Lecture 4: Neuroimaging Flashcards
What is an MRI scanner?
originally developed for structural imaging, more recently also used for functional brain imaging (functional Magnetic Resonance Imaging – fMRI)
What are the benefits of MRI scanners?
- Non-invasive
- High spatial resolution
- Reasonably affordable
What are the magnetic details of an MRI?
- Very 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!!!
What is a Blood Oxygen level Dependent (BOLD) signal?
- When neurons become active, blood flows to the part of the brain to provide oxygen to fuel the cells.
- Hemoglobin (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
What does an MRI scanner detect?
Small changes in the magnetic field
What does an fMRI do?
Measure blood flow
- the magnetic properties of oxygenated vs deoxygenated blood
What are the steps from raw data to functional brain activation maps?
- Design a task to be used in the scanner
- Collect some data
- Preprocess the data
- Data analysis
- Interpret your results
What is an experimental design of an fMRI?
- BOLD signal is arbitrary: It has no stable baseline.
- 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.
What is a good baseline condition for an fMRI?
- One that differs from the experimental condition only by the process of interest
- E.g. Face processing.
- Baseline = scrambled faces
What are block vs event-related designs?
- 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
What are the disadvantages of block designs?
- Block designs often group together lots of trials
- Limitations (Psychological)
- 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
What is an alternative to a block design?
Event related design
What is an event related design?
Trials of different conditions are randomly intermixed and occur close together in time
What are the advantages of event -related designs?
In freeing us from the necessity of block designs, event-related fMRI enables us to design more complex and novel experiments.
- 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 artifacts from activation
- Dissociate components of delay tasks
- Mental chronometry
How is best to collect data in an fMRI?
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)
How to preprocess the data in an fMRI?
Correcting for non-task-related variability in experimental data
- Getting rid of the ‘noise’
What are the steps of preprocessing the data of an fMRI?
- High pass filtering
- Remove low frequency oscillations such as scanner drift that introduce noise into your data
- Standard low pass filter is approx 120 secs
- Motion correction
- Keep track of how the head moves through relaxation versus stress
- Slice time correction
- Temporal difference between slices collected so need to shift back into alingment
- Coregistration
- align functional data with structural data
- Normalisation
- Standardised Space- Montreal Neurological Institute (MNI) space
- Combination of 352 MRI scans on normal controls
- Why normalize? So that we can compare activation across subjects and do group analyses.
- Montreal Neurological Institute (MNI) space
- Spatial smoothing
- Application of Gaussian kernel
- Why smooth?
- 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.
How best to analyse data in an fMRI?
- 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 – resulting beta values tell us how close this correlation is.
- 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)
What are the effects of difference thresholds on brain activation maps?
- Where do we ‘draw the line’?
- Standard alpha level in psychological research is p < 0.05, i.e. we accept a 5% rate of false positives
What is the correlation for multiple comparisons?
- 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)
What are the two difference approaches to data analysis?
- Whole brain analysis - as we have just seen, examine effects on a voxel by voxel basis across the whole brian
- Region of interest (ROI) analysis - restrict out anaylsis to a particular brain region
What are the advantages of a whole brain analysis?
- Requires no prior hypotheses about areas involved
- Includes entire brain
What are the disadvantages of a while brain analysis?
- 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
What are the advantages of a region of interest analysis?
- 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.
What are the disadvantages of a region of interest analysis?
- Easy to miss things going on elsewhere in the brain.
- Not always simple how to define ROIs.
How do you interpret the results from an fMRI?
From raw data to functional brain ‘activation’ maps
What are the limitations of an fMRI?
- 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.
