Skip to main content
Log in

Neural similarity across task load relates to cognitive reserve and brain maintenance measures on the Letter Sternberg task: a longitudinal study

  • Original Research
  • Published:
Brain Imaging and Behavior Aims and scope Submit manuscript

Abstract

The aging process is characterized by change across several measures that index cognitive status and brain integrity. In the present study, 54 cognitively-healthy younger and older adults, were analyzed, longitudinally, on a verbal working memory task to investigate the effect of brain maintenance (i.e., cortical thickness) and cognitive reserve (i.e., NART IQ as proxy) factors on a derived measure of neural efficiency. Participants were scanned using fMRI while presented with the Letter Sternberg task, a verbal working memory task consisting of encoding, maintenance and retrieval phases, where cognitive load is manipulated by varying the number of presented items (i.e., between one and six letters). Via correlation analysis, we looked at region-level and whole-brain relationships between load levels within each phase and then computed a global task measure, what we term phase specificity, to analyze how similar neural responses were across load levels within each phase compared to between each phase. We found that longitudinal change in phase specificity was positively related to longitudinal change in cortical thickness, at both the whole-brain and regional level. Additionally, baseline NART IQ was positively related to longitudinal change in phase specificity over time. Furthermore, we found a longitudinal effect of sex on change in phase specificity, such that females displayed higher phase specificity over time. Cross-sectional findings aligned with longitudinal findings, with the notable exception of behavioral performance being positively linked to phase specificity cross-sectionally at baseline. Taken together, our findings suggest that phase specificity positively relates to brain maintenance and reserve factors and should be better investigated as a measure of neural efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Custom-written code detailing analyses can be made available upon reasonable request.

References

  • Altamura, M., Elvevåg, B., Blasi, G., Bertolino, A., Callicott, J. H., Weinberger, D. R., & Goldberg, T. E. (2007). Dissociating the effects of Sternberg working memory demands in prefrontal cortex. Psychiatry Research: Neuroimaging, 154(2), 103–114.

    Article  Google Scholar 

  • Andersson, J. L., Jenkinson, M., & Smith, S. (2007). Non-linear registration, aka Spatial normalisation FMRIB technical report TR07JA2. FMRIB Analysis Group of the University of Oxford, 2(1), e21.

  • Atkinson, A. L., Baddeley, A. D., & Allen, R. J. (2018). Remember some or remember all? Ageing and strategy effects in visual working memory. Quarterly Journal of Experimental Psychology, 71(7), 1561–1573.

    Article  Google Scholar 

  • Baddeley, A. (1992). Working memory. Science, 255(5044), 556–559.

    Article  CAS  PubMed  Google Scholar 

  • Baddeley, A. (2012). Working memory: theories, models, and controversies. Annual Review of Psychology, 63, 1–29.

    Article  PubMed  Google Scholar 

  • Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509.

    Article  PubMed  Google Scholar 

  • 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, 415458. https://doi.org/10.1155/2015/415458

  • Bedwell, J. S., Horner, M. D., Yamanaka, K., Li, X., Myrick, H., Nahas, Z., & George, M. S. (2005). Functional neuroanatomy of subcomponent cognitive processes involved in verbal working memory. International Journal of Neuroscience, 115(7), 1017–1032.

    Article  PubMed  Google Scholar 

  • Bergmann, J., Genç, E., Kohler, A., Singer, W., & Pearson, J. (2016). Neural anatomy of primary visual cortex limits visual working memory. Cerebral Cortex, 26(1), 43–50.

    Article  PubMed  Google Scholar 

  • Bopp, K. L., & Verhaeghen, P. (2005). Aging and verbal memory span: A meta-analysis. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60(5), P223–P233.

    Article  PubMed  Google Scholar 

  • Brockmole, J. R., & Logie, R. H. (2013). Age-related change in visual working memory: A study of 55,753 participants aged 8–75. Frontiers in Psychology, 4, 12.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bunge, S. A., Ochsner, K. N., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. (2001). Prefrontal regions involved in keeping information in and out of mind. Brain, 124(10), 2074–2086.

    Article  CAS  PubMed  Google Scholar 

  • Burzynska, A. Z., Nagel, I. E., Preuschhof, C., Li, S. C., Lindenberger, U., Bäckman, L., & Heekeren, H. R. (2011). Microstructure of frontoparietal connections predicts cortical responsivity and working memory performance. Cerebral Cortex, 21(10), 2261–2271.

    Article  CAS  PubMed  Google Scholar 

  • Cairo, T. A., Liddle, P. F., Woodward, T. S., & Ngan, E. T. (2004). The influence of working memory load on phase specific patterns of cortical activity. Cognitive Brain Research, 21(3), 377–387.

    Article  PubMed  Google Scholar 

  • Cansino, S., Hernández-Ramos, E., Estrada-Manilla, C., Torres-Trejo, F., Martínez-Galindo, J. G., Ayala-Hernández, M., & Rodríguez-Ortiz, M. D. (2013). The decline of verbal and visuospatial working memory across the adult life span. Age, 35(6), 2283–2302.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: a review of its functional anatomy and behavioural correlates. Brain, 129(3), 564–583.

    Article  PubMed  Google Scholar 

  • Curtis, C. E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7(9), 415–423.

    Article  PubMed  Google Scholar 

  • D’Esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working memory. Annual Review of Psychology, 66, 115–142.

    Article  PubMed  Google Scholar 

  • Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194.

    Article  CAS  PubMed  Google Scholar 

  • Deen, B., Koldewyn, K., Kanwisher, N., & Saxe, R. (2015). Functional organization of social perception and cognition in the superior temporal sulcus. Cerebral Cortex, 25(11), 4596–4609.

    Article  PubMed  PubMed Central  Google Scholar 

  • Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980.

    Article  PubMed  Google Scholar 

  • Dominguez, E. N., Stark, S. M., Ren, Y., Corrada, M. M., Kawas, C. H., & Stark, C. E. (2021). Regional cortical thickness predicts top cognitive performance in the elderly. Frontiers in Aging Neuroscience, 758. https://doi.org/10.3389/fnagi.2021.751375

  • Dunst, B., Benedek, M., Jauk, E., Bergner, S., Koschutnig, K., Sommer, M., & Neubauer, A. C. (2014). Neural efficiency as a function of task demands. Intelligence, 42, 22–30.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ebaid, D., Crewther, S. G., MacCalman, K., Brown, A., & Crewther, D. P. (2017). Cognitive processing speed across the lifespan: beyond the influence of motor speed. Frontiers in Aging Neuroscience, 9, 62.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ecker, U. K., Lewandowsky, S., Oberauer, K., & Chee, A. E. (2010). The components of working memory updating: An experimental decomposition and individual differences. Journal of Experimental Psychology: Learning Memory and Cognition, 36(1), 170.

    PubMed  Google Scholar 

  • Elmer, S. (2016). Broca pars triangularis constitutes a “hub” of the language-control network during simultaneous language translation. Frontiers in Human Neuroscience, 10, 491.

    Article  PubMed  PubMed Central  Google Scholar 

  • Emch, M., Von Bastian, C. C., & Koch, K. (2019). Neural correlates of verbal working memory: An fMRI meta-analysis. Frontiers in Human Neuroscience, 13, 180.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fakhri, M., Sikaroodi, H., Maleki, F., Ghanaati, H., & Oghabian, M. A. (2013). Impacts of normal aging on different working memory tasks: Implications from an fMRI study. Behavioural Neurology, 27(3), 235–244.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fjell, A. M., Grydeland, H., Krogsrud, S. K., Amlien, I., Rohani, D. A., Ferschmann, L., & Walhovd, K. B. (2015). Development and aging of cortical thickness correspond to genetic organization patterns. Proceedings of the National Academy of Sciences, 112(50), 15462–15467.

  • Fjell, A. M., Westlye, L. T., Amlien, I., Espeseth, T., Reinvang, I., Raz, N., & Walhovd, K. B. (2009). High consistency of regional cortical thinning in aging across multiple samples. Cerebral Cortex, 19(9), 2001–2012.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fleischman, D. A., Arfanakis, K., Kelly, J. F., Rajendran, N., Buchman, A. S., Morris, M. C., & Bennett, D. A. (2010). Regional cortical thinning and systemic inflammation in older persons without dementia. Journal of the American Geriatrics Society, 58(9), 1823.

    Article  PubMed  PubMed Central  Google Scholar 

  • Funahashi, S. (2017). Working memory in the prefrontal cortex. Brain Sciences, 7(5), 49.

    Article  PubMed  PubMed Central  Google Scholar 

  • Glahn, D. C., Kim, J., Cohen, M. S., Poutanen, V. P., Therman, S., Bava, S., & Cannon, T. D. (2002). Maintenance and manipulation in spatial working memory: dissociations in the prefrontal cortex. NeuroImage, 17(1), 201–213.

    Article  CAS  PubMed  Google Scholar 

  • Habeck, C., Rakitin, B. C., Moeller, J., Scarmeas, N., Zarahn, E., Brown, T., & Stern, Y. (2005). An event-related fMRI study of the neural networks underlying the encoding, maintenance, and retrieval phase in a delayed-match-to-sample task. Cognitive Brain Research, 23(2–3), 207–220.

    Article  PubMed  Google Scholar 

  • Hale, S., Rose, N. S., Myerson, J., Strube, M. J., Sommers, M., Tye-Murray, N., & Spehar, B. (2011). The structure of working memory abilities across the adult life span. Psychology and Aging, 26(1), 92–110.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hartwigsen, G., Baumgaertner, A., Price, C. J., Koehnke, M., Ulmer, S., & Siebner, H. R. (2010). Phonological decisions require both the left and right supramarginal gyri. Proceedings of the National Academy of Sciences, 107(38), 16494–16499.

  • Kennedy, K. M., Boylan, M. A., Rieck, J. R., Foster, C. M., & Rodrigue, K. M. (2017). Dynamic range in BOLD modulation: Lifespan aging trajectories and association with performance. Neurobiology of Aging, 60, 153–163.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kim, H. (2019). Neural activity during working memory encoding, maintenance, and retrieval: A network-based model and meta‐analysis. Human Brain Mapping, 40(17), 4912–4933.

    Article  PubMed  PubMed Central  Google Scholar 

  • Koen, J. D., & Rugg, M. D. (2019). Neural dedifferentiation in the aging brain. Trends in Cognitive Sciences, 23(7), 547–559.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kragel, J. E., Ezzyat, Y., Sperling, M. R., Gorniak, R., Worrell, G. A., & Berry, B. M., & Kahana, M. J. (2017). Similar patterns of neural activity predict memory function during encoding and retrieval. NeuroImage, 155, 60–71.

  • Kumar, N., & Priyadarshi, B. (2013). Differential effect of aging on verbal and visuo-spatial working memory. Aging and Disease, 4(4), 170.

    PubMed  PubMed Central  Google Scholar 

  • Lejbak, L., Crossley, M., & Vrbancic, M. (2011). A male advantage for spatial and object but not verbal working memory using the n-back task. Brain and Cognition, 76(1), 191–196.

    Article  PubMed  Google Scholar 

  • Maillet, D., & Rajah, M. N. (2013). Association between prefrontal activity and volume change in prefrontal and medial temporal lobes in aging and dementia: a review. Ageing Research Reviews, 12(2), 479–489.

    Article  PubMed  Google Scholar 

  • Mattis S (1988) Dementia Rating Scale. Professional manual. Psychological Assessment Resourses, Odessa

  • Morcom, A. M., & Henson, R. N. (2018). Increased prefrontal activity with aging reflects nonspecific neural responses rather than compensation. Journal of Neuroscience, 38(33), 7303–7313.

    Article  CAS  PubMed  Google Scholar 

  • Nee, D. E., Brown, J. W., Askren, M. K., Berman, M. G., Demiralp, E., Krawitz, A., & Jonides, J. (2013). A meta-analysis of executive components of working memory. Cerebral Cortex, 23(2), 264–282.

    Article  PubMed  Google Scholar 

  • Nussbaumer, D., Grabner, R. H., & Stern, E. (2015). Neural efficiency in working memory tasks: The impact of task demand. Intelligence, 50, 196–208.

    Article  Google Scholar 

  • Oldfield RC (1971) The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9(1), 97–113. https://doi.org/10.1016/0028-3932(71)90067-4

  • Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychology and Aging, 17(2), 299.

    Article  PubMed  Google Scholar 

  • Pliatsikas, C., Veríssimo, J., Babcock, L., Pullman, M. Y., Glei, D. A., Weinstein, M., & Ullman, M. T. (2019). Working memory in older adults declines with age, but is modulated by sex and education. Quarterly Journal of Experimental Psychology, 72(6), 1308–1327.

    Article  Google Scholar 

  • Rajah, M. N., & D’Esposito, M. (2005). Region-specific changes in prefrontal function with age: A review of PET and fMRI studies on working and episodic memory. Brain: A Journal of Neurology, 128(9), 1964–1983.

    Article  PubMed  Google Scholar 

  • Reed, J. L., Gallagher, N. M., Sullivan, M., Callicott, J. H., & Green, A. E. (2017). Sex differences in verbal working memory performance emerge at very high loads of common neuroimaging tasks. Brain and Cognition, 113, 56–64.

    Article  PubMed  Google Scholar 

  • Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012). Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage, 61(4), 1402–1418.

    Article  PubMed  Google Scholar 

  • Reuter, M., Rosas, H. D., & Fischl, B. (2010). Highly accurate inverse consistent registration: a robust approach. NeuroImage, 53(4), 1181–1196.

    Article  PubMed  Google Scholar 

  • Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science, 17(3), 177–182.

    Article  Google Scholar 

  • Reuter-Lorenz, P. A., & Sylvester, C. Y. (2005). The cognitive neuroscience of aging and working memory. In R. Cabeza, L. Nyberg, & D. Park (Eds.), The cognitive neuroscience of aging (pp. 186–217). Oxford University Press.

    Google Scholar 

  • Rypma B, D’Esposito M (1999) The roles of prefrontal brain regions in components of working memory: Effects of memory load and individual differences. Proceedings of the National Academy of Sciences, 96(11), 6558–6563. https://doi.org/10.1073/pnas.96.11.6558

  • Rypma, B., Berger, J. S., Prabhakaran, V., Bly, B. M., Kimberg, D. Y., Biswal, B. B., & Esposito, D. (2006). Neural correlates of cognitive efficiency. NeuroImage, 33(3), 969–979.

    Article  PubMed  Google Scholar 

  • Rypma, B., Berger, J. S., & D’esposito, M. (2002). The influence of working-memory demand and subject performance on prefrontal cortical activity. Journal of Cognitive Neuroscience, 14(5), 721–731.

    Article  PubMed  Google Scholar 

  • Sambataro, F., Murty, V. P., Callicott, J. H., Tan, H. Y., Das, S., Weinberger, D. R., & Mattay, V. S. (2010). Age-related alterations in default mode network: impact on working memory performance. Neurobiology of Aging, 31(5), 839–852.

    Article  PubMed  Google Scholar 

  • Schneider-Garces, N. J., Gordon, B. A., Brumback-Peltz, C. R., Shin, E., Lee, Y., Sutton, B. P., & Fabiani, M. (2010). Span, CRUNCH, and beyond: working memory capacity and the aging brain. Journal of Cognitive Neuroscience, 22(4), 655–669.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sele, S., Liem, F., Mérillat, S., & Jäncke, L. (2021). Age-related decline in the brain: A longitudinal study on inter-individual variability of cortical thickness, area, volume, and cognition. Neuroimage, 240.

  • Speck, O., Ernst, T., Braun, J., Koch, C., Miller, E., & Chang, L. (2000). Gender differences in the functional organization of the brain for working memory. NeuroReport, 11(11), 2581–2585.

    Article  CAS  PubMed  Google Scholar 

  • Stern, Y., Arenaza-Urquijo, E. M., Bartrés‐Faz, D., Belleville, S., Cantilon, M., Chetelat, G., & Reserve (2020). Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer’s & Dementia, 16(9), 1305–1311. Resilience and Protective Factors PIA Empirical Definitions and Conceptual Frameworks Workgroup.

  • Sternberg, S. (1966). High-speed scanning in human memory. Science, 153(3736), 652–654.

    Article  CAS  PubMed  Google Scholar 

  • Storsve, A. B., Fjell, A. M., Tamnes, C. K., Westlye, L. T., Overbye, K., Aasland, H. W., & Walhovd, K. B. (2014). Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. Journal of Neuroscience, 34(25), 8488–8498.

    Article  CAS  PubMed  Google Scholar 

  • Suzuki, M., Kawagoe, T., Nishiguchi, S., Abe, N., Otsuka, Y., Nakai, R., & Sekiyama, K. (2018). Neural correlates of working memory maintenance in advanced aging: evidence from fMRI. Frontiers in Aging Neuroscience, 10, 358. https://doi.org/10.3389/fnagi.2018.00358

  • Toepper, M., Gebhardt, H., Bauer, E., Haberkamp, A., Beblo, T., Gallhofer, B., & Sammer, G. (2014). The impact of age on load-related dorsolateral prefrontal cortex activation. Frontiers in Aging Neuroscience, 6, 9.

    Article  PubMed  PubMed Central  Google Scholar 

  • Tucker, A., & Stern, Y. (2011). Cognitive reserve in aging. Current Alzheimer Research, 8(4), 354–360.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Van Gerven, P. W., Meijer, W. A., Prickaerts, J. H., & Van der Veen, F. M. (2008). Aging and focus switching in working memory: excluding the potential role of memory load. Experimental Aging Research, 34(4), 367–378.

    Article  PubMed  Google Scholar 

  • Vandierendonck, A. (2017). A comparison of methods to combine speed and accuracy measures of performance: A rejoinder on the binning procedure. Behavior Research Methods, 49(2), 653–673.

    Article  PubMed  Google Scholar 

  • Veltman, D. J., Rombouts, S. A., & Dolan, R. J. (2003). Maintenance versus manipulation in verbal working memory revisited: an fMRI study. NeuroImage, 18(2), 247–256.

    Article  PubMed  Google Scholar 

  • Vermeij, A., Kessels, R. P., Heskamp, L., Simons, E. M., Dautzenberg, P. L., & Claassen, J. A. (2017). Prefrontal activation may predict working-memory training gain in normal aging and mild cognitive impairment. Brain Imaging and Behavior, 11(1), 141–154.

    Article  PubMed  Google Scholar 

  • Voyer, D., Saint Aubin, J., Altman, K., & Gallant, G. (2021). Sex differences in verbal working memory: A systematic review and meta-analysis. Psychological Bulletin, 147(4), 352.

    Article  PubMed  Google Scholar 

  • Wang, H., He, W., Wu, J., Zhang, J., Jin, Z., & Li, L. (2019). A coordinate-based meta-analysis of the n-back working memory paradigm using activation likelihood estimation. Brain and Cognition, 132, 1–12.

    Article  PubMed  Google Scholar 

  • Zarahn, E., Rakitin, B., Abela, D., Flynn, J., & Stern, Y. (2007). Age-related changes in brain activation during a delayed item recognition task. Neurobiology of Aging, 28(5), 784–798.

    Article  PubMed  Google Scholar 

  • Zarahn, E., Rakitin, B., Abela, D., Flynn, J., & Stern, Y. (2005). Positive evidence against human hippocampal involvement in working memory maintenance of familiar stimuli. Cerebral Cortex, 15(3), 303–316.

    Article  PubMed  Google Scholar 

Download references

Funding

We wish to gratefully acknowledge support from the grant NIH/NIA R01AG038465-06.

Author information

Authors and Affiliations

Authors

Contributions

G.A. analyzed the data and wrote the manuscript. G.A. and C.H. conceived and verified the analytical methods. C.H. and Y.S. conceived the study and designed the experiments. All authors contributed to the final version of the manuscript.

Corresponding author

Correspondence to Christian Habeck.

Ethics declarations

Ethics approval

The Columbia University Institutional Review Board approved all study procedures.

Consent to participate

All participants provided written informed consent prior to participation. 

Consent for publication

All participants were informed of the research scope and inclusion of data to publish and could opt out at any time.

Conflict of interest

The authors confirm that they have no conflict of interest to declare.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Below is the link to the electronic supplementary material.

ESM 1

(PDF 158 KB)

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Argiris, G., Stern, Y. & Habeck, C. Neural similarity across task load relates to cognitive reserve and brain maintenance measures on the Letter Sternberg task: a longitudinal study. Brain Imaging and Behavior 17, 100–113 (2023). https://doi.org/10.1007/s11682-022-00746-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-022-00746-2

Keywords

Navigation