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Dynamic corticostriatal activity biases social bonding in monogamous female prairie voles

Abstract

Adult pair bonding involves dramatic changes in the perception and valuation of another individual1. One key change is that partners come to reliably activate the brain’s reward system2,3,4,5,6, although the precise neural mechanisms by which partners become rewarding during sociosexual interactions leading to a bond remain unclear. Here we show, using a prairie vole (Microtus ochrogaster) model of social bonding7, how a functional circuit from the medial prefrontal cortex to nucleus accumbens is dynamically modulated to enhance females’ affiliative behaviour towards a partner. Individual variation in the strength of this functional connectivity, particularly after the first mating encounter, predicts how quickly animals begin affiliative huddling with their partner. Rhythmically activating this circuit in a social context without mating biases later preference towards a partner, indicating that this circuit’s activity is not just correlated with how quickly animals become affiliative but causally accelerates it. These results provide the first dynamic view of corticostriatal activity during bond formation, revealing how social interactions can recruit brain reward systems to drive changes in affiliative behaviour.

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Figure 1: Mating enhances low-frequency coherence across multiple brain areas.
Figure 2: mPFC–NAcc cross-frequency coupling is dynamically modulated and behaviour-dependent.
Figure 3: mPFC–NAcc cross-frequency coupling correlates with huddling latency.
Figure 4: Low-frequency stimulation of mPFC-to-NAcc projections biases behavioural preference towards a partner.

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References

  1. Hazan, C. & Shaver, P. Romantic love conceptualized as an attachment process. J. Pers. Soc. Psychol. 52, 511–524 (1987)

    Article  CAS  PubMed  Google Scholar 

  2. Bartels, A. & Zeki, S. The neural basis of romantic love. Neuroreport 11, 3829–3834 (2000)

    Article  CAS  PubMed  Google Scholar 

  3. Young, L. J., Lim, M. M., Gingrich, B. & Insel, T. R. Cellular mechanisms of social attachment. Horm. Behav. 40, 133–138 (2001)

    Article  CAS  PubMed  Google Scholar 

  4. Aragona, B. J. et al. Nucleus accumbens dopamine differentially mediates the formation and maintenance of monogamous pair bonds. Nat. Neurosci. 9, 133–139 (2006)

    Article  CAS  PubMed  Google Scholar 

  5. Ross, H. E. et al. Variation in oxytocin receptor density in the nucleus accumbens has differential effects on affiliative behaviors in monogamous and polygamous voles. J. Neurosci. 29, 1312–1318 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Johnson, Z. V. et al. Central oxytocin receptors mediate mating-induced partner preferences and enhance correlated activation across forebrain nuclei in male prairie voles. Horm. Behav. 79, 8–17 (2016)

    Article  CAS  PubMed  Google Scholar 

  7. Young, L. J. & Wang, Z. The neurobiology of pair bonding. Nat. Neurosci. 7, 1048–1054 (2004)

    Article  CAS  PubMed  Google Scholar 

  8. Kleiman, D. G. Monogamy in mammals. Q. Rev. Biol. 52, 39–69 (1977)

    Article  CAS  PubMed  Google Scholar 

  9. Christie, M. J., Summers, R. J., Stephenson, J. A., Cook, C. J. & Beart, P. M. Excitatory amino acid projections to the nucleus accumbens septi in the rat: a retrograde transport study utilizing d[3H]aspartate and [3H]GABA. Neuroscience 22, 425–439 (1987)

    Article  CAS  PubMed  Google Scholar 

  10. Ross, H. E. et al. Characterization of the oxytocin system regulating affiliative behavior in female prairie voles. Neuroscience 162, 892–903 (2009)

    Article  CAS  PubMed  Google Scholar 

  11. Nicola, S. M. The nucleus accumbens as part of a basal ganglia action selection circuit. Psychopharmacology (Berl.) 191, 521–550 (2007)

    Article  CAS  Google Scholar 

  12. Floresco, S. B. The nucleus accumbens: an interface between cognition, emotion, and action. Annu. Rev. Psychol. 66, 25–52 (2015)

    Article  PubMed  Google Scholar 

  13. Block, A. E., Dhanji, H., Thompson-Tardif, S. F. & Floresco, S. B. Thalamic–prefrontal cortical–ventral striatal circuitry mediates dissociable components of strategy set shifting. Cereb. Cortex 17, 1625–1636 (2007)

    Article  PubMed  Google Scholar 

  14. Paxinos, G & Watson, C. The Rat Brain in Stereotaxic Coordinates compact 6th edn (Academic, 2009)

  15. Williams, J. R., Catania, K. C. & Carter, C. S. Development of partner preferences in female prairie voles (Microtus ochrogaster): the role of social and sexual experience. Horm. Behav. 26, 339–349 (1992)

    Article  CAS  PubMed  Google Scholar 

  16. Ahern, T. H., Modi, M. E., Burkett, J. P. & Young, L. J. Evaluation of two automated metrics for analyzing partner preference tests. J. Neurosci. Methods 182, 180–188 (2009)

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lim, M. M. et al. Enhanced partner preference in a promiscuous species by manipulating the expression of a single gene. Nature 429, 754–757 (2004)

    Article  ADS  CAS  PubMed  Google Scholar 

  18. Bagot, R. C. et al. Ventral hippocampal afferents to the nucleus accumbens regulate susceptibility to depression. Nat. Commun. 6, 7062 (2015)

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Britt, J. P. et al. Synaptic and behavioral profile of multiple glutamatergic inputs to the nucleus accumbens. Neuron 76, 790–803 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Buzsáki, G. & Wang, X.-J. Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203–225 (2012)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Kalenscher, T., Lansink, C. S., Lankelma, J. V. & Pennartz, C. M. A. Reward-associated gamma oscillations in ventral striatum are regionally differentiated and modulate local firing activity. J. Neurophysiol. 103, 1658–1672 (2010)

    Article  PubMed  Google Scholar 

  22. van der Meer, M. A. & Redish, A. D. Low and high gamma oscillations in rat ventral striatum have distinct relationships to behavior, reward, and spiking activity on a learned spatial decision task. Front. Integr. Neurosci. 3, 9 (2009)

    PubMed  PubMed Central  Google Scholar 

  23. Berke, J. D. Fast oscillations in cortical–striatal networks switch frequency following rewarding events and stimulant drugs. Eur. J. Neurosci. 30, 848–859 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lakatos, P. et al. An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. J. Neurophysiol. 94, 1904–1911 (2005)

    Article  PubMed  Google Scholar 

  25. Tort, A. B. et al. Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a T-maze task. Proc. Natl Acad. Sci. USA 105, 20517–20522 (2008)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ragozzino, M. E., Kim, J., Hassert, D., Minniti, N. & Kiang, C. The contribution of the rat prelimbic-infralimbic areas to different forms of task switching. Behav. Neurosci. 117, 1054–1065 (2003)

    Article  PubMed  Google Scholar 

  27. Jutras, M. J. & Buffalo, E. A. Synchronous neural activity and memory formation. Curr. Opin. Neurobiol. 20, 150–155 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Trimper, J. B., Stefanescu, R. A. & Manns, J. R. Recognition memory and theta–gamma interactions in the hippocampus. Hippocampus 24, 341–353 (2014)

    Article  PubMed  Google Scholar 

  29. Stuber, G. D. et al. Excitatory transmission from the amygdala to nucleus accumbens facilitates reward seeking. Nature 475, 377–380 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Okuyama, T., Kitamura, T., Roy, D. S., Itohara, S. & Tonegawa, S. Ventral CA1 neurons store social memory. Science 353, 1536–1541 (2016)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gruber, A. J., Hussain, R. J. & O’Donnell, P. The nucleus accumbens: a switchboard for goal-directed behaviors. PLoS ONE 4, e5062 (2009)

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  32. Vertes, R. P. Differential projections of the infralimbic and prelimbic cortex in the rat. Synapse 51, 32–58 (2004)

    Article  CAS  PubMed  Google Scholar 

  33. Gutman, D. A. et al. A DTI tractography analysis of infralimbic and prelimbic connectivity in the mouse using high-throughput MRI. Neuroimage 63, 800–811 (2012)

    Article  PubMed  Google Scholar 

  34. Donaldson, Z. R., Spiegel, L. & Young, L. J. Central vasopressin V1a receptor activation is independently necessary for both partner preference formation and expression in socially monogamous male prairie voles. Behav. Neurosci. 124, 159–163 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  35. Etholm, L., Arabadzisz, D., Lipp, H. P. & Heggelund, P. Seizure logging: a new approach to synchronized cable-free EEG and video recordings of seizure activity in mice. J. Neurosci. Methods 192, 254–260 (2010)

    Article  PubMed  Google Scholar 

  36. Ryan, S. J. et al. Spike-timing precision and neuronal synchrony are enhanced by an interaction between synaptic inhibition and membrane oscillations in the amygdala. PLoS ONE 7, e35320 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mitra, P. P. & Pesaran, B. Analysis of dynamic brain imaging data. Biophys. J. 76, 691–708 (1999)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mitra, P. & Bokil, H. Observed Brain Dynamics (Oxford Univ. Press, 2008)

  39. Slepian, D. & Pollack, H. O. Prolate spheroidal wave functions, Fourier analysis and uncertainty – I. Bell Syst. Tech. J. 40, 43–63 (1961)

    Article  MathSciNet  MATH  Google Scholar 

  40. Jutras, M. J., Fries, P. & Buffalo, E. A. Gamma-band synchronization in the macaque hippocampus and memory formation. J. Neurosci. 29, 12521–12531 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Tort, A. B., Komorowski, R., Eichenbaum, H. & Kopell, N. Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. J. Neurophysiol. 104, 1195–1210 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  42. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004)

    Article  PubMed  Google Scholar 

  43. Brovelli, A. et al. Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality. Proc. Natl Acad. Sci. USA 101, 9849–9854 (2004)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  44. Dhamala, M. in Encyclopedia of Computational Neuroscience (eds. Jaeger, D. & Jung, R. ) 2789–2793 (Springer, 2014)

  45. Granger, C. W. J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969)

    Article  MATH  Google Scholar 

  46. Gregoriou, G. G., Gotts, S. J., Zhou, H. & Desimone, R. High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324, 1207–1210 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. de Waele, S. & Broersen, M. T. Order selection for vector autoregressive models. IEEE Trans. Signal Process. 51, 427–433 (2003)

    Article  ADS  Google Scholar 

  48. Bokil, H., Purpura, K., Schoffelen, J.-M., Thomson, D. & Mitra, P. Comparing spectra and coherences for groups of unequal size. J. Neurosci. Methods 159, 337–345 (2007)

    Article  PubMed  Google Scholar 

  49. Cohen, J. A power primer. Psychol. Bull. 112, 155–159 (1992)

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank H.-P. Lipp for Neurologgers; F. Lin for initial testing of Neurologgers; J. Manns, G. Berman, and T. Madsen for methodological feedback and discussions on the manuscript; G. Wong for behavioural scoring; M. Zhang, R. Tangutoori, and R. Stanford for assistance with implant design and construction; the members of the Liu, Young and Rainnie laboratories for training, manuscript feedback, and discussions; L. Matthews and the Yerkes animal care and veterinary staff for vole husbandry and care; G. Feldpausch for custom cage design and machining; and J. LaPrairie and L.-L. Shen for assistance. This work was funded by an Emory Neuroscience Initiative grant (R.C.L., L.J.Y.), National Institute of Mental Health (NIMH) R21MH97187 (R.C.L.), NIMH P50MH100023 (L.J.Y., R.C.L.), National Institute of Neurological Disorders and Stroke R90 DA033462 (V.S.), Emory University Biology Graduate Student Award (E.A.A.), and Office of Research Infrastructure Programs’ Primate centers P51OD11132 (YNPRC).

Author information

Authors and Affiliations

Authors

Contributions

E.A.A. adapted the Neurologger to a vole preparation and designed and performed in vivo electrophysiology experiments, which motivated an optogenetics approach; optogenetics experiments were designed and performed by E.A.A. and Z.V.J., assisted by Y.J.K.; Z.V.J. validated viral techniques and performed optogenetics surgeries and histology; S.J.R. and E.A.A. designed slice electrophysiology experiments; Z.V.J. performed all surgeries and histology for slice electrophysiology experiments; S.J.R. performed slice electrophysiology experiments, assisted and supervised by E.A.A. and D.G.R., respectively. E.A.A., Z.V.J., Y.J.K., S.J.R., H.W., A.C.S., V.S., and W.D.M. analysed data; E.A.A. drafted the manuscript; Z.V.J., A.C.S., Y.J.K., S.J.R., H.W., and V.S. contributed to the writing; R.C.L. and L.J.Y. edited the manuscript and supervised all aspects of the study.

Corresponding author

Correspondence to Robert C. Liu.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks R. Fernald and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Preparations for electrophysiological and optogenetic experiments.

a, Neurologger recording device secured to a female during cohabitation with a male. Neurologger interfaces with a chronic electrode implant targeting mPFC and NAcc. b, Schematic of experimental setup. Simultaneous video and neural recording is synchronized by periodic timestamps. c, Summarized ethogram definitions of mating, self-grooming, and huddling used to score experimental videos. d, Arena used for cohabitation in optogenetics experiments. Arena is divided into social, neutral, and non-social zones. Food is placed in the centre of the neutral zone. Male is contained under a cup in the social zone, and female, implanted with optical fibres, is allowed to freely explore the arena. Optical stimulation is triggered whenever she is in the social zone (green hatched area; red circle is visualization of tracking) for up to 1 h within the cohabitation period. Social zone is defined as consistently as possible across experiments on the basis of physical features of arena. e, Schematic of cohabitation setup, additionally showing how laser is controlled by video recording to automatically deliver optical stimulation when female is in the social zone.

Extended Data Figure 2 Placement of LFP electrodes in all subjects.

a, b, Electrodes in hit subjects (n = 9) targeting mPFC and NAcc (a), verified with electrolytic lesions (b; scale bar, 500 μm). Anterior/posterior locations of brain sections (units of rat brain atlas14; see Methods) are indicated. c, Electrodes in non-hit subjects (n = 6) targeting mPFC and posterior to NAcc (within or bordering the bed nucleus of the stria terminalis (BNST)). SHy, septohypothalamic nucleus.

Extended Data Figure 3 Behavioural characterization of hit and non-hit subjects.

a, Number of bouts, total duration, and latency for mating, self-grooming, and huddling in hit (n = 9) and non-hit (n = 6) subjects. No significant differences exist between subject groups (all P > 0.05). b, Measures of mating and self-grooming duration and latency do not correlate with huddling latency (n = 15; all P > 0.05). ‘Percent M [or SG] before Hud latency’ refers to percentage of time each animal spent mating or self-grooming before reaching its huddling latency. c, Latency is modulated across behaviours (n = 15;  = 18.53, P < 0.001, Friedman test), with mating and self-grooming showing shorter latencies compared with huddling but similar latencies to each other (self-grooming versus huddling, P < 0.001; mating versus huddling, P = 0.001; mating versus self-grooming, P = 0.454, Wilcoxon signed-rank test). Reported P values in ac are Bonferroni-corrected for multiple comparisons (see Methods). Boxplots show median and interquartile range.

Source data

Extended Data Figure 4 Net modulation data for all subjects.

Net modulation values (2-s, non-overlapping windows) sampled over a baseline solo period (gold points) and 6-h cohabitation for all hit (numbers 1–9) and non-hit (numbers 10–15) subjects. Values that temporally overlap with mating, self-grooming, and huddling behaviours (top hashes) are colour-coded accordingly. All non-scored values are indicated as ‘other-cohab,’ which together with mating and self-grooming represent ‘non-huddling’ values. Cumulative distributions of net modulation values coded by behaviour are shown in the right panel for each subject.

Source data

Extended Data Figure 5 Granger causality in mPFC–NAcc circuit during mating.

a, Granger causality spectra in the mPFC-to-NAcc and NAcc-to-mPFC directions for example subject. Solid lines and shaded regions show mean and mid-95th percentile range, respectively, of the n = 40 Granger causality estimates for a given brain-area direction (see Methods). b, Comparison of Granger causality at 5 Hz in the two directions across hit subjects (n = 9). Granger causality is significantly higher in the mPFC-to-NAcc direction (t8 = 3.29, P = 0.011). Data are mean ± s.e.m.

Source data

Extended Data Figure 6 Specificity of correlation between non-huddling net modulation and huddling latency.

a, b, Mean huddling net modulation is uncorrelated with huddling latency in hits (a; n = 9) and non-hits (b; n = 6) (all P > 0.05). c, d, Mean non-huddling net modulation is uncorrelated with electrode placement (mPFC anterior (A)–posterior (P) location or NAcc/non-hit medial (M)–lateral (L) location; units of rat brain atlas14) in both hits (c) and non-hits (d) (all P > 0.05). e, f, Mean non-huddling net modulation is uncorrelated with mating and self-grooming latency and total duration in hits (e) and non-hits (f) (all P > 0.05).

Source data

Extended Data Figure 7 Net modulation during early and late mating and self-grooming.

a, b, Mean net modulation during mating increases over time in hits (a; n = 9; P = 0.008) but not non-hits (b; n = 6; P = 0.438). c, d, Mean net modulation during self-grooming shows no significant change in either hits (c; P = 0.406) or non-hits (d; P = 0.438). P values in ad are Bonferroni-corrected for multiple comparisons (see Methods). Mean early and late values for mating are derived from the first and last mating bouts. Mean values for self-grooming are derived from early and late self-grooming samples matched in number to the first and last mating bouts (see Methods). Boxplots show median and interquartile range.

Source data

Extended Data Figure 8 Behavioural specificity of correlation between local change in net modulation around mating and huddling latency.

a, h, Mean non-huddling (NHud) net modulation values within 1 min moving windows (stepped by 0.1 min) before (SG−) and after (SG+) the first self-grooming bout of hits (a; n = 9) and non-hits (h; n = 6). Each subject’s values are colour-coded by that subject’s latency to huddle from the end of the self-grooming bout (latencySG+). b, g, Change in mean net modulation from immediately before to after the first self-grooming bout is uncorrelated with huddling latencySG+ in hits (b; R2 = 0.01, P = 0.787; indicated by line segments in a) and non-hits (g; R2 = 0.27, P = 0.290; line segments in h). c, j, Strength of correlation between mean net modulation and huddling latencySG+ shows no consistent increase in either hits (c) or non-hits (j). d, e, i, l, Subtracting out the mean baseline net modulation from the local values around self-grooming confirms no significant increase in correlation strength in either hits (d, e; P = 0.164; permutation test on difference in R2 (0.27) between bracketed time-points) or non-hits (i, l; P = 0.655, observed R2 difference of 0.07). f, k, Change in mean net modulation from immediately before to after first self-grooming bout is uncorrelated with mean non-huddling net modulation in the 15 min after self-grooming in hits (f; R2 = 0.24, P = 0.180) and non-hits (k; R2 = 0.55, P = 0.090). mr, Change in net modulation around first mating bout (Fig. 3e, j x axis) is uncorrelated with local behavioural parameters (change in self-grooming duration around bout and mating duration within bout) in hits (m) and non-hits (o) (all P > 0.05). It is further uncorrelated with the latency to next mating or self-grooming bouts (n, p) and the mean net modulation during the baseline solo period (q, r) in hits and non-hits (all P > 0.05).

Source data

Extended Data Figure 9 Validation of virus injection and optical implant locations.

a, b, Representative coronal sections showing estimated centres of (a) bilateral virus injection and (b) optical implant placement for in vivo optogenetics subjects. Virus injection localization was based on minor tissue damage at dorsal-most surface of the coronal section where injection syringe initially entered the brain, the densest concentration of fluorescence, and physical tracts of damage left by injection syringe. Optical implant localization was based on physical tracts of damage left by optical implant. Morphology of corpus callosum was used to determine anterior/posterior position of injections and implants. c, Virus injection and (d) optical implant locations for all in vivo optogenetics subjects. ChR2-expressing subjects (n = 12) are indicated by circles with dotted centres. Control subjects (n = 11) are indicated by circles with empty centres. Each colour is a separate subject, with two circles per subject (bilateral injection and optical implant). All injection centre locations fell within the prelimbic cortex and all optical implant locations fell within the medial NAcc. In ad, the anterior/posterior location of each section (units of rat brain atlas14) is indicated on left-hand side of the section. IL, infralimbic cortex; MO, medial orbital cortex.

Extended Data Figure 10 Validation of light-induced electrophysiological responses in mPFC and NAcc.

a, Representative image of whole-cell patch-clamp recording from a prelimbic mPFC neuron cell body in slice preparation. Recording electrode (tip denoted with white arrowhead) is patched onto a cell, and an optical fibre is oriented towards the cell for optogenetic stimulation. b, Example light-evoked potential (average response to five, 1-ms light pulses; see Methods) in a prelimbic mPFC neuron in the presence of tetrodotoxin (TTX; 1 μM) to show a direct effect of light stimulation. c, Whole-cell patch-clamp recordings were obtained from n = 7 putative medium spiny neurons (from four subjects) in NAcc. Anterior/posterior location of each section (units of rat brain atlas14) indicated on bottom-right of the section. d, Average electrophysiological responses (excitatory postsynaptic potentials (EPSPs; cells 1–4) or currents (EPSCs; cells 5–7)) to five, 1-ms light pulses delivered onto the cell. Application of picrotoxin (Picro; second column) had no consistent effect on electrophysiological responses, whereas DNQX (third column) disrupted them, indicating that electrophysiological responses were due to glutamatergic transmission. Abbreviation ‘cc’, corpus callosum.

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Amadei, E., Johnson, Z., Jun Kwon, Y. et al. Dynamic corticostriatal activity biases social bonding in monogamous female prairie voles. Nature 546, 297–301 (2017). https://doi.org/10.1038/nature22381

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