1. functional magnetic resonance imaging (fMRI)

2. Basic principles magnetic field: 1.5T, 3T, 7T (earth: 0.0001T)
structural (anatomical) vs. functional imaging
functional: sacrifise spatial for temporal resolution (6 min vs. 2 sec)
radio frequency pulse (T1: anatomical, T2*: functional)
hydrogen nuclei in water (different amounts in different tissues gives the structural picture)
functional by detecting changes in blood oxygenation associated with neural activity: neuron consumes oxygen: oxyhaemoglobin => deoxyhaemoglobin (strong paramagnetic agent, produces distortions in magnetic field)
BOLD: blood oxygen-level-dependent contrast (not blood flow like PET)
HRF: haemodynamic response function (they way BOLD signal evolves over time in response to an increase in neural activity)
use EPI (fast acquisition technique) to take BOLD contrast weighted echoplanar images
TR, volume, number of slices, whole/partial brain coverage, voxel (~105)

3. The HRF (~linear system/convolution possible)

4. some characteristics of fMRI
indirect measure (downstream consequence of neural activity – not produced by neurons themselves)
non-invasive (versus PET)
good spatial resolution (~ 1mm)
shows all participating areas at once (e.g. to direct electrophysiology)
repeated scanning is no problem
no precision of measurement in real time (~ 6 s)
poor temporal resolution 1-4 s (unlike MEG, EEG)
no differentiation between neuro-electrical excitation and inhibition?
relatively weak signal increases (1-3% with 1.5T)
problematic for auditory, speech, temporal & orbitofrontal cortex
expensive to acquire, use & maintain
personnel intensive: Neuro-radiologist, Nurse, Physicist, Statistician, Computer Scientist, Cognitive Psychologists

6. How do we do fMRI? (cognitive) subtraction method (contrast, t-contrast, ttest)
an example:
brain activation during moving dot stimuli (experimental condition)
brain activation during stationary dot presentation (control condition)
brain activation due to motion (unique part of experimental cond.)

8. pure insertion: we assume that adding an extra component does not affect the operation of the other ones
interactions: above assumption not true
simple subtraction: measuring differences in brain activity between two or more conditions
several conditions, control condition, baseline, fixation
factorial designs (usually 2x2)
parametric designs: measuring associations (e.g. correlation) between brain activity and changes in the variable of interest (continuous, not categorical – no baseline/subtraction necessary)

9. basic experimental setup protocols:
block design (stimuli that belong together in one condition are presented together in a block) vs. event related designs (different stimuli/conditions are interspersed with each other)
more sensitivity /statistical power vs. flexibility (proper psychophysical designs, cases with no prior knowledge e.g. hallucinations)

10. data analysis (e.g. SPM): pre-processing of data & statistical testing (single voxel)
pre-processing: realignment (correction for head movement), normalisation (Talairach coordinates), smoothing (better SNR, group analysis)

11. statistical testing
for each single voxel: compare between conditions (contrast, ttest), set some statistical threshold for significance (p value), correct for multiple comparisons (60k), corrected (0.05) vs. uncorrected (0.001) statistics, region of interest (ROI) vs. whole brain analysis, small volume correction, clustering
group analysis (2nd level): fixed effects (6 but problematic), random effects (12), individual-subject results (4)
interpretation: non-specific factors (e.g. attention, low level effects, eye-movements)
input, processing, or output?
excitation of inhibition?
‘brain reading’ possible? (read perception, feeling, intention, racial bias etc.)

12. Area V1 has a retinotopic mapf o v e a
left visual cortex
right visual field 90 45
inferior vertical meridian 40
270 f o v e a 20
315 10 HM
0 = HM 10 45 90
superior vertical meridian
1 cm
315
270

13. ROI definition in visual studies
which visual area? human retinotopy using fMRI

15. The GLM model – why? what do we do with the activation signal?
why no just take averages?
what about the time-lag?
lets time-shift!
what about the shape of the HRF?
lets convolve!
but how do we take the average?
remember linear regression?
predictors (linear combination of)?
minimising SSE?
so lets use GLM 
predictors, betas, error, t-contrast, t-value, p-value
(beta1-beta2)/error
the problem of overfitting

16. Convolution with the HRF

21. Matrix multiplication

22. The activation of a single voxel over time (n time-points, p predictors)

24. statistical thresholding visualisation