MIT OpenCourseWare http://ocw.mit.edu HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2006 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
HST.583: Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2006 Harvard-MIT Division of Health Sciences and Technology Course Director: Dr. Randy Gollub. Statistical Signal Processing for fmri Douglas N. Greve Mark Vangel Anastasia Yendiki
Overview First-Level Univariate Analysis Signal Modeling Nuisance Modeling Noise Modeling Hypothesis Testing Correction for Multiple Comparisons Cross-Subject/Higher Level Analysis Lab
Analysis Goals Quantify Neural Correlates in fmri Amplitude of Hemodynamic Response Delay/Shape of Hemodynamic Response Extent/Size of Activation Localization of function Quantify Uncertainty Cross-subject (within group) Cross-group eg, Normals, Clinical Populations Within-subject EEG/MEG/Optical/Surgical Planning
Challenges Large Noise thermal, physiological, motion Small Signal delay, dispersion Structural/Functional Alignment within subject Intersubject Alignment Copious amounts of data eg, 20 subjects, 5 runs per subject, 100 time points per run, 64x64x30 volume = 1.2G data points More spatial voxels than time points (multiple comparisons problem). Model Validation
Method Correlational synchronized stimulus and acquistion Linear/Gaussian Assumptions GLM General Linear Model MSE Minimum Square Error LMS Least Mean Squares Massively Univariate
Hemodynamic Response (BOLD) Time-to-Peak (~6sec) Dispersion TR (~2sec) Equilibrium (~16-32sec) Undershoot Delay (~1-2sec)
fmri Noise Synthetic data.
Averaging Synthetic data.
Typical Analysis Stream Preprocessing Univariate First-Level GLM Analysis Univariate Higher-Level GLM Analysis Multivariate Analysis Packages: SPM Statistical Parametric Mapping AFNI Analysis of Functional NeuroImages FSL fmri Software Library FS-FAST FreeSurfer Functional Analysis STream
Preprocessing k-space reconstruction Slice-Timing Correction (?) Motion Correction Spatial Filtering (Smoothing - FWHM) Intensity Normalization Temporal Filtering (or in analysis) Per-run, within subject
Univariate First-Level Analysis Per-voxel, per-subject Postulate model of the observable (ie raw time course) Signal model (eg, hemodynamic response) Noise model (eg, autocorrelation function) Drift (eg, mean offset, linear, quadratic) General Linear Model (GLM) Parameterized Linear (superposition) Least-mean-square estimation of parameters Hypothesis Test = Contrast of Parameters Assemble into a map
Univariate High-level Analysis Per-voxel, Cross-subject Requires intersubject registration Dave Kennedy Uses information from First/Lower Levels GLM to describe relationship Random Effects Fixed Effects
Multivariate Statistics Cross-voxel (within map) Thresholding and multiple comparisons problem Gaussian Random Fields (GRF) Principal Component Analysis (PCA/SVD) Independent Component Analysis Region-of-Interest
Hemodynamic Response Model Time-to-Peak (~6sec) Dispersion TR (~2sec) Equilibrium (~16-32sec) Undershoot Delay (~1-2sec)
Visual Activation Paradigm Flickering Checkerboard Visual, Auditory, Motor, Tactile, Pain, Perceptual, Recognition, Memory, Emotion, Reward/Punishment, Olfactory, Taste, Gastral, Gambling, Economic, Acupuncture, Meditation, The Pepsi Challenge, Scientific Clinical Pharmaceutical
Blood Oxygen Level Dependence (BOLD) Oxygenated Hemoglobin (DiaMagnetic) Neurons Lungs Deoxygenated Hemoglobin (ParaMagnetic) Oxygen CO2
Functional MRI (fmri) Stimulus Localized Neural Firing Localized Increased Blood Flow Localized BOLD Changes Sample BOLD response in 4D Space (3D) voxels (64x64x35, 3x3x5mm^3) Time (1D) time points (100, 2 sec) Time 1 Time 2 Time 3
Analysis Goals Given: raw fmri time course and stimulus presentation times Compute: Hemodynamic Response (HRF) Amplitude HRF Confidence Interval } P-Value Quantify Uncertainty Noise Amplitude
Final Results: Maps Assign values to each voxel Display as pseudo-color images Threshold?
Final Results: Tables List of active regions Cluster TalX TalY TalZ Volume Sig Number (mm) (mm) (mm) (mm^3) (log10) 1-30.5 13.2 0.2 125.6 5.7 2 4.5 9.7-20.2 878.1 4.1 3 2.9-18.0 17.7 400.3 3.2
Final Results: Waveforms Average raw data over time and space