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 Satisfactory Make 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-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

Scanner and Physiologic Noise Models

fMRI Time Series Model Baseline Activation Drift AR(1)+White Activation Model = time, = spatial location

Correlated Noise Model Pixelwise 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)

Ballistocardiogram Noise Outside Magnet Inside Magnet

Faraday’s Induced Noise B v   = 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  1.5T in many cases Motion: > 200  1.5T The Signal: ERPs: 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 Induced noise True underlying EEG 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

Outside Magnet

Results: Alpha Waves Frequency (Hz) Time (sec) After Adaptive Filtering Time (sec) Frequency (Hz) 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

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.