1. Neuroimaging: from image to Inference
Chris Rorden
fMRI limitations: relative to other tools used to infer brain function.
fMRI signal: tiny, slow, hidden in noise.
fMRI processing: a sample experiment.
fMRI anatomy: stereotaxic space.
See also:

2. Modern neuroscience
Different tools exist for inferring brain function.
No single tool dominates, as each has limitations.
This course focuses on fMRI.
poor
(whole brain)
iap
eeg
lesions
erp
nirs
pet
tms
Spatial resolution
fmri
good
(neuron)
scr
good (millisecond)
poor (months)
Temporal resolution

3. Directly measure neural activity. Exquisite timing information
Single Cell Recording
Directly measure neural activity.
Exquisite timing information
Precise spatial information
Often, no statistics required!
Each line is one trial.
Each stripe is neuron firing.
Note: firing increases whenever monkey reaches or watches reaching.

4. With SCR, we are very close to the data.
Single Cell Recording
With SCR, we are very close to the data.
We can clearly see big effects without processing.
Unfortunately, there are limitations:
Invasive (needle in brain)
Typically constrained to animals, so difficult to directly infer human brain function.
Limited field of view: just a few neurons at a time.

5. fMRI Processing Processing Steps
Unlike SCR, we must heavily process fMRI data to extract a signal.
The signal in the raw fMRI data is influenced by many factors other than brain activity.
We need to filter the data to remove these artifacts.
We will examine why each of these steps is used.
Processing Steps
Motion Correct
Spatial
Intensity
Physiological Noise Removal
Temporal Filtering
Temporal Slice Time Correct
Spatial Smoothing
Normalize
Statistics

6. fMRI signal sluggish
Unlike SCR, huge delay between activity and signal change.
Visual cortex shows peak response ~5s after visual stimuli.
Indirect measure of brain activity 2 1
Time (seconds)

7. What is the fMRI signal
fMRI is ‘Blood Oxygenation Level Dependent’ measure (BOLD).
Brain regions become oxygen rich after activity.
Very indirect measure.

8. Lets conduct a study
Anatomical Hypothesis: lesion studies suggest location for motor-hand areas.
Ask person to tap finger while in MRI scanner – predict contralateral activity in motor hand area..
M1: movement
S1: sensation

9. Task Task has three conditions:
Up arrows: do nothing
Left arrows: press left button each time arrow flashes.
Right arrows: press right button every time arrow flashes.
Block design: each condition repeats rapidly for 11.2 sec.
No sequential repeats: block of left arrows always followed by block of either up or right arrows.

10. Data Collection Participant Lies in scanner watching computer screen.
Taps left/right finger after seeing left/right arrows.
Collect 120 3D volumes of data, one every 3s (total time = 6min).

11. Raw Data The scanner reconstructs 120 3D volumes.
Each volume = 64x64x36 voxels
Each voxel is 3x3x3mm.
We need to process this raw data to detect task-related changes.

12. Motion Correction
Unfortunately, people move their heads a little during scanning.
We need to process the data to create motion-stabilized images.
Otherwise, we will not be looking at the same brain area over time.

13. Spatial smoothing Each voxel is noisy
By blurring the image, we can get a more stable signal (neighbors show similar effects, noise spikes attenuated).

14. Predicted fMRI signal We need to generate a statistical model.
We convolve expected brain activity with hemodynamic response to get predicted signal.
Neural Signal
HRF
Predicted fMRI signal =

15. Predicted fMRI signal
We generate predictions for neural responses for the left and right arrows across our dataset.
Statistics will identify which areas show this pattern of activity.
Several possible statistical contrasts (crucial to inference):
Activity correlated with left arrows: visual cortex, bilateral motor.
More activity for left than right arrows: contralateral motor.

16. Voxelwise statistics
We compute the probability for every voxel in the brain.
We observe that right arrows precede activation in the left motor cortex and right cerebellum.

17. fMRI signal change is tiny, noise is high
Right motor cortex becomes brighter following movement of left hand.
Note signal increases from ~12950 to ~13100, only about 1.2%
And this is after all of our complicated processing to reduce noise.

18. Coordinates - normalization
Different people’s brains look different
‘Normalizing’ adjusts overall size and orientation
Normalized Images
Raw Images

19. Why normalize? Stereotaxic coordinates analogous to longitude
Universal description for anatomical location
Allows other to replicate findings
Allows between-subject analysis: crucial for inference that effects generalize across humanity.

20. fMRI is notoriously difficult technique
Goals for this course
fMRI is notoriously difficult technique
Sluggish signal
Poor signal/noise
Must find meaningful statistical contrasts
This seminar reveals how to
Devise meaningful contrasts
Maximize signal, minimize noise
Control for statistical errors.

21. MRI uses very strong magnet and radiofrequencies
Safety
MRI uses very strong magnet and radiofrequencies
3T= ~x60,000 field that aligns compass
Metal and electronic devices are not compatible.
MRI scanning makes loud sounds
Rapid gradient switching creates auditory noise.
Auditory protection crucial.
MRI scanning is confined
Claustrophobia is a concern.

22. Summary of Lectures Introduction Physics I: Hardware and Acquisition
Physics II: Contrasts and Protocols
fMRI Paradigm Design
fMRI Statistics and Thresholding
fMRI Spatial Processing
fMRI Temporal Processing
VBM & DTI: subtle structural changes
Lesion Mapping: overt structural changes
Advanced and Alternative Techniques

23. There are many tools available for analysis. Different strengths.
Which tools
There are many tools available for analysis.
Different strengths.
We predominantly focus on SPM and FSL.
These are both free, popular and have good user support.
Tool
SFN04
SPM
78.5%
AFNI
9.1%
FSL
7.4%
BrainVoyager
4.1%

24. Reporting findings How do we describe anatomy to others?
We could use anatomical names, but often hard to identify.
We could use Brodmann’s Areas, but this requires histology – not suitable for invivo research.
Both show large between-subject variability.
Requires anatomical coordinate system.

25. Relative Coordinates
On the globe we talk about North, South, East and West.
Lets explore the coordinates for the brain.

26. Orientation - animals Dorsal back Caudal tail Ventral belly
Rostral
Caudal
Ventral
Cranial head
Rostral beak
Caudal tail
Ventral belly

27. Coordinates – Dorsal Ventral
Human dorsal/ventral differ for brain and spine.
Head/Foot, Superior/Inferior, Anterior/Posterior not ambiguous.
Dorsal
Ventral
Dorsal
Ventral
Dorsal
Ventral

28. Coordinates – Human Human rostral/caudal differ for brain and spine.
Head/Foot, Superior/Inferior, Anterior/Posterior not ambiguous. C R R R C C

29. Orientation Human anatomy described as if person is standing
If person is lying down, we would still say the head is superior to feet.

30. Anatomy – Relative Directions
lateral < medial > lateral
Posterior <> Anterior
Ventral/Dorsal
aka Inferior/Superior
aka Foot/Head
Ventral <> Dorsal
Anterior/Posterior
aka Rostral/Caudal
Posterior <> Anterior

31. Coordinates - Anatomy 3 Common Views of Brain: Coronal (head on)
sagittal
coronal
axial
3 Common Views of Brain:
Coronal (head on)
Axial (bird’s eye), aka Transverse.
Sagittal (profile)

32. Coronal
Corona: a coronal plane is parallel to crown that passes from ear to ear

33. Transverse/Axial: perpendicular to the long axis
Example: cucumber slices are transverse to long axis.

34. Sagittal – ‘arrow like’
Sagittal cut divides object into left and right
sagittal suture looks like an arrow.
top view

35. Sagittal and Midsagittal
A Sagittal slice down the midline is called the ‘midsagittal’ view.
midsagittal
sagittal

36. Oblique Slices
Slices that are not cut parallel to an orthogonal plane are called ‘oblique’.
The oblique blue slice is neither Coronal nor Axial.
Cor
Oblique Ax