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Principles of CN Proper > L9: MEG Event Related Analyses > Flashcards

L9: MEG Event Related Analyses Flashcards

1
Q

Event-related analyses in MEG happens when

A

we got data preprocessed and taking as much artefacts as we can

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2
Q

The most popular statistical analysis for EEG/MEG is

A

event-related analyses

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3
Q

The effects of averaging time course over multiple repetitions of condition, to increase SNR, depends on - (2)

A

kind of response - only work if responses are consistent and happen same time in different trials

can improve SNR but lose signal if responses are not consistent

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4
Q

What does this diagram show - evoked responses? - (3)

A

Perfect sample as responses on 3 different trials is identical and has same time course - (looking 100ms after stimulus onset) = time-locked

The averaging of 3 different trial’s response gets same strong peak (all peaks line up - phase-locked)

(evoked response)

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5
Q

What is evoked response?

A

Time locked and phase lock

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6
Q

What does time-locked mean?

A

Same amount of time after stimulus onset

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7
Q

What does phase lock means

A

At that time its the same phase

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8
Q

Diagram of cycle, amplitude phase and explain what phase is (2)

A

phase is where we are in oscillation

we have up and down measure of activity and phase is whether we are at baseline, peak or second hit to baseline or at bottom

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9
Q

Evoked responses are good to average across trials and thus use

A

event-related analyses

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10
Q

Induced responses is when responses are not

A

phase-locked

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11
Q

Diagram of evoked vs induced responses

A
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12
Q

Diagram of evoked vs induced

For induced - (3)

A

Responses are at slightly different times and because slightly different phases

Time locked as 100 milliseconds after stimulus onset, all responses are happening so all around 100 ms

Not phase locked as peaks are not same and when we average we get reduced signal and unlikely to miss effects so no longer improving SNR

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13
Q

Event-related analyses depend on averaging so

A

can used on evoked responses but not induced responses (can’t average)

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14
Q

We don’t average when - (2)

A

responses that are not time-locked (e.g., changes in attention or steady-state or resting state-analyses) as well as responses that are not phase-locked

bad to average across time course of trials

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15
Q

For responses that are not time-locked and phase-locked we instead calculate

A

power in a given frequency per trial and average those (lose phase info as it is varying)

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16
Q

When we have induced responses, what analysis we do?

A

frequency-based analyses

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17
Q

An event-related potential (ERP) is created In EEG when

A

averaging over many trials across their time courses gets a complex waveform

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18
Q

An ERP asks in EEG

A

when (and broadly where -topography spatially) is there a change in strength of the electric potential

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19
Q

What does event-related analyses give us in EEG

A

ERP

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20
Q

Diagram of ERP in EEG and what graph shows? - (4)

A

N = negative effects

P = positive effects

Label by number they happen in data (e.g., N1, N2, P1,P2) - label positive and negative deflections sequentially

Negative voltages are plotted upwards

21
Q

In ERP in EEG, you are measuring the - (2)

A

amplitude (strength) of responses and response latency (how long after stimulus presentation [0ms] it occurs

ERPs can be compared across conditions e.g., clinical groups etc..

22
Q

Diagram of latency and amplitude what it shows EEG ERP - (2)

A
  • Amplitude = how high peak is from baseline
  • latency - was response after 100 or 200 ms
23
Q

ERP components can be given standard labels

A

by approximate time they occur in milliseconds

24
Q

Why is it useful to give ERP components standard labels by the approximate time they occur in milliseconds?

A

Able to discuss EEG findings across studies

25
Q

ERP components can be given standard labels by the (approximate) time they occur in milliseconds - (6)

A

P100 – basic visual response (occipital) - positive effect after 100 ms

N100 – basic auditory response (more anterior) - negative effect after 100 ms

N170 – to face stimuli - negative effect after 170 ms

P300 – decision making

Lateralized readiness potential (LRP) ( an indicator of motor planning)

N400 – semantic processing (e.g., differences in response at N400 when given sentence ‘John ate democry at dinner’ vs ‘John ate broccili’ at dinner’)

26
Q

Event-related analyses is most widely used approach in

A

MEG

27
Q

In event-related analyses in MEG it pools over

A

lots of trials and average out the noise to get clearer signals

28
Q

In event-related analyses in MEG, it assesses

A

the time course of changes in magnetic field after stimulus presentation

29
Q

In MEG event-related analyses we record a

A

magnetic evoked potential (MEP) - MEG version of ERP

30
Q

The MEP asks

A

when (and broadly where) is there a change in the magnetic field strength

31
Q

Note that MEP can also stand for

A

Motor Evoked Potential - a paradigm where electrical or magnetic stimulation causes a motor response – make sure you don’t get confused!

32
Q

Diagram of butterfly plot in MEG shows

A

Different sensors and magnetic field strength shown across time and black is average

33
Q

In components of MEP, the sign of deflections is less stereotyped than ERPs in EEG as…

A

we usually prefix them with M for magnetic rather than P for positive and N for negative

34
Q

In components of MEP, the sign of deflections is less stereotyped than ERPs in EEG

for example.. - (2)

A

For example, the M100 is an early visual response, the M170 is associated with stimuli such as faces

The M170 can appear as a positive or negative change in the magnetic strength

35
Q

Diagram example of
MEP components less stereotyped - (3)

A

Around 170ms we get negative effect for faces and scrambled faces on left

In another graph to right around 170ms we get positive effect for faces and scrambled faces

For incidental reasons (e.g., cortical folding)

36
Q

Typical in MEG to use senor topographies of magnetic field strength at a single time point (e.g., 170 ms) or averaged across time window to show

A

spatial distribution of MEPs

37
Q

Diagram of sensor topography in MEG

A
38
Q

The sensor topography in MEG can differ between participants because of

A

individual differences in cortical folding and overall brain anatomy

39
Q
A
40
Q

The sensor topography in MEG can look different from EEG topographies for identical experimental condition because

A

magnetic fields are at right angles to electrical potential differences

41
Q

Diagram of how we make comparisons between conditions in MEG using butterfly plots - explain

A

take butterfly plot of faces subtracted from scrambled faces to give our difference

42
Q

In MEG we can compare its response in two conditions - (2) butterfly plots

A

We can subtract the time courses for each condition and look at the difference in activity

Can test for significant differences at the group level e.g., with t-test per time point (details in lecture 11) - see if its different from 0 and difference greaer than 0 and outside CI -sig

Asks – when do the responses differ between two conditions?

43
Q

In MEG we can compare its response in two conditions - (3) topographies of head spatially -where is the difference?

A

Also possible to subtract topographies across the whole head

In general, we will do group-level statistics such as t-tests on data like this (more details in lecture 8)

Asks – (broadly) where do the responses differ between two conditions? (until we go into source space – lecture 11)

44
Q

In MEG calculate global power by…

A

summarising the MEG activity across all sensors -quick check of seeing if something is different in responses in conditions

45
Q

Global field power can be thought of

A

average amount of activity detected by scanner -summing everything across

46
Q

Global field power sometimes can show… but can not… - (2)

A

coarse differences between conditions and when they occur

but can not tell us where

47
Q

What does power mean in global field power?

A

amplitude squares so values are always positive

48
Q

Diagram of global field power shown in the bottom of all the time coruse we run

A

Principles of CN Proper (19 decks)

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