Subdural Grid

1. Subdural Grid • Intracranial electrodes typically cannot be used in human studies • It is possible to record from the cortical surface Subdural grid on surface of Human cortex

2. Electroencephalography and the Event-Related Potential • Could you measure these electric fields without inserting electrodes through the skull?

3. Electroencephalography and the Event-Related Potential • 1929 – first measurement of brain electrical activity from scalp electrodes (Berger, 1929)

4. Electroencephalography and the Event-Related Potential Voltage Time • Place an electrode on the scalp and another one somewhere else on the body • Amplify the signal to record the voltage difference across these electrodes • Keep a running measurement of how that voltage changes over time • This is the human EEG

5. Electroencephalography and the Event-Related Potential • 1929 – first measurement of brain electrical activity from scalp electrodes (Berger, 1929) • Initially believed to be artifactual and/or of no significance

6. Electroencephalography • pyramidal cells span layers of cortex and have parallel cell bodies • their combined extracellular field is small but measurable at the scalp!

7. Electroencephalography • The field generated by a patch of cortex can be modeled as a single equivalent dipolar current source with some orientation (assumed to be perpendicular to cortical surface) Duracell

8. Electroencephalography • Electrical potential is usually measured at many sites on the head surface

9. Magnetoencephalography • For any electric current, there is an associated magnetic field Electric Current Magnetic Field

10. Magnetoencephalography • For any electric current, there is an associated magnetic field • magnetic sensors called “SQuID”s can measure very small fields associated with current flowing through extracellular space Electric Current Magnetic Field SQuID Amplifier

11. Magnetoencephalography • MEG systems use many sensors to accomplish source analysis • MEG and EEG are complementary because they are sensitive to orthogonal current flows • MEG is very expensive

12. EEG/MEG • EEG/MEG changes with various states and in response to stimuli Electroencephalogram

13. EEG/MEG • Any complex waveform can be decomposed into component frequencies • E.g. • White light decomposes into the visible spectrum • Musical chords decompose into individual notes

14. EEG/MEG • EEG is characterized by various patterns of oscillations • These oscillations superpose in the raw data 4 Hz 4 Hz + 8 Hz + 15 Hz + 21 Hz = 8 Hz 15 Hz 21 Hz

15. How can we visualize these oscillations? • The amount of energy at any frequency is expressed as % power change relative to pre-stimulus baseline • Power can change over time 48 Hz % change From Pre-stimulus 24 Hz 16 Hz Frequency 8 Hz 4 Hz +200 +400 +600 0 (onset) Time

16. Where in the brain are these oscillations coming from? • We can select and collapse any time/frequency window and plot relative power across all sensors Win Lose

17. The Event-Related Potential (ERP) • Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor actions, etc.

18. The Event-Related Potential (ERP) • Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor actions, etc. • Averaging all such events together isolates this event-related potential

19. The Event-Related Potential (ERP) • We have an ERP waveform for every electrode

20. The Event-Related Potential (ERP) • We have an ERP waveform for every electrode

21. The Event-Related Potential (ERP) • We have an ERP waveform for every electrode • Sometimes that isn’t very useful

22. The Event-Related Potential (ERP) • We have an ERP waveform for every electrode • Sometimes that isn’t very useful • Sometimes we want to know the overall pattern of potentials across the head surface • isopotential map

23. The Event-Related Potential (ERP) • We have an ERP waveform for every electrode • Sometimes that isn’t very useful • Sometimes we want to know the overall pattern of potentials across the head surface • isopotential map Sometimes that isn’t very useful - we want to know the generator source in 3D

24. Brain Electrical Source Analysis • Given this pattern on the scalp, can you guess where the current generator was?

25. Brain Electrical Source Analysis • Given this pattern on the scalp, can you guess where the current generator was? • Source Imaging in EEG/MEG attempts to model the intracranial space and “back out” the configuration of electrical generators that gave rise to a particular pattern of EEG on the scalp Duracell

26. Brain Electrical Source Analysis • EEG data can be coregistered with high-resolution MRI image Source Imaging Result Structural MRI with EEG electrodes coregistered

27. Intracranial and “single” Unit • Single or multiple electrodes are inserted into the brain • “chronic” implant may be left in place for long periods

28. Intracranial and “single” Unit • Single electrodes may pick up action potentials from a single cell • An electrode may pick up thecombined activity from several nearby cells • spike-sorting attempts to isolate individual cells

29. Intracranial and “single” Unit • Simultaneous recording from many electrodes allows recording of multiple cells

30. Intracranial and “single” Unit • Output of unit recordings is often depicted as a “spike train” and measured in spikes/second • Spike rate is almost never zero, even without sensory input • in visual cortex this gives rise to “cortical grey” Stimulus on Spikes

31. Intracranial and “single” Unit • Local Field Potential reflects summed currents from many nearby cells Stimulus on Spikes

32. Relationship between EEG / LFP / spike trains • All three probably reflect related activities but probably don’t share a 1-to-1 mapping • For example: there could be some LFP or EEG signal that isn’t associated with a change in spike rates. • WHY? Whittingstall & Logothetis (2009)

33. Synthesize the Big Picture