Advertisement

GeroScience

, Volume 40, Issue 2, pp 123–137 | Cite as

Simultaneous assessment of cognitive function, circadian rhythm, and spontaneous activity in aging mice

  • Sreemathi LoganEmail author
  • Daniel Owen
  • Sixia Chen
  • Wei-Jen Chen
  • Zoltan Ungvari
  • Julie Farley
  • Anna Csiszar
  • Amanda Sharpe
  • Maarten Loos
  • Bastijn Koopmans
  • Arlan Richardson
  • William E. Sonntag
Original Article

Abstract

Cognitive function declines substantially with age in both humans and animal models. In humans, this decline is associated with decreases in independence and quality of life. Although the methodology for analysis of cognitive function in human models is relatively well established, similar analyses in animal models have many technical issues (e.g., unintended experimenter bias, motivational issues, stress, and testing during the light phase of the light dark cycle) that limit interpretation of the results. These caveats, and others, potentially bias the interpretation of studies in rodents and prevent the application of current tests of learning and memory as part of an overall healthspan assessment in rodent models of aging. The goal of this study was to establish the methodology to assess cognitive function in aging animals that addresses many of these concerns. Here, we use a food reward-based discrimination procedure with minimal stress in C57Bl/6J male mice at 6, 21, and 27 months of age, followed by a reversal task to assess behavioral flexibility. Importantly, the procedures minimize issues related to between-experimenter confounds and are conducted during both the dark and light phases of the light dark cycle in a home-cage setting. During cognitive testing, we were able to assess multiple measures of spontaneous movement and diurnal activity in young and aged mice including, distance moved, velocity, and acceleration over a 90-h period. Both initial discrimination and reversal learning significantly decreased with age and, similar to rats and humans, not all old mice demonstrated impairments in learning with age. These results permitted classification of animals based on their cognitive status. Analysis of movement parameters indicated decreases in distance moved as well as velocity and acceleration with increasing age. Based on these data, we developed preliminary models indicating, as in humans, a close relationship exists between age-related movement parameters and cognitive ability. Our results provide a reliable method for assessing cognitive performance with minimal stress and simultaneously provide key information on movement and diurnal activity. These methods represent a novel approach to developing non-invasive healthspan measures in rodent models that allow standardization across laboratories.

Keywords

Aging Phenotyper Behaviour Automated home-cage Spatial memory 

Notes

Acknowledgements

This work was supported by the funding sources: T32AG052363; NIH R01AG038747; R01NS056218; R01AG057424 to WES.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

References

  1. Aarts E, Maroteaux G, Loos M, Koopmans B, Kovačević J, Smit AB, Verhage M, Sluis Sv, Neuro-BSIK Mouse Phenomics Consortium (2015) The light spot test: measuring anxiety in mice in an automated home-cage environment. Behav Brain Res 294:123–130CrossRefPubMedGoogle Scholar
  2. Arakawa H, Iguchi Y (2018) Ethological and multi-behavioral analysis of learning and memory performance in laboratory rodent models. Neurosci Res S0168-0102(17):30714–9.  https://doi.org/10.1016/j.neures.2018.02.001
  3. Ashpole NM, Logan S, Yabluchanskiy A, Mitschelen MC, Yan H, Farley JA, Hodges EL, Ungvari Z, Csiszar A, Chen S, Georgescu C, Hubbard GB, Ikeno Y, Sonntag WE (2017) IGF-1 has sexually dimorphic, pleiotropic, and time-dependent effects on healthspan, pathology, and lifespan. Geroscience 39(2):129–145CrossRefPubMedPubMedCentralGoogle Scholar
  4. Ayala JE, Bracy DP, McGuinness OP, Wasserman DH (2006) Considerations in the design of hyperinsulinemic-euglycemic clamps in the conscious mouse. Diabetes 55(2):390–397CrossRefPubMedGoogle Scholar
  5. Ayala JE, Samuel VT, Morton GJ, Obici S, Croniger CM, Shulman GI, Wasserman DH, McGuinness OP, for the NIH Mouse Metabolic Phenotyping Center Consortium (2010) Standard operating procedures for describing and performing metabolic tests of glucose homeostasis in mice. Dis Model Mech 3(9–10):525–534CrossRefPubMedPubMedCentralGoogle Scholar
  6. Baker DJ, Wijshake T, Tchkonia T, LeBrasseur NK, Childs BG, van de Sluis B, Kirkland JL, van Deursen JM (2011) Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders. Nature 479(7372):232–236CrossRefPubMedPubMedCentralGoogle Scholar
  7. Barzilai N, Huffman DM, Muzumdar RH, Bartke A (2012) The critical role of metabolic pathways in aging. Diabetes 61(6):1315–1322CrossRefPubMedPubMedCentralGoogle Scholar
  8. Beauchet O, Launay CP, Sekhon H, Barthelemy JC, Roche F, Chabot J, Levinoff EJ, Allali G (2017) Association of increased gait variability while dual tasking and cognitive decline: results from a prospective longitudinal cohort pilot study. Geroscience 39:439–445CrossRefPubMedCentralGoogle Scholar
  9. Bergman H, Ferrucci L, Guralnik J, Hogan DB, Hummel S, Karunananthan S, Wolfson C (2007) Frailty: an emerging research and clinical paradigm—issues and controversies. J Gerontol A Biol Sci Med Sci 62(7):731–737Google Scholar
  10. Dahle CL, Jacobs BS, Raz N (2009) Aging, vascular risk, and cognition: blood glucose, pulse pressure, and cognitive performance in healthy adults. Psychol Aging 24(1):154–162CrossRefPubMedPubMedCentralGoogle Scholar
  11. Destici E, Jacobs EH, Tamanini F, Loos M, van der Horst GTJ, Oklejewicz M (2013) Altered phase-relationship between peripheral oscillators and environmental time in Cry1 or Cry2 deficient mouse models for early and late chronotypes. PLoS One 8(12):e83602CrossRefPubMedPubMedCentralGoogle Scholar
  12. Dooves S, Bugiani M, Postma NL, Polder E, Land N, Horan ST, van Deijk ALF, van de Kreeke A, Jacobs G, Vuong C, Klooster J, Kamermans M, Wortel J, Loos M, Wisse LE, Scheper GC, Abbink TEM, Heine VM, van der Knaap MS (2016) Astrocytes are central in the pathomechanisms of vanishing white matter. J Clin Invest 126(4):1512–1524CrossRefPubMedPubMedCentralGoogle Scholar
  13. Forster MJ, Lal H (1999) Estimating age-related changes in psychomotor function: influence of practice and of level of caloric intake in different genotypes. Neurobiol Aging 20(2):167–176CrossRefPubMedGoogle Scholar
  14. Foster TC, Defazio RA, Bizon JL (2012) Characterizing cognitive aging of spatial and contextual memory in animal models. Front Aging Neurosci 4:12PubMedPubMedCentralGoogle Scholar
  15. Freeman WM, VanGuilder HD, Bennett C, Sonntag WE (2009) Cognitive performance and age-related changes in the hippocampal proteome. Neuroscience 159(1):183–195CrossRefPubMedGoogle Scholar
  16. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA (2001) Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 56(3):M146–M156CrossRefPubMedPubMedCentralGoogle Scholar
  17. Gerlai R (2001) Behavioral tests of hippocampal function: simple paradigms complex problems. Behav Brain Res 125(1–2):269–277CrossRefPubMedGoogle Scholar
  18. Giblin W, Skinner ME, Lombard DB (2014) Sirtuins: guardians of mammalian healthspan. Trends Genet 30(7):271–286CrossRefPubMedPubMedCentralGoogle Scholar
  19. Gulinello M, Mitchell HA, Chang Q, Timothy O'Brien W, Zhou Z, Abel T, et al (2018) Rigor and reproducibility in rodent behavioral research. Neurobiol Learn Mem S1074-7427(18):30001–7.  https://doi.org/10.1016/j.nlm.2018.01.001
  20. Hanell A, Marklund N (2014) Structured evaluation of rodent behavioral tests used in drug discovery research. Front Behav Neurosci 8:252Google Scholar
  21. Harrison DE, Strong R, Allison DB, Ames BN, Astle CM, Atamna H, Fernandez E, Flurkey K, Javors MA, Nadon NL, Nelson JF, Pletcher S, Simpkins JW, Smith D, Wilkinson JE, Miller RA (2014) Acarbose, 17-alpha-estradiol, and nordihydroguaiaretic acid extend mouse lifespan preferentially in males. Aging Cell 13(2):273–282CrossRefPubMedGoogle Scholar
  22. Hedden T, Gabrieli JD (2004) Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci 5(2):87–96CrossRefPubMedGoogle Scholar
  23. Huffman DM, Schafer MJ, LeBrasseur NK (2016) Energetic interventions for healthspan and resiliency with aging. Exp Gerontol 86:73–83CrossRefPubMedPubMedCentralGoogle Scholar
  24. Kim S, Myers L, Wyckoff J, Cherry KE, Jazwinski SM (2017) The frailty index outperforms DNA methylation age and its derivatives as an indicator of biological age. Geroscience 39(1):83–92CrossRefPubMedPubMedCentralGoogle Scholar
  25. Ko SU, Jerome GJ, Simonsick EM, Studenski S, Ferrucci L (2018) Differential gait patterns by falls history and knee pain status in healthy older adults: results from the Baltimore longitudinal study of aging. J Aging Phys Act:1–18Google Scholar
  26. Koopmans B, Smit AB, Verhage M, Loos M (2017) AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data. BMC Bioinformatics 18(1):200CrossRefPubMedPubMedCentralGoogle Scholar
  27. Kramvis I et al (2013) Hyperactivity, perseveration and increased responding during attentional rule acquisition in the fragile X mouse model. Front Behav Neurosci 7:172CrossRefPubMedPubMedCentralGoogle Scholar
  28. Logan S, Pharaoh GA, Marlin MC, Masser DR, Matsuzaki S, Wronowski B, Yeganeh A, Parks EE, Premkumar P, Farley JA, Owen DB, Humphries KM, Kinter M, Freeman WM, Szweda LI, van Remmen H, Sonntag WE (2018) Insulin-like growth factor receptor signaling regulates working memory, mitochondrial metabolism, and amyloid-beta uptake in astrocytes. Mol Metab 9:141–155CrossRefPubMedPubMedCentralGoogle Scholar
  29. Longordo F, Fan J, Steimer T, Kopp C, Lüthi A (2011) Do mice habituate to “gentle handling?” A comparison of resting behavior, corticosterone levels and synaptic function in handled and undisturbed C57BL/6J mice. Sleep 34(5):679–681CrossRefPubMedPubMedCentralGoogle Scholar
  30. Loos M, Koopmans B, Aarts E, Maroteaux G, van der Sluis S, Neuro-BSIK Mouse Phenomics Consortium, Verhage M, Smit AB (2014) Sheltering behavior and locomotor activity in 11 genetically diverse common inbred mouse strains using home-cage monitoring. PLoS One 9(9):e108563CrossRefPubMedPubMedCentralGoogle Scholar
  31. Loos M et al (2015) Within-strain variation in behavior differs consistently between common inbred strains of mice. Mamm Genome 26(7–8):348–354CrossRefPubMedGoogle Scholar
  32. Maire M, Reichert CF, Gabel V, Viola AU, Phillips C, Berthomier C, Borgwardt S, Cajochen C, Schmidt C (2018) Human brain patterns underlying vigilant attention: impact of sleep debt, circadian phase and attentional engagement. Sci Rep 8(1):970CrossRefPubMedPubMedCentralGoogle Scholar
  33. Mandillo S, Tucci V, Hölter SM, Meziane H, Banchaabouchi MA, Kallnik M, Lad HV, Nolan PM, Ouagazzal AM, Coghill EL, Gale K, Golini E, Jacquot S, Krezel W, Parker A, Riet F, Schneider I, Marazziti D, Auwerx J, Brown SDM, Chambon P, Rosenthal N, Tocchini-Valentini G, Wurst W (2008) Reliability, robustness, and reproducibility in mouse behavioral phenotyping: a cross-laboratory study. Physiol Genomics 34(3):243–255CrossRefPubMedPubMedCentralGoogle Scholar
  34. Maroteaux G, Loos M, van der Sluis S, Koopmans B, Aarts E, van Gassen K, Geurts A, The NeuroBSIK Mouse Phenomics Consortium, Largaespada DA, Spruijt BM, Stiedl O, Smit AB, Verhage M (2012) High-throughput phenotyping of avoidance learning in mice discriminates different genotypes and identifies a novel gene. Genes Brain Behav 11(7):772–784CrossRefPubMedPubMedCentralGoogle Scholar
  35. Martin-Montalvo A et al (2013) Metformin improves healthspan and lifespan in mice. Nat Commun 4:2192CrossRefPubMedPubMedCentralGoogle Scholar
  36. McCoy JG, Strecker RE (2011) The cognitive cost of sleep lost. Neurobiol Learn Mem 96(4):564–582CrossRefPubMedPubMedCentralGoogle Scholar
  37. Mielke MM, Roberts RO, Savica R, Cha R, Drubach DI, Christianson T, Pankratz VS, Geda YE, Machulda MM, Ivnik RJ, Knopman DS, Boeve BF, Rocca WA, Petersen RC (2013) Assessing the temporal relationship between cognition and gait: slow gait predicts cognitive decline in the Mayo Clinic Study of Aging. J Gerontol A Biol Sci Med Sci 68(8):929–937CrossRefPubMedGoogle Scholar
  38. Okonkwo OC, Alosco ML, Griffith HR, Mielke MM, Shaw LM, Trojanowski JQ, Tremont G, Alzheimer's Disease Neuroimaging Initiative (2010a) Cerebrospinal fluid abnormalities and rate of decline in everyday function across the dementia spectrum: normal aging, mild cognitive impairment, and Alzheimer disease. Arch Neurol 67(6):688–696CrossRefPubMedPubMedCentralGoogle Scholar
  39. Okonkwo OC, Cohen RA, Gunstad J, Tremont G, Alosco ML, Poppas A (2010b) Longitudinal trajectories of cognitive decline among older adults with cardiovascular disease. Cerebrovasc Dis 30(4):362–373CrossRefPubMedPubMedCentralGoogle Scholar
  40. Qiu C, Winblad B, Fratiglioni L (2005) The age-dependent relation of blood pressure to cognitive function and dementia. Lancet Neurol 4(8):487–499CrossRefPubMedGoogle Scholar
  41. Remmelink E, Loos M, Koopmans B, Aarts E, van der Sluis S, Smit AB, Verhage M, Neuro-BSIK Mouse Phenomics Consortium (2015) A 1-night operant learning task without food-restriction differentiates among mouse strains in an automated home-cage environment. Behav Brain Res 283:53–60CrossRefPubMedGoogle Scholar
  42. Remmelink E, Aartsma-Rus A, Smit AB, Verhage M, Loos M, van Putten M (2016) Cognitive flexibility deficits in a mouse model for the absence of full-length dystrophin. Genes Brain Behav 15(6):558–567CrossRefPubMedGoogle Scholar
  43. Richardson A, Fischer KE, Speakman JR, de Cabo R, Mitchell SJ, Peterson CA, Rabinovitch P, Chiao YA, Taffet G, Miller RA, Rentería RC, Bower J, Ingram DK, Ladiges WC, Ikeno Y, Sierra F, Austad SN (2016) Measures of healthspan as indices of aging in mice—a recommendation. J Gerontol A Biol Sci Med Sci 71(4):427–430CrossRefPubMedGoogle Scholar
  44. Rosso AL, Verghese J, Metti AL, Boudreau RM, Aizenstein HJ, Kritchevsky S, Harris T, Yaffe K, Satterfield S, Studenski S, Rosano C (2017) Slowing gait and risk for cognitive impairment: the hippocampus as a shared neural substrate. Neurology 89(4):336–342CrossRefPubMedPubMedCentralGoogle Scholar
  45. Schorr A, Carter C, Ladiges W (2018) The potential use of physical resilience to predict healthy aging. Pathobiol Aging Age Relat Dis 8(1):1403844CrossRefPubMedGoogle Scholar
  46. Soultoukis GA, Partridge L (2016) Dietary protein, metabolism, and aging. Annu Rev Biochem 85:5–34CrossRefPubMedGoogle Scholar
  47. Suire CN, Eitan E, Shaffer NC, Tian Q, Studenski S, Mattson MP, Kapogiannis D (2017) Walking speed decline in older adults is associated with elevated pro-BDNF in plasma extracellular vesicles. Exp Gerontol 98:209–216CrossRefPubMedGoogle Scholar
  48. Tanila H (2017) Testing cognitive functions in rodent disease models: Present pitfalls and future perspectives. Behav Brain Res S0166-4328(17)30634–4.  https://doi.org/10.1016/j.bbr.2017.05.040
  49. VanGuilder Starkey HD, Sonntag WE, Freeman WM (2013) Increased hippocampal NgR1 signaling machinery in aged rats with deficits of spatial cognition. Eur J Neurosci 37(10):1643–1658CrossRefPubMedPubMedCentralGoogle Scholar
  50. VanGuilder HD, Farley JA, Yan H, van Kirk CA, Mitschelen M, Sonntag WE, Freeman WM (2011) Hippocampal dysregulation of synaptic plasticity-associated proteins with age-related cognitive decline. Neurobiol Dis 43(1):201–212CrossRefPubMedPubMedCentralGoogle Scholar
  51. Watson NL et al (2010) Executive function, memory, and gait speed decline in well-functioning older adults. J Gerontol A Biol Sci Med Sci 65(10):1093–1100CrossRefPubMedGoogle Scholar
  52. Weindruch R et al (2001) Caloric restriction mimetics: metabolic interventions. J Gerontol A Biol Sci Med Sci 56 Spec No 1:20–33CrossRefPubMedGoogle Scholar
  53. Zhu Y, Tchkonia T, Pirtskhalava T, Gower AC, Ding H, Giorgadze N, Palmer AK, Ikeno Y, Hubbard GB, Lenburg M, O'Hara SP, LaRusso NF, Miller JD, Roos CM, Verzosa GC, LeBrasseur NK, Wren JD, Farr JN, Khosla S, Stout MB, McGowan SJ, Fuhrmann-Stroissnigg H, Gurkar AU, Zhao J, Colangelo D, Dorronsoro A, Ling YY, Barghouthy AS, Navarro DC, Sano T, Robbins PD, Niedernhofer LJ, Kirkland JL (2015) The Achilles’ heel of senescent cells: from transcriptome to senolytic drugs. Aging Cell 14(4):644–658CrossRefPubMedPubMedCentralGoogle Scholar
  54. Zhu Y, Tchkonia T, Fuhrmann-Stroissnigg H, Dai HM, Ling YY, Stout MB, Pirtskhalava T, Giorgadze N, Johnson KO, Giles CB, Wren JD, Niedernhofer LJ, Robbins PD, Kirkland JL (2016) Identification of a novel senolytic agent, navitoclax, targeting the Bcl-2 family of anti-apoptotic factors. Aging Cell 15(3):428–435CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© American Aging Association 2018

Authors and Affiliations

  • Sreemathi Logan
    • 1
    Email author
  • Daniel Owen
    • 1
  • Sixia Chen
    • 2
  • Wei-Jen Chen
    • 2
  • Zoltan Ungvari
    • 1
  • Julie Farley
    • 1
  • Anna Csiszar
    • 1
  • Amanda Sharpe
    • 3
  • Maarten Loos
    • 4
  • Bastijn Koopmans
    • 4
  • Arlan Richardson
    • 1
  • William E. Sonntag
    • 1
  1. 1.Reynolds Oklahoma Center on AgingUniversity of Oklahoma Health Sciences CenterOklahoma CityUSA
  2. 2.Department of Biostatistics and EpidemiologyUniversity of Oklahoma Health Sciences CenterOklahoma CityUSA
  3. 3.College of Pharmaceutical SciencesUniversity of Oklahoma Health Sciences CenterOklahoma CityUSA
  4. 4.Sylics (Synaptologics BV)AmsterdamThe Netherlands

Personalised recommendations