Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Mar 24;17(3):778-797.
doi: 10.18632/aging.206225. Epub 2025 Mar 24.

Parallel patterns of age-related working memory impairment in marmosets and macaques

Affiliations

Parallel patterns of age-related working memory impairment in marmosets and macaques

Casey R Vanderlip et al. Aging (Albany NY). .

Abstract

As humans age, some experience cognitive impairment while others do not. When impairment does occur, it is not expressed uniformly across cognitive domains and varies in severity across individuals. Translationally relevant model systems are critical for understanding the neurobiological drivers of this variability, which is essential to uncovering the mechanisms underlying the brain's susceptibility to the effects of aging. As such, non-human primates (NHPs) are particularly important due to shared behavioral, neuroanatomical, and age-related neuropathological features with humans. For many decades, macaque monkeys have served as the primary NHP model for studying the neurobiology of cognitive aging. More recently, the common marmoset has emerged as an advantageous model for this work due to its short lifespan that facilitates longitudinal studies. Despite their growing popularity as a model, whether marmosets exhibit patterns of age-related cognitive impairment comparable to those observed in macaques and humans remains unexplored. To address this major limitation for the development and evaluation of the marmoset as a model of cognitive aging, we directly compared working memory ability as a function of age in macaques and marmosets on the identical task. We also implemented varying delays to further tax working memory capacity. Our findings demonstrate that marmosets and macaques exhibit remarkably similar age-related working memory deficits, with macaques performing better than marmosets on longer delays. These results highlight the similarities and differences between the two most commonly used NHP models and support the value of the marmoset as a model for cognitive aging research within the neuroscience community.

Keywords: aging; animal model; cognition; comparative cognition; monkey.

PubMed Disclaimer

Conflict of interest statement

CONFLICTS OF INTEREST: None of the authors have financial or conflict of interests related to the manuscript.

Figures

Figure 1
Figure 1
Age-dependent impairment in performance on the DRST in macaques and marmosets. (A) Depiction of a single DRST trial, reproduced from Glavis-Bloom et al., 2022. (B) Marmoset and (C) macaque individual learning curves. Each line denotes an individual animal, with color indicating the age during testing. The dashed black line represents chance level performance. Marmoset learning curves are reproduced from Glavis-Bloom et al., 2022. Correlations show that increasing macaque (red) and marmoset (blue) age is associated with (D) more trials needed to perform above chance in the Novice Phase, (E) reduced maximum learning rates in the Learning Phase, and (F) smaller working memory capacity. When averaging across ages, no interspecies differences were observed in (G) trials to above-chance performance, (H) maximum learning rates, or (I) working memory capacity. Each circle in (DF) represents one individual. Ages in (DF) correspond to the age at the time of assessment. Each circle in boxplots in (GI) represents one subject, colors according to age of subject in color scales in (B) for marmosets and (C) for macaques. Marmoset data presented in (DG) are re-plotted from Glavis-Bloom et al., 2022.
Figure 2
Figure 2
Error patterns related to age and trial difficulty level. (A) Increased age is correlated with committing a larger number of errors before reaching the performance criterion in the DNMS section of the DRST for both macaques (red) and marmosets (blue). (B) There were no significant species-specific differences in errors to reach the criterion. Similar patterns were identified in trials to criterion with (C) increased age associated with requiring more trials needed to reach criterion. (D) There were no significant interspecies differences in the number of trials to criterion. (E) Reduction in perseverative errors across Novice, Learner, and Expert Phases in both species. (F) Concurrent increase in primacy errors observed through these Phases for both macaques and marmosets. (G) There were significant associations between increasing age and more trials to transition from predominantly perseverative to predominantly primacy errors for macaques (red) and marmosets (blue). (H) During the Expert Phase, macaques more frequently misidentified remote (higher n-back) stimuli as novel compared to recent stimuli (lower n-back), suggesting retroactive interference. (I) Marmosets exhibit a similar pattern during the Expert Phase, also suggesting vulnerability to retroactive interference; mean ± SEM, *p < 0.05. Marmoset error distributions reproduced from Glavis-Bloom et al., 2022. Each circle in boxplots in (B and D) represents one subject, colors according to age of subject in color scales in Figure 1B for marmosets and 1C for macaques. Marmoset data presented in (AG) are re-plotted from Glavis-Bloom et al., 2022.
Figure 3
Figure 3
Choice latencies change as a function of DRST Phase and Trial Difficulty Level. Changes in (A) correct choice latencies and (B) incorrect choice latencies across the Novice, Learner, and Expert Phases for macaques (red) and marmosets (blue). Latencies decreased with increased task experience. In the Expert Phase, significant positive Spearman’s correlations were observed between trial difficulty level and (C) correct choice latencies and (D) incorrect choice latencies, reflecting increased cognitive load on more challenging portions of trials. mean ± SEM, *p < 0.05. Marmoset data presented in (AD) are re-plotted from Glavis-Bloom et al., 2022.
Figure 4
Figure 4
Delay-related effects on DRST performance. Marmosets (blue) show significant delay-dependent decreased DRST performance, whereas macaques (red) do not. Also, macaques have significantly higher performance than marmosets at delays longer than 2 seconds. These results are seen on several measures of performance including (A) average Final Span Length, (B) accuracy on the DNMS (TDL2) portion of the DRST, and (C) accuracy on TDL3 trials. (D) On TDL3 trials, marmosets’ perseverative errors increased in a delay-dependent manner, whereas macaque perseverative errors remained consistent across varying delays. (E) Marmosets’ primacy error rate showed a corresponding delay-dependent decrease, and macaque primacy errors remained consistent across the varying delays. Lightly shaded lines in (AC) depict individual animal performance as a function of delay. Bold colored lines in (AC) depict species average performance as a function of delay. mean ± SEM, *p < 0.05.

Update of

References

    1. Small SA, Perera GM, DeLaPaz R, Mayeux R, Stern Y. Differential regional dysfunction of the hippocampal formation among elderly with memory decline and Alzheimer's disease. Ann Neurol. 1999; 45:466–72. 10.1002/1531-8249(199904)45:4<466::aid-ana8>3.0.co;2-q - DOI - PubMed
    1. Cook Maher A, Makowski-Woidan B, Kuang A, Zhang H, Weintraub S, Mesulam MM, Rogalski E. Neuropsychological Profiles of Older Adults with Superior versus Average Episodic Memory: The Northwestern "SuperAger" Cohort. J Int Neuropsychol Soc. 2022; 28:563–73. 10.1017/S1355617721000837 - DOI - PMC - PubMed
    1. Stern Y, Albert M, Barnes CA, Cabeza R, Pascual-Leone A, Rapp PR. A framework for concepts of reserve and resilience in aging. Neurobiol Aging. 2023; 124:100–3. 10.1016/j.neurobiolaging.2022.10.015 - DOI - PMC - PubMed
    1. Vanderlip CR, Stark CEL, Initiative ADN. Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer’s spectrum. bioRxiv. 2024; 2024.05.23.595638. Available from: https://www.biorxiv.org/content/10.1101/2024.05.23.595638v1. - DOI - PMC - PubMed
    1. Izpisua Belmonte JC, Callaway EM, Caddick SJ, Churchland P, Feng G, Homanics GE, Lee KF, Leopold DA, Miller CT, Mitchell JF, Mitalipov S, Moutri AR, Movshon JA, et al.. Brains, genes, and primates. Neuron. 2015; 86:617–31. 10.1016/j.neuron.2015.03.021 - DOI - PMC - PubMed