Retention of verbal and nonverbal information in the working memory. An analysis of functional and effective connectivity

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Abstract

In this work we estimated differences in the structure of brain systems that ensure encoding and retention in working memory (WM) of two types of information: verbal (letters) and non-verbal (segments of an open broken line) sequences presented either statically or dynamically. Brain systems were characterized by the strength of functional and effective connections between eight approximately bilaterally symmetrical cortical loci, including the dorsolateral prefrontal cortex (dlPFC) and regions of the temporal (STG), parietal (IPS), and occipital (v2) cortices.

Using an 8-channel vector autoregressive model in the space of cortical EEG sources, it was shown in a group of subjects in whom high-density EEG was recorded that: (1) the brain organization of the WM when holding a sequence of letters differs from that when holding a sequence of broken line segments; (2) the brain organization of the WM depends on the mode of presentation of sequences: the strength of the functional connection is different during dynamic and static presentation of the sequence; (3) differences in the structure of functional and effective connections are not of a pronounced frequency-selective nature and are observed in all studied EEG frequency ranges from theta (4–8 Hz) to high-frequency gamma (50–60 Hz); (4) the most reliable differences between the task of retaining a sequence of letters and the task of retaining a sequence of broken line segments are observed in the alpha and beta frequency ranges during static visual presentation of sequences in the strength of functional connectivity measured using coherence between the left hemisphere dlPFC and the right hemisphere STG, as well as in theta range between the right hemisphere dlPFC and the left visual cortex v2; (5) the most reliable difference between static and dynamic presentation modes is observed in the task of holding broken line segments in the gamma frequency range (50–60 Hz) between the dlPFC in the right hemisphere and the left visual cortex v2.

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About the authors

А. V. Kurgansky

Institute of Child Development; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences; The Presidential Academy (RANEPA)

Author for correspondence.
Email: akurg@yandex.ru
Russian Federation, Moscow; Moscow; Moscow

A. A. Korneev

Institute of Child Development; Moscow State University

Email: akurg@yandex.ru
Russian Federation, Moscow; Moscow

D. I. Lomakin

Institute of Child Development

Email: akurg@yandex.ru
Russian Federation, Moscow

R. I. Machinskaya

Institute of Child Development; The Presidential Academy (RANEPA)

Email: akurg@yandex.ru
Russian Federation, Moscow; Moscow

References

  1. Величковский Б.Б. Рабочая память человека: структура и механизмы. М.: Когито-центр Москва, 2015. 247.
  2. Величковский Б.М. Когнитивная наука: Основы психологии познания в 2 т. Т. 1. М: “Смысл”, 2006. 488.
  3. Корнеев А.А., Ломакин Д.И., Курганский А.В. Отсроченное копирование незнакомых контурных изображений: отражает ли убывание времени реакции с ростом задержки изменение внутреннего представления будущего движения? Журнал высшей нервной деятельности им. И.П. Павлова. 2016а. 66 (1): 51–61.
  4. Корнеев А.А., Ломакин Д.И., Курганский А.В., Мачинская Р.И. Отсроченное копирование незнакомых контурных изображений: анализ потенциалов, связанных с предъявлением стимулов. Журнал высшей нервной деятельности им. И.П. Павлова. 2016б. 66 (4): 470–483.
  5. Корнеев А.А., Ломакин Д.И., Курганский А.В., Мачинская Р.И. Удержание вербальной и невербальной серийной информации в рабочей памяти. Психология. Журнал Высшей школы экономики. 2022. 19 (2): 87–106.
  6. Корнеев А.А., Курганский А.В. Преобразование порядка движений в серии, заданной зрительным образцом. Вестник Московского университета. Серия 14. Психология. 2014. 2: 61–74.
  7. Курганский А.В., Ломакин Д.И., Корнеев А.А., Мачинская Р.И. Мозговая организация рабочей памяти при отсроченном копировании ломаной линии: анализ потенциалов, связанных с императивным сигналом. Журнал высшей нервной деятельности им. И.П. Павлова. 2022. 72 (3): 387–404.
  8. Курганский А.В. Некоторые вопросы исследования кортико-кортикальных функциональных связей с помощью векторной авторегрессионной модели многоканальной ЭЭГ. Журн. высш. нервн. деят. им. И.П. Павлова. 2010. 60 (6): 740–759.
  9. Курганский А.В., Григал П.П. Направленные кортикокортикальные функциональные взаимодействия на ранних стадиях серийного научения у взрослых и детей 7–8 лет. Физиология человека. 2010. 36 (4); 44–56.
  10. Фарбер Д.А., Бетелева Т.Г. Формирование мозговой организации рабочей памяти в младшем школьном возрасте. Физиология человека. 2011. 37 (1): 5–15.
  11. Absatova K.A., Kurgansky A.V., Machinskaya R.I. The recall modality affects the source-space effective connectivity in the θ-band during the retention of visual information. Psychology & Neuroscience.2016. 9 (3): 344–361.
  12. Ahveninen J., Uluç I, Raij T., Nummenmaa A., Mamashli F. Spectrotemporal content of human auditory working memory represented in functional connectivity patterns. Commun Biol. 2023 Mar 20; 6 (1): 294. https://doi.org/10.1038/s42003-023-04675-8
  13. Akalin Acar Z., Makeig S. Effects of forward model errors on EEG source localization. Brain Topogr. 2013. 26 (3): 378–396. https://doi.org/10.1007/s10548-012-0274-6
  14. Babiloni C., Babiloni F., Carducci F., Cincotti F., Vecchio F., Cola B. et al. Functional frontoparietal connectivity during short-term memory as revealed by highresolution EEG coherence analysis. Behav Neurosci. 2004. 118 (4): 687–697. https://doi.org/10.1037/0735-7044.118.4.687
  15. Baccalá L.A., Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol Cybern. 2001. 84 (6): 463–474. https://doi.org/10.1007/PL00007990
  16. Baddeley A.D., Hitch G. Working Memory. Psychology of Learning and Motivation, edited by G.H. Bower, Academic Press 1974. 8: 47–89. https://doi.org/10.1016/S0079-7421(08)60452-1
  17. Baddeley A.D. Developing the Concept of Working Memory: The Role of Neuropsychology1. Arch Clin Neuropsychol. 2021. 36 (6): 861–873. https://doi.org/10.1093/arclin/acab060
  18. Barbey A.K., Koenigs M., Grafman J. Orbitofrontal contributions to human working memory. Cereb Cortex. 2011. 21 (4): 789–795. https://doi.org/10.1093/cercor/bhq153
  19. Barrouillet P., Camos V. Working Memory and Executive Control: A Time-based Resource-sharing Account. Psychologica Belgica. 2010. 50 (3–4): 353–382. https://doi.org/10.5334/pb-50-3-4-353
  20. Bezdicek O., Ballarini T., Albrecht F., Libon D.J., Lamar M., Růžička F. et al. Serial-order recall in working memory across the cognitive spectrum of Parkinson’s disease and neuroimaging correlates. J. Neuropsychol. 2021. 15 (1): 88–111. https://doi.org/10.1111/jnp.12208
  21. Binder J.R. The Wernicke area: Modern evidence and a reinterpretation. Neurology. 2015. 85 (24): 2170–2175. https://doi.org/10.1212/WNL.0000000000002219
  22. Carpenter A.F., Baud-Bovy G., Georgopoulos A.P., Pellizzer G. Encoding of serial order in working memory: neuronal activity in motor, premotor, and prefrontal cortex during a memory scanning task. J. Neurosci. 2018. 38 (21): 4912–4933. https://doi.org/10.1523/JNEUROSCI.3294-17.2018
  23. Carreiras M., Quiñones I., Hernández-Cabrera J.A., Duñabeitia J.A. Orthographic coding: brain activation for letters, symbols, and digits. Cereb.Cortex. 2015. 25 (12): 4748– 4760. https://doi.org/10.1093/cercor/bhu163
  24. Cui J., Xu L., Bressler S.L., Ding M., Liang H. BSMART: a Matlab/C toolbox for analysis of multichannel neural time series. Neural. Netw. 2008. 21 (8): 1094–104. https://doi.org/10.1016/j.neunet.2008.05.007
  25. D’Esposito M., Postle B.R. The cognitive neuroscience of working memory. Annu. Rev. Psychol. 2015. 66: 115–142. https://doi.org/10.1146/annurev-psych-010814-015031
  26. Eickhoff S.B., Stephan K.E., Mohlberg H., Grefkes C., Fink G.R., Amunts K., Zilles K. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage. 2005. 25 (4): 1325–1335. https://doi.org/10.1016/j.neuroimage.2004.12.034
  27. Eriksson J., Vogel E.K., Lansner A., Bergström F., Nyberg L. Neurocognitive architecture of working memory. Neuron. 2015. 88 (1): 33–46. https://doi.org/10.1016/j.neuron.2015.09.020
  28. Fougnie D., Marois R. Working memory capacity is modalityspecific: Evidence of separate stores for auditory and visuospatial stimuli [Abstract]. J. Vision. 2008. 8 (6): 1169, 1169a.
  29. Freunberger R., Fellinger R., Sauseng P., Gruber W., Klimesch W. Dissociation between phase-locked and nonphase-locked alpha oscillations in a working memory task. Hum. Brain Mapp. 2009. 30 (10): 3417–3425.: https://doi.org/10.1002/hbm.20766
  30. Frost A., Moussaoui S., Kaur J., Aziz S., Fukuda K., Niemeier M. Is the n-back task a measure of unstructured working memory capacity? Towards understanding its connection to other working memory tasks. Acta Psychol. (Amst). 2021. 219: 103398. https://doi.org/10.1016/j.actpsy.2021.103398
  31. Gazzaley A., Rissman J., D’Esposito M. Functional connectivity during working memory maintenance. Cogn. Affect. Behav. Neurosci. 2004. 4 (4): 580–599. https://doi.org/10.3758/cabn.4.4.580
  32. Ikkai A., Curtis C.E.A. Common neural mechanisms supporting spatial working memory, attention and motor intention. Neuropsychologia. 2011. 9 (6): 1428–1434. https://doi.org/10.1016/j.neuropsychologia.2010.12.020
  33. Jackson J.B., Feredoes E., Rich A.N., Lindner M., Woolgar A. Concurrent neuroimaging and neurostimulation reveals a causal role for dlPFC in coding of task-relevant information. Commun. Biol. 2021. 4 (1): 588. https://doi.org/10.1038/s42003-021-02109-x
  34. Johnson E.L., Chang W.K., Dewar C.D., Sorensen D., Lin J.J., Solbakk A.K. et al. Orbitofrontal cortex governs working memory for temporal order. Curr. Biol. 2022. 32 (9): R410–R411. https://doi.org/10.1016/j.cub.2022.03.074
  35. Kawasaki M., Kitajo K., Yamaguchi Y. Dynamic links between theta executive functions and alpha storage buffers in auditory and visual working memory. Eur. J. Neurosci. 2010. 31 (9): 1683–1689.
  36. Koenigs M., Barbey A.K., Postle B.R., Grafman J. Superior parietal cortex is critical for the manipulation of information in working memory. J. Neurosci. 2009. 29 (47): 14980–14986. https://doi.org/10.1523/JNEUROSCI.3706-09.2009
  37. Lenartowicz A., McIntosh A.R. The role of anterior cingulate cortex in working memory is shaped by functional connectivity. J. Cogn. Neurosci. 2005. 17 (7): 1026– 1042. https://doi.org/10.1162/0898929054475127
  38. Li J., Cao D., Yu S., Xiao X., Imbach L., Stieglitz L. et al. Functional specialization and interaction in the amygdala-hippocampus circuit during working memory processing. Nat. Commun. 2023. 14 (1): 2921. https://doi.org/10.1038/s41467-023-38571-w
  39. Li Y., Cowan N. Constraints of attention, stimulus modality, and feature similarity in working memory. Atten. Percept. Psychophys. 2022. 84 (8): 2519–2539. https://doi.org/10.3758/s13414-022-02549-5
  40. Mackey W.E., Devinsky O., Doyle W.K., Golfinos J.G., Curtis C.E. Human parietal cortex lesions impact the precision of spatial working memory. J. Neurophysiol. 2016. 116 (3): 1049–1054. https://doi.org/10.1152/jn.00380.2016
  41. Nystrom L.E., Braver T.S., Sabb F.W., Delgado M.R., Noll D.C., Cohen J.D. Working memory for letters, shapes, and locations: fMRI evidence against stimulus-based regional organization in human prefrontal cortex. Neuroimage. 2000. 11 (5 Pt 1): 424–446. https://doi.org/10.1006/nimg.2000.0572
  42. Pascual-Marqui R.D., Lehmann D., Koukkou M., Kochi K., Anderer P., Saletu B. et al. Assessing interactions in the brain with exact low-resolution electromagnetic to mography. Philos. Trans. A Math. Phys. Eng. Sci. 2011. 369 (1952): 3768–3784. https://doi.org/10.1098/rsta.2011.0081
  43. Postle B.R. Cognitive neuroscience of visual working memory. In R.H. Logie, V. Camos, & N. Cowan (Eds.) Working memory: State of the science (pp. 333–357). Oxford University Pres. 2021. https://doi.org/10.1093/oso/9780198842286.003.0012
  44. Pratte M.S., Tong F. Spatial specificity of working memory representations in the early visual cortex. J. Vis. 2014. 14 (3): 22. https://doi.org/10.1167/14.3.22
  45. Ren Z., Zhang Y., He H., Feng Q., Bi T., Qiu J. The different brain mechanisms of object and spatial working memory: voxel-based morphometry and resting-state functional connectivity. Front. Hum. Neurosci. 2019. 13: 248. https://doi.org/10.3389/fnhum.2019.00248
  46. Robert S., Ungerleider L.G., Vaziri-Pashkam M. Disentangling object category representations driven by dynamic and static visual input. J. Neurosci. 2023. 43 (4): 621–634. https://doi.org/10.1523/JNEUROSCI.0371-22.2022
  47. Sauseng P., Klimesch W., Heise K.F., Gruber W.R., Holz E., Karim A.A. et al. Brain oscillatory substrates of visual short-term memory capacity. Curr. Biol. 2009. 19 (21): 1846–1852. https://doi.org/1016/j.cub.2009.08.062
  48. Sheth B.R., Young R. Two visual pathways in primates based on sampling of space: exploitation and exploration of visual information. Front. Integr. Neurosci. 2016. 10: 37. https://doi.org/10.3389/fnint.2016.00037
  49. Shirazi S.Y., Huang H.J. More reliable EEG electrode digitizing methods can reduce source estimation uncertainty, but current methods already accurately identify Brodmann areas. Front. Neurosci. 2019. 13: 1159. https://doi.org/10.3389/fnins.2019.01159
  50. Wager T.D., Smith E.E. Neuroimaging studies of working memory: a meta-analysis. Cogn. Affect. Behav. Neurosci. 2003. 3 (4): 255–274. https://doi.org/10.3758/cabn.3.4.255
  51. Webler R.D., Fox J., McTeague L.M., Burton P.C., Dowdle L., Short E.B. et al. DLPFC stimulation alters working memory related activations and performance: An interleaved TMS-fMRI study. Brain Stimul. 2022. 15 (3): 823–832. https://doi.org/10.1016/j.brs.2022.05.014
  52. Working memory: State of the science. H. Logie, V. Camos & N. Cowan (Eds.) Oxford University Press., 2021.
  53. Yu Q., Postle B.R. The neural codes underlying internally generated representations in visual working memory. J. Cogn. Neurosci. 2021. 33 (6): 1142–1157. https://doi.org/10.1162/jocn_a_01702
  54. Zhao Y.J., Kay K.N., Tian Y., Ku Y. Sensory recruitment revisited: ipsilateral V1 involved in visual working memory. Cereb. Cortex. 2022. 32 (7): 1470–1479. https://doi.org/10.1093/cercor/bhab300

Supplementary files

Supplementary Files
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1. JATS XML
2. Fig. 1. Temporal structure of a typical experimental trial. Along the time axis from left to right, shown are following stages of the trial separated by vertical lines (SB – start of stimulation; SE – end of stimulation; Go – presentation of an imperative signal) shown are: the stage of prestimulus attention (ATT), the period of visual stimulation – presentation of a sequence of stimuli (STIM), the stage of holding the sequence in working memory (RET) and the stage of motor reproduction of the retained sequence in response to the presentation of an imperative signal (Go signal). Above the portion of the time axis corresponding to visual stimulation are shown the two types of visual sequences used: letter strings (LET) and broken lines or “trajectories” (TRJ), composed of vertical and horizontal straight segments. Each type of sequence was presented either statically (STAT) or dynamically (DYN) (details in the text). Connectivity was assessed in segments lasting 500 ms (marked with EEG label); one of them corresponded to the ATT phase and immediately preceded the start of stimulation (SB), and the other consisted of the first 500 ms of EEG immediately after the end of stimulation (SE).

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3. Fig. 2. Shown are the COH values averaged over the group of subjects for the F-T, F-P and F-O links. ((a), (б), (в)) in two tasks: LET and TRJ (averaged over the levels of factors FBAND, HEM_F, MODE); ((г), (д), (е)) – COH values in two modes of sequence presentation: STAT and DYN (averaged over the levels of factors FBAND, HEM_F, TASK). White circles correspond to the location of the temporal (T), parietal (P) and occipital (O) ROIs in the left hemisphere, and black circles correspond to the location in the right hemisphere.

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4. Fig. 3. Shown are the DTF values averaged over the group of subjects for the F-T, F-P and F-O links. ((a), (б), (в)) in two tasks: LET and TRJ (averaged over the levels of factors FBAND, DIR, HEM_F, MODE); ((г), (д), (е)) – COH values in two modes of sequence presentation: STAT and DYN (averaged over the levels of factors FBAND, DIR, HEM_F, TASK). White circles correspond to the location of the temporal (T), parietal (P) and occipital (O) ROIs in the left hemisphere, and black circles correspond to the location in the right hemisphere.

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5. Fig. 4. Coherence versus frequency function averaged over all subjects for two conditions (upper row) and examples of individual functions for these conditions (lower row). The top row of panels ((a), (б), (в)) shows graphs of the dependence averaged over the group of subjects. (a) in a pair of ROIs formed by the dlPFC in the left hemisphere and the right hemisphere STG in static visual presentation condition; The solid curve corresponds to the LET task, and the dashed curve corresponds to the TRJ task; (б) in a pair of ROIs formed by the dlPFC in the right hemisphere and the left visual cortex v2, with a static visual presentation; The solid curve corresponds to the LET task, and the dashed curve corresponds to the TRJ task; (в) in the pair formed by dlPFC in the right hemisphere and the left visual cortex v2, in the TRJ task; The solid curve corresponds to the static STAT presentation mode, and the dashed curve corresponds to the dynamic DYN. In the top row of panels ((a), (б), (в)), the thick curve (solid or dashed) indicates the average function for the group of subjects, and the corresponding solid or dashed thin lines above and below it indicate the corridor (mean ± standard error). The bottom row of panels ((г), (д), (е)) shows examples of individual functions corresponding to the upper curves: (г) contains an individual example for (a), (д) for (б) and (е) for (в). The light gray vertical bar marks the frequency regions in which the two curves shown in panels ((a), (б), (в)) differ statistically significantly. The same vertical stripes are reproduced in the corresponding examples of individual curves ((г), (д), (е)).

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