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The European journal of neuroscience
12, 2023;58 (11): 4341-4356.

Theta oscillatory power decreases in humans are associated with spatial learning in a virtual water maze task.

Conor Thornberry, Michelle Caffrey, Sean Commins
Author Information
  1. Conor Thornberry: Department of Psychology, Maynooth University, Maynooth, Ireland. ORCID
  2. Michelle Caffrey: Department of Psychology, Maynooth University, Maynooth, Ireland.
  3. Sean Commins: Department of Psychology, Maynooth University, Maynooth, Ireland.
PMID: 37957526 DOI: 10.1111/ejn.16185

Abstract

Theta oscillations (4-8 Hz) in humans play a role in navigation processes, including spatial encoding, retrieval and sensorimotor integration. Increased theta power at frontal and parietal midline regions is known to contribute to successful navigation. However, the dynamics of cortical theta and its role in spatial learning are not fully understood. This study aimed to investigate theta oscillations via electroencephalogram (EEG) during spatial learning in a virtual water maze. Participants were separated into a learning group (n = 25) who learned the location of a hidden goal across 12 trials, or a time-matched non-learning group (n = 25) who were required to simply navigate the same arena, but without a goal. We compared all trials, at two phases of learning, the trial start and the goal approach. We also compared the first six trials with the last six trials within-groups. The learning group showed reduced low-frequency theta power at the frontal and parietal midline during the start phase and largely reduced theta combined with a short increase at both midlines during the goal-approach phase. These patterns were not found in the non-learning group, who instead displayed extensive increases in low-frequency oscillations at both regions during the trial start and at the parietal midline during goal approach. Our results support the theory that theta plays a crucial role in spatial encoding during exploration, as opposed to sensorimotor integration. We suggest our findings provide evidence for a link between learning and a reduction of theta oscillations in humans.

Keywords

efficiency learning navigation spatial memory theta oscillations

References

  1. Astur, R. S., Ortiz, M. L., & Sutherland, R. J. (1998). A characterization of performance by men and women in a virtual Morris water task: A large and reliable sex difference. Behavioural Brain Research, 93, 185-190. https://doi.org/10.1016/S0166-4328(98)00019-9
  2. Barnhart, C. D., Yang, D., & Lein, P. J. (2015). Using the Morris water maze to assess spatial learning and memory in weanling mice. PLoS ONE, 10, e0124521. https://doi.org/10.1371/journal.pone.0124521
  3. Battery, A. I. T. (1944). Manual of directions and scoring. War Department, Adjutant General's Office.
  4. Bohbot, V. D., Copara, M. S., Gotman, J., & Ekstrom, A. D. (2017). Low-frequency theta oscillations in the human hippocampus during real-world and virtual navigation. Nature Communications, 8, 1-7, 14415. https://doi.org/10.1038/ncomms14415
  5. Boudewyn, M. A., Luck, S. J., Farrens, J. L., & Kappenman, E. S. (2018). How many trials does it take to get a significant ERP effect? It depends. Psychophysiology, 55, e13049. https://doi.org/10.1111/psyp.13049
  6. Burgess, A. P. (2019). How conventional visual representations of time-frequency analyses bias our perception of EEG/MEG signals and what to do about it. Frontiers in Human Neuroscience, 13, 212. https://doi.org/10.3389/fnhum.2019.00212
  7. Burgess, A. P., & Gruzelier, J. H. (1997). Short duration synchronization of human theta rhythm during recognition memory. Neuroreport, 8, 1039-1042. https://doi.org/10.1097/00001756-199703030-00044
  8. Burgess, N., & O'Keefe, J. (2011). Models of place and grid cell firing and theta rhythmicity. Current Opinion in Neurobiology, 21, 734-744. https://doi.org/10.1016/j.conb.2011.07.002
  9. Bush, D., Bisby, J. A., Bird, C. M., Gollwitzer, S., Rodionov, R., Diehl, B., McEvoy, A. W., Walker, M. C., & Burgess, N. (2017). Human hippocampal theta power indicates movement onset and distance travelled. Proceedings of the National Academy of Sciences, 114, 12297-12302. https://doi.org/10.1073/pnas.1708716114
  10. Buzsáki, G. (2005). Theta rhythm of navigation: Link between path integration and landmark navigation, episodic and semantic memory. Hippocampus, 15, 827-840. https://doi.org/10.1002/hipo.20113
  11. Buzsáki, G., & Moser, E. I. (2013). Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nature Neuroscience, 16, 130-138. https://doi.org/10.1038/nn.3304
  12. Caplan, J. B., & Glaholt, M. G. (2007). The roles of EEG oscillations in learning relational information. NeuroImage, 38, 604-616. https://doi.org/10.1016/j.neuroimage.2007.07.054
  13. Chattopadhyay, S., Zary, L., Quek, C., & Prasad, D. K. (2021). Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network. Expert Systems with Applications, 184, 115548. https://doi.org/10.1016/j.eswa.2021.115548
  14. Chen, A. C. N., Feng, W., Zhao, H., Yin, Y., & Wang, P. (2008). EEG default mode network in the human brain: Spectral regional field powers. NeuroImage, 41, 561-574. https://doi.org/10.1016/j.neuroimage.2007.12.064
  15. Cheng, B., Wunderlich, A., Gramann, K., Lin, E., & Fabrikant, S. I. (2022). The effect of landmark visualization in mobile maps on brain activity during navigation: A virtual reality study. Frontiers in Virtual Reality, 3, 981625. https://doi.org/10.3389/frvir.2022.981625
  16. Chrastil, E. R., Rice, C., Goncalves, M., Moore, K. N., Wynn, S. C., Stern, C. E., & Nyhus, E. (2022). Theta oscillations support active exploration in human spatial navigation. NeuroImage, 262, 119581. https://doi.org/10.1016/j.neuroimage.2022.119581
  17. Cohen, M. X. (2014). Analyzing neural time series data: Theory and practice. MIT press. https://doi.org/10.7551/mitpress/9609.001.0001
  18. Colgin, L. L. (2020). Five decades of hippocampal place cells and EEG rhythms in behaving rats. Journal of Neuroscience, 40, 54-60. https://doi.org/10.1523/JNEUROSCI.0741-19.2019
  19. Commins, S. (2018). Efficiency: an underlying principle of learning? Reviews in the Neurosciences, 29(2), 183-197.
  20. Commins, S., Coutrot, A., Hornberger, M., Spiers, H. J., & de Andrade, R. (2022). Examining individual learning patterns using generalized linear mixed models.
  21. Commins, S., Duffin, J., Chaves, K., Leahy, D., Corcoran, K., Caffrey, M., Keenan, L., Finan, D., & Thornberry, C. (2020). NavWell: A simplified virtual-reality platform for spatial navigation and memory experiments. Behavior Research Methods, 52, 1189-1207. https://doi.org/10.3758/s13428-019-01310-5
  22. Cornwell, B. R., Arkin, N., Overstreet, C., Carver, F. W., & Grillon, C. (2012). Distinct contributions of human hippocampal theta to spatial cognition and anxiety. Hippocampus, 22, 1848-1859. https://doi.org/10.1002/hipo.22019
  23. Crespo-García, M., Zeiller, M., Leupold, C., Kreiselmeyer, G., Rampp, S., Hamer, H. M., & Dalal, S. S. (2016). Slow-theta power decreases during item-place encoding predict spatial accuracy of subsequent context recall. NeuroImage, 142, 533-543. https://doi.org/10.1016/j.neuroimage.2016.08.021
  24. de Araújo, D. B., Baffa, O., & Wakai, R. T. (2002). Theta oscillations and human navigation: A magnetoencephalography study. Journal of Cognitive Neuroscience, 14, 70-78. https://doi.org/10.1162/089892902317205339
  25. Delaux, A., de Saint Aubert, J. B., Ramanoël, S., Bécu, M., Gehrke, L., Klug, M., Chavarriaga, R., Sahel, J. A., Gramann, K., & Arleo, A. (2021). Mobile brain/body imaging of landmark-based navigation with high-density EEG. European Journal of Neuroscience, 54, 8256-8282. https://doi.org/10.1111/ejn.15190
  26. Do, T.-T. N., Lin, C.-T., & Gramann, K. (2021). Human brain dynamics in active spatial navigation. Scientific Reports, 11, 13036. https://doi.org/10.1038/s41598-021-92246-4
  27. Du, Y. K., Liang, M., McAvan, A. S., Wilson, R. C., & Ekstrom, A. D. (2023). Frontal-midline oscillations index the evolution of spatial memory during active navigation. bioRxiv.
  28. Ekstrom, A. D., Caplan, J. B., Ho, E., Shattuck, K., Fried, I., & Kahana, M. J. (2005). Human hippocampal theta activity during virtual navigation. Hippocampus, 15, 881-889. https://doi.org/10.1002/hipo.20109
  29. Ekstrom, A. D., Huffman, D. J., & Starrett, M. (2017). Interacting networks of brain regions underlie human spatial navigation: A review and novel synthesis of the literature. Journal of Neurophysiology, 118, 3328-3344. https://doi.org/10.1152/jn.00531.2017
  30. Ekstrom, A. D., Kahana, M. J., Caplan, J. B., Fields, T. A., Isham, E. A., Newman, E. L., & Fried, I. (2003). Cellular networks underlying human spatial navigation. Nature, 425, 184-188. https://doi.org/10.1038/nature01964
  31. Epstein, R. A. (2008). Parahippocampal and retrosplenial contributions to human spatial navigation. Trends in Cognitive Sciences, 12, 388-396. https://doi.org/10.1016/j.tics.2008.07.004
  32. Epstein, R. A., Patai, E. Z., Julian, J. B., & Spiers, H. J. (2017). The cognitive map in humans: Spatial navigation and beyond. Nature Neuroscience, 20, 1504-1513. https://doi.org/10.1038/nn.4656
  33. Fell, J., & Axmacher, N. (2011). The role of phase synchronization in memory processes. Nature Reviews Neuroscience, 12, 105-118. https://doi.org/10.1038/nrn2979
  34. Fellrath, J., Mottaz, A., Schnider, A., Guggisberg, A. G., & Ptak, R. (2016). Theta-band functional connectivity in the dorsal fronto-parietal network predicts goal-directed attention. Neuropsychologia, 92, 20-30. https://doi.org/10.1016/j.neuropsychologia.2016.07.012
  35. Goyal, A., Miller, J., Qasim, S. E., Watrous, A. J., Zhang, H., Stein, J. M., Inman, C. S., Gross, R. E., Willie, J. T., Lega, B., Lin, J.-J., Sharan, A., Wu, C., Sperling, M. R., Sheth, S. A., McKhann, G. M., Smith, E. H., Schevon, C., & Jacobs, J. (2020). Functionally distinct high and low theta oscillations in the human hippocampus. Nature Communications, 11, 2469. https://doi.org/10.1038/s41467-020-15670-6
  36. Greenberg, J. A., Burke, J. F., Haque, R., Kahana, M. J., & Zaghloul, K. A. (2015). Decreases in theta and increases in high frequency activity underlie associative memory encoding. NeuroImage, 114, 257-263. https://doi.org/10.1016/j.neuroimage.2015.03.077
  37. Griffiths, B., Mazaheri, A., Debener, S., & Hanslmayr, S. (2016). Brain oscillations track the formation of episodic memories in the real world. NeuroImage, 143, 256-266. https://doi.org/10.1016/j.neuroimage.2016.09.021
  38. Guderian, S., Schott, B. H., Richardson-Klavehn, A., & Düzel, E. (2009). Medial temporal theta state before an event predicts episodic encoding success in humans. Proceedings of the National Academy of Sciences, 106, 5365-5370. https://doi.org/10.1073/pnas.0900289106
  39. Gyurkovics, M., Clements, G. M., Low, K. A., Fabiani, M., & Gratton, G. (2021). The impact of 1/f activity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale. NeuroImage, 237, 118192. https://doi.org/10.1016/j.neuroimage.2021.118192
  40. Hanslmayr, S., Spitzer, B., & Bäuml, K.-H. (2009). Brain oscillations dissociate between semantic and nonsemantic encoding of episodic memories. Cerebral Cortex, 19, 1631-1640. https://doi.org/10.1093/cercor/bhn197
  41. Harris, A. M., Dux, P. E., Jones, C. N., & Mattingley, J. B. (2017). Distinct roles of theta and alpha oscillations in the involuntary capture of goal-directed attention. NeuroImage, 152, 171-183. https://doi.org/10.1016/j.neuroimage.2017.03.008
  42. Heimrath, K., Sandmann, P., Becke, A., Müller, N. G., & Zaehle, T. (2012). Behavioral and electrophysiological effects of transcranial direct current stimulation of the parietal cortex in a visuo-spatial working memory task. Frontiers in Psychiatry, 3, 56. https://doi.org/10.3389/fpsyt.2012.00056
  43. Herweg, N. A., Solomon, E. A., & Kahana, M. J. (2020). Theta oscillations in human memory. Trends in Cognitive Sciences, 24, 208-227. https://doi.org/10.1016/j.tics.2019.12.006
  44. Jabès, A., Klencklen, G., Ruggeri, P., Antonietti, J.-P., Banta Lavenex, P., & Lavenex, P. (2021). Age-related differences in resting-state EEG and allocentric spatial working memory performance. Frontiers in Aging neuroscience, 13, 704362. https://doi.org/10.3389/fnagi.2021.704362
  45. Jaiswal, N., Ray, W., & Slobounov, S. (2010). Encoding of visual-spatial information in working memory requires more cerebral efforts than retrieval: Evidence from an EEG and virtual reality study. Brain Research, 1347, 80-89. https://doi.org/10.1016/j.brainres.2010.05.086
  46. Kahana, M. J., Sekuler, R., Caplan, J. B., Kirschen, M., & Madsen, J. R. (1999). Human theta oscillations exhibit task dependence during virtual maze navigation. Nature, 399, 781-784. https://doi.org/10.1038/21645
  47. Kane, J., Cavanagh, J. F., & Dillon, D. G. (2019). Reduced theta power during memory retrieval in depressed adults. Biol Psychiatry Cogn Neurosci Neuroimaging, 4, 636-643. https://doi.org/10.1016/j.bpsc.2019.03.004
  48. Kaplan, R., Bush, D., Bonnefond, M., Bandettini, P. A., Barnes, G. R., Doeller, C. F., & Burgess, N. (2014). Medial prefrontal theta phase coupling during spatial memory retrieval. Hippocampus, 24, 656-665. https://doi.org/10.1002/hipo.22255
  49. Kaplan, R., Doeller, C. F., Barnes, G. R., Litvak, V., Düzel, E., Bandettini, P. A., & Burgess, N. (2012). Movement-related theta rhythm in humans: Coordinating self-directed hippocampal learning. PLoS Biology, 10, e1001267. https://doi.org/10.1371/journal.pbio.1001267
  50. Kennedy, J. P., Zhou, Y., Qin, Y., Lovett, S. D., Sheremet, A., Burke, S., & Maurer, A. (2022). A direct comparison of theta power and frequency to speed and acceleration. Journal of Neuroscience, 42, 4326-4341. https://doi.org/10.1523/JNEUROSCI.0987-21.2022
  51. Kerrén, C., Linde-Domingo, J., Hanslmayr, S., & Wimber, M. (2018). An optimal oscillatory phase for pattern reactivation during memory retrieval. Current Biology, 28, 3383-3392.e3386. https://doi.org/10.1016/j.cub.2018.08.065
  52. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29, 169-195. https://doi.org/10.1016/S0165-0173(98)00056-3
  53. Klimesch, W. (2012). Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences, 16, 606-617. https://doi.org/10.1016/j.tics.2012.10.007
  54. Kunz, L., Wang, L., Lachner-Piza, D., Zhang, H., Brandt, A., Dümpelmann, M., Reinacher, P. C., Coenen, V. A., Chen, D., & Wang, W.-X. (2019). Hippocampal theta phases organize the reactivation of large-scale electrophysiological representations during goal-directed navigation. Science Advances, 5, eaav8192. https://doi.org/10.1126/sciadv.aav8192
  55. Larson, M. J., & Carbine, K. A. (2017). Sample size calculations in human electrophysiology (EEG and ERP) studies: A systematic review and recommendations for increased rigor. International Journal of Psychophysiology, 111, 33-41. https://doi.org/10.1016/j.ijpsycho.2016.06.015
  56. Lega, B. C., Jacobs, J., & Kahana, M. (2012). Human hippocampal theta oscillations and the formation of episodic memories. Hippocampus, 22, 748-761. https://doi.org/10.1002/hipo.20937
  57. Liang, M., Starrett, M. J., & Ekstrom, A. D. (2018). Dissociation of frontal-midline delta-theta and posterior alpha oscillations: A mobile EEG study. Psychophysiology, 55, e13090. https://doi.org/10.1111/psyp.13090
  58. Liang, M., Zheng, J., Isham, E., & Ekstrom, A. (2021). Common and distinct roles of frontal midline theta and occipital alpha oscillations in coding temporal intervals and spatial distances. Journal of Cognitive Neuroscience, 33, 2311-2327. https://doi.org/10.1162/jocn_a_01765
  59. Lin, J. J., Rugg, M. D., Das, S., Stein, J., Rizzuto, D. S., Kahana, M. J., & Lega, B. C. (2017). Theta band power increases in the posterior hippocampus predict successful episodic memory encoding in humans. Hippocampus, 27, 1040-1053. https://doi.org/10.1002/hipo.22751
  60. Lin, M.-H., Liran, O., Bauer, N., & Baker, T. E. (2022). Scalp recorded theta activity is modulated by reward, direction, and speed during virtual navigation in freely moving humans. Scientific Reports, 12, 2041. https://doi.org/10.1038/s41598-022-05955-9
  61. Meltzer, J. A., Fonzo, G. A., & Constable, R. T. (2009). Transverse patterning dissociates human EEG theta power and hippocampal BOLD activation. Psychophysiology, 46, 153-162. https://doi.org/10.1111/j.1469-8986.2008.00719.x
  62. Miller, J., Watrous, A. J., Tsitsiklis, M., Lee, S. A., Sheth, S. A., Schevon, C. A., Smith, E. H., Sperling, M. R., Sharan, A., & Asadi-Pooya, A. A. (2018). Lateralized hippocampal oscillations underlie distinct aspects of human spatial memory and navigation. Nature Communications, 9, 1-12, 2423. https://doi.org/10.1038/s41467-018-04847-9
  63. Miyakoshi, M., Gehrke, L., Gramann, K., Makeig, S., & Iversen, J. (2021). The AudioMaze: An EEG and motion capture study of human spatial navigation in sparse augmented reality. The European Journal of Neuroscience, 54, 8283-8307. https://doi.org/10.1111/ejn.15131
  64. Morales, S., & Bowers, M. E. (2022). Time-frequency analysis methods and their application in developmental EEG data. Developmental Cognitive Neuroscience, 54, 101067. https://doi.org/10.1016/j.dcn.2022.101067
  65. Mussel, P., Ulrich, N., Allen, J. J. B., Osinsky, R., & Hewig, J. (2016). Patterns of theta oscillation reflect the neural basis of individual differences in epistemic motivation. Scientific Reports, 6, 29245. https://doi.org/10.1038/srep29245
  66. Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L., & Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53, 695-699. https://doi.org/10.1111/j.1532-5415.2005.53221.x
  67. Nelson, H. E., & Willison, J. (1991). National adult reading test (NART). Nfer-Nelson Windsor.
  68. Nyberg, N., Duvelle, É., Barry, C., & Spiers, H. J. (2022). Spatial goal coding in the hippocampal formation. Neuron, 110, 394-422. https://doi.org/10.1016/j.neuron.2021.12.012
  69. Park, Y. M., Park, J., Baek, J. H., Kim, S. I., Kim, I. Y., Kang, J. K., & Jang, D. P. (2019). Differences in theta coherence between spatial and nonspatial attention using intracranial electroencephalographic signals in humans. Human Brain Mapping, 40, 2336-2346. https://doi.org/10.1002/hbm.24526
  70. Pastötter, B., & Bäuml, K.-H. T. (2014). Distinct slow and fast cortical theta dynamics in episodic memory retrieval. NeuroImage, 94, 155-161. https://doi.org/10.1016/j.neuroimage.2014.03.002
  71. Patai, E. Z., & Spiers, H. J. (2021). The versatile wayfinder: Prefrontal contributions to spatial navigation. Trends in Cognitive Sciences, 25, 520-533. https://doi.org/10.1016/j.tics.2021.02.010
  72. Pereira, J., Ofner, P., Schwarz, A., Sburlea, A. I., & Müller-Putz, G. R. (2017). EEG neural correlates of goal-directed movement intention. NeuroImage, 149, 129-140. https://doi.org/10.1016/j.neuroimage.2017.01.030
  73. Peylo, C., Hilla, Y., & Sauseng, P. (2021). Cause or consequence? Alpha oscillations in visuospatial attention. Trends in Neurosciences, 44, 705-713. https://doi.org/10.1016/j.tins.2021.05.004
  74. Pezzulo, G., Donnarumma, F., Maisto, D., & Stoianov, I. (2019). Planning at decision time and in the background during spatial navigation. Current Opinion in Behavioral Sciences, 29, 69-76. https://doi.org/10.1016/j.cobeha.2019.04.009
  75. Pu, Y., Cornwell, B. R., Cheyne, D., & Johnson, B. W. (2020). Gender differences in navigation performance are associated with differential theta and high-gamma activities in the hippocampus and parahippocampus. Behavioural Brain Research, 391, 112664. https://doi.org/10.1016/j.bbr.2020.112664
  76. Quintana, J., & Fuster, J. M. (1993). Spatial and temporal factors in the role of prefrontal and parietal cortex in visuomotor integration. Cerebral Cortex, 3, 122-132. https://doi.org/10.1093/cercor/3.2.122
  77. Reitan, R. M., & Wolfson, D. (1992). Neuropsychological evaluation of older children. Neuropsychology Press.
  78. Roberts, B. M., Hsieh, L. T., & Ranganath, C. (2013). Oscillatory activity during maintenance of spatial and temporal information in working memory. Neuropsychologia, 51, 349-357. https://doi.org/10.1016/j.neuropsychologia.2012.10.009
  79. Rodriguez, P. F. (2010). Neural decoding of goal locations in spatial navigation in humans with fMRI. Human Brain Mapping, 31, 391-397. https://doi.org/10.1002/hbm.20873
  80. Sánchez-Cubillo, I., Periáñez, J. A., Adrover-Roig, D., Rodríguez-Sánchez, J. M., Ríos-Lago, M., Tirapu, J., & Barceló, F. (2009). Construct validity of the trail making test: Role of task-switching, working memory, inhibition/interference control, and visuomotor abilities. Journal of the International Neuropsychological Society, 15, 438-450. https://doi.org/10.1017/S1355617709090626
  81. Sauseng, P., Klimesch, W., Schabus, M., & Doppelmayr, M. (2005). Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. International Journal of Psychophysiology, 57, 97-103. https://doi.org/10.1016/j.ijpsycho.2005.03.018
  82. Scheeringa, R., Bastiaansen, M. C. M., Petersson, K. M., Oostenveld, R., Norris, D. G., & Hagoort, P. (2008). Frontal theta EEG activity correlates negatively with the default mode network in resting state. International Journal of Psychophysiology, 67, 242-251. https://doi.org/10.1016/j.ijpsycho.2007.05.017
  83. Sestieri, C., Shulman, G. L., & Corbetta, M. (2017). The contribution of the human posterior parietal cortex to episodic memory. Nature Reviews Neuroscience, 18, 183-192. https://doi.org/10.1038/nrn.2017.6
  84. Smallwood, J., Bernhardt, B. C., Leech, R., Bzdok, D., Jefferies, E., & Margulies, D. S. (2021). The default mode network in cognition: A topographical perspective. Nature Reviews Neuroscience, 22, 503-513. https://doi.org/10.1038/s41583-021-00474-4
  85. Sosa, M., & Giocomo, L. M. (2021). Navigating for reward. Nature Reviews Neuroscience, 22, 472-487. https://doi.org/10.1038/s41583-021-00479-z
  86. Spiers, H. J., & Maguire, E. A. (2008). The dynamic nature of cognition during wayfinding. Journal of Environmental Psychology, 28, 232-249. https://doi.org/10.1016/j.jenvp.2008.02.006
  87. Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., & Leahy, R. M. (2011). (2011) brainstorm: A user-friendly application for MEG/EEG analysis. Computational Intelligence and Neuroscience, 2011, 1-13, 879716. https://doi.org/10.1155/2011/879716
  88. Vivekananda, U., Bush, D., Bisby, J. A., Baxendale, S., Rodionov, R., Diehl, B., Chowdhury, F. A., McEvoy, A. W., Miserocchi, A., Walker, M. C., & Burgess, N. (2021). Theta power and theta-gamma coupling support long-term spatial memory retrieval. Hippocampus, 31, 213-220. https://doi.org/10.1002/hipo.23284
  89. Vorhees, C. V., & Williams, M. T. (2006). Morris water maze: Procedures for assessing spatial and related forms of learning and memory. Nature Protocols, 1, 848-858. https://doi.org/10.1038/nprot.2006.116
  90. Vorhees, C. V., & Williams, M. T. (2014). Assessing spatial learning and memory in rodents. ILAR Journal, 55, 310-332. https://doi.org/10.1093/ilar/ilu013
  91. Yassa, M. A. (2018). Brain rhythms: Higher-frequency theta oscillations make sense in moving humans. Current Biology, 28, R70-R72. https://doi.org/10.1016/j.cub.2017.11.045

MeSH Term

Humans
Spatial Learning
Theta Rhythm
Electroencephalography
Parietal Lobe
Maze Learning
Journal Article Research Support, Non-U.S. Gov't
Article has an altmetric score of 7

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