Foundations of Neuroimaging (online course)
CURRENT VERSION 2023/2024
 Professor:

Marty Sereno
(homepage)
— email: msereno@ucsd.edu OR msereno@sdsu.edu
 Example lecture times: Mon/Wed/Fri, 9:009:50 AM
 Example advanced/grad section: Fri, 8:008:50 AM
 See also Systems Neuroscience (online course)
 Course Description:

This course covers (1) the physical, biological, and mathematical
foundations of data acquisition and reconstruction of structural,
functional, diffusion, and perfusion MRI images, (2) fMRI time series
analysis, (3) cortical surfacebased reconstruction and data analysis, and
(4) the neural basis, recording, and localization of EEG and MEG signals.
Although we don't assume a background in linear algebra, vector calculus,
differential equations, electromagnetism, the Fourier transform, and
convolution, students will be expected to develop a solid grasp of a
number of key equations underlying the different neuroimaging methods and
solve simple Matlab problems. We go slower than a typical engineering
class, but don't skip the math.
Special thanks to SDSU Physics Professor Matt Anderson and the SDSU
Instructional Technologies Services for the use of Matt's wonderful
Learning Glass lecture recording system, and David Poddig and Stan Greene
for inspiration and production. This course was developed and taught
at UCSD, Birkbeck/UCL (London), and SDSU.
 Resources:
 • Sereno lecture recordings:
direct video links or
youtube playlist (equivalent)

(58 onehour lectures: about one semester or two quarters)
 • Sereno lecture notes PDF
(124page PDF [19MB]— singlepage links below)
 • References: background reading PDFs
 • Reference texts:

Huettel, S., A.W. Song, G. McCarthy (2014)
Functional Magnetic Resonance Imaging
 Liang, Z.P. and P.C Lauterbur (1999)
Principles of Magnetic Resonance Imaging
 Nishimura, D.G. (1996)
Principles of Magnetic Resonance Imaging
 Buxton, R., 2nd ed. (2009)
Introduction to Functional Magnetic Resonance Imaging
 Haacke, E.M., R.W. Brown, et al. (2014) Magnetic Resonance Imaging
 Bernstein, M.A., K.F. King, and X.J. Zhou (2004)
Handbook of MRI Pulse Sequences
 Nunez, P.L (1981)
Electric Fields of the Brain
 Assignments:
 • Homework #0
(warmup)
 • Homework #1
(due end of 6th week as email PDF)
 • Homework #2
(due end of 12th week as email PDF, test brain image is
here)
 • Final Paper: (ugrad=5pg, grad=10pg) literature review on narrow methodological topic

(start search: Magnetic Resonance in Medicine, Neuroimage, Human Brain Mapping)
 Lecture Topics: (e.g., Fall semester course) (1page PDF syllabus)

Week 1 (Mon/Wed/Fri) — Introduction
 Introduction to Neuroimaging — MRI, fMRI, EEG, MEG
 MRI hardware
 Spin and Precession
Week 2 (Mon/Wed/Fri) — Bloch Equation
 Dot/Cross/Complex Products
 Bloch Equation
 Precession Solution
 Effects of Change (M, B, Angle) On Precession Freq
 InitialValue Solution to T2 Differential Equation
 Verify T1 ReGrowth Solution
 Simple Matrix Operations
 Bloch Equation, Solution — Matrix Version
 Bloch Equation: Excitation In Rotating Frame
 Bloch Equation: Summary
Week 3 (Wed/Fri) — Signal Equation
 [no class Mon]
 RF Excitation
 Signal Equation
 PhaseSensitive Detection
Week 4 (Mon/Wed/Fri) — Echoes
 Free Induction Decay
 Spin Echo
 Spin Echo Equations
 Stimulated Echo, Spin Echo Trains
 Extended Phase Graphs
 3Pulse Echo Amplitudes
 HyperEcho Train
 Gradient Echo, Gradient Echo Trains
Week 5 (Mon/Wed/Fri) — Using the Bloch Equation
 SaturationRecovery Signal
 Imperfect 90 deg Approach to State State
 Inversion Recovery Signal
 Spin Echo Signal
 Gradient Echo Signal
 Generalized MDEFT Signal
 Magnetization Transfer Contrast
 SNR/tSNR/CNR (e.g., Gray/White)
 SignaltoNoise
Week 6 (Mon/Wed/Fri) — Fourier transform
 Complex Algebra
 Fourier Transform
 Negative Exponents, Orthogonality
 Inverse Fourier as Correlation w/Cos,Sin
 Spatial Frequency Space (kSpace) — 3 Equivalent Representations
 One kSpace Point
 Simple Image (1 Freq), Shifted Simple Image
 Center of kSpace, Complex Image
 Kx plus Ky Rotates, Special Case Sum of Reals
Week 7 (Mon/Wed/Fri) — Gradients, Slice Selection
 Gradient Fields
 Gradient Combination
 Slice Selection
 RF Pulse Details
 Slice Select RF Pulses
 Introduction to FrequencyEncoding —It's A Misnomer
 FrequencyEncoding—Avoid This Intuition
 FrequencyEncoding—Correct Intuition (=PhaseEncoding)
Week 8 (Mon/Wed/Fri) — MRI Image Formation
 1st TakeHome Due
 Imaging Equation (1D)
 Phase Encoding
 3D Imaging (2nd PhaseEncode)
 Spin Phase in Image Space
 Gradients Move Signal in kSpace
Week 9 (Mon/Wed/Fri) — Image Reconstruction
 Image Space and kspace
(311K MPEG, R.S. Huang)
 Image Reconstruction
 Aliasing and FOV
 Under/Over Sample
 Replicas, FTs
 PointSpread Function
 General Linear Inverse for MRI Reconstruction
Week 10 (Mon/Wed/Fri) — Practical Pulse Sequences
 Fast Spin Echo
 3D MultiSlab Fast Spin Echo
 3D SingleSlab Fast Spin Echo (SPACE)
 Fast Gradient Echo
 Quantitative T1 (Intro, Other Methods)
 Quantitative T1 (2FlipAngle Method)
 Gradient Echo EPI
 Spin Echo Size Selectivity
 Spin Echo EPI
 Coil FallOff, Undersampling, GRAPPA, SENSE
 Simultaneous MultiSlice (blipped CAIPI)
 SMS cont.: sliceGRAPPA, splitsliceGRAPPA
 3D Echo Volume Imaging
 SingleShot Spiral
 3D Spiral Inversionrecovery FSE
Week 11 (Mon/Wed/Fri) — Image Artifacts
 Fourier Shift Artifacts
 EPI vs. Spiral Artifacts
 Imagespace View Localized B0 Defect
 Effect Local B0 Defect on Reconstruction
 Shimming and B0Mapping
 Gradient Nonlinearities
 Motiondetection with Navigator Echoes
 RF Field Inhomogeneities
Week 12 (Mon/Wed) — Diffusion, Perfusion, and Spectroscopy
 DiffusionWeighted Imaging and Tract Tracing
 Practical DiffusionWeighted Sequences
 Perfusion Imaging (Arterial Spin Labeling)
 pCASL
 OffResonance RF Excitation
 Combining Spectroscopy with Imaging
 PRESS, MEGAPRESS
 [no class Fri]
Week 13 (Mon/Wed/Fri) — Block Design, PhaseEncoded Design
 PhaseEncoded Stimulus and Mapping
 Convolution
 General Linear Model and Solution
 General Linear Model — Geometric Picture
 Cluster Correction — 3D and SurfaceBased
 Why Use Surfaces?
 Normalize, Strip Skull
 Spring Force in Detail
 Nonisotropic Filter, RegionGrowing
 Tessellation: 3D > 2D
 Energy Functional for Smooth/Inflate/Final
 Cortical Unfolding and Flattening
 SulcusBased Alignment
Week 14 (Mononly) — Cortical Surface Based methods
 2nd TakeHome Due
 Cortical Thickness Measurement
 Mapping Cortical Visual Areas
 [no class Wed/Fri]
Week 15 (Mon/Wed/Fri) — Source of EEG/MEG
 Neural Source of EEG/MEG
 Intracortical Circuits and EEG/MEG
 Grad, Div, Curl
 1D/2D/3D Current Source Density
 1D/2D Current Source Density Experiments
 Intracortical Basis of EEG
 Maxwell Equations — Low Frequency Limit
 Why We Can Ignore Magnetic Induction
 Monopole, Dipole Current Sources
Week 16 (Mon/Wed/Fri) — Neuroimaging EEG/MEG
 Forward Solution — Analytic, Boundary Element (incomplete)
 Matrix Formulation of the Linear Forward Solution
 Why Localize?
 Derivation of IllPosed Minimum Norm Linear Inverse
 Minimum Norm: Why It Appropriately 'Devalues' Deeper Sources
 Two Equivalent Formulations — One Is Easier to Compute
 SurfaceNormal Constraint and its Problems
 fMRIweighted Inverse Solutions
 NoiseSensitivity Normalization of Mininum Norm
 NoiseSensitivity Normalization — Intuition
 PointSpread/Crosstalk, Conclusions
Week 17 (Mononly) — Final Paper/Exam
 Using Spatiotemporal Covariance of Sensors (MUSIC)
 Sensor Covariance: Calculate Weights
 Sensor Covariance: Insert Weights into Inverse
 Sensor Covariance: How it Helps
 Nonlinear Fitting of Moveable Dipoles
Final paper due
last modified: Jul 2023
Scanned/video'd class notes (pdf, links above) © 2023 Martin I. Sereno
Supported by NSF 0224321, NIH MH081990, Royal Society Wolfson
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