HST 583 fMRI DATA ANALYSIS AND ACQUISITION

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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

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Outline

• Spatial Temporal Scales of Neurophysiologic
  Measurements
• Neural Signal Processing for fMRI
• Signal Processing for EEG in the fMRI Scanner
• Combined EEG/fMRI
• Conclusion

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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
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Spatio-Temporal Scales
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Neurons
Kandel, Schwartz Jessell
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Action Potentials (Spike Trains)
Neuron
Stimuli
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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)
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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
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Physiologic Response Model Block Design
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Physiologic Model Event-Related Design
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Physiologic Response Flow,Volume and Interaction
Models
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Scanner and Physiologic Noise Models
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fMRI Time Series Model

• Baseline
  Activation
• Drift
  AR(1)White
• Activation Model

time, spatial location
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Correlated Noise Model Pixelwise Activation
Confidence Intervals for the Slice
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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)
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Ballistocardiogram Noise
Outside Magnet
Inside Magnet
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Faradays Induced Noise
?? e N ? t
B
v

• 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

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Noise vs. Signal...

• The Noise
• Ballistocardiogram gt150 mV _at_ 1.5T in many cases
• Motion gt 200 mV _at_ 1.5T

• The Signal
• ERPs lt 10 mV, reject epochs if gt 50 mV
• Alpha waves lt 100 mV

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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

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Adaptive Filter Algorithm

• Observed signal
• Linear time-varying FIR model for induced noise

Induced noise
True underlying EEG
Motion sensor signal
FIR kernel
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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

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Results Alpha Waves
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Results Alpha Waves
Outside Magnet
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Results Alpha Waves
Frequency (Hz)
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COMBINED EEG/fMRI What are the advantages to
combining EEG and fMRI?( Liu, Belliveau and Dale
1998)
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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

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The Sequence used in Simultaneous EEG/fMRI
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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

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Spatiotemporal Dynamics of Brain Activity
following visual stimulation
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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.

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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.