Associative responses to visual shape stimuli in the mouse auditory cortex
Autoři:
Manabu Ogi aff001; Tatsuya Yamagishi aff001; Hiroaki Tsukano aff001; Nana Nishio aff001; Ryuichi Hishida aff001; Kuniyuki Takahashi aff002; Arata Horii aff002; Katsuei Shibuki aff001
Působiště autorů:
Department of Neurophysiology, Brain Research Institute, Niigata University, Asahi-machi, Chuo-ku, Niigata, Japan
aff001; Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medical and Dental Sciences, Niigata University, Asahi-machi, Chuo-ku, Niigata, Japan
aff002
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0223242
Souhrn
Humans can recall various aspects of a characteristic sound as a whole when they see a visual shape stimulus that has been intimately associated with the sound. In subjects with audio-visual associative memory, auditory responses that code the associated sound may be induced in the auditory cortex in response to presentation of the associated visual shape stimulus. To test this possibility, mice were pre-exposed to a combination of an artificial sound mimicking a cat’s “meow” and a visual shape stimulus of concentric circles or stars for more than two weeks, since such passive exposure is known to be sufficient for inducing audio-visual associative memory in mice. After the exposure, we anesthetized the mice, and presented them with the associated visual shape stimulus. We found that associative responses in the auditory cortex were induced in response to the visual stimulus. The associative auditory responses were observed when complex sounds such as “meow” were used for formation of audio-visual associative memory, but not when a pure tone was used. These results suggest that associative auditory responses in the auditory cortex represent the characteristics of the complex sound stimulus as a whole.
Klíčová slova:
Fluorescence imaging – Imaging techniques – Memory – Memory recall – Neurons – Vision – Auditory cortex – Audio equipment
Zdroje
1. Kaas JH, Hackett TA. 'What' and 'where' processing in auditory cortex. Nat Neurosci. 1999; 2: 1045–1047.
2. Rauschecker JP, Tian B. Mechanisms and streams for processing of "what" and "where" in auditory cortex. Proc Natl Acad Sci USA. 2000; 97: 11800–11806. doi: 10.1073/pnas.97.22.11800 11050212
3. King AJ1, Teki S, Willmore BDB. Recent advances in understanding the auditory cortex. F1000Res. 2018; 7: F1000 Faculty Rev-1555.
4. Hackett TA1, Barkat TR, O'Brien BM, Hensch TK, Polley DB. Linking topography to tonotopy in the mouse auditory thalamocortical circuit. J Neurosci. 2011; 31: 2983–2995. doi: 10.1523/JNEUROSCI.5333-10.2011 21414920
5. Tsukano H, Horie M, Hishida R, Takahashi K, Takebayashi H, Shibuki K. Quantitative map of multiple auditory cortical regions with a stereotaxic fine-scale atlas of the mouse brain. Sci Rep. 2016; 6: 22315. doi: 10.1038/srep22315 26924462
6. Tsukano H, Horie M, Ohga S, Takahashi K, Kubota Y, Hishida R, et al. Reconsidering tonotopic maps in the auditory cortex and lemniscal auditory thalamus in mice. Front Neural Circuits. 2017; 11: 14. doi: 10.3389/fncir.2017.00014 28293178
7. Feldman DE. The spike-timing dependence of plasticity. Neuron. 2012; 75: 556–571. doi: 10.1016/j.neuron.2012.08.001 22920249
8. Kudoh M, Shibuki K. Importance of polysynaptic inputs and horizontal connectivity in the generation of tetanus-induced long-term potentiation in the rat auditory cortex. J Neurosci. 1997; 17: 9458–9465. 9391001
9. Atencio CA, Schreiner CE. Auditory cortical local subnetworks are characterized by sharply synchronous activity. J Neurosci. 2013; 33: 18503–18514. doi: 10.1523/JNEUROSCI.2014-13.2013 24259573
10. Happel MF, Jeschke M, Ohl FW. Spectral integration in primary auditory cortex attributable to temporally precise convergence of thalamocortical and intracortical input. J Neurosci. 2010; 30: 11114–11127. doi: 10.1523/JNEUROSCI.0689-10.2010 20720119
11. Burwick T. The binding problem. Wiley Interdiscip Rev Cogn Sci. 2014; 5: 305–315. doi: 10.1002/wcs.1279 26308565
12. Jack BN, Le Pelley ME, Griffiths O, Luque D, Whitford TJ. Semantic prediction-errors are context-dependent: An ERP study. Brain Res. 2019; 1706: 86–92. doi: 10.1016/j.brainres.2018.10.034 30391305
13. Fairhurst MT, Scott M, Deroy O. Voice over: Audio-visual congruency and content recall in the gallery setting. PLoS One. 2017; 12: e0177622. doi: 10.1371/journal.pone.0177622 28636667
14. Yamagishi T, Yoshitake K, Kamatani D, Watanabe K, Tsukano H, Hishida R, et al. Molecular diversity of clustered protocadherin-α required for sensory integration and short-term memory in mice. Sci Rep. 2018; 8: 9616. doi: 10.1038/s41598-018-28034-4 29941942
15. Takahashi K, Hishida R, Kubota Y, Kudoh M, Takahashi S, Shibuki K. Transcranial fluorescence imaging of auditory cortical plasticity regulated by acoustic environments in mice. Eur J Neurosci. 2006; 23: 1365–1376. doi: 10.1111/j.1460-9568.2006.04662.x 16553797
16. Ohshima S, Tsukano H, Kubota Y, Takahashi K, Hishida R, Takahashi S, et al. Cortical depression in the mouse auditory cortex after sound discrimination learning. Neurosci Res. 2010; 67: 51–58. doi: 10.1016/j.neures.2010.01.005 20096737
17. Tohmi M, Kitaura H, Komagata S, Kudoh M, Shibuki K. Enduring critical period plasticity visualized by transcranial flavoprotein imaging in mouse primary visual cortex. J Neurosci. 2006; 26: 11775–11785. doi: 10.1523/JNEUROSCI.1643-06.2006 17093098
18. Komagata S, Chen S, Suzuki A, Yamashita H, Hishida R, Maeda T, et al. Initial phase of neuropathic pain within a few hours after nerve injury in mice. J Neurosci. 2011; 31: 4896–4905. doi: 10.1523/JNEUROSCI.6753-10.2011 21451028
19. Yoshitake K, Tsukano H, Tohmi M, Komagata S, Hishida R, Yagi T, et al. Visual map shifts based on whisker-guided cues in the young mouse visual cortex. Cell Rep. 2013; 5: 1365–1374. doi: 10.1016/j.celrep.2013.11.006 24316077
20. Baba H, Tsukano H, Hishida R, Takahashi K, Horii A, Takahashi S, et al. Auditory cortical field coding long-lasting tonal offsets in mice. Sci Rep. 2016; 6: 34421. doi: 10.1038/srep34421 27687766
21. Ohga S, Tsukano H, Horie M, Terashima H, Nishio N, Kubota Y, et al. Direct relay pathways from lemniscal auditory thalamus to secondary auditory field in mice. Cereb Cortex. 2018; 28: 4424–4439. doi: 10.1093/cercor/bhy234 30272122
22. Kanda Y. Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transplant. 2013; 48: 452–458. doi: 10.1038/bmt.2012.244 23208313
23. Shibuki K, Hishida R, Murakami H, Kudoh M, Kawaguchi T, Watanabe M, et al. Dynamic imaging of somatosensory cortical activity in the rat visualized by flavoprotein autofluorescence. J Physiol (Lond). 2003; 549: 919–927.
24. Reinert KC, Dunbar RL, Gao W, Chen G, Ebner TJ. Flavoprotein autofluorescence imaging of neuronal activation in the cerebellar cortex in vivo. J Neurophysiol. 2004; 92: 199–211. doi: 10.1152/jn.01275.2003 14985415
25. Kitaura H, Uozumi N, Tohmi M, Yamazaki M, Sakimura K, Kudoh M, et al. Roles of nitric oxide as a vasodilator in neurovascular coupling of mouse somatosensory cortex. Neurosci Res. 2007; 59: 160–71. doi: 10.1016/j.neures.2007.06.1469 17655958
26. Pérez Koldenkova V, Nagai T. Genetically encoded Ca (2+) indicators: properties and evaluation. Biochim Biophys Acta. 2013; 1833: 1787–1797. doi: 10.1016/j.bbamcr.2013.01.011 23352808
27. Ohkura M, Sasaki T, Sadakari J, Gengyo-Ando K, Kagawa-Nagamura Y, et al. Genetically encoded green fluorescent Ca2+ indicators with improved detectability for neuronal Ca2+ signals. PLoS One. 2012; 7: e51286. doi: 10.1371/journal.pone.0051286 23240011
28. Chen TW, Wardill TJ, Sun Y, Pulver SR, Renninger SL, Baohan A, et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature. 2013; 499: 295–300. doi: 10.1038/nature12354 23868258
29. Nishio N, Tsukano H, Hishida R, Abe M, Nakai J, Kawamura M, et al. Higher visual responses in the temporal cortex of mice. Sci Rep. 2018; 8: 11136. doi: 10.1038/s41598-018-29530-3 30042474
30. von Kriegstein K, Giraud AL. Implicit multisensory associations influence voice recognition. PLoS Biol. 2006; 4: e326. doi: 10.1371/journal.pbio.0040326 17002519
31. Bliss TV, Collingridge GL. A synaptic model of memory: long-term potentiation in the hippocampus. Nature. 1993; 361: 31–39. doi: 10.1038/361031a0 8421494
32. Gruart A, Leal-Campanario R, López-Ramos JC, Delgado-García JM. Functional basis of associative learning and its relationships with long-term potentiation evoked in the involved neural circuits: Lessons from studies in behaving mammals. Neurobiol Learn Mem. 2015; 124: 3–18. doi: 10.1016/j.nlm.2015.04.006 25916668
33. Miniaci MC, Lippiello P, Monda M, Scotto P. Role of hippocampus in polymodal-cue guided tasks in rats. Brain Res. 2016; 1646: 426–432. doi: 10.1016/j.brainres.2016.06.030 27342815
34. Watanabe K, Kamatani D, Hishida R, Kudoh M, Shibuki K. Long-term depression induced by local tetanic stimulation in the rat auditory cortex. Brain Res. 2007; 1166: 20–28. doi: 10.1016/j.brainres.2007.06.049 17669373
35. D'amour JA, Froemke RC. Inhibitory and excitatory spike-timing-dependent plasticity in the auditory cortex. Neuron. 2015; 86: 514–528. doi: 10.1016/j.neuron.2015.03.014 25843405
36. Rose D. Some reflections on (or by?) grandmother cells. Perception. 1996; 25: 881–886. doi: 10.1068/p250881 8938002
37. Gross CG. Genealogy of the "grandmother cell". Neuroscientist. 2002; 8: 512–518. doi: 10.1177/107385802237175 12374433
38. Abbott LF. Decoding neuronal firing and modelling neural networks. Q Rev Biophys. 1994; 27: 291–331. 7899551
39. deCharms RC, Zador A. Neural representation and the cortical code. Annu Rev Neurosci. 2000; 23: 613–647. doi: 10.1146/annurev.neuro.23.1.613 10845077
40. Maniwa K, Yamashita H, Tsukano H, Hishida R, Endo N, Shibata M, et al. Tomographic optical imaging of cortical responses after crossing nerve transfer in mice. PLoS One. 2018; 13: e0193017. doi: 10.1371/journal.pone.0193017 29444175
41. D'Souza RD, Burkhalter A. A laminar organization for selective cortico-cortical communication. Front Neuroanat. 2017; 11: 71. doi: 10.3389/fnana.2017.00071 28878631
Článek vyšel v časopise
PLOS One
2019 Číslo 10
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Correction: Low dose naltrexone: Effects on medication in rheumatoid and seropositive arthritis. A nationwide register-based controlled quasi-experimental before-after study
- Combining CDK4/6 inhibitors ribociclib and palbociclib with cytotoxic agents does not enhance cytotoxicity
- Experimentally validated simulation of coronary stents considering different dogboning ratios and asymmetric stent positioning
- Risk factors associated with IgA vasculitis with nephritis (Henoch–Schönlein purpura nephritis) progressing to unfavorable outcomes: A meta-analysis
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy