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HST 583 fMRI DATA ANALYSIS AND ACQUISITION
Neural Signal Processing for Functional Neuroimaging
Emery N. Brown
Neuroscience Statistics Research Laboratory
Massachusetts General Hospital
Harvard Medical School/MIT Division of Health, Sciences and Technology
September 9, 2002
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 SatisfactoryMake an Inference
Make a Decision
Spatio-Temporal Scales
EEG + fMRI
Kandel, Schwartz & Jessell
Neurons
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-relatedb) block
ii) blood flow iii) blood volume iv) hemoglobin and deoxy hemoglobin contentNoise Stochastic i) physiologic ii) scanner noiseSystematic i) motion artifact ii) drift iii) [distortion] iv) [registration], [susceptibility]
Physiologic Response Model: Block Design
Physiologic Model:
Event-Related Design
0 20 40 60 80 100 1200
0.5
1
Flow Term
0 20 40 60 80 100 1200
0.5
1
Volume Term
0 20 40 60 80 100 1200
0.5
1
Interaction Term
0 20 40 60 80 100 120-0.2
0
0.2
0.4
0.6
Modeled BOLD Signal
fa=1 fb=-0.5
fc=0.2
Physiologic Response: Flow,Volume and Interaction Models
Scanner and Physiologic Noise Models
fMRI Time Series Model Baseline Activation
Drift AR(1)+White
Activation Model
x t m b t s t v tP P P P P( ) ( ) ( )
= time, = spatial locationt P
s t - DP p( ) (base + Blood O stimulus)
(base + Blood volume stimulus)
O 2 IR
vol IR
2
2 2
Correlated Noise ModelPixelwise Activation Confidence
Intervals for the Slice
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)
0 1 2 3 4 5 6 7 8 9 10-150
-100
-50
0
50
100
150
EE
G S
igna
l (uV
)
Time (sec)
0 1 2 3 4 5 6 7 8 9 10-150
-100
-50
0
50
100
150
Time (sec)
EE
G s
igna
l (uV
)Ballistocardiogram NoiseOutside Magnet
Inside Magnet
Faraday’s Induced Noise
Bv
= 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 V @ 1.5T in many
cases• Motion: > 200 V @ 1.5T
The Signal:• ERPs: < 10 V, reject epochs if > 50 V• Alpha waves: < 100 V
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
• Linear time-varying FIR model for induced noise
)()()( tntsty Induced noiseTrue underlying
EEG
1
0
)()()(N
kt ktmkwtn Motion sensor
signal
FIR kernel
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 WavesOutside Magnet
Results: Alpha Waves
Fre
quen
cy (
Hz)
Time (sec)0 20 40 60 80
0
5
10
15
20
25
30
35
After Adaptive Filtering
Time (sec)
Fre
quen
cy (
Hz)
0 20 40 60 800
5
10
15
20
25
30
35
Eyes Closed
Eyes Open
Before 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
The Sequence used in Simultaneous EEG/fMRI
fMRI Window30 sec
15 sec of 4-8 HzCheckerboard
Reversal
100 msec
Time
EEG/VEPWindow
30 sec
RT
15 sec offixation
15 sec of 4-8 HzCheckerboard
Reversal
15 sec offixation
TO
Stim
ulus
Pres
enta
tion
fMR
Itr
igge
rE
EG
trig
ger
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.