Presentation - Translational Neuromodeling Unit

Event-related Potential (ERP)
EEG Course
Translational Neuromodeling Unit
Frederike Petzschner
Let’s start with a concrete example…
Let’s start with a simple experiment: variant of the oddball task
Luck, 2005
Simple experiment
EEG from one electrode site midline over parietal lobes
Luck, 2005
Simple experiment
P = Positive
N = Negative
P1 = P100
P2 = P200
P3 = P300
ERP components have labels like P1 and N1 refer to polarity and position in the
waveform. These labels are NOT linked to the nature of the underlying brain
Luck, 2005
ERP Components – P1
Sensory peak, elicited by visual
stimuli no matter which task is used
strongly influenced by stimulus
parameters (luminance)
Onset: 60-90 ms, peak:100-130ms
Latency varies with contrast
Early portion may come from middle
occipital gyrus, late portion from
fusiform gyrus
Luck, 2005
ERP Components – P3 group
Depends on which task is
no clear consensus about what
neural or cognitive process the
P3 wave reflects. (‘contextupdating’)
P3 amplitude gets larger as
target probability gets smaller.
Also local probability matters,
because the P3 wave elicited
by a target becomes larger
when it has been preceded by
more and more nontargets.
Luck, 2005
ERP Components –Mismatch Negativity (MMN)
observed when subjects are exposed to a
repetitive train of identical stimuli with
occasional mismatching stimuli
negative-going wave that is largest at central
midline scalp sites and typically peaks
between 160 and 220 ms.
Several other components are sensitive to
mismatches if they are task-relevant, but the
MMN is observed even if subjects are not
using the stimulus stream for a task
thought to reflect a fairly automatic process
that compares incoming stimuli to a sensory
memory trace of preceding stimuli.
ERP Components –Error-related negativity (ERN)
Can be elicited by
- Being aware of an error
- negative feedback following an incorrect
- observing someone else making
an incorrect response
Most investigators believe that the ERN
reflects the activity of a system that either
monitors responses or is sensitive to
conflict between intended and actual
Source could be ACC.
Why are ERP Components interesting?
Advantage over pure behavior
1. Provide a continuous measure of processing
between stimulus and response
Research Question: Are slowed responses
(RTs) due to slower perceptual processes or
slower response processes?
- Latency of the P3 wave becomes longer
when perceptual processes are delayed, no
increase in latency in the Stroop Task
 ERPs might proof useful for determining which stage
of processing is influenced by a task
Luck, 2005
Advantage over pure behavior
2. Can provide an online measure of the
processing of stimuli even when there is no
behavioral response
• Attended versus ignored stimuli
• Language comprehension can assess
processing of a word embedded in a sentence
at the time the word is processed
Luck, 2005
ERP changes in Psychiatric Disorders
Alcohol (N1, P2, N2, P3, …)
Schizophrenia (N1, P2, N2, P3, MMN, …)
Bipolar Disorder (P50, P3)
Depression (P3)
Phobia (P3)
Panic disorder (P3)
Generalized anxiety disorder (P3)
OCD (P3, N2, ERN)
Posttraumatic stress disorder (P50, P3)
Dissociative disorder (P3)
Personality disorder (N2, P3)
Disadvantage over behavior
• 1. Very small signal, requires a large number of trials
to measure them accurately. (50, 100 or even 1000
• 2. Functional significance: we don’t know the
specific biophysical events that underlie the
production of a given ERP
Luck, 2005
What are ERPs?
Evoked Model
What is an ERP?
• Two main types of electrical activity in the brain:
action potentials and postsynaptic potentials
• Action potentials are discrete voltage spikes that
travel from the beginning of the axon to the cell body
to the axon terminals where neurotransmitters are
• Postsynaptic potentials are voltages that arise
when the neurotransmitters bind to receptors to
open or close and leading to a graded change in the
potential across the cell membrane
Luck, 2005
Which of the two, action potentials or postsynaptic
potentials, do you think we see reflected in EEG?
What is the ERP?
• Mostly surface electrodes
can not detect action
potentials due to the
timing and physical
arrangement of axons.
Will most likely cancel
each other out
Luck, 2005
What is an ERP? - Postsynaptic Potential
• If an excitatory
neurotransmitter is released
at the apical dentrides:
 current will flow from
extracellular space into the
cell, yielding a net negativity
on the outside in the region of
the dentrides.
 Current will also flow out of the
cell body and basal dentrides
yielding a net positivity in this
Cortical pyramidal cell
Luck, 2005
Best guess of a biophysical event that gives rise
to a scalp ERP
This creates a tiny
dur: 10-100ms
location: largely dentrides &
cell body
delay: occur instantaneously
and do not travel down the
size: Can summate under
certain conditions rather then
cancel each other out and
then be recorded at great
distance (scalp)
Luck, 2005
Best guess of a biophysical event that gives rise
to a scalp ERP
• The dipole of a single neuron is tiny
• But under certain conditions the dipoles from
many neurons will summate
1. occur approximately at the same time across
1000 – 1000 000 neurons
2. be spatially aligned
3. receive all excitatory or all inhibitory input
This is most likely in cortical pyramidal
cells, which are aligned perpendicular to
the surface of the cortex
Luck, 2005
Purkinje cells in the cerebellar cortex are
beautifully aligned with each other and oriented
perpendicular to the cortical surface. Can we
measure them in EEG?
Assume we have many aligned dipoles now..
…then the signal still needs to reach the scalp
The brain is conductive material (volume
The voltage on the surface will thus depend
- The position and orientation of the
generator dipole (equivalent current
- The resistance and shape of the various
components of the head (brain, skull,
scalp, eye holes)
Luck, 2005
Assume the signal has reached the skull…
…what does it look like?
Electricity spreads out through the
conductor  blur
Tends to follow the path of least
The skull has high resistance
 Travels laterally when reaching the
 More blur *
BUT nicely electricity travels nearly as fast a light. So the signal is instantaneous!
Luck, 2005
* There are clever algorithms that calculate the ‘skull’-blur and reduce it.
How do I find out where the signal comes from?
• Forward problem: If you knew the location and
orientation of the dipoles and the conductance of the
volume, the you could compute the distribution of
• Inverse problem: ‘ill-posed’. An infinite number or
different dipole configurations can produce any given
voltage.  use specific constraints
• No perfect tool out there yet
 talks on source reconstruction
Luck, 2005
Sum: ERP components in the Evoked Model
tx = xth point in time
i = trial index
k = total number of single trials
- reflect neural activity in rather localized brain
regions that are involved in the processing of a
stimulus and/or task.
- reflect a sequential process
- independent of the background activity
- ERP components do not interact with
prestimulus EEG
Klimesch et al., 2007; Luck, 2005
Critique ERP Components in the Evoked Model
• (e1) EEG oscillations do not serve a specific function 
Negative evidence
• (e2) No correlation/ interaction between preand poststimulus EEG
 Negative evidence
• (e3) ERP components and (power of) ongoing EEG are additive 
Preliminary negative evidence
• (e4) ERP components do not interact with prestimulus EEG 
Negative evidence
• (e5) ERP latencies/ interpeak latencies and evoked power are not
associated with frequencies of dominant EEG oscillations 
Negative evidence
• (e6) Dipole source analysis yields meaningful results 
Preliminary positive evidence
• (e7) ERP components are generated along a pathway of localized
neural activation  Preliminary positive evidence
Klimesch et al., 2007
Oscillations in EEG
Klimesch et al., 2007
Event-Related Phase Reorganization (ERPR) Model
a= amplitude
w = frequency
tx = xth time point
y = trial index
No noise term
For optimal processing of a stimulus, phase reorganization is obligatory.
This doesn’t mean a phase reset, but instantaneous phase alignment (IPA)
This means: Event-related alignment in phase between (task relevant)
Klimesch et al., 2007
How is noise embedded in the ERPR Model?
Oscillations and ERPs
Klimesch et al., 2007
ER Components
Early processing of a
stimulus in subcortical
regions, oscillatory activity
might be induced in the
cortex (much earlier then
the first event-related
components)  widely
distributed neuronal process
Evoked components are
localized processes.
Distribution can be
explained through volume
Klimesch et al., 2007
What would you consider to be positive
evidence for the ERPR Model?
Implications of the ERPR Model
• (p1) EEG oscillations are associated with specific functions Positive
• (p2) Interaction between pre- and poststimulus EEG  Positive evidence
• (p3) Phase reset of task relevant ongoing oscillations, generation of task
relevant evoked oscillations, IPA between task relevant oscillations 
Positive evidence
• (p4) ERP components are determined by IPA; reset and/or IPA takes
place not necessarily at pos. or neg. peak  Positive evidence
• (p5) ERP latencies/ interpeak latencies and evoked power reflect
frequency characteristics of functionally relevant oscillations  Positive
• (p6) Dipole source analysis may not be considered an adequate method
 Not investigated
• (p7) Evoked components reflect parallel distributed neural processes
Not investigated
Voltage Peaks are not special
Rule 1. It doesn’t make sense to measure
peak amplitude and peak latency to measure
the magnitude and timing of ERP components.
Luck, 2005
Peak Shapes Are not the Same as Component Shapes
Rule 2. It is impossible to
estimate the time course or
peak latency of a latent ERP
component by looking at a
single ERP waveform.
Rule 3. It is dangerous to
compare an experimental
effect (i.e.,the difference
between two ERP waveforms)
with the raw ERP waveforms.
Luck, 2005
Peak amplitudes are different from component sizes…
Rule 4. Differences in peak
amplitude do not necessarily
correspond with differences in
component size, and differences in
peak latency do not necessarily
correspond with changes in
component timing.
Luck, 2005
Averaging changes
your data
Rule 5. Never assume
that an averaged ERP
waveform accurately
represents the individual
waveforms that were
averaged together.
In particular, the onset
and offset times in the
averaged waveform
will represent the
earliest onsets and
latest offsets from the
trials or individual
subjects that contribute
to the average.
Luck, 2005
The way to go…
Strategy 1. Focus on a Specific Component
Strategy 2. Use Well-Studied Experimental Manipulations
Strategy 3. Focus on Large Components
Strategy 4. Isolate Components with Difference Waves
Strategy 5. Focus on Components That Are Easily Isolated
Strategy 6. Component-Independent Experimental Designs
Avoiding Confounds and Misinterpretations
Luck, 2005
Klimesch, 2007

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