fMRI Flashcards

1
Q

What is fMRI also known as?

A

Blood Oxygenation Level Dependent
Magnetic Resonance Imaging (BOLD MRI)

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2
Q

What are activation maps?

A

Use MRI to produce activation maps in reponse to a specific task condition = visualisation of which areas of the brain are activated

Aligns with the main goals of fMRI and neuroscience more broadly

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3
Q

What is the simple mechanism of fMRI?

A

fMRI BOLD signal is altered due to increase of blood flow in response to brain activity
This brain activity can be the result of a stimulus or due to resting state

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4
Q

Describe the basic process of fMRI

A

Stimulus/modulation in back ground activity
> Neuronal activity
>NVC
> Haemodynamic response
> Detection by MRI scanner
>fMRI BOLD response

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5
Q

Briefly describe Mosso’s experiment

A

Built a balance which individuals could lie on
> balance remained perfectly balanced until the individual engaged in some type of emotional or cognitive process
>here the balance tipped down on the side of the head as the blood flow redistributed in the brain

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6
Q

What did Mosso’s “human circulation balance” establish?

A

The relationship between blood flow and neuronal activity

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7
Q

How can a computer in the fMRI setup be helpful if you are using a response box to record participant responses?

A

Computer can receive feedback from the response box and this response will be synchronised in time with the scan

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8
Q

How does blood flow change the fMRI signal?

A

The BOLD sequence isn’t measuring the blood flow, but is sensitive to the different magnetic properties of oxygenated and deoxygenated red blood cells

The increased magnetic field in the deoxygenated blood causes a magnetic field gradient resulting in adjacent water molecules precessing at different frequencies.

As water molecules are precessing at different frequencies in the gradient field, they dephase (i.e. spread out in the x-y plane).

The variable dephasing results in signal loss around deoxygenated blood compared to oxygenated.

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9
Q

What is magnetic suceptibility?

A

Characterises the magnetic field

It’s the degree of magnetization in an object in response to a external magnetic field

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10
Q

What is the difference between diamagnetic and paramagnetic?

A

Diamagnetic = slightly reduces the magnetic field and this is caused by oxygenated blood

Paramagnetic = adds to the magnetic field and is caused by deoxygenated blood

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11
Q

What does it mean if there is an increase in the magnetic field?

A

Protons speed up

Related to the Lamour equation which shows that frequency of proton precession is proportional to the magnetic strength

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12
Q

How is the T2* signal affected at rest? Activation contrast

A

At rest oxygenated blood travels through the arteries exchanges in the capillary bed and deoxygenated blood leaves via the veins

The deoxygenated blood increases dephasing, reducing T2* signal.

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13
Q

How is the T2* signal affected during activation? Activation contrast

A

During activation an excess of oxygenated blood flows into the activated region, swamping the deoxygenated blood

The excess of oxygenated blood reduces dephasing, increasing the T2* signal.

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14
Q

Why is there an excess/oversupply of oxygenated blood during activation?

A

Still unclear but:

-could be related to supply of other nutrients e.g. glucose

-could be to provide a strong enough concentration gradient across the capillary wall so that the oxygenated blood can diffuse efficiently

-or could be related to blood flow being controlled at the arteriole level and this leads to an oversupply in smaller vessels

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15
Q

How can we get the optimum echo time when considering the task signal and rest signal in terms of exponential decay?

A

The longer the echo time is, the bigger the gap between the task signal and rest signal is, when this gap between these is too big, there is no longer any signal to detect
There is also a noise level as well which, once reached, means that the signal would be indistinguishable from the noise

Need a compromise between getting enough difference in signal between task and rest signal before they decay and not hitting the noise floor, so we can find an optimum echo time mathematically to do this

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16
Q

What are we imaging with fMRI?

A

Imaging voxels are an average signal over mm3, typically 4 x 4 x 4 = 64 mm3 in fMRI

An fMRI voxel will contain many different vessels, but these vessels still only make up around 3% of the voxel volume

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17
Q

Why is it important to consider macroscopic vs microscopic changes in fMRI?

A

Within imaging we tend to image at macroscopic levels so on the order of mms but theres a lot going on at the microscopic level too

We might not see an effect that’s expected- this could be that there is something going on at a microscopic level e.g. something could be happening to drown out the BOLD signal

Important to consider that we aren’t just imaging BOLD and vessels, other things also occur which may explain why you don’t see a positive result in an exp where you would expect there to be one

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18
Q

Where we move around K space is dictated by what?

A

The area under gradients

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19
Q

What is an issue with acquring the image through k-space?

A

Its too slow for fMRI

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20
Q

What method can we use to measure the BOLD response instead that is faster?

A

EPI- echo planar imaging

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21
Q

How can echo planar imaging help us quickly capture brain volumes quickly?

A

It means the whole of k-space is acquired after a single 90 degree pulse – single shot imaging
-we can image the BOLD effect with a spin echo sequence

So we can acquire a single slice in less than 100ms and so whole brain volume can be acquired within a second or two

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22
Q

What is an issue with BOLD EPI images?

A

They are low quality images and often have artefacts

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23
Q

Why are BOLD EPI images such low quality?

A

Because we acquire the echo signal after one excitation, we don’t get a lot of signal at the two extremes, and those spaces at the edge of k space are what give a sharper image with more detail

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24
Q

What is the order of acquiring the slices in whole brain coverage?

A

Slices are presented continuously but often not acquired in this way

Often we interleave slices when exciting them

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25
Q

What is the difference between sequential and interleaved slice aquisition?

A

Sequential slice acquisition acquires each adjacent slice consecutively, either bottom-to-top or top-to-bottom. Interleaved slice acquisition acquires every other slice, and then fills in the gaps on the second pass

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26
Q

Why do we interleave slices when acquiring images using the 90 degree pulse?

A

When we excite the slices with the 90 degree pulse, it doesn’t excite the whole slice, it bleeds a little into the adjacent slice so this means the first excitation effects the adjacent slice

So we interleave them so that by the time we return to that neighbouring slice the signal will have returned to a more normal baseline

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27
Q

What are some advantages of interleaved slice aquisition?

A

Increased signal to noise
Post processing is not required to remove distortion

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28
Q

Why do we normally acquire a few dummy scans?

A

Takes a few cycles for the magnetisation to become steady between acquisition

Tend to specify 3 or 4 dummy scans first to allow the signal to steady itself

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29
Q

What do we want to do once we’ve acquired these images and got the time-series for these images?

A

Plot how the signal varies voxel by voxel across the whole time series

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30
Q

What are the two main tequniques for modelling the BOLD signal?

A

Linear regressions (i.e. GLM)

Non-linear regression

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31
Q

Why do we want to model the data?

A

We want to fit a model to the data to analyse which voxels show significant activation

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32
Q

What is the easiest way to model the data?

A

Apply a simple model (e.g. boxcar- rest then activation then rest) to the data

If this is a poor fit we can always shift the timecourse a little to fit the data better

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33
Q

What is a better way to model the data than arbitrarily shifting the data?

A

Could try to model the physiology

Done using the haemodynamic response function which characterises the response to an impulse stimulus

Haemodynamic response function characterises what our BOLD signal is going to do in response to the impulse stimulus - these have been measured experimentally

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34
Q

What does a basic haemodynamic response look like?

A

We have an initial undershoot
Then we get the peak, followed by a decay, and another undershoot

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35
Q

Once we have the haemodynamic response function what do we do?

A

Convovle the HRF with our fMRI experimental model to produce a more realistic estimate of the expected signal change

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36
Q

If there is still a residual signal drift, what can we also do?

A

To account for the signal drift over time, we can add a linear ramp to our model

this then fits the voxel timecourse reasonably well

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37
Q

Once we’ve fitted the model to our data how do we know what it tells us about activation?

A

The data is a series of discrete signal measurements taken every few seconds

What we can do is estimate the amplitude (a measure of how activated our voxel is) and then the residual noise (how good our fit is to the data)

38
Q

Summarise the General Linear Model (GLM)

A

y= x B + e

y- voxel time series data
x- design matrix
b- regression parameter
e- gaussian noise

39
Q

What is the t-statistic?

A

The ratio of the fitted amplitude to the residual noise

The t-statistic is defined as “the ratio of the departure of the estimated value of a parameter from its hypothesised value to its standard error.

40
Q

What do you do once you’ve got the t-statistic?

A

We threshold the t-value map to great an fMRI activation map

41
Q

What is the most important thing to understand about fMRI?

A

It’s not quantitative

We can’t get a single subject to perform a task and measure activation in any meaningful units, i.e. neurons firing per millisecond, blood flow increase in ml/100ml/s

42
Q

What does it mean for us if fMRI data is not quantitative?

A

fMRI relied on the signal contrast generated by different states

So the contrast we develop needs to be a comparison between two different states e.g. rest vs task

43
Q

What is a run?

A

Continuous period of data acquisition that has the same parameters and task

44
Q

What is an event?

A

An isolated occurance of a stimulus being presented or a response being made

45
Q

What is an epoch?

A

A period of sustained neural activity

46
Q

What is a block trial?

A

It consists of many closely spaced successive trials over a short interval of time

Block design experiments utilise blocks of identical trial types to establish a task-specific condition

For a two condition block design, block lengths of around 20 seconds are statistically optimal.

47
Q

How is a model fitted to block designs?

A

Block design is convovled with the HRF to generate a GLM regressor

This is our prediction of what the measured (noise free) signal in an activated voxel should look like following our block design stimuli.

48
Q

What are the advatanges of block designs?

A

It is the most efficient design for detecting BOLD signal amplitude differences between conditions

Fairly robust when there is uncertainty in the timing / shape of the HRF, as block duration is usually larger than HRF response

Can acquire more trials in less time than other designs because you don’t have to worry about spacing individual trials apart to get an estimate of each individual event

49
Q

What are the disadvantages of block designs?

A

Stimuli are highly predictable – subjects know what is coming and may alter strategies accordingly (this may, or may not be what you want!)

Inflexible for more complex tasks – impact of oddball stimuli, or stimuli / events that occur uncontrollably, cannot distinguish between trial types within a block

Does not account for transient responses at start / end of a block- participants mayb behave differently at the start/end of the block

Does blocking trials change the psychological process you are interested in? Comes down to what you want to measure

Determining an appropriate baseline condition can be challenging. If you do task vs rest, you cant be sure what occurs during the rest phase so hard to estalbish a baseline, may be worth having a control task that isnt rest

50
Q

What are slow event related designs?

A

Slow event related designs consist of short stimuli separated by a fairly long inter-stimulus interval (ISI) to enable the HRF to fall back to baseline before the next trial.

Essentially you wait enough time for the haemodynamic response to decay close enough to 0 before you do the next trial with the next stimulus

51
Q

What is an issue with slow event related designs?

A

Get an effective loss of power of about 35% where power= ability to discriminate between two conditions

So you need about 9 mins of a slow event design to be equivalent in power to detecting difference in BOLD signal to around 6 minutes of a block design

52
Q

What are slow event related designs good for?

A

Good if you dont know what the haemodynamic repsonse is - e.g. if you have a group with impaired blood flow

Rather than model it we have a good estimate of the haemodynamic response after each trial as we allow the haemodynamic data to return to baseline

53
Q

How is a model fitted to slow event related designs?

A

As with the block design, the slow ER design is convolved with the HRF to generate a GLM regressor

Again, this is the prediction of what the measured (noise free) signal in an activated voxel should look like following the slow ER design stimuli

Note how the ISI is set so that the HRF has decayed back to baseline before the following stimulus is presented

This paradigm represents three different stimuli, each giving a different response magnitude.

54
Q

What are fast event related designs?

A

Fast event related designs consist of short stimuli separated by variable ISI

The inclusion of jittered fixation frames allows for more closely placed trials

Events can be truly randomised as you would do in a behavioural study

BOLD signal change is much lower even than in slow ER designs, ≈ 1% vs 3% for block designs

55
Q

Why do we need to take care with a fast event related design?

A

Care with design is required: every combination of trial sequences must be used, i.e. every trial type must be preceded and followed by every other trial type an equal number of times

56
Q

What is jittering and why is it essential?

A

Jittering is randomised, variable inter stimulus intervals

Adding jitter to experiments with conditions that are relatively close to each other allows the independent estimation of the hemodynamic response to each condition

57
Q

How is a model fitted to fast event related designs?

A

As with the other designs, the fast ER design is convolved with the HRF to generate a GLM regressor

Again, this is the prediction of what the measured (noise free) signal in an activated voxel should look like following the fast ER design stimuli

Note how there are a mix of ISI, sometimes allowing the HRF to decay back to baseline

This paradigm represents two different stimuli

There is no colour coding of the convolved model because the average signal cannot be clearly separated by stimuli type

58
Q

What are 7 things for good practice infMRI experimental design?

A
  1. Evoke the cognitive or other process of interest
  2. Collect as much (fMRI) data as possible- because data is small and quite noisy
  3. Collect data on as many subjects as possible
  4. Choose stimulus and timing to create maximal change in BOLD signal for the cognitive process of interes
  5. Time stimuli presentation of different conditions to minimise overlap in signal
  6. Use software to optimise design efficiency for ER designs
  7. Get measure of subject behaviour in the scanner (ideally related to task)- e.g. falling asleep or not concentrating, check paying attention and doing things correctly through feedback, ensures they are correctly engaging with the task
59
Q

When can we reject the null hypothesis in a simple block design?

A

For a simple block design, we only have one regressor / EV, therefore all we can test is whether there is significant activation

If our t-statistic is greater than a specified value, we can reject the null hypothesis and conclude that there is significant activation in our voxel

60
Q

What are COPEs?

A

Contrast Of Parameter Estimates

These describe the statistical comparisons we want to make
These are input into software using contrast weight vectors

61
Q

Imagine that our two stimuli correspond to displaying faces (B1) and displaying objects (B2).

Describe the vectors.

A

Vector [1 0], COPE B1 = Voxels where activation occurs due to faces
Vector [0 1], COPE B2 = Voxels where activation occurs due to objects
Vector [1 1], COPE B1 + B2 = Mean activation due to faces and objects
Vector [1 - 1], COPE B1 - B2 = Voxels where facts activate more than objects
Vector [-1 1], COPE B2 - B1 = Voxels where objects activate more than face

62
Q

How can COPEs be applied to imaging?

A

The GLM is fitted in every voxel so we can produce images of COPEs

t = COPE/ std deviation of COPE

63
Q

How many regression parameters do we now have in a slow event related design?

A

3 regression parameters - B1, B2, B3

64
Q

How do we decide which of our voxels show statistically significant activation?

A

Simple thresholding - In the early days of fMRI, had a slider to threshold the t-value (or z-value) until our maps looked “about right”! (If we were lucky, we could also specify the minimum cluster size

But this is very biased ! Not experimentally sound in terms of methodology

65
Q

What does a small p-value imply?

A

That the null hypothesis is unlikely to be true

66
Q

What p-value in fMRI terms is considered to be statistically significant?

A

In fMRI terms, if the p-value is less than 0.05, the voxel is “active”

A threshold can be set for the p-value (normally 0.05) which corresponds to a false positive rate of 5%

67
Q

What does the t-value depend upon?

A

Depends on the degrees of freedom (sq root of n)

Therefore, similar t-values may mean very different things depending on the DOF

68
Q

What can we do to make sure t-values are interpreted in the same way?

A

Transform t-values into z-values

The z-distribution is just a standard normal distribution

This means that our t-values can always be interpreted in the same way

69
Q

What is the multiple comparisons problem?

A

Remember, a p-value < 0.05 sets the false positive rate at 5%, which means we expect around 5% of activations to be false positive

fMRI: 64 x 64 x 40 scan = 163840 voxels
0.05 (p-value) x 163840 = 8192 voxels
So, we expect over 8000 false positive voxels in a typical dataset!
There’s a reasonable chance that some of these will form small clusters of activation
A 5% error rate is fine when we’re just doing one or two tests, but not when we’re doing over 100000

70
Q

What can we do to overcome the multiple comparisons problem?

A

Use multiple comparison correction methods

71
Q

What is the most commonly used multiple comparisons correction method?

A

Bonferroni is the most commonly used- strong control over false positives, least sensitive.

72
Q

What are some other multiple comparison correction methods?

A

Gaussian Random Fields- strong control over false positives, somewhat conservative.

False-Discovery Rate- admits false positives, more lenient

Cluster Enhancement

73
Q

What is the problem with Bonferroni?

A

Bonferroni is too harsh. It assumes all tests are independent and they clearly aren’t in a fMRI study, each test is not an independent test, its done on the same subject and its likely that neighbouring voxels share the same value so are correlated to an extent

74
Q

What is the main weak point of fMRI?

A

This inference is not brilliantly worked out - we don’t have a great way for acurately assigning which voxels have activated as its an incredibly complicated process

75
Q

What is first level analysis?

A

Analysis of fMRI data in individual subjects with statistical inferences about which regions activate in those subjects

76
Q

What is second level analysis?

A

Analysing groups of data

Also known as higher level anaylsis

77
Q

What is the aim/ purpose of second level (group) analysis?

A

To find out if this group activate on average

Which voxels activate in that group

78
Q

What is essential for making voxel-by-voxel comparisons?

A

Registering all of the images into a common anatomical coordinate system, usually a “standard space”

79
Q

Describe the steps for second level analysis.

A
  1. Spatial normalisation
  2. Template- registering the images into a “standard space” - then every voxel in every subject should be nominally located in the same place and so we can then do a second level analysis
  3. Second level analysis - what is the average activation across the group, is activation consistent
80
Q

From first level analysis what do subjects have associated with then?

A

Each subject has a beta value and an error value from the first level analysis of GLM

81
Q

What are the two ways we can formulate the second level GLM?

A

Fixed effects model
Mixed effects model

82
Q

What is the fixed effects model?

A

The fixed effects model ignores the group variance, only considering the variance of the original MRI data

Only relevant to those specific subjects but dont consider between group variance

83
Q

What is the mixed effects model?

A

The mixed effects model considers the group (between subject) variance as well as the variance in the MRI data

84
Q

How do fixed effects and mixed effects models generalise to the population?

A

The mixed effects analysis generalises to the population as a whole, while the fixed effects analysis is only applicable to the specific subjects studied

In practice, this means there are very few occasions where we would want to perform a fixed effects analysis

85
Q

What pre-processing is needed?

A

Brain extraction
Motion correction
Slice timing
Spatial smoothing
Temporal filtering
Unwarping
Registration

86
Q

What does brain extraction do?

A

Remove non-brain tissue for registration and masking purposes

87
Q

What does motion correction do?

A

Ensures consistent anatomical coordinates between images

88
Q

What does slice timing do?

A

Ensures consistent acquisition timing (use temporal derivative in practice)

89
Q

What does spatial smoothing do?

A

Improve SNR and GRF conformity

90
Q

What does temporal filtering do?

A

Highpass filtering to remove slow signal drifts

91
Q

What does unwarping do?

A

Corrects for B0 inhomogeneity induced image distortion

92
Q

What does registration do?

A

Registers images into a consistent coordinate frame (e.g. standard space for group analysis)