Subdural Grid Intracranial electrodes typically cannot be used in human studies It is possible to...

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

Electroencephalography and the Event-Related Potential

• Could you measure these electric fields without inserting electrodes through the skull?

Electroencephalography and the Event-Related Potential

• 1929 – first measurement of brain electrical activity from scalp electrodes (Berger, 1929)

Electroencephalography and the Event-Related Potential

Time

Volta

ge

-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

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

Electroencephalography

• pyramidal cells span layers of cortex and have parallel cell bodies

• their combined extracellular field is small but measurable at the scalp!

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)

Electroencephalography

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

Magnetoencephalography

• For any electric current, there is an associated magnetic field

Magnetic Field

Electric Current

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

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

EEG/MEG

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

Electroencephalogram

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

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 =

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

Freq

uenc

y

Time0

(onset)+200 +400

4 Hz

8 Hz

16 Hz

24 Hz

48 Hz

% changeFromPre-stimulus

+600

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

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.

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

The Event-Related Potential (ERP)

• We have an ERP waveform for every electrode

The Event-Related Potential (ERP)

• We have an ERP waveform for every electrode

The Event-Related Potential (ERP)

• We have an ERP waveform for every electrode

• Sometimes that isn’t very useful

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

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

Brain Electrical Source Analysis

• Given this pattern on the scalp, can you guess where the current generator was?

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

Brain Electrical Source Analysis

• EEG data can be coregistered with high-resolution MRI image

Source ImagingResult

Structural MRI with EEG electrodes coregistered

Intracranial and “single” Unit

• Single or multiple electrodes are inserted into the brain

• “chronic” implant may be left in place for long periods

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

Intracranial and “single” Unit

• Simultaneous recording from many electrodes allows recording of multiple cells

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

Intracranial and “single” Unit

• Local Field Potential reflects summed currents from many nearby cells

Stimulus on

Spikes

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)

Synthesize the Big Picture