Principles of Functional Neuroimaging: Measurement, Design and Analysis - MIT Course 9.S913

 

 

This course is an intensive introduction to the issues associated with using structural and functional MRI in a range of research applications. Presentations will cover both theory and practice related to the basic principles of MRI measurement, techniques for stimulus presentation and response recording in high magnetic fields, basic statistical methods, individual subject experimental design and analysis techniques, group design and analysis techniques, study design principles and guidelines for reporting neuroimaging studies.

The level of the presentations will be appropriate for both early and experienced investigators planning to use measures of brain structure and function in both clinical and basic studies. Each 3 hour meeting will include lecture and laboratory time. The course website will provide guidance for subsequent self-study.
 

Course instructors:

Susan Whitfield-Gabrieli
Tom Zeffiro
John Gabrieli
Christina Triantafyllou
Satrajit Gosh
Sheeba Arnold

The course will focus on using SPM8, FSL and Nipype for preprocessing, statistical modeling and visualization of data associated with a range of basic and clinical fMRI experimental designs. While the primary emphasis will involve using the core SPM8 programs for these purposes, there will also be extensive discussion of a variety of software tools that extend the power of SPM8. Some of these tools facilitate fMRI quality assurance through artifact detection and mitigation at various analysis stages. Other tools support a variety of data visualization methods, including MRIcron, xjView, FSLview, and FreeSurfer.

Lectures will be held in Room 46-4062 on Monday 3-6PM beginning February 13, 2012.

The MIT Stellar website for the course is here.

 

Educational Objectives 

  • Understand the basic organization of the SPM and FSL GUI
  • Understand the organization of the SPM "toolbox"
  • Be able to construct batch processing scripts for preprocessing and statistical modeling using SPM and Nipyp
  • Understand the basic fMRI data preprocessing steps
  • Be able to construct a preprocessing sequence including slice time correction, realignment, and spatial filtering
  • Understand the origins of the artifacts most commonly encountered in fMRI datasets
  • Be able to utilize explore an fMRI dataset for artifacts and effect repairs as needed
  • Understand the basic fMRI single subject experimental design types
  • Be able to implement statistical analysis procedures for the basic single subject fMRI designs
  • Understand the basic fMRI single group experimental design types
  • Be able to implement statistical analysis procedures for the basic single group fMRI designs
  • Understand the basic fMRI multiple group experimental design types
  • Be able to implement statistical analysis procedures for the basic multiple group fMRI designs
  • Understand the process of incorporation of covariates in fMRI experimental designs
  • Be able to construct and estimate statistical models involving covariates
  • Be able to utilize the RIC Talairach Daemon, SPM Anatomy toolbox and FreeSurfer for region labeling
  • Be able to export SPM statistical maps to FreeSurfer to visualize results on the cortical surface
  • Be able to use MRIcron for visualization, including volume rendering
  • Be able to use xjView for visualization and labeling

 

Schedule

 

FEB 13 - Session 1

 

Course Introduction (Whitfield-Gabrieli)

 

Planning and Running an fMRI Study (Zeffiro)

Clinical research questions and hypotheses
Meta-analysis
Sample selection
Task design
Study design
Power analysis
fMRI data acquisition
fMRI data preprocessing
fMRI data modeling - individuals
fMRI quality assurance
fMRI data modeling - groups
Critical thresholds for inference
Structure/function integration
Reporting results

 

Mechanisms of Structural and Functional MRI Image Contrast

MRI system components
Basic NMR principles - spins, excitation, relaxation
MR image formation - frequency encoding, phase encoding and slice selection
T1, T2 and T2* contrast
Morphology revealed with T1 contrast
Pathology revealed with T2 contrast
Function revealed with T2* contrast
Neurovascular Coupling
Neurovascular system anatomy
Regional control of blood flow
Neurovascular coupling mechanisms
BOLD-contrast mechanisms - linearity?
Effects on vascular dynamics of aging, neurological disease, psychiatric disease and drugs

Laboratory: MRI Contrast Mechanisms (Triantafyllou)

 

 

FEB 21 - Session 2 (Tuesday meeting because of President's Day)

 

Optimal fMRI Data Acquisition: Spatial (Zeffiro)

Field strength
Head coil
Sequence type - EPI, spiral
Echo time
Voxel dimensions
Slice angle
Field map
Whole vs. partial head coverage

Optimal fMRI Data Acquisition: Temporal (Zeffiro)

Slice acquisition order
Repetition time
Session length
Fixed vs. distributed temporal sampling
Noise source recording
Prospective motion correction

 

Laboratory: Acquisition Protocol Design (Zeffiro)

 

 

FEB 27 - Session 3

 

Task Design for fMRI (Zeffiro)

Block
Event
Other
Sparse temporal sampling
Mixed designs
Efficiency in experimental design

Participant Screening, Characterization and Selection (Zeffiro)

Random samples and samples of convenience
Inclusion criteria
Exclusion criteria - medical issues, smoking, drug use
Psychometric testing
Psychiatric testing
The uses of covariates

 

Laboratory: Task design (Zeffiro, Ghosh, Whitfield-Gabrieli)

 

 

MAR 5 - Session 4

 

Preprocessing: Unwarping, Realignment, Slice Timing (Zeffiro)

Preprocessing overview
Correction of geometric distortion
Head motion correction
Slice timing correction
Spatial filtering

 

Preprocessing: Spatial Normalization (Zeffiro)

Structural image spatial normalization
Functional image direct spatial normalization
Functional image indirect spatial normalization
Preprocessing summary
Preprocessing: Clinical Considerations
Regional atrophy in structural images
Partial volume effects in functional images
Peripheral effects on brain hemodynamics
Pharmacological effects on brain hemodynamics
Atypical head motion
Imaging activity in structures sensitive to magnetic susceptibility gradients

 

Laboratory: Preprocessing I (Zeffiro, Ghosh, Whitfield-Gabrieli)

 

 

MAR 12 - Session 5

 

Optimal Structural MRI

Multi-echo MPRAGE
prospective moition correction

 

Nypipe Introduction (Ghosh)

Python Scripting
Introduction to Nypipe
First-level fMRI analysis with Nipype

Laboratory: Preprocessing II (Zeffiro, Ghosh, Whitfield-Gabrieli)

 

 

MAR 19 - Session 6

 

Correlation, Regression and the General Linear Model (Zeffiro)

Measures of central tendency and variability
Correlation
Simple regression
Multiple regression
First-Level Modeling and Estimation
Design matrix specification
Task condition specification
Hemodynamic response modeling
Covariate specification
Behavioral Measurements
Why measure performance?
Measures of performance - accuracy, response time, recall
Performance as an independent variable
Performance as a modulating variable
Performance as a nuisance variable
Binary and continuous responses

 

Laboratory: Building First-Level Models (Zeffiro, Whitfield-Gabrieli)

 

 

MAR 26 - Spring Break - no class

 

 

APR 2 - Session 7

 

First-Level Inference and Critical Thresholds (Zeffiro)

Temporal autocorrelation
Spatial autocorrelation
Gaussian field theory
Multiple comparison correction

 

Laboratory: Critical thresholds (Zeffiro, Whitfield-Gabrieli)

 

 

APR 9 - Session 8

 

 

fMRI Group Modeling and Estimation (Zeffiro)

Summary statistics
Within and between subject factors
Regression models
ANOVA models
ANCOVA models
fMRI Group Inference
Sample size considerations
Serial testing

 

Laboratory: Building Second-Level Models (Zeffiro, Whitfield-Gabrieli)

 

 

APR 16 - Patriots Day - no class

 

 

APR 23 - Session 9

 

 

Neuroimaging Meta-Analysis (Zeffiro)

Theory
Practice

Neuroimaging Power Analysis (Zeffiro)

Theory
Practice

 

Outlier Detection and Quality Assurance (Whitfield-Gabrieli)

Detection and rejection of outliers (fMRI task and connectivity)
Motion regression, PACE, MoCo (parameters/matrix regression)
Stimulus Correlated Motion (SCM)
Power Spectra (high pass filter selection)
Mask images
Top Down QA (ResMS, RPV, Beta, Con, Tmaps)
 

Laboratory: Outlier Detection and Removal (Whitfield-Gabrieli, Zeffiro)

 

 

APR 30 - Session 10

 

Intrinsic Activity: Measuring Functional Connectivity (Whitfield-Gabrieli)

Bivariate correlation
Seed-Voxel
ROI-ROI
Voxel-Voxel
Semipartial correlation and multivariate regression
Single-subject analysis
Group analysis
QA: Motion, Artifacts
CompCor, Global Signal Regression, Anticorrelations

Laboratory: Seed-drive resting state functional connectivity (Whitfield-Gabrieli)
 

Suggested Reading: Behzadi et al., 2007, Chai et al., 2012, Power et al., 2012, Satterthwaite et al, 2012, Van Dijk et al, 2012

 

 

 

MAY 7 - Session 11

 

 

Intrinsic Activity: Measuring Functional Connectivity (Whitfield-Gabrieli)

Graph Theory
Independent component analysis
Psychophysiological Interaction

 

Laboratory:

 

 

MAY 14 - Session 12

 

Multivariate Pattern Analysis and Machine Learning (Ghosh)

 

 

Laboratory: MVPA and machine learning (Ghosh, Whitield-Gabrieli)

 

 

MAY 21 - FINAL EXAM

 

 READINGS

 

Session 1

MRI physics videos

 

Session 2

Slice acquisition order effects

 

Echo time effects

 

Field strength effects

 

Geometric distortion

Andersson, J.L.R., Hutton, C, Ashburner, C., Turner, R., and Friston, K. (2001). Modeling geometric deformations in EPI time series. NeuroImage, 13, 903-919.

Session 4

Head motion

Ardekani, B.A., Bachman, A.H., and Helpern, J.A. (2001). A quantitative comparison of motion detection algorithms in fMRI. Magnetic Resonance Imaging, 19, 959-963.
 

Birn, R.M., Bandettini, P.A., Coz, R.W., and Shaker, R. (1999). Event-related fMRI of tasks involving brief motion. Human Brain Mapping, 7, 106-114.
 

Bullmore, E.T., Brammer, M.J., Rabe-Hesketh, S.R., Curtis, V.A., Morris, R.G., Williams, S.C.R., Sharma, T., and McGuire, P.K. (1999). Methods for diagnosis and treatment of stimulus-correlated motion in generic brain activation studies using fMRI. Human Brain Mapping, 7, 38-48.
 

Field, A.S., Yen, Y.F., Burdette, J.H., and Elster, A.D. (2000). False cerebral activation on BOLD functional MR images: study of love-amplitude motion weakly correlated to stimulus. American Journal of Neuroradiology, 21, 1388-1396.
 

Freire, L. and Mangin, J.F. (2001). Motion correction algorithms may create spurious brain activations in the absence of subject motion. NeuroImage, 14, 709-722.
 

Grootoonk, S., Hutton, C., Ashburner, J., Howseman, A.M., Josephs, O., Rees, G., Friston, K.J., and Turner, R. (2000). Characterization and correction of interpolation effects in the realignment of fMRI time series. NeuroImage, 11, 49-57.
 

Morgan, V.L., Pickens, D.R., Hartmann, S.L., and Prince, R.R. (2001). Comparison of functional MRI image realignment tools using a computer-generated phantom. Magnetic Resonance Imaging, 46, 510-514.
 

Stocker, T., Schneider, F., Klein, M., Habel, U., Kellermann, T., Zilles, K., and Shah, N.J. (2005). Automated quality assurance routines for fMRI data applied to a multicenter study. Human Brain Mapping, 25, 237-246.
 

Ward, H.A., Riederer, S.J., Grimm, R.C., Ehman, R.L., Felmlee, J.P., and Jack, Jr., C.R. (2000). Prospective multiaxial motion correction for fMRI. Magnetic Resonance in Medicine, 43, 459-469.

Cardiac and respiratory effects

 

Artifact Detection/Quality Assurance

Diedrichsen, J. and Shadmehr, R. (2005). Detecting and adjusting for artifacts in fMRI time series data. NeuroImage, 27, 624-634

 

Functional connectivity

Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. (In Press) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage.

Van Dijk KR, Sabuncu MR, Buckner RL. 2012 The influence of head motion on intrinsic functional connectivity MRI. Neuroimage. 59:431-8.
 

Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, Hakonarson H, Gur RC, Gur RE. 2012 Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. Neuroimage. 2012 Jan 2;60(1):623-632.

Preprocessing
 

Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage, 37(1), 90-101. doi:10.1016/j.neuroimage.2007.04.042
 

Evans, J. W., Todd, R. M., Taylor, M. J., & Strother, S. C. (2010). Group specific optimisation of fMRI processing steps for child and adult data. NeuroImage, 50(2), 479-90. Elsevier Inc. doi:10.1016/j.neuroimage.2009.11.039
 

Friedman, L., & Glover, G. H. (2006). Report on a multicenter fMRI quality assurance protocol. Journal of magnetic resonance imaging : JMRI, 23(6), 827-39. doi:10.1002/jmri.20583
 

Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63-72. Elsevier Inc. doi:10.1016/j.neuroimage.2009.06.060
 

Jezzard, P., & Balaban, R. S. (1995). Correction for geometric distortion in echo planar images from B0 field variations. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 34(1), 65-73. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7674900
 

Johnstone, Tom, Ores Walsh, K. S., Greischar, L. L., Alexander, A. L., Fox, A. S., Davidson, R. J., & Oakes, T. R. (2006). Motion correction and the use of motion covariates in multiple-subject fMRI analysis. Human brain mapping, 27(10), 779-88. doi:10.1002/hbm.20219
 

Klein, A., Andersson, J., Ardekani, B. a, Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., et al. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3), 786-802. Elsevier B.V. doi:10.1016/j.neuroimage.2008.12.037
 

Klein, A., Ghosh, S. S., Avants, B., Yeo, B. T. T., Fischl, B., Ardekani, B., Gee, J. C., et al. (2010). Evaluation of volume-based and surface-based brain image registration methods. NeuroImage, 51(1), 214-20. Elsevier B.V. doi:10.1016/j.neuroimage.2010.01.091
 

Lund, T. E., Madsen, K. H., Sidaros, K., Luo, W.-L., & Nichols, T. E. (2006). Non-white noise in fMRI: does modelling have an impact? NeuroImage, 29(1), 54-66. doi:10.1016/j.neuroimage.2005.07.005
 

Oakes, T R, Johnstone, T., Ores Walsh, K. S., Greischar, L. L., Alexander, a L., Fox, a S., & Davidson, R. J. (2005). Comparison of fMRI motion correction software tools. NeuroImage, 28(3), 529-43. doi:10.1016/j.neuroimage.2005.05.058
 

Sladky, R., Friston, K. J., Tröstl, J., Cunnington, R., Moser, E., & Windischberger, C. (2011). Slice-timing effects and their correction in functional MRI. NeuroImage. Elsevier Inc. doi:10.1016/j.neuroimage.2011.06.078
 

Smith, Stephen M, & Brady, J. M. (1997). SUSAN — A New Approach to Low Level Image Processing. International Journal of Computer Vision, 23(1), 45-78.
 

Stöcker, T., Schneider, F., Klein, M., Habel, U., Kellermann, T., Zilles, K., & Shah, N. J. (2005). Automated quality assurance routines for fMRI data applied to a multicenter study. Human brain mapping, 25(2), 237-46. doi:10.1002/hbm.20096
 

Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., & Poline, J.-B. (2006). Dealing with the shortcomings of spatial normalization: multi-subject parcellation of fMRI datasets. Human brain mapping, 27(8), 678-93. doi:10.1002/hbm.20210
 

Thirion, B., Pinel, P., Mériaux, S., Roche, A., Dehaene, S., & Poline, J.-B. (2007). Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses. NeuroImage, 35(1), 105-20. doi:10.1016/j.neuroimage.2006.11.054

 

fMRI Task Analysis

Henson Rik, ANOVAs in SPM
 

Independence
 

Vul E, Pashler H (2012) Voodoo and circularity errors NeuroImage

 

 

Functional Connectivity
 

Fox M.D, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 102: 9673-9678.
 

Chai XJ, Castañón AN, Ongür D, Whitfield-Gabrieli S. 2012. Anticorrelations in resting state networks without global signal regression. Neuroimage. 59: 1420-8.
 

Behzadi Y, Restom K, Liau J, Liu TT. 2007. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage, 37: 90-101.
 

Weissenbacher, A., Kasess, C., Gerstl, F., Lanzenberger, R., Moser, E., Windischberger, C.,2009. Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. Neuroimage 47,1408–1416.
 

Watts DJ, & Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393: 440–442.

 

Multivariate Pattern Analysis

Cohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ, Poldrack RA. (2011) Decoding continuous variables from neuroimaging data: basic and clinical applications. Front Neurosci. 5:75. Link: http://www.frontiersin.org/neuroscience/10.3389/fnins.2011.00075/abstract

Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P. (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science. 293(5539):2425-30.

Haxby JV, Guntupalli JS, Connolly AC, Halchenko YO, Conroy BR, Gobbini MI, Hanke M, Ramadge PJ. (2011) A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron. 72(2):404-16.
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