SDSU Foundations of Neuroimaging (PSY 596 — Fall 2017)
UNDER CONSTRUCTION
Professor:
Marty Sereno -- email: msereno - AT - sdsu
time: MWF 1:00 - 1:50 AM (graduate: TBA)
location:
SSW 2667
(Learning Glass Studio, Student Services West)
Required Readings:
Huettel, S., A.W. Song, and G. McCarthy (2014)
Functional Magnetic Resonance Imaging, 3rd ed.
Sereno Lecture Notes
(69-page PDF [11MB, updated: Mar 2016] -- single-page links below)
Recommended Readings:
Additional books/articles below
Assignments:
Homework #1
(due ##/##/##, paper printout, incl. code and graphs)
Homework #2
(due ##/##/##, paper printout, incl. code and graphs, brain image is
here)
Final Paper: 10 page literature review on narrow methodological topic
(start search in
Magnetic Resonance in Medicine, Neuroimage, Human Brain Mapping)
Learning Objectives:
Students will be able to do the following:
(1) explain excitation/recording/contrast of
magnetic resonance signals and echoes using the Bloch equation
(2) compute Fourier transform, use it to explain
how RF stimulation, gradients, and RF coil signals generate brain images
(3) describe origin/localization of EEG/MEG signals,
cortical surface-based methods, and how to combine them w/fMRI
N.B.: consult with me if a disability hinders your
performance so we can use University resources to maximize learning
Description and Prerequisites:
This course covers the physical and mathematical foundations of
structural, functional, and diffusion MRI, and fMRI time series
analysis. It also covers cortical-surface based reconstruction and
data analysis, and the neural basis, recording, and localization
of EEG and MEG signals. Although this course does not assume a
background in linear algebra, vector calculus, electromagnetism,
the Fourier transform, and convolution, students will be expected to
develop a solid grasp of the key equations underlying the different
neuroimaging methods and solve simple Matlab problems. There are two
other neuroimaging courses available at UCSD. The first is Tom Liu's
Bioengineering
280A -- Principles of Biomedical Imaging (Fall 2015), which provides
a stronger mathematical foundation in the fundamentals of the Fourier
transform and linear systems theory and covers ultrasound and CT in
addition to MRI. The second is the team-taught two-quarter SOMI 276 -- School of Medicine fMRI
Course in 2016/2017 offered by the Radiology Department and organized
by David Dubowitz and Rick Buxton.
Lecture Topics -- Fall 2017 --
this page: http://www.cogsci.ucsd.edu/~sereno/596i/
Week 1 -- MRI Signals I --
Huettel Chap. 2,3 (Buxton Chap. 3,4,6,7)
Week 2 -- MRI Signals II --
Huettel Chap. 5,9 (Buxton Chap. 4,8)
Week 3 -- Fourier Transform, Slice Selection --
Huettel Chap. 4 (Buxton Chap. 5,10)
Week 4 -- MRI Image Formation --
Huettel Chap. 4 (Buxton Chap. 5,10)
Week 5 -- Image Reconstruction, Structural Pulse Sequences --
Huettel Chap. 4 (Buxton Chap. 11)
Week 6 -- Functional MRI, Artifacts, Correction --
Huettel Chap. 6,7,8,10 (Buxton Chap. 12,16,17)
Week 7 -- Diffusion and Perfusion, General Linear Model --
Huettel Chap. 11, 12 (Buxton Chap. 9, 15)
Week 8 -- Surface-Based Methods and Cortical Mapping --
Online PDFs
Week 9 -- Sources of EEG/MEG --
Huettel Chap. 15, Online PDFs
Week 10 -- EEG/MEG Localization --
Online PDFs
READINGS
Math Resources:
MATLAB Primer
Free Software:
FreeSurfer (MGH)
FreeSurfer (retinotopy-enabled) -- contact msereno at ucsd edu
AFNI
FSL
SPM12
Web MRI Resources:
Hornak, J.P. (1996-2002)
The Basics of MRI
D.M. Higgins (2006)
K-space Tool
Jody Culham (2002)
fMRI for Dummies
Carl D. Gregory (1998)
MRI Artifact Gallery
Neuroimaging Books:
Buxton, R. (2009)
Introduction to Functional Magnetic Resonance Imaging, 2nd ed.
Huettel, S., A.W. Song, and G. McCarthy, 3rd ed. (2014)
Functional Magnetic Resonance Imaging
Haacke, E.M., R.W. Brown, M.R. Thompson, and R. Venkatesan (1999)
Magnetic Resonance Imaging
Bernstein, M.A., K.F. King, and X.J. Zhou (2004)
Handbook of MRI Pulse Sequences
Liang, Z.-P. and P.C Lauterbur (1999)
Principles of Magnetic Resonance Imaging
Nishimura, D.G. (1996)
Principles of Magnetic Resonance Imaging [available from author]
Hashemi, R.H., W.G. Bradley, C.J. Lisanti (2004)
MRI: The Basics
Nunez, P.L (1981)
Electric Fields of the Brain
Background:
Bracewell, R.N. (2000) The Fourier Transform and its Applications
Oppenheim, A.V. and A.S. Willsky (1997) Signals and Systems
Karu, Z.Z. (1995) Signals and Systems Made Ridiculously Easy
Wangsness, R.K. (1986) Electromagnetic Fields
Greenberg, M.D. (1978) Foundations of Applied Mathematics
Koch, C. and I. Segev (1998) Methods in Neuronal Modeling
Johnston, D. and S.M.-S. Wu (1995) Foundations of Cellular Neurophysiology
[updates of following papers in progress]
Pulse Sequence Papers:
QUIPSS II
Turbo ASL
Spherical Navigators
SENSE
SPACE RIP
Inverse Method Distortion Correction
Point-Spread Function Mapping Distortion Correction
MRI Data Analysis Papers:
Estimation, and Predictability
Detection, Estimation, and Predictability
3dDeconvolve
Visual Mapping Papers:
Analysis of Retinotopy
Human Visual Areas
Visual Mapping Review '98
Visual Mapping Review '05
Retinotopy Step-by-Step
Human V1
Human V3A
Ipsilateral Representation
Human V4
Human V6
Human V7
Human V8
Human MT/MST
Human LIP
Human VIP Multisensory
Visual Tracking
Frontal Maps
Auditory Mapping Papers:
Auditory Phase-Map
Somatosensory Mapping Papers:
Somatosensory Phase-Map
Dale and Sereno, 93:(cortical surface, EEG/MEG inverse)
manuscript
(equations more readable), or
journal scan
Cortical Surface Reconstruction Papers:
Segmentation and Reconstruction
Inflation, Flattening, Coordinates
Inter-Subject Morphing
Cortical Thickness
Topology Repair
Montreal Cortical Surface Method
Van Essen Surface-Based Atlas
EEG and MEG Localization Papers:
fMRI/EEG Attention
Noise Sensitivity Normalized Inverse--Data
Noise Sensitivity Normalized Inverse--Models
Mapping the Conductivity Tensor
N400 Localization
Intracortical EEG Papers:
Surface Plus Laminar Analysis
3D CSD of Rat BAER
last modified: May 5, 2017
Scanned class notes (links above) © 2017 Martin I. Sereno.
Don't redistribute notes without attribution and link to this page.
Supported by NSF 0224321, NIH MH081990, Royal Society Wolfson.