Presentation on theme: "HST 583 fMRI DATA ANALYSIS AND ACQUISITION Neural Signal Processing for Functional Neuroimaging Emery N. Brown Neuroscience Statistics Research Laboratory."— Presentation transcript:
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
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)
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 V @ 1.5T in many cases Motion: > 200 V @ 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
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.