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 Time Voltage -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)

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 Magnetic Field Electric Current

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 Magnetic Field Electric Current 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 8 Hz 15 Hz 21 Hz 4 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 Frequency Time 0 (onset) +200+400 4 Hz 8 Hz 16 Hz 24 Hz 48 Hz % change From Pre-stimulus +600

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 WinLose

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

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 the combined 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 Understanding Brain-wide neural circuits Extracranial electrophysiology EEG/MEG Metabolic Imaging fMRI/PET Intracranial LFP/single-unit