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The rodent object-in-context task: A systematic review and meta-analysis of important variables

Sep, Milou S. C., Vellinga, Marijn, Sarabdjitsingh, R. Angela, Joëls, Marian·2021·PLOS ONE
DOI 10.1371/journal.pone.0249102PMC PMC8284613
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Abstract

Environmental information plays an important role in remembering events. Information about stable aspects of the environment (here referred to as ‘context’) and the event are combined by the hippocampal system and stored as context-dependent memory. In rodents (such as rats and mice), context-dependent memory is often investigated with the object-in-context task. However, the implementation and interpretation of this task varies considerably across studies. This variation hampers the comparison between studies and—for those who design a new experiment or carry out pilot experiments–the estimation of whether observed behavior is within the expected range. Also, it is currently unclear which of the variables critically influence the outcome of the task. To address these issues, we carried out a preregistered systematic review (PROSPERO CRD42020191340) and provide an up-to-date overview of the animal-, task-, and protocol-related variations in the object-in-context task for rodents. Using a data-driven explorative meta-analysis we next identified critical factors influencing the outcome of this task, such as sex, testbox size and the delay between the learning trials. Based on these observations we provide recommendations on sex, strain, prior arousal, context (size, walls, shape, etc.) and timing (habituation, learning, and memory phase) to create more consensus in the set-up, procedure, and interpretation of the object-in-context task for rodents. This could contribute to a more robust and evidence-based design in future animal experiments.

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Figures

Fig 1 Schematic overview of the rodent object-in-context task (OIC). During the sample phase (learning) an animal encounters a unique set of two objects in context A and next a different set in B. In the test phase (memory) the animal is exposed to either context A or B, with one object of each unique set.
Fig 1 Schematic overview of the rodent object-in-context task (OIC). During the sample phase (learning) an animal encounters a unique set of two objects in context A and next a different set in B. In the test phase (memory) the animal is exposed to either context A or B, with one object of each unique set.CC BY · PubMed Central
Fig 2 Flowchart of the study.
Fig 2 Flowchart of the study.CC BY · PubMed Central
Fig 3 Assessment of study quality (QA) and risk of bias. The risk of bias according to SYRCLE’s risk of bias (RoB) tool [34], for each study, indicated by PubMed ID (PMID), included in the systematic review. The figure also shows if an a priori sample size calculation was performed and -if behavior was scored manually- an inter-observer rate was calculated. Unreported details were scored as an ‘unclear’ risk of bias.
Fig 3 Assessment of study quality (QA) and risk of bias. The risk of bias according to SYRCLE’s risk of bias (RoB) tool [34], for each study, indicated by PubMed ID (PMID), included in the systematic review. The figure also shows if an a priori sample size calculation was performed and -if behavior was scored manually- an inter-observer rate was calculated. Unreported details were scored as an ‘unclear’ risk of bias.CC BY · PubMed Central
Fig 4 Forest plot. Forest plot visualizing the Discrimination Ratio (DR) per experimental group, per study; and the overall mean DR with 95% Confidence Interval. Studies are presented by publication year in ascending order.
Fig 4 Forest plot. Forest plot visualizing the Discrimination Ratio (DR) per experimental group, per study; and the overall mean DR with 95% Confidence Interval. Studies are presented by publication year in ascending order.CC BY · PubMed Central
Fig 5 Funnel plot. Funnel plot showing the DR of individual control groups on the x-axis against their standard errors (i.e., the square root of the sampling variances) on the y-axis. Vertical reference lines indicate the 0, i.e. no context-dependent memory; and the estimated overall mean DR based on the model. Colors indicate unique studies (PMID).
Fig 5 Funnel plot. Funnel plot showing the DR of individual control groups on the x-axis against their standard errors (i.e., the square root of the sampling variances) on the y-axis. Vertical reference lines indicate the 0, i.e. no context-dependent memory; and the estimated overall mean DR based on the model. Colors indicate unique studies (PMID).CC BY · PubMed Central
Fig 6 Relative importance of potential moderators based on ‘permuted variable importance’ in the random forest-based meta-analyses. Moderators above the horizontal line belong to the top 50%. Full definitions of variables and sum scores can be found in A2 and A5 Appendices in S1 File respectively.
Fig 6 Relative importance of potential moderators based on ‘permuted variable importance’ in the random forest-based meta-analyses. Moderators above the horizontal line belong to the top 50%. Full definitions of variables and sum scores can be found in A2 and A5 Appendices in S1 File respectively.CC BY · PubMed Central
Fig 7 Partial dependence plots showing the predicted relation between DR and the upper half most important variables in the random forest-based meta-analysis, broken down by species: Mice in red vs. rat in blue. The y-axis shows the predicted DR and x-axis shows the values of the variable that is named above the graph (in gray). The values of context.size A and B are shown in in cm2 and the values of time variables (learning. learning.delay, learning.time.trial, habituation.time.total.context, habituation.time.trial) are shown in minutes. Higher context.difference.score values indicate more different contexts and higher arousal.prior values indicate more arousal prior to the experiment. Strains values are abbreviations: TMm (Tg(Sim1cre)KH21Gsat/Mmucd mice), SEm (SEm Sv/Ev mice), ICm (ICR mice), BLm (C57BL/6 mice), LHr (Lister hooded rats), LEr (Long-Evans rats), SDr (Sprague-Dawley rats), Wr (Wistar rats), pDr (pigmented DA strain rats), DAr (Dark Agouti rats). DR.formula values are also abbreviations: n-f/t ((Tnovel-Tfamiliar)/(Tnovel+Tfamiliar)), n-f/t*100 ([(Tnovel-Tfamiliar)/(Tnovel+Tfamiliar)]*100), n/t ((Tnovel)/(Tnovel+Tfamiliar)), n/t*100 ([(Tnovel)/(Tnovel+Tfamiliar)]*100). The context.order value AA indicates that memory was tested in the context of the second learning trial, the value AB indicates that memory was tested in the context of the first learning trial, ‘both’ indicates that AA and AB were randomized. Finally, context.habituation.freq values show the number of visits per context. Full definitions of variables and sum scores can be found in A2 and A5 Appendices in S1 File respectively. Note, predictions based on the random forest-model do not take into account that strains belong to a specific species (either rat or mouse), hence rat and mice predictions were generated of all strains.
Fig 7 Partial dependence plots showing the predicted relation between DR and the upper half most important variables in the random forest-based meta-analysis, broken down by species: Mice in red vs. rat in blue. The y-axis shows the predicted DR and x-axis shows the values of the variable that is named above the graph (in gray). The values of context.size A and B are shown in in cm2 and the values of time variables (learning. learning.delay, learning.time.trial, habituation.time.total.context, habituation.time.trial) are shown in minutes. Higher context.difference.score values indicate more different contexts and higher arousal.prior values indicate more arousal prior to the experiment. Strains values are abbreviations: TMm (Tg(Sim1cre)KH21Gsat/Mmucd mice), SEm (SEm Sv/Ev mice), ICm (ICR mice), BLm (C57BL/6 mice), LHr (Lister hooded rats), LEr (Long-Evans rats), SDr (Sprague-Dawley rats), Wr (Wistar rats), pDr (pigmented DA strain rats), DAr (Dark Agouti rats). DR.formula values are also abbreviations: n-f/t ((Tnovel-Tfamiliar)/(Tnovel+Tfamiliar)), n-f/t*100 ([(Tnovel-Tfamiliar)/(Tnovel+Tfamiliar)]*100), n/t ((Tnovel)/(Tnovel+Tfamiliar)), n/t*100 ([(Tnovel)/(Tnovel+Tfamiliar)]*100). The context.order value AA indicates that memory was tested in the context of the second learning trial, the value AB indicates that memory was tested in the context of the first learning trial, ‘both’ indicates that AA and AB were randomized. Finally, context.habituation.freq values show the number of visits per context. Full definitions of variables and sum scores can be found in A2 and A5 Appendices in S1 File respectively. Note, predictions based on the random forest-model do not take into account that strains belong to a specific species (either rat or mouse), hence rat and mice predictions were generated of all strains.CC BY · PubMed Central
Fig 8 Weighted scatter plots showing the distribution of the raw DR for the 50% most important variables in the random forest-based meta-analysis, broken down by species: Mice in red vs. rat in blue. Dot size indicates the sample size of each observation. Most studies were performed in rats. The y-axis shows the mean DR and x-axis shows the values of the variable that is named below the graph. The values of context.size A and B are shown in in cm2 and the values of time variables (learning. learning.delay, learning.time.trial, habituation.time.total.context, habituation.time.trial) are shown in minutes. Higher context.difference.score values indicate more different contexts and higher arousal.prior values indicate more arousal prior to the experiment. Strains values are abbreviations: TMm (Tg(Sim1cre)KH21Gsat/Mmucd mice), SEm (SEm Sv/Ev mice), ICm (ICR mice), BLm (C57BL/6 mice), LHr (Lister hooded rats), LEr (Long-Evans rats), SDr (Sprague-Dawley rats), Wr (Wistar rats), pDr (pigmented DA strain rats), DAr (Dark Agouti rats). DR.formula values are also abbreviations: n-f/t ((Tnovel-Tfamiliar)/(Tnovel+Tfamiliar)), n-f/t*100 ([(Tnovel-Tfamiliar)/(Tnovel+Tfamiliar)]*100), n/t ((Tnovel)/(Tnovel+Tfamiliar)), n/t*100 ([(Tnovel)/(Tnovel+Tfamiliar)]*100). The context.order value AA indicates that memory was tested in the context of the second learning trial, the value AB indicates that memory was tested in the context of the first learning trial, ‘both’ indicates that AA and AB were randomized. Finally, context.habituation.freq values show the number of visits per context. Full definitions of variables and sum scores can be found in A2 and A5 Appendices in S1 File respectively.
Fig 8 Weighted scatter plots showing the distribution of the raw DR for the 50% most important variables in the random forest-based meta-analysis, broken down by species: Mice in red vs. rat in blue. Dot size indicates the sample size of each observation. Most studies were performed in rats. The y-axis shows the mean DR and x-axis shows the values of the variable that is named below the graph. The values of context.size A and B are shown in in cm2 and the values of time variables (learning. learning.delay, learning.time.trial, habituation.time.total.context, habituation.time.trial) are shown in minutes. Higher context.difference.score values indicate more different contexts and higher arousal.prior values indicate more arousal prior to the experiment. Strains values are abbreviations: TMm (Tg(Sim1cre)KH21Gsat/Mmucd mice), SEm (SEm Sv/Ev mice), ICm (ICR mice), BLm (C57BL/6 mice), LHr (Lister hooded rats), LEr (Long-Evans rats), SDr (Sprague-Dawley rats), Wr (Wistar rats), pDr (pigmented DA strain rats), DAr (Dark Agouti rats). DR.formula values are also abbreviations: n-f/t ((Tnovel-Tfamiliar)/(Tnovel+Tfamiliar)), n-f/t*100 ([(Tnovel-Tfamiliar)/(Tnovel+Tfamiliar)]*100), n/t ((Tnovel)/(Tnovel+Tfamiliar)), n/t*100 ([(Tnovel)/(Tnovel+Tfamiliar)]*100). The context.order value AA indicates that memory was tested in the context of the second learning trial, the value AB indicates that memory was tested in the context of the first learning trial, ‘both’ indicates that AA and AB were randomized. Finally, context.habituation.freq values show the number of visits per context. Full definitions of variables and sum scores can be found in A2 and A5 Appendices in S1 File respectively.CC BY · PubMed Central

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Research works printed but not linked: 65

  1. JW Rudy, 2009, Context representations, context functions, and the parahippocampal-hippocampal system., Learn Mem, 16, 573, 10.1101/lm.1494409 · DOI · Paper page
  2. S Maren, 2013, The contextual brain: implications for fear conditioning, extinction and psychopathology., Nat Publ Gr., 14, 417
  3. DR Godden, 1975, Context-dependent memory in two natural environments: on land and underwater., Br J Psychol, 66, 325, 10.1111/j.2044-8295.1975.tb01468.x · DOI
  4. D De Quervain, 2017, Stress, glucocorticoids and memory: implications for treating fear-related disorders., Nat Publ Gr, 18, 7
  5. S Lissek, 2010, Overgeneralization of Conditioned Fear as a Pathogenic Marker of Panic Disorder., Am J Psychiatry., 167, 47, 10.1176/appi.ajp.2009.09030410 · DOI · Paper page
  6. M El, 2013, Context Memory in Alzheimer’s Disease
  7. GP Urcelay, 2014, The functions of contexts in associative learning., Behavioural Processes., 2, 10.1016/j.beproc.2014.02.008 · DOI
  8. MSC Sep, 2019, Time-dependent effects of psychosocial stress on the contextualization of neutral memories., Psychoneuroendocrinology, 108, 140, 10.1016/j.psyneuen.2019.06.021 · DOI
  9. D Clewett, 2019, Transcending time in the brain: How event memories are constructed from experience, Hippocampus, 29, 162, 10.1002/hipo.23074 · DOI
  10. Y Ezzyat, 2014, Similarity Breeds Proximity: Pattern Similarity within and across Contexts Is Related to Later Mnemonic Judgments of Temporal Proximity, Neuron, 81, 1179, 10.1016/j.neuron.2014.01.042 · DOI · Paper page
  11. D Clewett, 2020, Pupil-linked arousal signals track the temporal organization of events in memory., Nat Commun, 11, 4007, 10.1038/s41467-020-17851-9 · DOI · Paper page
  12. T Staudigl, 2013, Theta Oscillations at Encoding Mediate the Context-Dependent Nature of Human Episodic Memory, Curr Biol, 23, 1101, 10.1016/j.cub.2013.04.074 · DOI
  13. VA van Ast, 2014, Cortisol mediates the effects of stress on the contextual dependency of memories., Psychoneuroendocrinology., 41, 97, 10.1016/j.psyneuen.2013.12.007 · DOI
  14. AP Yonelinas, 2019, A contextual binding theory of episodic memory: systems consolidation reconsidered., Nat Rev Neurosci, 20, 364, 10.1038/s41583-019-0150-4 · DOI
  15. SL Dix, 1999, Extending the spontaneous preference test of recognition: Evidence of object-location and object-context recognition., Behav Brain Res
  16. MA Good, 2007, Context- but not familiarity-dependent forms of object recognition are impaired following excitotoxic hippocampal lesions in rats., Behav Neurosci, 121, 218, 10.1037/0735-7044.121.1.218 · DOI
  17. M La Spina, 2019, Pterostilbene Improves Cognitive Performance in Aged Rats: An in Vivo Study, Cell Physiol Biochem, 52, 232, 10.33594/000000017 · DOI
  18. R De Rosa, 2005, rescues recognition memory deficits in AD11 anti-NGF transgenic mice, Proc Natl Acad Sci U S A, 102, 3811, 10.1073/pnas.0500195102 · DOI
  19. EM Rodberg, 2017, Stress Facilitates the Development of Cognitive Dysfunction After Chronic Ethanol Exposure, Alcohol Clin Exp Res, 41, 1574, 10.1111/acer.13444 · DOI
  20. AG Pillai, 2018, Early life stress determines the effects of glucocorticoids and stress on hippocampal function: Electrophysiological and behavioral evidence respectively, Neuropharmacology, 133, 307, 10.1016/j.neuropharm.2018.02.001 · DOI
  21. A Ennaceur, 1988, A new one-trial test for neurobiological studies of memory in rats. 1: Behavioral data., Behav Brain Res, 31, 47, 10.1016/0166-4328(88)90157-X · DOI · Paper page
  22. A. Ennaceur, 2010, One-trial object recognition in rats and mice: methodological and theoretical issues, Behav Brain Res, 215, 244, 10.1016/j.bbr.2009.12.036 · DOI · Paper page
  23. RBM de Vries, 2015, A protocol format for the preparation, registration and publication of systematic reviews of animal intervention studies., Evidence-based Preclin Med, 2, e00007, 10.1002/ebm2.7 · DOI
  24. M Leenaars, 2012, A step-by-step guide to systematically identify all relevant animal studies., Lab Anim., 46, 24, 10.1258/la.2011.011087 · DOI
  25. D Moher, 2009, Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement., PLoS Med., 6, e1000097, 10.1371/journal.pmed.1000097 · DOI · Paper page
  26. M Vellinga, 2020, Measuring context-dependent memory in rodents: a systematic review and meta-analysis of important variables in the object in context-task (CRD42020191340)., PROSPERO.
  27. JA Huwaldt, 2015, Plot Digitizer., Source Forge
  28. M Antunes, 2012, The novel object recognition memory: neurobiology, test procedure, and its modifications., Cogn Process, 13, 93, 10.1007/s10339-011-0430-z · DOI · Paper page
  29. CR Hooijmans, 2014, SYRCLE’s risk of bias tool for animal studies., BMC Med Res Methodol., 14, 43, 10.1186/1471-2288-14-43 · DOI · Paper page
  30. V Bonapersona, 2019, The behavioral phenotype of early life adversity: A 3-level meta-analysis of rodent studies., Neurosci Biobehav Rev., 102, 299, 10.1016/j.neubiorev.2019.04.021 · DOI
  31. R Core Team, 2020, R: A language and environment for statistical computing
  32. H Wickham, 2020, dplyr: A Grammar of Data Manipulation., R package version 1.0.2.
  33. A Wolen, 2020, osfr: An R Interface to the Open Science Framework., J Open Source Softw, 5, 2071, 10.21105/joss.02071 · DOI
  34. W. Viechtbauer, 2010, Conducting Meta-Analyses in R with the metafor Package., J Stat Softw., 36, 1, 10.18637/jss.v036.i03 · DOI · Paper page
  35. H. Wickham, 2016, ggplot2: Elegant Graphics for Data Analysis, 10.1007/978-3-319-24277-4 · DOI
  36. M Borenstein, 2009, Introduction to Meta-Analysis, 10.1002/9780470743386 · DOI · Paper page
  37. JPT Higgins, 2003, Measuring inconsistency in meta-analyses, British Medical Journal
  38. R. Rosenthal, 1979, The file drawer problem and tolerance for null results
  39. S Duval, 2000, Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis, Biometrics
  40. M Egger, 1997, Bias in meta-analysis detected by a simple, graphical test, BMJ, 315, 629, 10.1136/bmj.315.7109.629 · DOI · Paper page
  41. CB Begg, 1994, Operating Characteristics of a Rank Correlation Test for Publication Bias, Biometrics
  42. W Viechtbauer, 2010, Outlier and influence diagnostics for meta-analysis., Res Synth Methods.
  43. DW Apley, 2020, Visualizing the effects of predictor variables in black box supervised learning models., J R Stat Soc Ser B Stat Methodol, 82, 1059, 10.1111/rssb.12377 · DOI · Paper page
  44. J. Cohen, 1969, Statistical power analysis for the behavioural sciences
  45. RP Kesner, 2018, An analysis of dentate gyrus function (an update)., Behav Brain Res, 354, 84, 10.1016/j.bbr.2017.07.033 · DOI
  46. C Kilkenny, 2010, Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research, PLoS Biol, 8, e1000412, 10.1371/journal.pbio.1000412 · DOI · Paper page
  47. A Antonic, 2013, Stem Cell Transplantation in Traumatic Spinal Cord Injury: A Systematic Review and Meta-Analysis of Animal Studies., PLoS Biol, 11, e1001738, 10.1371/journal.pbio.1001738 · DOI
  48. A Verbitsky, 2020, Rodent models of post-traumatic stress disorder: behavioral assessment., Transl Psychiatry, 10, 132, 10.1038/s41398-020-0806-x · DOI
  49. JPA Ioannidis, 2014, Increasing value and reducing waste in research design, conduct, and analysis, Lancet, 383, 166, 10.1016/S0140-6736(13)62227-8 · DOI · Paper page
  50. R Watzlawick, 2019, Outcome heterogeneity and bias in acute experimental spinal cord injury: A meta-analysis, Neurology, 93, e40, 10.1212/WNL.0000000000007718 · DOI
  51. ML Head, 2015, The Extent and Consequences of P-Hacking in Science, PLOS Biol, 13, e1002106, 10.1371/journal.pbio.1002106 · DOI · Paper page
  52. C. Van Lissa, 2017, MetaForest: Exploring heterogeneity in meta-analysis using random forests
  53. V Bonapersona, 2021, Increasing the statistical power of animal experiments with historical control data, Nat Neurosci
  54. R Blaser, 2015, Spontaneous object recognition: a promising approach to the comparative study of memory., Front Behav Neurosci, 9, 183, 10.3389/fnbeh.2015.00183 · DOI
  55. S Binder, 2015, A critical appraisal of the what-where-when episodic-like memory test in rodents: Achievements, caveats and future directions, Prog Neurobiol, 130, 71, 10.1016/j.pneurobio.2015.04.002 · DOI
  56. MSC Sep, 2020, Individual differences in the encoding of contextual details following acute stress: An explorative study, Eur J Neurosci
  57. K Imuta, 2018, Children’s learning and memory of an interactive science lesson: Does the context matter?, Dev Psychol., 54, 1029, 10.1037/dev0000487 · DOI
  58. PS Powell, 2018, Decoding selective attention to context memory: An aging study., Neuroimage, 181, 95, 10.1016/j.neuroimage.2018.06.085 · DOI
  59. J Strunk, 2017, Age-related changes in neural oscillations supporting context memory retrieval., Cortex, 91, 40, 10.1016/j.cortex.2017.01.020 · DOI
  60. GW Kirschen, 2019, Young at heart: Insights into hippocampal neurogenesis in the aged brain, Behav Brain Res, 369, 111934, 10.1016/j.bbr.2019.111934 · DOI
  61. MJ Alam, 2018, Adult neurogenesis conserves hippocampal memory capacity, J Neurosci, 38, 6854, 10.1523/JNEUROSCI.2976-17.2018 · DOI
  62. JM Andreano, 2009, Sex influences on the neurobiology of learning and memory., Learn Mem., 16, 248, 10.1101/lm.918309 · DOI
  63. RJ Keeley, 2015, Strain and sex differences in brain and behaviour of adult rats: Learning and memory, anxiety and volumetric estimates., Behav Brain Res., 288, 118, 10.1016/j.bbr.2014.10.039 · DOI · Paper page
  64. S Yagi, 2019, Sex differences in hippocampal cognition and neurogenesis, Neuropsychopharmacology, 44, 200, 10.1038/s41386-018-0208-4 · DOI
  65. CWEM Quaedflieg, 2018, Memory dynamics under stress., Memory, 26, 364, 10.1080/09658211.2017.1338299 · DOI

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Metadata

Authors
Sep, M. S. C., Vellinga, M., Sarabdjitsingh, R. A., & Joëls, M.
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2021-07-16
Volume
16
Issue
7
Pages
e0249102
ISSN
1932-6203
DOI
10.1371/journal.pone.0249102
PMC
PMC8284613
Cited by
17
References
13
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • ZonMw (114024150)
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (022.003.003)
  • ministerie van defensie
  • Ministerie van Onderwijs, Cultuur en Wetenschap (024.001.003)

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Sep, M. S. C., Vellinga, M., Sarabdjitsingh, R. A., & Joëls, M. (2021). The rodent object-in-context task: A systematic review and meta-analysis of important variables. PLOS ONE. https://doi.org/10.1371/journal.pone.0249102

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