HST 583
fMRI DATA ANALYSIS AND ACQUISITION
Neural Signal Processing for Functional
Neuroimaging
Neuroscience Statistics Research Laboratory
Massachusetts General Hospital
Harvard Medical School/MIT Division of
Health, Sciences and Technology
HST.583: Functional Magnetic Resonance Imaging: Data Acquisition and Analysis
Harvard-MIT Division of Health Sciences and Technology
Dr. Emery Brown
Outline
• Spatial Temporal Scales of Neurophysiologic
Measurements
• Neural Signal Processing for fMRI
• Signal Processing for EEG in the fMRI Scanner
• Combined EEG/fMRI
• Conclusion
THE STATISTICAL PARADIGM (Box, Tukey)
Question
Preliminary Data (Exploration Data Analysis)
Models
Experiment
(Confirmatory
Analysis)
Model Fit
Goodness-of-fit
not satisfactory
Assessment
Satisfactory
Make an Inference
Make a Decision
Spatio-Temporal Scales
EEG +
fMRI
Neurons
Kandel, Schwartz & Jessell
Action Potentials (Spike Trains)
Neuron
Stimuli
2. SIGNAL PROCESSING for fMRI DATA ANALYSIS
Question: Can we construct an accurate statistical model
to describe the spatial temporal patterns of activation in fMRI
images from visual and motor cortices during combined motor
and visual tasks? (Purdon et al., 2001; Solo et al., 2001)
What Makes Up An fMRI Signal?
Hemodynamic Response/MR Physics
i) stimulus paradigm
a) event-related
b) block
ii) blood flow
iii) blood volume
iv) hemoglobin and deoxy hemoglobin content
Noise
Stochastic
i) physiologic
ii) scanner noise
Systematic
i) motion artifact
ii) drift
iii) [distortion]
iv) [registration], [susceptibility]
Physiologic Response
Model: Block Design
Physiologic Model:
Event-Related Design
Physiologic Response: Flow,Volume
and Interaction Models
Volume Term
Flow Term
1
1
0.5
0.5
0
0
20
40
60
80
100
120
0
0
fa=1
Interaction Term
120
100
80
60
40
20
fb=-0.5
1
Modeled BOLD Signal
fc=0.2
0.5
0.6
0.4
0
0
20
40
60
80
100
120
0.2
0
-0.2
0
20
40
60
80
100
120
Scanner and Physiologic Noise Models
fMRI Time Series Model
Baseline
Activation
Drift
AR(1)+White
xP (t ) = mP + bPt + sP (t ) + vP (t )
Activation Model
t
= time,
P
= spatial location
sP ( t - Dp ) = (baseO2 +Blood O2IR ∗stimulus) ×
(basevol +Blood volumeIR ∗stimulus)
Correlated Noise Model
Pixelwise Activation Confidence
Intervals for the Slice
β − 2σ β
β
β + 2σ β
Signal Processing for EEG in the
fMRI Scanner
How can we remove the artefacts from
EEG signals recorded simultaneously with
fMRI measurements? (Bonmassar et al.
2002)
Ballistocardiogram Noise
Outside Magnet
150
EEG signal (uV)
100
50
0
-50
-100
-150
0
1
2
3
4
5
Time (sec)
6
7
8
9
10
4
5
Time (sec)
6
7
8
9
10
Inside Magnet
150
EEG Signal (uV)
100
50
0
-50
-100
-150
0
1
2
3
Faraday’s Induced Noise
∂φ
v
B
ε=N—
∂t
• A Fundamental Physical Problem w/ EEG/fMRI:
– Motion of the EEG electrodes and leads generates noise currents!
• Machine Motion
– helium pump, vibration of table, ventilation system
• Physiological Motion
– heart beat (ballistocardiogram), breathing, subject motion
Noise vs. Signal...
The Noise:
• Ballistocardiogram: >150 µV @ 1.5T in many
cases
• Motion: > 200 µV @ 1.5T
The Signal:
• ERPs: < 10 µV, reject epochs if > 50 µV
• Alpha waves: < 100 µV
Adaptive Filtering
• Use a motion sensor to measure the
ballistocardiogram and head motion
– Place near temporal artery to pick up
ballistocardiogram
• Use motion signal to remove induced noise
Adaptive Filter Algorithm
• Observed signal
y (t ) = s (t ) + n(t )
Induced noise
True underlying
EEG
• Linear time-varying FIR model for induced noise
N −1
n(t ) =
∑ w (k )m(t − k )
t
k =0
FIR kernel
Motion sensor
signal
Data
• 5 subjects
• Alpha waves
– 10 seconds eyes open, 20 seconds eyes closed over 3
minutes
• Visual Evoked Potentials (VEPs)
• Motion
– Head-nod once per 7-10 seconds for 5 minutes
– Added simulated epileptic spikes
Results: Alpha Waves
Results: Alpha Waves
Outside Magnet
Results: Alpha Waves
35
35
30
30
25
25
Frequency (Hz)
Frequency (Hz)
Eyes Closed
Eyes Open
20
15
20
15
10
10
5
5
0
0
20
40
Time (sec)
60
80
Before Adaptive Filtering
0
0
20
40
Time (sec)
60
80
After Adaptive Filtering
COMBINED EEG/fMRI
What are the advantages to combining
EEG and fMRI?( Liu, Belliveau and
Dale 1998)
Combined EEG/fMRI
• Combines high temporal resolution of EEG with
high spatial resolution of fMRI
• Applications
–
–
–
–
Event related potentials
EEG-Triggered fMRI of Epilepsy
Sleep
Anesthesia
EEG
trigger
fMRI
trigger
Stimulus
Presentation
The Sequence used in
Simultaneous EEG/fMRI
15 sec of 4-8 Hz
Checkerboard
Reversal
15 sec of
fixation
15 sec of 4-8 Hz
Checkerboard
Reversal
EEG/VEP
Window
30 sec
fMRI Window
30 sec
100 msec
15 sec of
fixation
TO
RT
Time
Combining EEG and fMRI
• (A) fMRI regions of activation for 2 subjects.
The fMRI activity was consistently localized to the
posterior portion of the calcarine sulcus.
• (B) Anatomically constrained EEG (aEEG).
The cortical activity was localized along the entire length
of the calcarine sulcus.
• (C) Combined EEG/fMRI (fEEG).
The localizations are similar to the fMRI results and
considerably more focal than the unconstrained EEG
localizations
Spatiotemporal Dynamics of Brain Activity
following visual stimulation
Cortical activations changes over
time
• Seven snapshots of the cortical activity movie, without and
with fMRI constraint.
• The peaks of activity occur at the same time for both the
EEG (alone) localization and the fMRI constrained
localization.
• Spatial extent of the fMRI constrained EEG localization is
more focal than the results based on EEG measurements
alone.
Conclusion
• Well Poised Question
• Careful Experimental Design/Measurement
Techniques
• Signal Processing Analysis Is An Important
Feature of Experimental Design, Data
Acquisition and Analysis.
• Data Analysis Should Be Carried Out Within
the Statistical Paradigm.