Neural Signal Processing

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.