factors for Advantages & Disadvantages different methods
Spatial resolution —> how fine grained we can look at brain
temporal resolution —> how frequently the brain's activity is sampled
interference —> interfering with brain activity in certain areas temporarily -> more than mere correlation
mobility
good temporal resolution
EEG
MEG
ERP
& bad spatial resolution
good spatial resolution
CT
MRT
PET
fMRI
Fmri immobile
DTI immobile
MEG less mobile
EEG mobile
interference
TMS
fMRI construction
main magnet —> constant magnetic field
along length of MRI scanner—> high field strength
gradient coils —> spatial encoding
additional magnets with varying magnetic fields
along different spatial axes
radio frequency Coil —> disturbs protons from alignment
fMRI physics
magnet influences protons in nucleus of hydrogen molecules
main magnet aligns protons along the direction of magnetic fielt —> spin around direction of magnetic field
gradient coil induces another short impulse aligning protons in antiparallel way
return time differs for each type of matter (tissue)
fmri physiological bases
oxygen is natural contrast agent
oxygenated hemoglobin —> blood less magnetic —> less distortions —> stronger signal
desoxygenated hemoglobin —> blood more magnetig —> more distortion —> weaker signal
hemoglobin transports oxygen to activated neural regions
BOLD Contrast
Blood oxygenation level dependent contrast
more activity in brain region —> more metabolism —> more oxygen needed —> more oxygenated blood delivered
BOLD signal is INDIRECT —> does not measure activity directly, therefore delayed peak (4-6 seconds to maximum) and long time to return to rest level (up to 30 sec)
signal is slow —> 2 seconds per slice in worst spatial resolution
fMRI summarized
setting is challenging -> noise, nothing magnetic can go in, takes 20-40 min
tradeoff between temporal & spatial resolution
voxel sizes 3x3x3mm
Outcome: large & complex 4D datasets
100.000 voxels
1 set (1 volume) takes about 2 sec
typically: more than 40 slices per volume
fmri Data analysis step 1: classical subtraction technique
Problem: always multiple activated brain regions
—> how to identify the regions that are involved in specific mental process?
measurement under stimulation
measurement in control condition
generating a difference image
Classical subtraction technique
originally subtraction
today: statistics
fmri Data analysis step 2: between subject averaging
problem: sizes of brains & anatomy of gyri and sulci differ between subjects
—> how to identify whether area activated across all participants
spatial normalization to standard brain
outdated: Atlas of Talairach & Tournoux 1988
basis: right hemisphere of an older lady —> much smaller than standard brain
Montreal Neurological Institute MNI brain
average brain across 152
Data analysis 3: between subject averaging
Problem: noisy data
—> how to conduct analyses across subjects in noisy data
3D spatial smoothing before statistics via 3D Gauss curve
single voxel results —> sometimes single voxels seem to not be activated —> if that happens across multiple participants effect for whole area may not be significant
therefore smoothing in order to be able to perform group statistics
—> Consequence: weakening of the signal but improves chance of finding group statistics
modern pipelines of fmri data analysis
motion correction —> realignment when participants head moves
activation patterns in typical studies are not strengths per se but may be p-values —> other statistical values!
borders are the threshholds of significance
General Linear model
y=x*ß+E
Y: Matrix of BOLD Signals (time x voxels)
X: Design Matrix (time x regressors) Experimental design & model of BOLD response
ß: Matrix Parameters (regressors x voxels) —> reflected in brain maps
E: Error Matrix (time x voxels)
Statistics of fMRI Analysis
preprocessing
1st level analysis: GLM for every participant —> maps of statistical parameters = one ß-map per voxel
2nd level analysis: group level analysis
t-test, ANOVAs, regression
one test per voxel —> statistically problematic, might be up to 10.000 t-tests
Software packages used fmri statistics
SPM Matlab based
FSL
AFNI
Brain Voyager
Python
Experimental designs for functional MRI
Blocked designs
Event-related or single-trial designs
repeated presentation of same stimulus / same type of stimulus
two ways
1 experimental condition multiple blocks
1 experimental condition per block
often relatively fast timing: 1 block typically 20-60 seconds
Blocked designs advantages
statistical power because many repetitions
greater efficiency —> more stimuli in shorter time
event related/single trial designs
randomized sequence of stimuli
multiple experimental conditions
variable intervals between trials
Advantages & Problems single trial/event related designs
Advantages:
psychologically better paradigm because subjects perform decision once —> evoked response by single trial instead of whole block
trial-specific activation can be estimated
Disadvantage:
Bold signals are slow, we need time between stimuli to separate different signals
—> can be solved by optimal randomization of trials
Design considerations when planning
efficiency vs. event-related activation
psychological plausibility / adequateness
duration
behavioral correlates
statistical power
Fundamental issues in fmri
dead salmon —> without proper statistical correction (e.g. correction for multiple comparisons) fmri data can show false positives —> brain activity where there is none
multiple comparisons problem in fmri —> each voxel compared to other brains —> false positives
unexpectedly high correlations in social neuroscience studies with fmri
correlations of 1 unlikely —> brains responses are influenced by multitude of factors
problem of non-independence (circularity) —> hypothesis & data selection selecting significantly activated voxels and then correlations on them
reverse inference not possible —> interpretation ist limited
just because amygdala is activated —> person is not necessarily scared
Default Mode Network
Resting state activity
constant deactivation of same brain areas under task conditions in 9 PET studies
Raichle proposal 2006: task positive and task negative network (Default Mode Netwirk)
task positive: active during task performance, deactivated during rest
But: there are multiple resting state networks (7)
resting state networks
visual
somatomotor
dorsal attention
ventral attention
limbic
frontoparietal
default mode network
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