Download Temporal Aspects of Visual Extinction

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:
– http://www.biac.duke.edu/education/courses/fall05/fmri/
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 Different tools exist
for inferring brain
function.
 No single tool
dominates, as each
has limitations.
 This course focuses
on fMRI.
Spatial resolution
Modern neuroscience
poor
(whole
brain)
eeg
erp
nirs
tms
good
(neuron)
iap
lesions
pet
fmri
scr
good (millisecond)
Temporal resolution
poor
(months)
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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.
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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.
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fMRI Processing




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
1. Motion Correct
1. Spatial
2. Intensity
2.
3.
4.
5.
6.
7.
Physiological Noise Removal
Temporal Filtering
Temporal Slice Time Correct
Spatial Smoothing
Normalize
Statistics
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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
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1
0
0
6
12
18
Time (seconds)
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What is the fMRI signal
 fMRI is ‘Blood Oxygenation Level Dependent’
measure (BOLD).
 Brain regions become oxygen rich after activity.
 Very indirect measure.
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Lets conduct a study
 Anatomical Hypothesis: lesion studies suggest location for motorhand areas.
 Ask person to tap finger while in MRI scanner – predict contralateral
activity in motor hand area..
M1: movement
S1: sensation
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Task
 Task has three conditions:
1. Up arrows: do nothing
2. Left arrows: press left button each
time arrow flashes.
3. 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.
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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).
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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 taskrelated changes.
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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.
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Spatial smoothing
 Each voxel is noisy
 By blurring the image, we can get a more stable signal
(neighbors show similar effects, noise spikes
attenuated).
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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
=
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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):
1. Activity correlated with left arrows: visual cortex, bilateral motor.
2. More activity for left than right arrows: contralateral motor.
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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.
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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.
L_Tap
right
13100
13000
12900
0
100
200
300
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Coordinates - normalization


Different people’s brains look different
‘Normalizing’ adjusts overall size and orientation
Raw Images
Normalized Images
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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.
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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.
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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.
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Summary of Lectures
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
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
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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%
https://cirl.berkeley.edu/view/Grants/BrainPyMotivation
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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 betweensubject variability.
 Requires anatomical coordinate
system.
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Relative Coordinates
On the globe we talk about North, South, East
and West.
Lets explore the coordinates for the brain.
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Orientation - animals
Dorsal
back
Dorsal
Rostral
Caudal
Ventral
 Cranial
head
 Rostral
beak
Caudal
tail
Ventral
belly
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Coordinates – Dorsal Ventral
 Human dorsal/ventral differ for brain and spine.
– Head/Foot, Superior/Inferior, Anterior/Posterior not ambiguous.
Dorsal
Ventral
Dorsal
Ventral
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Coordinates – Human
 Human rostral/caudal differ for brain and spine.
– Head/Foot, Superior/Inferior, Anterior/Posterior not ambiguous.
R
R
C
R
C
C
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Orientation
 Human anatomy described
as if person is standing
 If person is lying down, we
would still say the head is
superior to feet.
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Anatomy – Relative Directions
Anterior/Posterior
aka Rostral/Caudal
Posterior <> Anterior
Ventral/Dorsal
aka Inferior/Superior
aka Foot/Head
Posterior <> Anterior
Ventral <> Dorsal
lateral < medial > lateral
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Coordinates - Anatomy
3 Common Views of
Brain:
coronal
sagittal
– Coronal (head on)
– Axial (bird’s eye), aka
Transverse.
– Sagittal (profile)
axial
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Coronal
Corona: a coronal plane is parallel to crown
that passes from ear to ear
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Transverse
Transverse/Axial: perpendicular to the long
axis
Example:
cucumber slices
are transverse
to long axis.
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Sagittal
Sagittal – ‘arrow like’
– Sagittal cut divides object into left
and right
– sagittal suture looks like an arrow.
top view
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Sagittal and Midsagittal
A Sagittal slice down the
midline is called the
‘midsagittal’ view.
midsagittal
sagittal
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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
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