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Precise temporal memories are supported by the lateral entorhinal cortex in humans


There is accumulating evidence that the entorhinal-hippocampal network is important for temporal memory. However, relatively little is known about the precise neurobiological mechanisms underlying memory for time. In particular, whether the lateral entorhinal cortex (LEC) is involved in temporal processing remains an open question. During high-resolution functional magnetic resonance imaging (fMRI) scanning, participants watched a ~28-min episode of a television show. During the test, they viewed still-frames and indicated on a continuous timeline the precise time each still-frame was viewed during the study. This procedure allowed us to measure error in seconds for each trial. We analyzed fMRI data from retrieval and found that high temporal precision was associated with increased blood-oxygen-level-dependent fMRI activity in the anterolateral entorhinal (a homolog of the LEC in rodents) and perirhinal cortices, but not in the posteromedial entorhinal and parahippocampal cortices. This suggests a previously unknown role for the LEC in processing of high-precision, minute-scale temporal memories.

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

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  1. 1.

    Kesner, R. P. & Hunsaker, M. R. The temporal attributes of episodic memory. Behav. Brain Res. 215, 299–309 (2010).

  2. 2.

    Ekstrom, A. D. & Bookheimer, S. Y. S. Spatial and temporal episodic memory retrieval recruit dissociable functional networks in the human brain. Learn. Mem. 14, 645–654 (2007).

  3. 3.

    Ekstrom, A. D. & Ranganath, C. Space, time, and episodic memory: the hippocampus is all over the cognitive map. Hippocampus 28, 680–687 (2018).

  4. 4.

    Hartley, T., Lever, C., Burgess, N. & O’Keefe, J. Space in the brain: how the hippocampal formation supports spatial cognition. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20120510 (2013).

  5. 5.

    Hafting, T., Fyhn, M., Molden, S., Moser, M. B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005).

  6. 6.

    Save, E. & Sargolini, F. Disentangling the role of the mec and lec in the processing of spatial and non-spatial information: contribution of lesion studies. Front. Syst. Neurosci. 11, 81 (2017).

  7. 7.

    McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I. & Moser, M. B. Path integration and the neural basis of the ‘cognitive map’. Nat. Rev. Neurosci. 7, 663–678 (2006).

  8. 8.

    MacDonald, C. J., Lepage, K. Q., Eden, U. T. & Eichenbaum, H. Hippocampal ‘time cells’ bridge the gap in memory for discontiguous events. Neuron 71, 737–749 (2011).

  9. 9.

    MacDonald, C. J., Carrow, S., Place, R. & Eichenbaum, H. Distinct hippocampal time cell sequences represent odor memories in immobilized rats. J. Neurosci. 33, 14607–14616 (2013).

  10. 10.

    Kraus, B. J. et al. During running in place, grid cells integrate elapsed time and distance run. Neuron 88, 578–589 (2015).

  11. 11.

    Pastalkova, E., Itskov, V., Amarasingham, A. & Buzsáki, G. Internally generated cell assembly sequences in the rat hippocampus. Science 321, 1322–1327 (2008).

  12. 12.

    Salz, X. D. M. et al. Time cells in hippocampal area ca3. J. Neurosci. 36, 7476–7484 (2016).

  13. 13.

    Eichenbaum, H. On the integration of space, time, and memory. Neuron 95, 1007–1018 (2017).

  14. 14.

    Deshmukh, S. S. & Knierim, J. J. Representation of non-spatial and spatial information in the lateral entorhinal cortex. Front. Behav. Neurosci. 5, 69 (2011).

  15. 15.

    Knierim, J. J., Neunuebel, J. P., Deshmukh, S. S. & Knierim, J. J. Functional correlates of the lateral and medial entorhinal cortex: objects, path integration and local–global reference frames. Phil. Trans. R. Soc. Lond. B 369, 20130369 (2013).

  16. 16.

    Reagh, Z. M. & Yassa, M. A. Object and spatial mnemonic interference differentially engage lateral and medial entorhinal cortex in humans. Proc. Natl. Acad. Sci. USA 111, E4264–E4273 (2014).

  17. 17.

    Reagh, Z. M., Noche, J. A., Tustison, N. J., Delisle, D., Murray, E. A., & Yassa, M. A. Functional imbalance of anterolateral entorhinal cortex and hippocampal dentate/CA3 underlies age-related object pattern separation deficits. Neuron 97, 1187–1198.e4 (2018).

  18. 18.

    Suzuki, W. A. & Amaral, D. G. Perirhinal and parahippocampal cortices of the macaque monkey: cortical afferents. J. Comp. Neurol. 350, 497–533 (1994).

  19. 19.

    Maass, A., Berron, D., Libby, L. A., Ranganath, C. & Düzel, E. Functional subregions of the human entorhinal cortex. eLife 4, e06426 (2015).

  20. 20.

    Navarro Schröder, T., Haak, K. V., Zaragoza, Jimenez,N. I., Beckmann, C. F. & Doeller, C. F. Functional topography of the human entorhinal cortex. eLife 4, e06738 (2015).

  21. 21.

    Ranganath, C. & Ritchey, M. Two cortical systems for memory-guided behaviour. Nat. Rev. Neurosci. 13, 713–726 (2012).

  22. 22.

    Knierim, J. J., Neunuebel, J. P. & Deshmukh, S. S. Functional correlates of the lateral and medial entorhinal cortex: objects, path integration and local-global reference frames. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130369 (2013).

  23. 23.

    Lositsky, O. et al. Neural pattern change during encoding of a narrative predicts retrospective duration estimates. eLife 5, 1–40 (2016).

  24. 24.

    Tsao, A., Sugar, J., Lu, L., Wang, C., Knierim, J. J., Moser, M. B. & Moser, E. I. Integrating time from experience in the lateral entorhinal cortex. Nature 561, 57–62 (2018).

  25. 25.

    Hannesson, D. K., Howland, J. G. & Phillips, A. G. Interaction between perirhinal and medial prefrontal cortex is required for temporal order but not recognition memory for objects in rats. J. Neurosci. 24, 4596–4604 (2004).

  26. 26.

    Brown, M. W. Neuronal responses and recognition memory. Semin. Neurosci. 8, 23–32 (1996).

  27. 27.

    Eichenbaum, H., Yonelinas, A. P. & Ranganath, C. The medial temporal lobe and recognition memory. Annu. Rev. Neurosci. 30, 123–152 (2007).

  28. 28.

    Hsieh, L. T., Gruber, M. J., Jenkins, L. J. & Ranganath, C. Hippocampal activity patterns carry information about objects in temporal context. Neuron 81, 1165–1178 (2014).

  29. 29.

    Jenkins, L. J. & Ranganath, C. Prefrontal and medial temporal lobe activity at encoding predicts temporal context memory. J. Neurosci. 30, 15558–15565 (2010).

  30. 30.

    Tubridy, S. & Davachi, L. Medial temporal lobe contributions to episodic sequence encoding. Cereb. Cortex 21, 272–280 (2011).

  31. 31.

    Lehn, H. et al. A specific role of the human hippocampus in recall of temporal sequences. J. Neurosci. 29, 3475–3484 (2009).

  32. 32.

    Furman, O., Dorfman, N., Hasson, U., Davachi, L. & Dudai, Y. They saw a movie: long-term memory for an extended audiovisual narrative. Learn. Mem. 14, 457–467 (2007).

  33. 33.

    Peirce, J. W. PsychoPy--Psychophysics software in Python. J. Neurosci. Methods 162, 8–13 (2007).

  34. 34.

    GraphPad Prism v.7.00 (GraphPad Software, Inc., 2017);

  35. 35.

    Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).

  36. 36.

    Avants, B. B., Tustison, N. & Song, G. Advanced Normalization Tools (ANTS) Sherbrooke Connectivity Imaging Lab (2009).

  37. 37.

    Hunter, B. J. D. (2007). Computing in Science & Engineering, 9(3), 90–95.

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We thank M. Tsai, J. Noche and A. Chun for assistance with data collection. We also thank C. Stark, N. Fortin and D. Huffman for helpful discussions. This work was supported by US NIH grants nos. P50AG05146, R01MH1023921 and R01AG053555 (PI: M.A.Y.), and Training Grant no. T32DC010775 (to M.E.M., PI: Metherate).

Author information

M.E.M. and M.A.Y. designed the experiment. M.E.M. collected and analyzed the data with contributions from Z.M.R. M.E.M., Z.M.R. and M.A.Y. contributed substantially to the interpretation of results. M.E.M. and M.A.Y. drafted and revised the manuscript with support from Z.M.R.

Competing interests

The authors declare no competing interests.

Correspondence to Michael A Yassa.

Integrated supplementary information

  1. Supplementary Fig 1 Effect of distance from segment boundary on performance.

    A one-way repeated-measures ANOVA was conducted to determine whether performance differed as a function of each trial’s distance from a segment boundary at encoding (n = 19 participants). A segment boundary is defined as the beginning or end of a video segment at encoding (the episode was split into three segments). We conducted a one-way repeated-measures ANOVA comparing trials that were of short (2–107 seconds), medium (108–186 seconds) and long (200–277 seconds) distances from a segment boundary, which was not statistically significant [F(2,18)= 3.29, p = 0.0506], indicating that error does not differ significantly based on a trial’s distance from a segment boundary.

  2. Supplementary Fig 2 Effect of vividness on MTL and cortical regions.

    After scanning, participants viewed the still-frames one more time and were asked to indicate how vividly they could recall the scene associated with each one on a 5 point scale (n = 12 participants). High, medium, and low vividness trials were entered into a GLM. Paired t-tests were conducted on high and low vividness beta coefficients, and no significant results were found after correcting for multiple comparisons using the Bonferroni-Holm method in the alEC [t = 0.4983, df = 11, two-tailed p = 0.6281], pmEC [t = 1.947, df = 11, p = 0.0774], angular gyrus [t = 3.06, df = 11, p = 0.0109], MPFC [t = 2.956, df = 11, two-tailed p = 0.0131; critical p is 0.0083], PRC [t = 0.4744, df = 11, two-tailed = 0.6445], PHC [t = 1.976, df = 11, two-tailed p = 0.0738; critical p is 0.01], ACC [t = 0.5422, df = 11, two-tailed p = 0.5985], PCC [t = 0.1654, df = 11, two-tailed p = 0.8716], DGCA3 [t = 0.7672, df = 11, two-tailed p = 0.4591], CA1 [t = 0.6167, df = 11, two-tailed p = 0.549], precuneus [t = 0.3441, df = 11, two-tailed p = 0.7373], RSC [t = 0.703, df = 11, two-tailed p = 0.4967]).

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Fig. 1: Task parameters and description.
Fig. 2: Behavioral performance.
Fig. 3: Effects of precision on MTL regions.
Fig. 4: Cortical reinstatement effects.
Supplementary Fig 1: Effect of distance from segment boundary on performance.
Supplementary Fig 2: Effect of vividness on MTL and cortical regions.