Lecture Notes: Functional Magnetic Resonance Imaging (fMRI)

Baars, B., & Gage, N. M. (2013). Fundamentals of cognitive neuroscience: a beginner's guide. Academic Press. Chapter 3.
Savoy, R. (2001). History and future direction of human brain mapping and functional neuroimaging. Acta Psychologica, 107, 9-42.
Ward, J. The student's guide to cognitive neuroscience. Chapter 4.
Note: Slide 7 contains an image of open brain surgery.

Animation 2.1: Magnetic Resonance Imaging (MRI)
- SLMENS
- MRI animations with narration
- http://sites.sinauer.com/cogneuro2e/animations02.01.html
fMRI for Newbies (from which I've grabbed images)
- http://www.fmri4newbies.com/

Uses a magnetic field (B_0) and radio energy to produce an image.
A large magnet (50,000 times Earth's magnetic field) aligns nuclei that have a net magnetic moment (from odd number of protons/neutrons), e.g., H_2O.
Nuclei absorb and re-emit radio frequency energy.

Nuclei spin around the main magnetic field.
RF pulse (oscillating magnetic field) tips M out of alignment with B_0 and synchronizes the phase of spins.
M gradually returns to alignment, and spins lose phase coherence.
These changes are detected as the ‘MRI signal’.
Spins oriented with or against B_0.
M = net magnetization.
M deflected by RF pulse.
M Applied Magnetic Field (B_0).

Examples:
- grey matter volume.
- commissural myelination.
- Myelination by age (mths).
- Grey-matter development.

Brain Mapping Publications: an exponential rise.
References to journals and abstracts related to brain mapping, illustrating the growth in the field.

Relative levels of de/oxyhaemoglobin change from regional cortical activity.
Momentary decrease in blood oxygenation immediately after neural activity increases, known as the “initial dip” in the haemodynamic response function (HRF).
Followed by a period where the blood flow increases to a level which overcompensates for the increased demand.
Regional blood oxygenation actually increases following neural activation.
The blood flow peaks after around 6 seconds and then falls back to baseline, often accompanied by a “post-stimulus undershoot”.

De/oxyhaemoglobin have different magnetic properties.
Local field strength is affected by relative levels.
This affects the local signal in the image.
Increased signal is obtained from ‘active’ regions.
Take Home Points:
- It’s complicated!!!
- Change to the image is far removed from neural events.

Originated with reaction time experiments (F. C. Donders, a Dutch physiologist).
Measure the time for a process to occur by comparing two reaction times, one which has the same components as the other + the process of interest.
Assumption of pure insertion: Can insert a component process into a task without disrupting the other components.
Task difficulty (a confound) may differ between conditions, resulting in changed attention.
Examples:
- T1: Hit a button when you see a light.
- T2: Hit a button when the light is green but not red.
- T3: Hit the left button when the light is green and the right button when the light is red.
- T2 – T1 = time to make discrimination between light color.
- T3 – T2 = time to make a decision.

Block model convolved with HRF
We regress our model of our experiment against the signal-change data.
Least-squares best-fit analysis of the data that best estimates the amplitudes of the predictors (minimize residuals).
A T-test tests whether the slope of the function differs from 0 (i.e. flat).

This analysis is done independently at every voxel (3D pixel/volume) - i.e. thousands of times!
Contrasts allow one to test for voxels where activation in one condition is greater than another.
Voxels with significant T statistics can then be coloured in according to the size of T.
The essential question – what do those blobs represent?

Blobs are clusters of significant statistics for either a main effect or a contrast between two sets of regressors at each voxel.
Shows areas where the signal change was significantly predicted by the model (or where the degree of prediction differed between contrasted conditions).
Importantly…! This is the end result after much preprocessing and analysis.
Change in the signal is due to regional hemodynamics.
Thus, activations are distantly related to the underlying neurological events.

Identified functional areas – e.g. Fusiform face area – stimuli; Anterior Cingulate - attention.
Corroborated findings from other methods (e.g. hippocampal involvement in memory).
Allowed the localization of function from undamaged brains.
Meta-analyses bring some order to the flood of data, but are these maps from multiple different studies any more useful than the 1957 electrophysiology map (slide 10)?
Cabeza & Nyberg. (2001). Journal of Cognitive Neuroscience 12:1, pp. 1–47.

Functional-connectivity analyses: calculate correlations between activations in different areas.
Dynamic causal modelling: explicit models of distributed networks are tested to see which best fits the observed data.
Both these techniques investigate distributed processing and overcome some of the limitations of lesion studies and earlier fMRI studies.
- Functional-connectivity analysis.
- Dynamic causal model.