TwoPhotonAnalysis.m 1.3 KB

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  1. edges = -2:1/fs:3;
  2. figure
  3. boundedline(edges(1:end-1), mean(prePSTCa,2), std(prePSTCa,[],2)/sqrt(size(prePSTCa,2)), 'k-', ...,
  4. edges(1:end-1), mean(postPSTCa,2), std(postPSTCa,[],2)/sqrt(size(postPSTCa,2)), 'r-')
  5. xlim([-1 2])
  6. legend('prelearning', 'postlearning')
  7. ylabel('dF/F0 (%)')
  8. xlabel('Time (s)')
  9. title('Mouse 16D')
  10. % Reward only
  11. figure
  12. boundedline(edges(1:end-1), mean(rewardPSTCa,2), std(rewardPSTCa,[],2)/sqrt(size(rewardPSTCa,2)), 'g-')
  13. legend('Reward only')
  14. ylabel('dF/F0 (%)')
  15. xlabel('Time (s)')
  16. title('Mouse 16D')
  17. xlim([-1 2])
  18. %% Statistics
  19. % Mean calcium amplitudes (between t = 0 and 1.5 s after stimulus) before and after learning were compared by Mann-Whitney U test.
  20. [p h] = ranksum(mean(Mouse15C.prePSTCa(61:105,:)), mean(Mouse15C.postPSTCa(61:105,:)))
  21. [p h] = ranksum(mean(Mouse16D.prePSTCa(61:105,:)), mean(Mouse16D.postPSTCa(61:105,:)))
  22. [p h] = ranksum(mean(Mouse31F.prePSTCa(61:105,:)), mean(Mouse31F.postPSTCa(61:105,:)))
  23. %% Pooled
  24. prePSTCa = [Mouse15C.prePSTCa Mouse16D.prePSTCa Mouse31F.prePSTCa];
  25. postPSTCa = [Mouse15C.postPSTCa Mouse16D.postPSTCa Mouse31F.postPSTCa];
  26. [p h] = ranksum(mean(prePSTCa(61:105,:)), mean(postPSTCa(61:105,:)))
  27. rewardPSTCa = [Mouse15C.rewardPSTCa Mouse16D.rewardPSTCa Mouse31F.rewardPSTCa];
  28. [p h] = signrank(mean(rewardPSTCa(31:60,:)), mean(rewardPSTCa(61:105,:)))