Event-related Potential (ERP) EEG Course Translational Neuromodeling Unit Frederike Petzschner 15.08.2014

Let’s start with a concrete example…

Let’s start with a simple experiment: variant of the oddball task 80% 20% X X O O X X Marker Also called continuuous performance task Luck, 2005

Simple experiment 80% 20% X X O O X X Also called continuuous performance task EEG from one electrode site midline over parietal lobes Luck, 2005

Simple experiment ERP Components: P = Positive N = Negative P1 = P100 etc. Also called continuuous performance task 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 activity! Luck, 2005

ERP Components – P1 P1: Luck, 2005 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 Also called continuuous performance task Luck, 2005

ERP Components – P3 group Depends on which task is performed no clear consensus about what neural or cognitive process the P3 wave reflects. (‘context-updating’) 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. know a great deal about the effects of various manipulations on P3 amplitude and latency, but there is Moreover, it is the probability of the task-defined stimulus class that matters, not the probability of the physical stimulus. For example, if subjects are asked to press a button when detecting male names embedded in a sequence containing male and female names, with each individual name occurring only once, the amplitude of the P3 wave will depend on the relative proportions of male and female names in the sequence (see Kutas, McCarthy, & Donchin, 1977). Similarly, if the target is the letter E, occurring on 10 percent of trials, and the nontargets are selected at random from the other letters of the alphabet, the target will elicit a very large P3 wave even though the target letter is approximately four times more probable than any individual nontarget letter (see Vogel, Luck, & Shapiro, 1998). e.g. here much larger for the rare O then for the frequent X stimuli 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. However, the MMN can be eliminated for stimuli presented in one ear if the subjects focus attention very strongly on a competing sequence of stimuli in the other.

ERP Components –Error-related negativity (ERN) Can be elicited by Being aware of an error negative feedback following an incorrect response - 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 responses. 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 trials) 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 EVOKED MODEL: The fact that ERPs consist of a sequence of components seems to imply a sequential activation process. As an example, the processing of a visual stimulus can be characterized by three early components, the C1 with a latency around 80ms which is generated in the striate cortex (area 17), the P1 with a latency around 110ms which is generated in the extrastriate cortex (areas 18 and 19) and the N1 with a latency around 160ms which probably is generated in the temporal cortex (cf. Clark et al., 1995 ; Di Russo et al., 2002 ; Allison et al., 2002 ). These findings invite the interpretation that a visual stimulus is first processed in the primary visual cortex and then in secondary areas before it can be identified as an object, a process that is related with areas in the temporal cortex. Thus, the timing for the generation of ERP components appears as sequential neural processes that results solely from anatomical properties.

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 released 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 If an electrode is lowered into the intercellular space in a living brain both types of potentials can be recorded. Its fairly easy to isolate the action potentials arising from a single neuron by inserting a microelectrode into the brain but it is virtually impossible to completely isolate a single neuron’s postsynaptic potential in an in vivo extracellular recording. Single unit recordings thus measure action potentials rather than postsynaptic potentials. When recording many neurons simultaneously it is possible to measure either tehor summed postsynaptic potential or their action potentials. Recordings of action potentials from large populations of neurons are called multi-unit recordings and recordings of postsynaptic potentials from large groups of neurons are called local field potentials 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 When an action potential is generated, current flows rapidly into and then out of the axon at one point along the axon, and then this same inflow and outflow occur at the next point along the axon, and so on until the action potential reaches a terminal. If two neurons send their action potentials down axons that run parallel to each other, and the action potentials occur at exactly the same time, then the voltages from the two neurons will summate and the voltage recorded from a nearby electrode will be approximately twice as large as the voltage recorded from a single action potential. However, if one neuron fires slightly after the other, then current at a given spatial location will be flowing into one axon at the same time that it is flowing out of the other axon, so they cancel each other and produce a much smaller signal at the nearby electrode. 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 area Action potential duration: 1ms Cortical pyramidal cell Luck, 2005

Best guess of a biophysical event that gives rise to a scalp ERP This creates a tiny dipole dur: 10-100ms location: largely dentrides & cell body delay: occur instantaneously and do not travel down the axon 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 conduction). The voltage on the surface will thus depend on The position and orientation of the generator dipole (equivalent current dipole) 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 resistance The skull has high resistance Travels laterally when reaching the skull 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 voltage. 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 Central assumption is that the noise component apporximates zero or many trials 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 Brain oscillation theory offers an alternative explanation for the generation of ERP components that is completely different from that of the evoked model. The interpretation of the event-related EEG response is a logical consequence from results obtained for the ongoing EEG: Oscillations reflect different sensory and cognitive processes and play an important role for the timing of neural processes also for the event-related EEG response. Klimesch et al., 2007

Event-Related Phase Reorganization (ERPR) Model a= amplitude w = frequency tx = xth time point y = trial index No noise term An ERP generated by ERPR can be understood as the sum of instantaneous amplitudes (a) of different task relevant frequencies o at time point t , averaged over trials k : Unlike the evoked model, ERP components generated by ERPR lack an additive noise component. Noise may be due to the influence of two different factors: (i) to the number of frequencies that are not task relevant and/or (ii) to the extent task relevant frequencies do not align in absolute phase within certain time windows. Both factors will reduce the amplitude of the ERP. Thus, for optimal processing of a stimulus, phase reorganization is obligatory. 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) frequencies Klimesch et al., 2007

How is noise embedded in the ERPR Model? Input from non-task relevant and thus not aligned oscillations Imperfect alignement between phases of task relevant oscillations

Oscillations and ERPs Klimesch et al., 2007 oscillatory model assumes that evoked components are generated by a combination of three processes Phase reorganization of ongoing oscillations, evoked oscillations IPA 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 conduction It should also be noted that the alignment of phase of different assemblies may lead to a large and transient increase in neural activity during the excitatory period of alignment that may resemble at least in part an evoked component. But in a physiological sense there would be a large difference in the functional meaning of evoked components and ERPRdriven components. Although ERPR-driven components might also appear as a serial sequence of components, they are most likely generated by parallel processes. As an example, after very early processing of a stimulus in subcortical regions (such as the reticular formation and thalamus) oscillatory activity might be induced in the cortex much earlier than the first event-related components appear. Dynamic interactions between oscillations and respective cell assemblies may be important for the latency and anatomical site of IPA and the appearance of an eventrelated component. As illustrated in Fig. 3 , oscillatory activity might be a very distributed neural process, and some components such as the P1 might be generated simultaneously—or with a latency shift—at different sites (cf. sites 1 and 2 in Fig. 3(A) ). Recent empirical evidence, showing that the P1 can be described as traveling alpha wave propagating from occipital to parietal sites suggests that early ERP components may be generated by a widely distributed neural process (Klimesch et al., 2007b ). Evoked components, on the other hand, are more compatible with a localized process and the appearance of this component at different scalp sites might be explained by volume conduction due to a large dipole at a particular region in the brain. 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 evidence (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 evidence (p6) Dipole source analysis may not be considered an adequate method  Not investigated (p7) Evoked components reflect parallel distributed neural processes Not investigated

Pitfalls

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 individual 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

THANK YOU References: Luck, 2005 Klimesch, 2007