E vent- r elated P otential ( ERP )

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

2. Let’s start with a concrete example…

3. Let’s start with a simple experiment: variant of the oddball task 20% 80% O X X X O X Marker Luck, 2005

4. Simple experiment 20% 80% O X X X O X EEG from one electrode site midline over parietal lobes Luck, 2005

5. Simple experiment ERP Components: P = Positive N = Negative P1 = P100 P2 = P200 P3 = P300 etc. 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

6. ERP Components – P1 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

7. ERP Components – P3 group P3: 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. Luck, 2005

8. 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.

9. 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 conflictbetween intended and actual responses. • Source could be ACC.

10. Why are ERP Components interesting?

11. 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

12. 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

13. 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)

14. 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

15. What are ERPs?Evoked Model

16. 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 Luck, 2005

17. ? Which of the two, action potentials or postsynaptic potentials, do you think we see reflected in EEG?

18. 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

19. 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 Cortical pyramidal cell Luck, 2005

20. 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

21. 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 summate1. 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

22. ? 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?

23. 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

24. 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.

25. 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

26. 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 prestimulusEEG Klimesch et al., 2007; Luck, 2005

27. Critique ERP Components in the Evoked Model • (e1) EEG oscillations do not serve a specific function  Negativeevidence • (e2) No correlation/ interaction between preandpoststimulus EEG  Negative evidence • (e3) ERP components and (power of) ongoing EEG are additive  Preliminary negativeevidence • (e4) ERP components do not interact with prestimulus EEG  Negative evidence • (e5) ERP latencies/ interpeaklatencies and evoked power are not associated with frequencies of dominant EEG oscillations  Negative evidence • (e6) Dipole source analysis yields meaningful results Preliminary positiveevidence • (e7) ERP components are generated along a pathway of localized neural activation  Preliminary positiveevidence Klimesch et al., 2007

28. Oscillations in EEG Klimesch et al., 2007

29. 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) frequencies Klimesch et al., 2007

30. ? How is noise embedded in the ERPR Model?

31. Oscillations and ERPs Klimesch et al., 2007

32. 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 Klimesch et al., 2007

33. ? What would you consider to be positive evidence for the ERPR Model?

34. Implications of the ERPR Model • (p1) EEG oscillations are associated with specific functionsPositive evidence • (p2) Interaction between pre- and poststimulusEEG  Positiveevidence • (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/ interpeaklatencies 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 Notinvestigated

35. Pitfalls

36. 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

37. 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.,thedifference between two ERP waveforms) with the raw ERP waveforms. Luck, 2005

38. 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 componenttiming. Luck, 2005

39. 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

40. 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

41. THANK YOU References: Luck, 2005 Klimesch, 2007