), 1585—1590. https://doi.org/10.1097/01.wnr.0000234745.27571.d1
  23. Friedman, N.P., Miyake, A. (2017). Unity and diversity of executive functions: Individual differences as a window on cognitive structure. Cortex, 86, 186—204. https://doi.org/10.1016/j.cortex.2016.04.023
  24. Fujimaki, N., Hayakawa, T., Matani, A., Okabe, Y. (2004). Right-lateralized neural activity during inner speech repeated by cues. NeuroReport, 15(15), 2341—2345. https://doi.org/10.1097/00001756-200410250-00008
  25. Gaston, T.E., Allendorfer, J.B., Nair, S., Bebin, E.M., Grayson, L.P., Martin, R.C., Szaflarski, J.P. (2020). Effects of highly purified cannabidiol (CBD) on fMRI of working memory in treatment-resistant epilepsy. Epilepsy & Behavior, 112, 107358. https://doi.org/10.1016/j.yebeh.2020.107358
  26. Germano, C., Kinsella, G.J. (2005). Working Memory and Learning in Early Alzheimer’s Disease. Neuropsychology Review, 15(1), 1—10. https://doi.org/10.1007/s11065-005-3583-7
  27. Goddard, E., Contini, E.W., Irish, M. (2022). Exploring Information Flow from Posteromedial Cortex during Visuospatial Working Memory: A Magnetoencephalography Study. The Journal of Neuroscience, 42(30), 5944—5955. https://doi.org/10.1523/JNEUROSCI.2129-21.2022
  28. Gregory, M.D., Kippenhan, J.S., Callicott, J.H., Rubinstein, D.Y., Mattay, V.S., Coppola, R., Berman, K.F. (2019). Sequence Variation Associated with SLC12A5 Gene Expression Is Linked to Brain Structure and Function in Healthy Adults. Cerebral Cortex, 29(11), 4654—4661. https://doi.org/10.1093/cercor/bhy344
  29. Hahn, B., Robinson, B.M., Leonard, C.J., Luck, S.J., Gold, J.M. (2018). Posterior Parietal Cortex Dysfunction Is Central to Working Memory Storage and Broad Cognitive Deficits in Schizophrenia. The Journal of Neuroscience, 38(39), 8378—8387. https://doi.org/10.1523/JNEUROSCI.0913-18.2018
  30. Hammar, Å., Neto, E., Clemo, L., Hjetland, G.J., Hugdahl, K., Elliott, R. (2016). Striatal hypoactivation and cognitive slowing in patients with partially remitted and remitted major depression. PsyCh Journal, 5(3), 191—205. https://doi.org/10.1002/pchj.134
  31. Harrington, D.L., Shen, Q., Vincent Filoteo, J., Litvan, I., Huang, M., Castillo, G.N., Lee, R. R., Bayram, E. (2020). Abnormal distraction and load-specific connectivity during working memory in cognitively normal Parkinson’s disease. Human Brain Mapping, 41(5), 1195—1211. https://doi.org/10.1002/hbm.24868
  32. Heinzel, S., Kaufmann, C., Grützmann, R., Klawohn, J., Riesel, A., Bey, K., Heilmann-Heimbach, S., Weinhold, L., Ramirez, A., Wagner, M., Kathmann, N. (2021). Polygenic risk for obsessive-compulsive disorder (OCD) predicts brain reacsponse during working memaory task in OCD, unaffected relatives, and healthy controls. Scientific Reports, 11(1), 18914. https://doi.org/10.1038/s41598-021-98333-w
  33. Hoffman, R.M., Trevarrow, M.P., Bergwell, H.R., Embury, C.M., Heinrichs-Graham, E., Wilson, T.W., Kurz, M.J. (2021). Cortical oscillations that underlie working memory are altered in adults with cerebral palsy. Clinical Neurophysiology, 132(4), 938—945. https://doi.org/10.1016/j.clinph.2020.12.029
  34. Huang, S., Seidman, L.J., Rossi, S., Ahveninen, J. (2013). Distinct cortical networks activated by auditory attention and working memory load. NeuroImage, 83, 1098—1108. https://doi.org/10.1016/j.neuroimage.2013.07.074
  35. Jia, K., Li, Y., Gong, M., Huang, H., Wang, Y., Li, S. (2021). Perceptual Learning beyond Perception: Mnemonic Representation in Early Visual Cortex and Intraparietal Sulcus. The Journal of Neuroscience, 41(20), 4476—4486. https://doi.org/10.1523/JNEUROSCI.2780-20.2021
  36. Jiang, Y., Li, J., Schmitt, F.A., Jicha, G.A., Munro, N.B., Zhao, X., Smith, C.D., Kryscio, R.J., Abner, E.L. (2021). Memory-Related Frontal Brainwaves Predict Transition to Mild Cognitive Impairment in Healthy Older Individuals Five Years Before Diagnosis. Journal of Alzheimer’s Disease, 79(2), 531—541. https://doi.org/10.3233/JAD-200931
  37. Koric, L., Volle, E., Seassau, M., Bernard, F.A., Mancini, J., Dubois, B., Pelissolo, A., Levy, R. (2012). How cognitive performance‐induced stress can influence right VLPFC activation: An fMRI study in healthy subjects and in patients with social phobia. Human Brain Mapping, 33(8), 1973—1986. https://doi.org/10.1002/hbm.21340
  38. Kustermann, T., Rockstroh, B., Miller, G.A., Popov, T. (2018). Neural network communication facilitates verbal working memory. Biological Psychology, 136, 119—126. https://doi.org/10.1016/j.biopsycho.2018.05.018
  39. Le, T.M., Borghi, J.A., Kujawa, A.J., Klein, D.N., Leung, H.-C. (2017). Alterations in visual cortical activation and connectivity with prefrontal cortex during working memory updating in major depressive disorder. NeuroImage: Clinical, 14, 43—53. https://doi.org/10.1016/j.nicl.2017.01.004
  40. Lee, B., Cai, W., Young, C.B., Yuan, R., Ryman, S., Kim, J., Santini, V., Henderson, V.W., Poston, K.L., Menon, V. (2022). Latent brain state dynamics and cognitive flexibility in older adults. Progress in Neurobiology, 208, 102180. https://doi.org/10.1016/j.pneurobio.2021.102180
  41. Li, X., Yi, Z., Lv, Q., Chu, M., Hu, H., Wang, J., Zhang, J., Cheung, E.E.F., Chan, R.C.K. (2019). Clinical utility of the dual n-back task in schizophrenia: A functional imaging approach. Psychiatry Research: Neuroimaging, 284, 37—44. https://doi.org/10.1016/j.pscychresns.2019.01.002
  42. Logie, R.H., Belletier, C., Doherty, J.M. (2020). Integrating Theories of Working Memory. In R. Logie, V. Camos, N. Cowan (Eds.), Working Memory: The state of the science (pp. 389—430). Oxford: Oxford University Press. https://doi.org/10.1093/oso/9780198842286.003.0014
  43. Luck, S.J., Vogel, E.K. (2013). Visual working memory capacity: from psychophysics and neurobiology to individual differences. Trends in Cognitive Sciences, 17(8), 391—400. https://doi.org/10.1016/j.tics.2013.06.006
  44. Lugtmeijer, S., Geerligs, L., Tsvetanov, K.A., Mitchell, D.J., Cam-CAN, Campbell, K.L. (2023). Lifespan differences in visual short-term memory load-modulated functional connectivity. NeuroImage, 270, 119982. https://doi.org/10.1016/j.neuroimage.2023.119982
  45. Markiewicz, C.J., Bohland, J.W. (2016). Mapping the cortical representation of speech sounds in a syllable repetition task. NeuroImage, 141, 174—190. https://doi.org/10.1016/j.neuroimage.2016.07.023
  46. Marvel, C.L., Desmond, J.E. (2012). From storage to manipulation: How the neural correlates of verbal working memory reflect varying demands on inner speech. Brain and Language, 120(1), 42—51. https://doi.org/10.1016/j.bandl.2011.08.005
  47. Meier, T.B., Nair, V.A., Meyerand, M.E., Birn, R.M., Prabhakaran, V. (2014). The neural correlates of age effects on verbal—spatial binding in working memory. Behavioural Brain Research, 266, 146—152. https://doi.org/10.1016/j.bbr.2014.03.005
  48. Miró-Padilla, A., Bueichekú, E., Ávila, C. (2020). Locating neural transfer effects of n-back training on the central executive: a longitudinal fMRI study. Scientific Reports, 10(1), 5226. https://doi.org/10.1038/s41598-020-62067-y
  49. Miyake, A., Shah, P. (1999). Models of Working Memory: Mechanisms of Active Maintenance and Executive Control (A. Miyake & P. Shah (eds.)). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139174909
  50. Mruczek, R.E.B., Killebrew, K.W., Berryhill, M.E. (2019). Individual differences reveal limited mixed-category effects during a visual working memory task. Neuropsychologia, 122, 1—10. https://doi.org/10.1016/j.neuropsychologia.2018.12.005
  51. Mukherjee, P., Hartanto, T., Iosif, A.-M., Dixon, J.F., Hinshaw, S.P., Pakyurek, M., van den Bos, W., Guyer, A. E., McClure, S. M., Schweitzer, J. B., Fassbender, C. (2021). Neural basis of working memory in ADHD: Load versus complexity. NeuroImage: Clinical, 30, 102662. https://doi.org/10.1016/j.nicl.2021.102662
  52. Noguchi, Y., Kakigi, R. (2020). Temporal codes of visual working memory in the human cerebral cortex. NeuroImage, 222, 117294. https://doi.org/10.1016/j.neuroimage.2020.117294
  53. Oberauer, K. (2020). Towards a Theory of Working Memory. In R. Logie, V. Camos, N. Cowan (Eds.), Working Memory: The state of the science (pp. 116—149). Oxford University Press. https://doi.org/10.1093/oso/9780198842286.003.0005
  54. Osaka, M., Kaneda, M., Azuma, M., Yaoi, K., Shimokawa, T., Osaka, N. (2021). Capacity differences in working memory based on resting state brain networks. Scientific Reports, 11(1), 19502. https://doi.org/10.1038/s41598-021-98848-2
  55. Osaka, M., Osaka, N., Kondo, H., Morishita, M., Fukuyama, H., Aso, T., Shibasaki, H. (2003). The neural basis of individual differences in working memory capacity: an fMRI study. NeuroImage, 18(3), 789—797. https://doi.org/10.1016/S1053-8119(02)00032-0
  56. Othman, E.A., Yusoff, A.N., Mohamad, M., Abdul Manan, H., Abd Hamid, A.I., Giampietro, V. (2020). Hemispheric Lateralization of Auditory Working Memory Regions During Stochastic Resonance: An fMRI Study. Journal of Magnetic Resonance Imaging, 51(6), 1821—1828. https://doi.org/10.1002/jmri.27016
  57. Pavlov, Y.G., Kotchoubey, B. (2022). Oscillatory brain activity and maintenance of verbal and visual working memory: A systematic review. Psychophysiology, 59(5), e13735. https://doi.org/10.1111/psyp.13735
  58. Pennock, I.M.L., Schmidt, T.T., Zorbek, D., Blankenburg, F. (2021). Representation of visual numerosity information during working memory in humans: An fMRI decoding study. Human Brain Mapping, 42(9), 2778—2789. https://doi.org/10.1002/hbm.25402
  59. Peterburs, J., Blevins, L.C., Sheu, Y.-S., Desmond, J.E. (2019). Cerebellar contributions to sequence prediction in verbal working memory. Brain Structure and Function, 224(1), 485—499. https://doi.org/10.1007/s00429-018-1784-0
  60. Peterburs, J., Liang, Y., Cheng, D.T., Desmond, J.E. (2021). Sensory acquisition functions of the cerebellum in verbal working memory. Brain Structure and Function, 226(3), 833—844. https://doi.org/10.1007/s00429-020-02212-5
  61. Postle, B.R. (2020). Cognitive Neuroscience of Visual Working Memory. In R. Logie, V. Camos, N. Cowan (Eds.), Working Memory: The state of the science (pp. 333—357). Oxford University Press. https://doi.org/10.1093/oso/9780198842286.003.0012
  62. Schmidt, T.T., Wu, Y., Blankenburg, F. (2017). Content-Specific Codes of Parametric Vibrotactile Working Memory in Humans. The Journal of Neuroscience, 37(40), 9771—9777. https://doi.org/10.1523/JNEUROSCI.1167-17.2017
  63. Serrano, N., López-Sanz, D., Bruña, R., Garcés, P., Rodríguez-Rojo, I. C., Marcos, A., Crespo, D.P., Maestú, F. (2020). Spatiotemporal Oscillatory Patterns During Working Memory Maintenance in Mild Cognitive Impairment and Subjective Cognitive Decline. International Journal of Neural Systems, 30(1), 1950019. https://doi.org/10.1142/S0129065719500199
  64. Sobczak-Edmans, M., Ng, T.H.B., Chan, Y.C., Chew, E., Chuang, K.H., Chen, S.H.A. (2016). Temporal dynamics of visual working memory. NeuroImage, 124(Pt A), 1021—1030. https://doi.org/10.1016/j.neuroimage.2015.09.038
  65. Soloveva, M.V, Jamadar, S.D., Velakoulis, D., Poudel, G., Georgiou-Karistianis, N. (2020). Brain compensation during visuospatial working memory in premanifest Huntington’s disease. Neuropsychologia, 136, 107262. https://doi.org/10.1016/j.neuropsychologia.2019.107262
  66. Stäblein, M., Storchak, H., Ghinea, D., Kraft, D., Knöchel, C., Prvulovic, D., Bittner, R.A., Reif, A., Oertel-Knöchel, V. (2019). Visual working memory encoding in schizophrenia and first-degree relatives: neurofunctional abnormalities and impaired consolidation. Psychological Medicine, 49(1), 75—83. https://doi.org/10.1017/S003329171800051X
  67. Steffener, J., Habeck, C., Franklin, D., Lau, M., Yakoub, Y., Gad, M. (2022). Subjective difficulty in a verbal recognition-based memory task: Exploring brain-behaviour relationships at the individual level in healthy young adults. NeuroImage, 257, 119301. https://doi.org/10.1016/j.neuroimage.2022.119301
  68. Sternberg, S. (1966). High-Speed Scanning in Human Memory. Science, 153(3736), 652—654. https://doi.org/10.1126/science.153.3736.652
  69. Swanson, H.L., Alloway, T.P. (2012). Working memory, learning, and academic achievement. In K.R. Harris, S. Graham, T. Urdan, C.B. McCormick, G.M. Sinatra, J. Sweller (Eds.), APA educational psychology handbook, Vol 1: Theories, constructs, and critical issues. (Vol. 1, pp. 327—366). American Psychological Association. https://doi.org/10.1037/13273-012
  70. Tang, R., Etzel, J.A., Kizhner, A., Braver, T.S. (2021). Frontoparietal pattern similarity analyses of cognitive control in monozygotic twins. NeuroImage, 241, 118415. https://doi.org/10.1016/j.neuroimage.2021.118415
  71. Tüdös, Z., Hok, P., Hrdina, L., Hluštík, P. (2014). Modality effects in paced serial addition task: Differential responses to auditory and visual stimuli. Neuroscience, 272, 10—20. https://doi.org/10.1016/j.neuroscience.2014.04.057
  72. van’t Westeinde, A., Zimmermann, M., Messina, V., Karlsson, L., Padilla, N., Lajic, S. (2020). First Trimester DEX Treatment Is Not Associated with Altered Brain Activity During Working Memory Performance in Adults. The Journal of Clinical Endocrinology & Metabolism, 105(11), e4074—e4082. https://doi.org/10.1210/clinem/dgaa611
  73. Wianda, E., Ross, B. (2019). The roles of alpha oscillation in working memory retention. Brain and Behavior, 9(4), e01263. https://doi.org/10.1002/brb3.1263
  74. Wijeakumar, S., Spencer, J. (2020). A Dynamic Field Theory of Visual Working Memory. In R. Logie, V. Camos, N. Cowan (Eds.), Working Memory: The state of the science (pp. 358—388). Oxford: Oxford University Press. https://doi.org/10.1093/oso/9780198842286.003.0013
  75. Winston, G.P., Stretton, J., Sidhu, M.K., Symms, M.R., Thompson, P.J., Duncan, J.S. (2013). Structural correlates of impaired working memory in hippocampal sclerosis. Epilepsia, 54(7), 1143—1153. https://doi.org/10.1111/epi.12193
  76. Witt, S. T., Drissi, N.M., Tapper, S., Wretman, A., Szakács, A., Hallböök, T., Landtblom, A.-M., Karlsson, T., Lundberg, P., Engström, M. (2018). Evidence for cognitive resource imbalance in adolescents with narcolepsy. Brain Imaging and Behavior, 12(2), 411—424. https://doi.org/10.1007/s11682-017-9706-y
  77. Yang, P., Fan, C., Wang, M., Fogelson, N., Li, L. (2017). The effects of changes in object location on object identity detection: A simultaneous EEG-fMRI study. NeuroImage, 157, 351—363. https://doi.org/10.1016/j.neuroimage.2017.06.031
  78. Ye, Z., Zhang, G., Li, S., Zhang, Y., Xiao, W., Zhou, X., Münte, T.F. (2020). Age differences in the fronto-striato-parietal network underlying serial ordering. Neurobiology of Aging, 87, 115—124. https://doi.org/10.1016/j.neurobiolaging.2019.12.007
  79. Zhao, W., Chen, X., Zhang, Q., Du, B., Deng, X., Ji, F., Xiang, Y.-T., Wang, C., Dong, Q., Chen, C., Li, J. (2020). Effect of ZNF804A gene polymorphism (rs1344706) on the plasticity of the functional coupling between the right dorsolateral prefrontal cortex and the contralateral hippocampal formation. NeuroImage: Clinical, 27, 102279. https://doi.org/10.1016/j.nicl.2020.102279

Information About the Authors

Ekaterina V. Pechenkova, Candidate of Science (Psychology), Senior Research Scientist, Moscow Center for Continuous Mathematical Education, Associate Professor, Faculty of Social Sciences, Department of Psychology, Head of Laboratory, Faculty of Social Sciences, Department of Psychology, Research and Educational Laboratory of Cognitive Research, HSE University, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-3409-3703, e-mail: evpech@gmail.com

Olga A. Korolkova, Candidate of Science (Psychology), Associate Professor, Leading Research Associate, Center of Experimental Psychology, Moscow State University of Psychology and Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-4814-7266, e-mail: olga.kurakova@gmail.com

Yana R. Panikratova, Candidate of Science (Psychology), Senior Research Scientist, Moscow Center for Continuous Mathematical Education, Mental Health Research Center, Research Scientist at the Laboratory of Neuroimaging and Multimodal Analysis, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-5698-4251, e-mail: panikratova@mail.ru

Mariia E. Pchelintseva, Research Scientist, Moscow Center for Continuous Mathematical Education, Laboratory Assistant, Faculty of Social Sciences, Department of Psychology, HSE University, Moscow, Russian Federation, ORCID: https://orcid.org/0009-0005-1347-1404, e-mail: mepchelintseva@edu.hse.ru

Valentin E. Sinitsyn, Doctor of Medicine, Professor, Leading Research Scientist, Moscow Center for Continuous Mathematical Education, Lomonosov Moscow State University Medical Research and Educational Center, Head of Radiology Department, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-5649-2193, e-mail: vsini@mail.ru

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