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Behavioural and cognitive neuroscience lecture 1 (fMRI (Basic features…
Behavioural and cognitive neuroscience lecture 1
fMRI
=functional magnetic resonance imaging
allows us to conclude something about cognitive processes based on neural activity
Basic features
strong magnetic field 1.5-9 Tesla magnetic field
Non-invasive and safe
only danger is if you have any metal on you or in the room
head is placed in a helmet
can see a projection
can use buttons/joystick to get participant responses
head coil receives radio waves
functioning
want to measure brain activity therefore functional MRI
oxygenated blood gives better signal quality
active areas of brain receive more o2
needed for glucose metabolism
brain always has over suppy of oxygen in regions (hence why you can see it in blood not cells)
Indirect measure of brain activity, not actually measuring activity
Measurements
BOLD signal
measuring BOLD when performing task vs not
Mass univariate analysis
repeated measurement of brain activity for whole brain during task
statistical parameter mapping
fitting general linear model to brain activity at each point (voxel)
compare how much the model fits and different time points?
Issues
Reverse inference
If region X was active during cognitive process Y can't conclude a that because region X, process Y is occurring
region X may fcn for numerous tasks
task may not be measuring correct cognitive process
If experimental method fails to manipulate region of interested don't learn very much
Multi-demand network
whole brain regions that work together for a particular task just at different proportions
Multivariate Pattern Analysis (MVPA)
Read out/decode content of cognitive processes
understanding what brain region represents during cognitive activity
Technique
Smallest unit of measurement =voxel
=average activity of neurons in that regio
Cells tend to cluster together that do similar activity
therefore different voxels will respond to differing degrees when doing a task
=small response biases
make brain activation patterns more meaningful
Haxby et al. 2001
correlations between activation patterns within objects compared to between
correlations within category should be higher than between (eg face 1 vs face 2 as opposed to face 1 vs basket ball)
doesn't necessarily rely on specific regions but more patterns of activity (distributed code)
Investigates similarity of distributed activation patterns rather than just activation of a particular region
can use "search light pattern"
helpful if you don't know where to look
test every section of the brain independently
Applications
Decoding hidden intentions
participants presented with numbers on screen and had to decide beforehand whether they would add or subtract
mPFC more activated when making decisions
decoding invisible objects
objects you can't report but brain detects
stimulus shown briefly and then followed by mask
extracted patterns from visual cortex
V1-v3
compared visible objects to invisible objects
found activity in V1 still occured
concluded that something needs to happen to info in V1 to be able to consciously detect object
Decoding changes in conscious perception of an object
eg binocular rivalry
stimulus does not change but conscious perception of it does
button press for what colour they perceive
Primary visual cortex predict subjective percept
Decoding things in imagination
participants asked to imagine X or O on a screen and compared to actually seeing X or O
imagination can be predicted from same regions that predict
decoding content of dreams
looking at visual content in dreams