1. 1. Introduction to fMRI
2. Basic fMRI Physics
3. Data Analysis
4. Localisation
5. Cortical Anatomy

2. 1. Introduction to fMRI

3. MRI vs. fMRI Functional MRI (fMRI) studies brain function.
MRI studies brain anatomy.

4. Brain Imaging: Anatomy
CAT
Photography
PET
MRI
Source: modified from Posner & Raichle, Images of Mind

5.  neural activity   blood oxygen   fMRI signal
MRI vs. fMRI
MRI
fMRI
high resolution
(1 mm)
low resolution
(~3 mm but can be better)
one image
fMRI
Blood Oxygenation Level Dependent (BOLD) signal
indirect measure of neural activity: active neurons shed oxygen and become more magnetic increasing the fMRI signal
many images
(e.g., every 2 sec for 5 mins)
 neural activity   blood oxygen   fMRI signal

6. fMRI Activation Flickering Checkerboard
OFF (60 s) - ON (60 s) -OFF (60 s) - ON (60 s) - OFF (60 s)
Time 
Brain
Activity
Source: Kwong et al., 1992

7. PET and fMRI Activation
Source: Posner & Raichle, Images of Mind

8. fMRI Setup

9. fMRI Experiment Stages: Prep
1) Prepare subject
Consent form
Safety screening
Instructions
2) Shimming
putting body in magnetic field makes it non-uniform
adjust 3 orthogonal weak magnets to make magnetic field as homogenous as possible
3) Sagittals
Take images along the midline to use to plan slices
Note: That’s one g, two t’s

10. fMRI Experiment Stages: Anatomicals
4) Take anatomical (T1) images
high-resolution images (e.g., 1x1x2.5 mm)
3D data: 3 spatial dimensions, sampled at one point in time
64 anatomical slices takes ~5 minutes

11. Slice Terminology SAGITTAL SLICE IN-PLANE SLICE VOXEL
Slice Thickness
e.g., 6 mm
Number of Slices
e.g., 10
SAGITTAL SLICE
VOXEL
(Volumetric Pixel)
3 mm
6 mm
Matrix Size
e.g., 64 x 64
In-plane resolution
e.g., 192 mm / 64
= 3 mm
IN-PLANE SLICE
Field of View (FOV)
e.g., 19.2 cm

12. fMRI Experiment Stages: Functionals
5) Take functional (T2*) images
images are indirectly related to neural activity
usually low resolution images (3x3x5 mm)
all slices at one time = a volume (sometimes also called an image)
sample many volumes (time points) (e.g., 1 volume every 2 seconds for 150 volumes = 300 sec = 5 minutes)
4D data: 3 spatial, 1 temporal
first volume
(2 sec to acquire) …

13. Activation Statistics
Functional images
Time
fMRI
Signal
(% change)
ROI Time Course
Condition
~2s
Region of interest (ROI)
Condition 1
Statistical Map
superimposed on anatomical MRI image
Condition 2
Time
...
~ 5 min

14. Statistical Maps & Time Courses
Use stat maps to pick regions
Then extract the time course

15. 2D  3D

16. Design Jargon: Runs
session: all of the scans collected from one subject in one day
run (or scan): one continuous period of fMRI scanning (~5-7 min)
experiment: a set of conditions you want to compare to each other
condition: one set of stimuli or one task
4 stimulus conditions
+ 1 baseline condition (fixation)
Note: Terminology can vary from one fMRI site to another (e.g., some places use “scan” to refer to what we’ve called a volume).
A session consists of one or more experiments.
Each experiment consists of several (e.g., 1-8) runs
More runs/expt are needed when signal:noise is low or the effect is weak.
Thus each session consists of numerous (e.g., 5-20) runs (e.g., 0.5 – 3 hours)

17. Design Jargon: Paradigm
paradigm (or protocol): the set of conditions and their order used in a particular run
volume #1
(time = 0)
volume #105
(time = 105 vol x 2 sec/vol = 210 sec = 3:30)
run
epoch: one instance of a condition
first “objects right” epoch
second “objects right” epoch
epoch
8 vol x 2 sec/vol = 16 sec
Time

18. 2. Basic fMRI Physics

19. Recipe for MRI 1) Put subject in big magnetic field (leave him there)
2) Transmit radio waves into subject [about 3 ms]
3) Turn off radio wave transmitter
4) Receive radio waves re-transmitted by subject
Manipulate re-transmission with magnetic fields during this readout interval [ ms: MRI is not a snapshot]
5) Store measured radio wave data vs. time
Now go back to 2) to get some more data
6) Process raw data to reconstruct images
7) Allow subject to leave scanner (this is optional)
Source: Robert Cox’s web slides

20. History of NMR NMR = nuclear magnetic resonance
Felix Block and Edward Purcell
1946: atomic nuclei absorb and re-emit radio frequency energy
1952: Nobel prize in physics
nuclear: properties of nuclei of atoms
magnetic: magnetic field required
resonance: interaction between magnetic field and radio frequency
Bloch
Purcell
NMR  MRI: Why the name change?
less likely but more amusing explanation:
subjects got nervous when fast-talking doctors suggested an NMR
most likely explanation:
nuclear has bad connotations

21. History of fMRI MRI -1971: MRI Tumor detection (Damadian)
-1973: Lauterbur suggests NMR could be used to form images
-1977: clinical MRI scanner patented
-1977: Mansfield proposes echo-planar imaging (EPI) to acquire images faster
fMRI
-1990: Ogawa observes BOLD effect with T2*
blood vessels became more visible as blood oxygen decreased
-1991: Belliveau observes first functional images using a contrast agent
-1992: Ogawa et al. and Kwong et al. publish first functional images using BOLD signal
Ogawa

22. Necessary Equipment Magnet Gradient Coil RF Coil 4T magnet RF Coil
(inside)
Magnet
Gradient Coil
RF Coil
Source: Joe Gati, photos

23. The Big Magnet B0 x 80,000 = Very strong 1 Tesla (T) = 10,000 Gauss
Source:
1 Tesla (T) = 10,000 Gauss
Earth’s magnetic field = 0.5 Gauss
x 80,000 =
4 Tesla = 4 x 10,000  0.5 = 80,000X Earth’s magnetic field
Robarts Research Institute 4T
Continuously on
Main field = B0 B0

24. Source: http://www.simplyphysics.com/
Magnet Safety
The whopping strength of the magnet makes safety essential.
Things fly – Even big things!
Source:
Screen subjects carefully
Make sure you and all your students & staff are aware of hazzards
Develop stratetgies for screening yourself every time you enter the magnet
Source:
flying_objects.html

25. Subject Safety
Anyone going near the magnet – subjects, staff and visitors – must be thoroughly screened:
Subjects must have no metal in their bodies:
pacemaker
aneurysm clips
metal implants (e.g., cochlear implants)
interuterine devices (IUDs)
some dental work (fillings okay)
Subjects must remove metal from their bodies
jewellery, watch, piercings
coins, etc.
wallet
any metal that may distort the field (e.g., underwire bra)
Subjects must be given ear plugs (acoustic noise can reach 120 dB)
This subject was wearing a hair band with a ~2 mm copper clamp. Left: with hair band. Right: without.
Source: Jorge Jovicich

26. Protons align with field
Outside magnetic field
Protons align with field
randomly oriented
Inside magnetic field
spins tend to align parallel or anti-parallel to B0
net magnetization (M) along B0
spins precess with random phase
no net magnetization in transverse plane
only % of protons/T align with field M
longitudinal
axis
Longitudinal
magnetization
transverse
plane
Source: Mark Cohen’s web slides
M = 0
Source: Robert Cox’s web slides

27. fMRI Basics – The functional magnetic resonance imaging
technique measures the amount of oxygen in the blood in small
regions of the brain. These regions are called voxels. Neural
activity uses up oxygen and the vasculature responds by
providing more highly oxygenated blood to local brain regions.
Thus a change in amount of oxygen in the blood is measured,
and this is taken as a proxy for the amount of local neural activity.
The measured signal is often called the BOLD signal (Blood
Oxygen Level Dependent). Because neural activity is not
measured directly, one needs to think about what the indirect
signal really tells us, and how it’s spatial and temporal resolution
are limited. Certainly, however,the BOLD signal tells us
something about localization of neural
activity in the brain.

28. BOLD signal Blood Oxygen Level Dependent signal
neural activity   blood flow   oxyhemoglobin   T2*   MR signal
Mxy
Signal
Mo sin
T2* task
T2* control
Stask S
Scontrol
time
TEoptimum
Source: fMRIB Brief Introduction to fMRI
Source: Jorge Jovicich

29. BOLD signal
Source: Doug Noll’s primer

30. 3. DATA ANALYSIS

31. Hypotheses vs. Data Hypothesis-driven Data-driven
Examples: t-tests, correlations, general linear model (GLM)
a priori model of activation is suggested
data is checked to see how closely it matches components of the model
most commonly used approach
Data-driven
Independent Component Analysis (ICA)
no prior hypotheses are necessary
multivariate techniques determine the patterns in the data that account for the most variance across all voxels
can be used to validate a model (see if the math comes up with the components you would’ve predicted)
can be inspected to see if there are things happening in your data that you didn’t predict
can be used to identify confounds (e.g., head motion)
need a way to organize the many possible components
new and upcoming

32. Comparing the two approaches
Region of Interest (ROI) Analyses
Gives you more statistical power because you do not have to correct for the number of comparisons
Hypothesis-driven
ROI is not smeared due to intersubject averaging
Easy to analyze and interpret
Neglects other areas which may play a fundamental role
Popular in North America
Whole Brain Analysis
Requires no prior hypotheses about areas involved
Includes entire brain
Can lose spatial resolution with intersubject averaging
Can produce meaningless “laundry lists of areas” that are difficult to interpret
Depends highly on statistics and threshold selected
Popular in Europe
NOTE: Though different experimenters tend to prefer one method over the other, they are NOT mutually exclusive. You can check ROIs you predicted and then check the data for other areas.
Source: Tootell et al., 1995

33. Why do we need statistics?
MR Signal intensities are arbitrary
-vary from magnet to magnet, coil to coil, within a coil (especially surface coil), day to day, even run to run
-may also vary from area to area (some areas may be more metabolically active)
We must always have a comparison condition within the same run
We need to know whether the “eyeball tests of significance” are real.
Because we do so many comparisons, we need a way to compensate.

34. Two approaches: ROI A. ROI approach MT
Do (a) localizer run(s) to find a region (e.g., show moving rings to find MT)
Extract time course information from that region in separate independent runs
See if the trends in that region are statistically significant
Because the runs that are used to generate the area are independent from those used to test the hypothesis, liberal statistics can be used
Example study: Tootell et al, 1995, Motion Aftereffect
Localize “motion area” MT in a run comparing moving vs. stationary rings
Extract time courses from MT in subsequent runs while subjects see illusory motion (motion aftereffect) MT
Source: Tootell et al., 1995

35. 4. LOCALISATION

36. BRAIN LOCALIZATION AND ANATOMY with an emphasis on cortical areas
Why so corticocentric?
cortex forms the bulk of the brain
subcortical structures are hard to image (more vulnerable to motion artifacts) and resolve with fMRI
cortex is relevant to many cognitive processes
neuroanatomy texts typically devote very little information to cortex
Caveats of corticocentrism:
other structures like the cerebellum are undoubtedly very important (contrary to popular belief it not only helps you “walk and chew gum at the same time” but also has many cognitive functions) but unfortunately are poorly understood as yet
need to remember there may be lots of subcortical regions we’re neglecting

37. How can we define regions?
Talairach coordinates
Anatomical localization
Functional localization
Region of interest (ROI) analyses

38. Talairach Coordinate System
Individual brains are different shapes and sizes…
How can we compare or average brains?
Talairach & Tournoux, 1988
squish or stretch brain into “shoe box”
extract 3D coordinate (x, y, z) for each activation focus
Note: That’s TalAIRach, not TAILarach!
Source: Brain Voyager course slides

39. Rotate brain into ACPC plane
Find anterior commisure (AC)
Corpus Callosum
Fornix
Find posterior commisure (PC)
ACPC line
= horizontal axis
Pineal Body
“bent asparagus”
Note: official Tal sez use top of AC and bottom of PC
Source: Duvernoy, 1999

40. Deform brain into Talairach space
Mark 8 points in the brain:
anterior commisure
posterior commisure
front
back
top
bottom (of temporal lobe)
left
right
Squish or stretch brain to fit in “shoebox” of Tal system
ACPC=0
y>0
y<0 z y
AC=0
y>0
y<0 x
Extract 3 coordinates

41. Left is what?!!! L R R L Neurologic (i.e. sensible) convention
left is left, right is right L R
x = 0 - +
Note: Make sure you know what your magnet and software are doing before publishing left/right info!
Radiologic (i.e. stupid) convention
left is right, right is left R L
Note: If you’re really unsure which side is which, tape a vitamin E capsule to the one side of the subject’s head. It will show up on the anatomical image.

42. How to Talairach For each subject: For the group:
Rotate the brain to the ACPC Plane (anatomical)
Deform the brain into the shoebox (anatomical)
Perform the same transformations on the functional data
For the group:
Either
Average all of the functionals together and perform stats on that
Perform the stats on all of the data (GLM) and superimpose the statmaps on an averaged anatomical (or for SPM, a reference brain)
Averaged anatomical for 6 subjects
Averaged functional for 7 subjects

43. Talairach Atlas

44. Brodmann’s Areas Brodmann (1905):
Based on cytoarchitectonics: study of differences in cortical layers between areas
Most common delineation of cortical areas
More recent schemes subdivide Brodmann’s areas into many smaller regions
Monkey and human Brodmann’s areas not necessarily homologous

45. Talairach Pros and Cons
Advantages
widespread system
allows averaging of fMRI data between subjects
allows researchers to compare activation foci
easy to use
Disadvantages
based on the squished brain of an elderly alcoholic woman (how representative is that?!)
not appropriate for all brains (e.g., Japanese brains don’t fit well)
activation foci can vary considerably – other landmarks like sulci may be more reliable

46. Anatomical Localization Sulci and Gyri
gray matter
(dendrites & synapses)
white matter
(axons)
FUNDUS
BANK
GYRUS
SULCUS
pial surface
gray/white border
SULCUS
FISSURE
GYRUS
Source: Ludwig & Klingler, 1956 in Tamraz & Comair, 2000

47. Variability of Sulci Variability of Sulci
Source: Szikla et al., 1977 in Tamraz & Comair, 2000

48. Variability of Functional Areas
Watson et al., 1995
-functional areas (e.g., MT) vary between subjects in their Talairach locations
-the location relative to sulci is more consistent
Source: Watson et al. 1995

49. Cortical Surfaces Advantages surfaces are topologically more accurate
segment gray-white
matter boundary
render cortical surface
inflate cortical surface
sulci = concave = dark gray
gyri = convex = light gray
Advantages
surfaces are topologically more accurate
alignment across sessions and experiments allows task comparisons
Source: Jody Culham

50. Cortical Inflation Movie
Movie: unfoldorig.mpeg
Source: Marty Sereno’s web page

51. Cortical Flattening 2) make cuts along the medial surface
(Note, one cut typically goes along the fundus of the calcarine sulcus though in this example the cut was placed below)
1) inflate the brain
3) unfold the medial surface so the cortical surface lies flat
4) correct for the distortions so that the true cortical distances are preserved
Source: Brain Voyager Getting Started Guide

52. Spherical Averaging
Future directions of fMRI: Use cortical surface mapping coordinates
Inflate the brain into a sphere
Use sulci and/or functional areas to match subject’s data to template
Cite “latitude” & “longitude” of spherical coordinates
Movie: brain2ellipse.mpeg
Source: Marty Sereno’s web page
Source: Fischl et al., 1999

53. Spherical Averaging
Source: MIT HST583 online course notes

54. 5. CORTICAL ANATOMY

55. 14 Major Sulci Main sulci are formed early in development
Fissures are really deep sulci
Typically continuous sulci
Interhemispheric fissure
Sylvian fissure
Parieto-occipital fissure
Collateral sulcus
Central sulcus
Calcarine Sulcus
Typically discontinuous sulci
Superior frontal sulcus
Inferior frontal sulcus
Postcentral sulcus
Intraparietal sulcus
Superior temporal sulcus
Inferior temporal sulcus
Cingulate sulcus
Precentral sulcus
Other minor sulci are much less reliable
Source: Ono, 1990

56. Interhemispheric Fissure
-hugely deep (down to corpus callosum)
-divides brain into 2 hemispheres

57. Sylvian Fissure -hugely deep -mostly horizontal
-insula (purple) is buried within it
-separates temporal lobe from parietal and frontal lobes
Sylvian Fissure

58. Parieto-occipital Fissure and Calcarine Sulcus
Cuneus (pink)
-visual areas on medial side above calcarine (lower visual field)
Parieto-occipital fissure (red)
-very deep
-often Y-shaped from sagittal view, X-shaped in horizontal and coronal views
Lingual gyrus (yellow)
-visual areas on medial side below calcarine and above collateral sulcus (upper visual field)
Calcarine sulcus (blue)
-contains V1

59. Collateral Sulcus
-divides lingual (yellow) and parahippocampal (green) gyri from fusiform gyrus (pink)

60. Cingulate Sulcus
-divides cingulate gyrus (turquoise) from precuneus (purple) and paracentral lobule (gold)

61. Central, Postcentral and Precentral Sulci
Central Sulcus (red)
-usually freestanding (no intersections)
-just anterior to ascending cingulate
Precentral Sulcus (red)
-often in two parts (superior and inferior)
-intersects with superior frontal sulcus (T-junction)
-marks anterior end of precentral gyrus (motor strip, yellow)
Postcentral Sulcus (red)
-often in two parts (superior and inferior)
-often intersects with intraparietal sulcus
-marks posterior end of postcentral gyrus (somatosensory strip, purple)
ascending band
of the cingulate

62. Intraparietal Sulcus
-anterior end usually intersects with inferior postcentral (some texts call inferior postcentral the ascending intraparietal sulcus)
-posterior end usually forms a T-junction with the transverse occipital sulcus (just posterior to the parieto-occipital fissure)
-IPS divides the superior parietal lobule from the inferior parietal lobule (angular gyrus, gold, and supramarginal gyrus, lime)
POF

63. Slice Views
inverted omega
= hand area of motor cortex

64. Superior and Inferior Temporal Sulci
Superior Temporal Sulcus (red)
-divides superior temporal gyrus (peach) from middle temporal gyrus (lime)
Inferior Temporal Sulcus (blue)
-not usually very continuous
-divides middle temporal gyrus from inferior temporal gyrus (lavender)

65. Superior and Inferior Frontal Sulci
Superior Frontal Sulcus (red)
-divides superior frontal gyrus (mocha) from middle frontal gyrus (pink)
Inferior Frontal Sulcus (blue)
-divides middle frontal gyrus from inferior frontal gyrus (gold)
orbital gyrus (green) and frontal pole (gray) also shown
Frontal Eye fields lie at this junction

66. Medial Frontal -superior frontal gyrus continues on medial side
-frontal pole (gray) and orbital gyrus (green) also shown