Abstract
While the storage capacity is limited, accumulating studies have indicated that working memory (WM) can be improved by cognitive training. However, understanding how exactly the brain copes with limited WM capacity and how cognitive training optimizes the brain remains inconclusive. Given the hierarchical functional organization of WM, we hypothesized that the activation profiles along the posterior-anterior gradient of the frontal and parietal cortices characterize WM load and training effects. To test this hypothesis, we recruited 51 healthy volunteers and adopted a parametric WM paradigm and training method. In contrast to exclusively strengthening the activation of posterior areas, a broader range of activation concurrently occurred in the anterior areas to cope with increased memory load for all subjects at baseline. Moreover, there was an imbalance in the responses of the posterior and anterior areas to the same increment of 1 item at different load levels. Although a general decrease in activation after adaptive training, the changes in the posterior and anterior areas were distinct at different memory loads. Particularly, we found that the activation gradient between the posterior and anterior areas was significantly increased at load 4-back after adaptive training, and the changes were correlated with improvement in WM performance. Together, our results demonstrate a shift in the predominant role of posterior and anterior areas in the frontal and parietal cortices when approaching WM capacity limits. Additionally, the training-induced performance improvement likely benefits from the elevated neural efficiency reflected in the increased activation gradient between the posterior and anterior areas.
This is a preview of subscription content, log in via an institution to check access.
Data availability
The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.
References
Awh E, Jonides J (2001) Overlap** mechanisms of attention and spatial working memory. Trends Cogn Sci 5(3):119–126. https://doi.org/10.1016/s1364-6613(00)01593-x
Baddeley A (2003) Working memory: looking back and looking forward. Nat Rev Neurosci 4(10):829–839. https://doi.org/10.1038/nrn1201
Badre D, Nee DE (2018) Frontal Cortex and the Hierarchical Control of Behavior. Trends Cogn Sci 22(2):170–188. https://doi.org/10.1016/j.tics.2017.11.005
Bastian C, Belleville S, Udale RC, Reinhartz A, Essounni M, Strobach T (2022) Mechanisms underlying training-induced cognitive change. Nat Rev Psychol 1:30–41. https://doi.org/10.1038/s44159-021-00001-3
Blacker KJ, Negoita S, Ewen JB, Courtney SM (2017) N-back versus Complex Span Working Memory Training. J Cogn Enhanc 1(4):434–454. https://doi.org/10.1007/s41465-017-0044-1
Bor D, Duncan J, Wiseman RJ, Owen AM (2003) Encoding strategies dissociate prefrontal activity from working memory demand. Neuron 37(2):361–367. https://doi.org/10.1016/S0896-6273(02)01171-6
Bor D, Cumming N, Scott CEL, Owen AM (2004) Prefrontal cortical involvement in verbal encoding strategies. Eur J Neurosci 19(12):3365–3370. https://doi.org/10.1111/j.1460-9568.2004.03438.x
Braun U, Schafer A, Walter H, Erk S, Romanczuk-Seiferth N, Haddad L, Schweiger JI, Grimm O, Heinz A, Tost H, Meyer-Lindenberg A, Bassett DS (2015) Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci U S A 112(37):11678–11683. https://doi.org/10.1073/pnas.1422487112
Braver TS, Cohen JD, Nystrom LE, Jonides J, Smith EE, Noll DC (1997) A parametric study of prefrontal cortex involvement in human working memory. NeuroImage 5(1):49–62. https://doi.org/10.1006/nimg.1996.0247
Brehmer Y, Rieckmann A, Bellander M, Westerberg H, Fischer H, Backman L (2011) Neural correlates of training-related working-memory gains in old age. NeuroImage 58(4):1110–1120. https://doi.org/10.1016/j.neuroimage.2011.06.079
Buschman TJ (2021) Balancing flexibility and interference in Working Memory. Annu Rev Vis Sci 7:367–388. https://doi.org/10.1146/annurev-vision-100419-104831
Buschman TJ, Siegel M, Roy JE, Miller EK (2011) Neural substrates of cognitive capacity limitations. Proc Natl Acad Sci U S A 108(27):11252–11255. https://doi.org/10.1073/pnas.1104666108
Chai WJ, Abd Hamid AI, Abdullah JM (2018) Working Memory from the psychological and Neurosciences perspectives: a review. Front Psychol 9:401. https://doi.org/10.3389/fpsyg.2018.00401
Christophel TB, Klink PC, Spitzer B, Roelfsema PR, Haynes JD (2017) The distributed nature of Working Memory. Trends Cogn Sci 21(2):111–124. https://doi.org/10.1016/j.tics.2016.12.007
Cohen JD, Perlstein WM, Braver TS, Nystrom LE, Noll DC, Jonides J, Smith EE (1997) Temporal dynamics of brain activation during a working memory task. Nature 386(6625):604–608. https://doi.org/10.1038/386604a0
Cole MW, Ito T, Bassett DS, Schultz DH (2016) Activity flow over resting-state networks shapes cognitive task activations. Nat Neurosci 19(12):1718–1726. https://doi.org/10.1038/nn.4406
Constantinidis C, Klingberg T (2016) The neuroscience of working memory capacity and training. Nat Rev Neurosci 17(7):438–449. https://doi.org/10.1038/nrn.2016.43
Cowan N (1988) Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information-processing system. Psychol Bull 104(2):163–191. https://doi.org/10.1037/0033-2909.104.2.163
Cowan N (2001) The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav Brain Sci 24(1):87–114. https://doi.org/10.1017/s0140525x01003922
D’Esposito M, Postle BR (2015) The cognitive neuroscience of working memory. Annu Rev Psychol 66:115–142. https://doi.org/10.1146/annurev-psych-010814-015031
D’Esposito M, Aguirre GK, Zarahn E, Ballard D, Shin RK, Lease J (1998) Functional MRI studies of spatial and nonspatial working memory. Brain Res Cogn Brain Res 7(1):1–13. https://doi.org/10.1016/s0926-6410(98)00004-4
Edin F, Klingberg T, Johansson P, McNab F, Tegner J, Compte A (2009) Mechanism for top-down control of working memory capacity. Proc Natl Acad Sci U S A 106(16):6802–6807. https://doi.org/10.1073/pnas.0901894106
Eriksson J, Vogel EK, Lansner A, Bergstrom F, Nyberg L (2015) Neurocognitive Architecture of Working Memory. Neuron 88(1):33–46. https://doi.org/10.1016/j.neuron.2015.09.020
Finc K, Bonna K, He X, Lydon-Staley DM, Kuhn S, Duch W, Bassett DS (2020) Dynamic reconfiguration of functional brain networks during working memory training. Nat Commun 11(1):2435. https://doi.org/10.1038/s41467-020-15631-z
Freedman DJ, Ibos G (2018) An integrative Framework for sensory, Motor, and cognitive functions of the posterior parietal cortex. Neuron 97(6):1219–1234. https://doi.org/10.1016/j.neuron.2018.01.044
Fuster JM, Alexander GE (1971) Neuron activity related to short-term memory. Science 173(3997):652–654. https://doi.org/10.1126/science.173.3997.652
Hasson U, Chen J, Honey CJ (2015) Hierarchical process memory: memory as an integral component of information processing. Trends Cogn Sci 19(6):304–313. https://doi.org/10.1016/j.tics.2015.04.006
Hempel A, Giesel FL, Garcia Caraballo NM, Amann M, Meyer H, Wustenberg T, Essig M, Schroder J (2004) Plasticity of cortical activation related to working memory during training. Am J Psychiatry 161(4):745–747. https://doi.org/10.1176/appi.ajp.161.4.745
Jaeggi SM, Seewer R, Nirkko AC, Eckstein D, Schroth G, Groner R, Gutbrod K (2003) Does excessive memory load attenuate activation in the prefrontal cortex? Load-dependent processing in single and dual tasks: functional magnetic resonance imaging study. NeuroImage 19:210–225. https://doi.org/10.1016/s1053-8119(03)00098-3
Jaeggi SM, Buschkuehl M, Etienne A, Ozdoba C, Perrig WJ, Nirkko AC (2007) On how high performers keep cool brains in situations of cognitive overload. Cogn Affect Behav Neurosci 7(2):75–89. https://doi.org/10.3758/cabn.7.2.75
Jonides J, Schumacher EH, Smith EE, Lauber EJ, Awh E, Minoshima S, Koeppe RA (1997) Verbal working memory load affects Regional Brain activation as measured by PET. J Cogn Neurosci 9(4):462–475. https://doi.org/10.1162/jocn.1997.9.4.462
Katsuki F, Constantinidis C (2012) Unique and shared roles of the posterior parietal and dorsolateral prefrontal cortex in cognitive functions. Front Integr Neurosci 6:17. https://doi.org/10.3389/fnint.2012.00017
Kitzbichler MG, Henson RN, Smith ML, Nathan PJ, Bullmore ET (2011) Cognitive effort drives workspace configuration of human brain functional networks. J Neurosci 31(22):8259–8270. https://doi.org/10.1523/JNEUROSCI.0440-11.2011
Klingberg T (2010) Training and plasticity of working memory. Trends Cogn Sci 14(7):317–324. https://doi.org/10.1016/j.tics.2010.05.002
Koechlin E, Ody C, Kouneiher F (2003) The architecture of cognitive control in the human prefrontal cortex. Science 302(5648):1181–1185. https://doi.org/10.1126/science.1088545
Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dip**. Nat Neurosci 12(5):535–540. https://doi.org/10.1038/nn.2303
Lamichhane B, Westbrook A, Cole MW, Braver TS (2020) Exploring brain-behavior relationships in the N-back task. NeuroImage 212:116683. https://doi.org/10.1016/j.neuroimage.2020.116683
Langer N, von Bastian CC, Wirz H, Oberauer K, Jancke L (2013) The effects of working memory training on functional brain network efficiency. Cortex 49(9):2424–2438. https://doi.org/10.1016/j.cortex.2013.01.008
Lee SH, Kravitz DJ, Baker CI (2013) Goal-dependent dissociation of visual and prefrontal cortices during working memory. Nat Neurosci 16(8):997–U935. https://doi.org/10.1038/nn.3452
Liang X, Zou Q, He Y, Yang Y (2016) Topologically reorganized Connectivity Architecture of Default-Mode, Executive-Control, and salience networks across Working Memory Task loads. Cereb Cortex 26(4):1501–1511. https://doi.org/10.1093/cercor/bhu316
Linden DE, Bittner RA, Muckli L, Waltz JA, Kriegeskorte N, Goebel R, Singer W, Munk MH (2003) Cortical capacity constraints for visual working memory: dissociation of fMRI load effects in a fronto-parietal network. NeuroImage 20(3):1518–1530. https://doi.org/10.1016/j.neuroimage.2003.07.021
Mackey WE, Curtis CE (2017) Distinct contributions by frontal and parietal cortices support working memory. Sci Rep 7(1):6188. https://doi.org/10.1038/s41598-017-06293-x
McNab F, Klingberg T (2008) Prefrontal cortex and basal ganglia control access to working memory. Nat Neurosci 11(1):103–107. https://doi.org/10.1038/nn2024
Meyer ML, Spunt RP, Berkman ET, Taylor SE, Lieberman MD (2012) Evidence for social working memory from a parametric functional MRI study. Proc Natl Acad Sci U S A 109(6):1883–1888. https://doi.org/10.1073/pnas.1121077109
Minamoto T, Yaoi K, Osaka M, Osaka N (2015) The rostral prefrontal cortex underlies individual differences in working memory capacity: an approach from the hierarchical model of the cognitive control. Cortex 71:277–290. https://doi.org/10.1016/j.cortex.2015.07.025
Miro-Padilla A, Bueicheku E, Ventura-Campos N, Flores-Compan MJ, Parcet MA, Avila C (2019) Long-term brain effects of N-back training: an fMRI study. Brain Imaging Behav 13(4):1115–1127. https://doi.org/10.1007/s11682-018-9925-x
Murray JD, Bernacchia A, Freedman DJ, Romo R, Wallis JD, Cai X, Padoa-Schioppa C, Pasternak T, Seo H, Lee D, Wang XJ (2014) A hierarchy of intrinsic timescales across primate cortex. Nat Neurosci 17(12):1661–1663. https://doi.org/10.1038/nn.3862
Murray JD, Jaramillo J, Wang XJ (2017) Working memory and decision-making in a Frontoparietal Circuit Model. J Neurosci 37(50):12167–12186. https://doi.org/10.1523/JNEUROSCI.0343-17.2017
Nee DE, Brown JW (2012) Rostral-caudal gradients of abstraction revealed by multi-variate pattern analysis of working memory. NeuroImage 63(3):1285–1294. https://doi.org/10.1016/j.neuroimage.2012.08.034
Nee DE, D’Esposito M (2016) The hierarchical organization of the lateral prefrontal cortex. Elife 5. https://doi.org/10.7554/eLife.12112
Olesen PJ, Westerberg H, Klingberg T (2004) Increased prefrontal and parietal activity after training of working memory. Nat Neurosci 7(1):75–79. https://doi.org/10.1038/nn1165
Owen AM, McMillan KM, Laird AR, Bullmore E (2005) N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp 25(1):46–59. https://doi.org/10.1002/hbm.20131
Palva JM, Monto S, Kulashekhar S, Palva S (2010) Neuronal synchrony reveals working memory networks and predicts individual memory capacity. Proc Natl Acad Sci U S A 107(16):7580–7585. https://doi.org/10.1073/pnas.0913113107
Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, Vogel AC, Laumann TO, Miezin FM, Schlaggar BL, Petersen SE (2011) Functional network organization of the human brain. Neuron 72(4):665–678. https://doi.org/10.1016/j.neuron.2011.09.006
Roggeman C, Klingberg T, Feenstra HE, Compte A, Almeida R (2014) Trade-off between capacity and precision in visuospatial working memory. J Cogn Neurosci 26(2):211–222. https://doi.org/10.1162/jocn_a_00485
Romo R, Brody CD, Hernandez A, Lemus L (1999) Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399(6735):470–473. https://doi.org/10.1038/20939
Rypma B, D’Esposito M (1999) The roles of prefrontal brain regions in components of working memory: effects of memory load and individual differences. Proc Natl Acad Sci U S A 96(11):6558–6563. https://doi.org/10.1073/pnas.96.11.6558
Rypma B, Prabhakaran V, Desmond JE, Glover GH, Gabrieli JDE (1999) Load-dependent roles of frontal brain regions in the maintenance of working memory. NeuroImage 9(2):216–226. https://doi.org/10.1006/nimg.1998.0404
Salmi J, Nyberg L, Laine M (2018) Working memory training mostly engages general-purpose large-scale networks for learning. Neurosci Biobehav Rev 93:108–122. https://doi.org/10.1016/j.neubiorev.2018.03.019
Schneiders JA, Opitz B, Krick CM, Mecklinger A (2011) Separating intra-modal and across-modal training effects in visual working memory: an fMRI investigation. Cereb Cortex 21(11):2555–2564. https://doi.org/10.1093/cercor/bhr037
Soveri A, Antfolk J, Karlsson L, Salo B, Laine M (2017) Working memory training revisited: a multi-level meta-analysis of n-back training studies. Psychon Bull Rev 24(4):1077–1096. https://doi.org/10.3758/s13423-016-1217-0
Sreenivasan KK, D’Esposito M (2019) The what, where and how of delay activity. Nat Rev Neurosci 20(8):466–481. https://doi.org/10.1038/s41583-019-0176-7
Susanne M., Jaeggi Martin, Buschkuehl John, Jonides Walter J., Perrig (2008) Improving fluid intelligence with training on working memory Proceedings of the National Academy of Sciences 105(19) 6829-6833 10.1073/pnas.0801268105
Thompson TW, Waskom ML, Gabrieli JD (2016) Intensive Working Memory Training produces functional changes in large-scale Frontoparietal Networks. J Cogn Neurosci 28(4):575–588. https://doi.org/10.1162/jocn_a_00916
Todd JJ, Marois R (2004) Capacity limit of visual short-term memory in human posterior parietal cortex. Nature 428(6984):751–754. https://doi.org/10.1038/nature02466
Van Snellenberg JX, Slifstein M, Read C, Weber J, Thompson JL, Wager TD, Shohamy D, Abi-Dargham A, Smith EE (2015) Dynamic shifts in brain network activation during supracapacity working memory task performance. Hum Brain Mapp 36(4):1245–1264. https://doi.org/10.1002/hbm.22699
Vergara J, Rivera N, Rossi-Pool R, Romo R (2016) A neural Parametric Code for storing information of more than one sensory modality in Working Memory. Neuron 89(1):54–62. https://doi.org/10.1016/j.neuron.2015.11.026
Vermeij A, Kessels RPC, Heskamp L, Simons EMF, Dautzenberg PLJ, Claassen J (2017) Prefrontal activation may predict working-memory training gain in normal aging and mild cognitive impairment. Brain Imaging Behav 11(1):141–154. https://doi.org/10.1007/s11682-016-9508-7
Wager TD, Smith EE (2003) Neuroimaging studies of working memory: a meta-analysis. Cogn Affect Behav Neurosci 3(4):255–274. https://doi.org/10.3758/cabn.3.4.255
Wang LP, Li XX, Hsiao SS, Lenz FA, Bodner M, Zhou YD, Fuster JM (2015) Differential roles of delay-period neural activity in the monkey dorsolateral prefrontal cortex in visual-haptic crossmodal working memory. Proc Natl Acad Sci U S A 112(2):E214–E219. https://doi.org/10.1073/pnas.1410130112
**e Y, Hu P, Li J, Chen J, Song W, Wang XJ, Yang T, Dehaene S, Tang S, Min B, Wang L (2022) Geometry of sequence working memory in macaque prefrontal cortex. Science 375(6581):632–639. https://doi.org/10.1126/science.abm0204
Xu Y, Chun MM (2006) Dissociable neural mechanisms supporting visual short-term memory for objects. Nature 440(7080):91–95. https://doi.org/10.1038/nature04262
Yin D, Liu W, Zeljic K, Lv Q, Wang Z, You M, Men W, Fan M, Cheng W, Wang Z (2017) Failure in cognitive suppression of negative affect in adolescents with generalized anxiety disorder. Sci Rep 7(1):6583. https://doi.org/10.1038/s41598-017-07063-5
Yin DZ, Wang XF, Zhang XY, Yu QR, Wei Y, Cai Q, Fan MX, Li L (2021) Dissociable plasticity of visual-motor system in functional specialization and flexibility in expert table tennis players. Brain Struct Funct 226(6):1973–1990. https://doi.org/10.1007/s00429-021-02304-w
Zanto TP, Rubens MT, Thangavel A, Gazzaley A (2011) Causal role of the prefrontal cortex in top-down modulation of visual processing and working memory. Nat Neurosci 14(5):656–661. https://doi.org/10.1038/nn.2773
Zhang H, Zhao R, Hu X, Guan S, Margulies DS, Meng C, Biswal BB (2022) Cortical connectivity gradients and local timescales during cognitive states are modulated by cognitive loads. Brain Struct Funct 227(8):2701–2712. https://doi.org/10.1007/s00429-022-02564-0
Zhu H, Huang Z, Yang Y, Su K, Fan M, Zou Y, Li T, Yin D (2023) Activity flow map** over probabilistic functional connectivity. Hum Brain Mapp 44(2):341–361. https://doi.org/10.1002/hbm.26044
Zimmer HD (2008) Visual and spatial working memory: from boxes to networks. Neurosci Biobehav Rev 32(8):1373–1395. https://doi.org/10.1016/j.neubiorev.2008.05.016
Funding
This work was supported by the National Natural Science Foundation of China (32271096 to D.Z.Y.; 81471651 and 11835003 to M.X.F.), the Science and Technology Innovation 2030-Major Projects (2021ZD0200500 to D.Z.Y.), Open Research Fund of Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention (SYS2024A10 to D.Z.Y.), and the Fundamental Research Funds for the Central Universities (D.Z.Y.).
Author information
Authors and Affiliations
Contributions
KQS, QWL, and ZYH performed data analysis; DZY, TL, and KQS designed the experiments and wrote the paper; KQS, QWL., and MXF conducted the experiments and data acquisition; DZY supervised the work; All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Compliance with ethical standards
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of East China Normal University.
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Su, K., Huang, Z., Li, Q. et al. Dissociable functional responses along the posterior-anterior gradient of the frontal and parietal cortices revealed by parametric working memory and training. Brain Struct Funct (2024). https://doi.org/10.1007/s00429-024-02834-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00429-024-02834-z