Spectral analysis II: Applications

1. Spectral analysis II: Applications Bijan Pesaran Center for Neural Science New York University

2. Outline Example I: LFP spectrograms Example II: Spike rates, spectra and coherence Example III: Spike-LFP coherence Example IV: Decoding and single trial analysis Example V: Combining SVD and spectra

3. Spiking and LFP activity Extracellular potential Here, is the extracellular potential you will see when you place an electrode in the brain and its principal features are spikes and the rhythms they ride on. The extracellular potential results from current flow in the extracellular space, which in turn is produced by transmembrane potentials in cells. These cellular events can be fast, 1 ms, for action potentials which give rise to spikes and slow, up to 100 ms, for post-synaptic potentials in dendrites and soma, which give rise to LFP. (Other sources are voltage-dependent membrane oscillations and AHPs, which last 10s of ms, exist at low frequency, delta waves and blocked by ACh). The LFP can be extracted by simply filtering the recording at low frequency.Here, is the extracellular potential you will see when you place an electrode in the brain and its principal features are spikes and the rhythms they ride on. The extracellular potential results from current flow in the extracellular space, which in turn is produced by transmembrane potentials in cells. These cellular events can be fast, 1 ms, for action potentials which give rise to spikes and slow, up to 100 ms, for post-synaptic potentials in dendrites and soma, which give rise to LFP. (Other sources are voltage-dependent membrane oscillations and AHPs, which last 10s of ms, exist at low frequency, delta waves and blocked by ACh). The LFP can be extracted by simply filtering the recording at low frequency.

4. How do we analyze spike trains and field potentials together? Use spectral methods for a hybrid point-continuous process

5. Spectral intuition The approach I recommend is to transform both signals into the frequency domain. The approach I recommend is to transform both signals into the frequency domain.

6. Example I: LFP spectrograms LFP recording from Macaque area LIP

7. Example I: LFP spectrograms Example recording

8. Example I: LFP spectrograms Estimation issues Bias Narrow band Broad band Variance

9. Example I: LFP spectrograms Confidence intervals Chi2 Assume Gaussian process Jackknife Does not assume Gaussian process

10. Example I: LFP spectrograms

11. Example I: LFP spectrograms

12. Example I: LFP spectrograms

13. Example I: LFP spectrograms

14. Example I: LFP spectrograms

15. Example I: LFP spectrograms

16. Example II: Spike rates, spectra and coherence Simultaneous two-cell recording from Macaque area LIP

17. Example II: Spike rates, spectra and coherence

18. Example II: Spike rates, spectra and coherence

19. Example II: Spike rates, spectra and coherence Inter-spike intervals

20. Example II: Spike rates, spectra and coherence Inter-spike intervals Spike spectrum properties High frequency limit Low freq suppression Spectral peaks

21. Example II: Spike rates, spectra and coherence

22. Example II: Spike rates, spectra and coherence

23. Example III: Spike-field coherence Experimental paradigm

24. Example III: Spike-field coherence Hypothesis testing

25. Example III: Spike-field coherence Single trial analysis

26. Example IV: Decoding and single trial analysis Decoding LFP spectra

27. Example V: SVD and spectra SVD Time-channel decomposition Reduced dimensionality data set Estimate spectra of modes

28. Example V: SVD and spectra

29. Summary Presented examples of neuronal time series Compared methods for their analysis Demonstrated advantages of spectral analysis