HST 583 fmri DATA ANALYSIS AND ACQUISITION

Transcription

1 HST 583 fmri DATA ANALYSIS AND ACQUISITION Neural Signal Processing for Functional Neuroimaging Neuroscience Statistics Research Laboratory Massachusetts General Hospital Harvard Medical School/MIT Division of Health, Sciences and Technology HST.583: Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Harvard-MIT Division of Health Sciences and Technology Dr. Emery Brown

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

3 THE STATISTICAL PARADIGM (Box, Tukey) Question Preliminary Data (Exploration Data Analysis) Models Experiment Model Fit (Confirmatory Analysis) Goodness-of-fit not satisfactory Assessment Satisfactory Make an Inference Make a Decision

4 Spatio-Temporal Scales EEG + fmri

5 Neurons Kandel, Schwartz & Jessell

6 Action Potentials (Spike Trains) Neuron Stimuli

7 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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9 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]

10 Physiologic Response Model: Block Design

11 Physiologic Model: Event-Related Design

12 Physiologic Response: Flow,Volume and Interaction Models 1 Flow Term 1 Volume Term Interaction Term f a =1 f b = f c = Modeled BOLD Signal

13 Scanner and Physiologic Noise Models

14 fmri Time Series Model Baseline Activation Drift AR(1)+White x () t = m + b t+ s () t + v () t P P P P P Activation Model t P = time, = spatial location s P ( t-d ) = (base +Blood O stimulus) p O 2 (base +Blood volume stimulus) vol 2 IR IR

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17 β 2σ β Correlated Noise Model Pixelwise Activation Confidence Intervals for the Slice β β + 2σ β

18 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)

19 Ballistocardiogram Noise 150 Outside Magnet 100 EEG signal (uv) Time (sec) 150 Inside Magnet 100 EEG Signal (uv) Time (sec)

20 Faraday s Induced Noise v B φ ε = 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

21 Noise vs. Signal... The Noise: Ballistocardiogram: > T in many cases Motion: > T The Signal: ERPs: < 10 µv, reject epochs if > 50 µv Alpha waves: < 100 µv

22 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

23 Adaptive Filter Algorithm Observed signal True underlying EEG y ( t) = s( t) + n( t) Induced noise Linear time-varying FIR model for induced noise n( t) = N 1 k = 0 w t ( k) m( t k) Motion sensor signal FIR kernel

24 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

25 Results: Alpha Waves

26 Results: Alpha Waves Outside Magnet

27 Results: Alpha Waves Eyes Closed Eyes Open Frequency (Hz) Frequency (Hz) Time (sec) Before Adaptive Filtering Time (sec) After Adaptive Filtering

28 COMBINED EEG/fMRI What are the advantages to combining EEG and fmri?( Liu, Belliveau and Dale 1998)

29 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

30 The Sequence used in Simultaneous EEG/fMRI Stimulus Presentation 15 sec of 4-8 Hz Checkerboard Reversal 15 sec of fixation 15 sec of 4-8 Hz Checkerboard Reversal 15 sec of fixation fmri trigger fmri Window 30 sec EEG/VEP Window 30 sec EEG trigger 100 msec RT TO Time

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32 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

33 Spatiotemporal Dynamics of Brain Activity following visual stimulation

34 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.

35 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.