Lecture 1 Part 2: Functional Magnetic Resonance Imaging (fMRI)

Required Reading

  • 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.

Further Reading

  • Ward, J. The student's guide to cognitive neuroscience. Chapter 4. Shelfmark: 612.8233 WAR

Web Resources

  • Animation 2.1: Magnetic Resonance Imaging (MRI) - SLMENS

    • MRI animations with narration.

  • fMRI for Newbies

The Essential Question

  • What do those blobs (in fMRI images) represent?

Historical Images of the Brain and Mind

Brain Structure

  • Some images are still in use today.

  • Often interpretive, labor/skill intensive.

  • Examples:

    • Christopher Wren (1664)

    • Brodmann cytoarchitecture maps

Brain Function

  • Phrenology (19th Century ).

  • Electrophysiological mapping (1957).

Magnetic Resonance Imaging (MRI) Physics

  • 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.

How is an Image Acquired?

  • 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).

Mapping Lesions

  • A new extension of an older technique for mapping structure to function.

  • Images make complex patterns of data intelligible.

  • Imaging the Structure of the Brain.

Mapping Normal Variability in Structure

  • Map second-order structure, such as sulcal asymmetry.

  • Color-coded maps for statistical significance of gyral/sulcal variability.

  • Large numbers of neurologically-normal participants.

  • Thompson, P. M. et al. (1998). Cereb. Cortex, 8, 492–509.

Mapping Change Over Time

  • Examples:

    • Grey matter volume.

    • Commissural myelination.

    • Myelination by age (months).

Functional MRI (fMRI)

  • Brain Mapping Publications exhibit an exponential rise.

The Blood Oxygen Level Dependent (BOLD) Response – the basis of fMRI

  • Relative levels of de/oxyhemoglobin change from regional cortical activity.

  • Momentary decrease in blood oxygenation immediately after neural activity increases, known as the “initial dip” in the hemodynamic 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”.

Importance of Hemoglobin

  • De/oxyhemoglobin 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.

fMRI Experiment Components

  • Magnet

  • Video projector

  • Video screen

  • Gradient coil

  • Radio-frequency coil

  • Prism glasses

  • Headphones

  • Radio-frequency amplifier

  • Button response box

  • Amplifiers control magnetic field in three dimensions

  • Stimulus control computer

  • Spectrometer control computer

fMRI – Experimental Logic

  • Cognitive subtraction originated with reaction time experiments (F. C. Donders).

  • 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.

  • BUT 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 a discrimination between light color.

    • T3 – T2 = time to make a decision.

Experimental Design 1: Block Design fMRI

  • Alternating blocks of task and rest.

Experimental Design 2: Event-Related fMRI

  • Allows (pseudo)random presentation of stimuli.

  • Allows retrospective coding of events (e.g., memory experiments).

fMRI Preprocessing and Analysis

  • Steps:

    • Realignment & motion correction.

    • Smoothing.

    • Normalization.

    • Image Data.

    • Anatomical Reference.

    • Parameter Estimation.

    • Kernel Data.

    • Design Matrix.

    • Statistical Parametric Mapping.

    • Contrasts.

Analysis - Modelling the data fMRI signal (data)

  • 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).

Voxel-wise Analysis

  • 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 colored in according to the size of T.

Interpreting Blobs

  • 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.

What has functional brain imaging told us?

  • 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?

  • Cabeza & Nyberg. (2001). Journal of Cognitive Neuroscience 12:1, pp. 1–47.

New Directions

  • 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.