Neuroimaging Flashcards
1
Q
MRI scanner
A
- Incredibly strong magnet: usually 1.5 - 7 tesla
- 1.5 tesla = 15,000 gauss
- Earth’s magnetic field strength = 0.5 gauss
- Exeter MRI scanner = 30,000 X earth’s magnetic field strength
- NO METAL IN THE SCANNER!!!
2
Q
what does BOLD stand for?
A
Blood Oxygen Level Dependent
3
Q
BOLD signal
A
- When neurons become active, blood flows to the part of the brain to provide oxygen to fuel the cells.
- Haemoglobin (the iron-containing oxygen transporting protein present in blood) differs in how it responds to magnetic fields, depending on whether it has a bound oxygen molecule
- The MRI scanner, which is basically a giant magnet, detects these small changes in the magnetic field.
- With fMRI we are not directly measuring brain activation.
- We are measuring blood flow (more precisely, the magnetic properties of oxygenated vs deoxygenated blood)
4
Q
from raw data to functional ‘activation’ maps
A
- Design a task to be used in the scanner
- Collect some data
- Preprocess the data
- Analyse the data
- Interpret your results
5
Q
fMRI experimental design
A
- BOLD signal is arbitrary: It has no stable baseline - means that the baseline BOLD signal may be something in one session for one subject, and completely different for the same subject on the next day
- Therefore, the most important aspect of any fMRI experiment is that we have to provide both a) an experimental condition and b) a baseline condition.
- Resulting functional brain map reflects the difference between these two conditions.
6
Q
what is a good baseline?
A
- One that differs from the experimental condition only by the process of interest
- In general, having the subject rest, i.e. do nothing, is not a good baseline, because there will be so many things differing between that and your experimental condition that you never know what process your brain activation reflects.
- Ideally you want a baseline that differs from the experimental condition only according to the cognitive process you’re interested in.
7
Q
block designs
A
- BOLD signal is slow - peaks 4-5 seconds after stimulus onset and takes around 16 seconds to return to baseline
- All fMRI experiments originally employed block designs - long periods of alternating task/baseline performance
8
Q
disadvantages of block designs
A
- Block designs often group together lots of trials
- Highly predictable occurrence of stimuli: subjects know what is coming and may alter strategies accordingly (not always a pro)
- Inflexible for more complex tasks: impact of oddball stimuli? or stimuli or events that occur uncontrollably?
- Ecological validity. Does blocking trials change the psychological process you are interested in?
- Can’t separate trials by performance - e.g. to look at activation associated with correct vs incorrect response
9
Q
event related design
A
Trials of different conditions are randomly intermixed and occur close together in time
10
Q
advantages of event-related design
A
- Flexibility and randomisation - eliminate predictability of block designs - avoid practice effects
- Post hoc sorting - (e.g., correct vs. incorrect, aware vs. unaware, remembered vs. forgotten items, fast vs. slow RTs)
- Can look at novelty and priming - rare or unpredictable events can be measured - e.g., P300
- Can look at temporal dynamics of response - Dissociation of motion artefacts from activation - Dissociate components of delay tasks - Mental chronometry
11
Q
collect some data
A
- 2 to 3 seconds to collect a single ‘volume’
- To reference each point in the brain in 3D space we divide image into cubes or ‘voxels’ - Typical voxel size = 3x3x3 mm - Refer to each voxel with coordinates (x, y, z)
12
Q
preprocess the data
A
- Correcting for non-task-related variability in experimental data - Getting rid of the ‘noise’
1. High pass filtering
2. Motion correction
3. Slice time correction
4. Co-registration
5. Normalisation
6. Spatial smoothing
13
Q
high pass filtering
A
- Remove low frequency oscillations such as scanner drift that introduce noise into your data
- Standard low pass filter is approx 120 secs
- The consequence of this is that we can’t contrast events more than a couple of minutes apart because they will be wiped out by our filter.
14
Q
slice time correction
A
- Because it takes a few seconds to collect a whole brain volume, different slices may be acquired up to a couple of seconds apart
- This means that our estimate of the haemodynamic response will be incorrect for slices acquired later
- Slice timing simply corrects this by moving them earlier in time
15
Q
coregistration
A
Each functional image is aligned with the subject’s structural image so that functional maps can subsequently be overlaid and anatomical landmarks identified
16
Q
normalisation
A
- Ultimately we want to do statistics on group activation maps, so we have to get all the brains into a standard space
- To do this we use complex algorithms to warp each subject’s brain into the shape of a template brain
17
Q
normalisation - standardises space
A
- Montreal Neurological Institute (MNI) space - Combination of 352 MRI scans on normal controls
- Can compare activation across subjects and do group analyses.
18
Q
smoothing
A
Application of Gaussian kernel - allows closer neurons to influence the value more strongly than distal ones.
19
Q
why smooth?
A
- Because neurons do not fire in isolation.
- If a neuron fires, neurons close to it will tend to fire as well.
- Smoothing attempts to model the data according to this property of neurons.
20
Q
analysis
A
- Multiple regression is used to determine the effect of a number of independent variables (our conditions) on a single dependent variable (brain activation)
- For each voxel, we use multiple regression to estimate how closely the BOLD signal correlates with the timecourse of each condition.
- Finally perform a contrast - simply a t-test comparing beta condition 1 beta values to condition 2 beta values
- Calculating the t-statistic for each of the 130,000 voxels in the brain is equivalent to asking “in which regions is activation greater in condition 2 than in condition 1?”
- Raw t-map indicates the magnitude of the t-statistic using a colour scale - Yellow = bigger t values
- The threshold we apply defines what we accept as a significant difference in activation between our 2 conditions
- Essentially arbitrary (as it is in any statistical procedure)
- Standard alpha level in psychological research is p < 0.05, i.e. we accept a 5% rate of false positives
21
Q
multiple regression
A
Simply a way to find out the explanatory effect of a number of independent variables on a dependent variable.
22
Q
correction for multiple comparisons
A
- Brain images divided into up to 130,000 voxels = 130,000 individual t-tests
- Chance of a type 1 error (false positive) increases with every test performed
- With an alpha level of p < 0.05 (standard used in psychological research) with one t-test we have a 5% chance of type 1 error.
- With 100 independent t-tests, we have a 99.4 % chance of type 1 error
- In other words, with 130,000 t-tests and an alpha level of 0.05 we are guaranteed to find some significant voxels simply by chance.
- Very important to correct for multiple comparisons - adjust alpha level (p-value)
23
Q
different approaches to data analysis
A
- Whole brain analysis
2. Region of Interest (ROI) analysis
24
Q
advantages of WBA
A
- Requires no prior hypotheses about areas involved
- Includes entire brain
25
Q
disadvantages of WBA
A
- Can lose spatial resolution with inter-subject averaging
- Can produce meaningless “laundry lists of areas” that are difficult to interpret
- Depends highly on statistics and threshold selected
- Multiple comparisons problem
26
Q
WBA
A
examine effects on a voxel by voxel basis across the whole brain
27
Q
ROI analysis
A
Restrict our analysis to a particular brain region
28
Q
advantages of ROI analysis
A
- Hypothesis driven - avoids meaningless ‘laundry lists’ of activated regions.
- Avoids multiple comparisons problem - data summarised in a single number per subject reflecting mean activation across voxels in ROI.
- Simple - data can be exported and treated as any other type of data, requiring no special software for further analysis
- Generalisable - data can easily be compared across studies, e.g. for meta analysis.
29
Q
disadvantages of ROI
A
- Easy to miss things going on elsewhere in the brain.
- Not always simple how to define ROIs
30
Q
limitations of fMRI
A
- fMRI data is correlative, therefore we can’t say that a region activated during a task/function is essential for that task/function.
- Need converging evidence from TMS/neuropsychology to make stronger inferences about causation.
- Temporal resolution of fMRI is low
- Need converging evidence from EEG/TMS to provide finer grained temporal information.
