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Source Analysis and
Multimodal Neuroimaging
Bin He Department of Biomedical Engineering Institute for Engineering in Medicine
University of Minnesota
Brain Activity is Spatio-temporal Process
• Brain activity is distributed in 3-dimensional space and evolves in time.
• High spatial resolution imaging modalities needed.
• High temporal resolution imaging modalities needed.
• Noninvasive imaging modalities needed for human brain imaging. 100 ms
Spatial & Temporal Resolution of Neuroimaging Modalities
He & Liu, IEEE Rev BME, 2008
Strategies
Increase temporal resolution of fMRI
Enhance spatial resolution of EEG/MEG Multimodal imaging combining fMRI
and electromagnetic imaging
Problem Formulation
Brain Electric Source
Volume Conductor E/MEG
Equivalent Brain Source
Volume Conductor
Model E/MEG
Inverse Problem/ Source Analysis
Forward Problem
Why Source Analysis?
The head volume conductor smears the EEG/MEG distribution over the scalp, thus it is difficult to relate scalp EEG/MEG to intra-cranial sources by only inspecting scalp EEG/MEG.
Electrophysiological Neuroimaging • Neuroimaging aimed at improving substantially
the spatial resolution of conventional EEG/MEG through deconvolution, and use of anatomic information on the brain and head as obtained from magnetic resonance imaging.
• We shall abbreviate it as EEGI below.
From Sensor to Source Space
Michel & He, Niedermeyer's Electroencephalography, 2011
Issues in EEGI
• Scalp Mapping • Equivalent Source Modeling • Volume Conductor Modeling • Inverse Source Solutions • Validation and Biomedical Applications
Electrophysiological Mapping of Binocular Rivalry
Zhang, Jamison, Engel, He, He, Neuron, 2012
Low density / high density array
32 electrodes 64 electrodes 128/256 electrodes
Effect of electrode numbers - Patient data
Mean and standard deviation of localization errors in clinical data analysis
Lu et al., Clin. Neurophysiol., 2012
Issues in EEGI
• Scalp Mapping • Equivalent Source Modeling • Volume Conductor Modeling • Inverse Source Solutions • Validation and Biomedical Applications
Electric Source Modeling
He et al, IEEE Trans BME, 2011
Equivalent Source Modeling
• Point Source Models - Moving Dipole - Fixed Dipole - Multipole • Distributed Source Models
- 2D Cortical Potential - 2D Cortical Dipole Layer - 3D Dipole Distribution - 3D Current Distribution
3D Source Models
Dipole Source Model
Distributed Source Model
• Amplitude, location, orientation
• Nonlinear problem
• Modeling focal and compact source
• Amplitude
• Linear problem
• Modeling distributed source network
Issues in EEGI
• Scalp Mapping • Equivalent Source Modeling • Volume Conductor Modeling • Inverse Source Solutions • Validation and Biomedical Applications
Head Conductor Modeling
• Homogeneous Sphere Model • Inhomogeneous 3-concentric-spheres Model • Inhomogeneous 4-concentric-spheres Model • Homogeneous Realistic Geometry (RG) Model • Piece-wise Homogeneous RG Model (BEM) • Inhomogeneous RG Model (FEM)
RG-Head based EEG Inverse Solution
He et al., IEEE Trans on BME, 1987
Brain-Head Modeling
He et al., NeuroImage, 2002
Issues in EEGI
• Scalp Mapping • Equivalent Source Modeling • Volume Conductor Modeling • Inverse Source Solutions • Validation and Biomedical Applications
Forward and Inverse Problems
Forward Problem
Inverse Problem
Source Model
- current dipoles
- discrete and distributed source models
Volume Conductor Model
- sphere head model
- boundary element model
- finite element model Source Imaging
- discrete source imaging
- distributed source imaging
Solving inverse problem
ill-posedness
regularization
Moving Dipole Localization
Dipole Source Model
Comparator
Volume Model
Parameter Modifier
Initial Parameters
Inverse Solution
Subject Mapping
System
EEG/MEG
Linear Inverse Problem
s
x
x - Scalp EEG/MEG
s - Unknown Source Vector
A - Transfer Matrix
b - Noise
• Tikhonov regularization:
k : truncation parameter
s(λ) = (AtA+λR)−1Atx
where
Regularization Approach
• Truncated singular value decomposition:
s(k) = A#x =VΣk−1Utx
L-Curve Approach
sFA φ−
F large k
small λ
small k large λ
Hansen 1992
Issues in EEGI
• Scalp Mapping • Equivalent Source Modeling • Volume Conductor Modeling • Inverse Source Solutions • Validation and Biomedical Applications
Electrophysiological
Source Imaging
MRI
Source Imaging
EEG
Cortical Imaging from EEG
Bai, He, et al., Brain Topography, 2011
Cortical Imaging of Epileptic Activity
Lai, He, et al., NeuroImage, 2011
Sparse Source Imaging from EEG
Ding & He, Human Brain Mapping, 2008
Spatio-temporal Seizure Imaging
Yang, He, et al., NeuroImage, 2011
EEG Imaging of Seizure Sources
Yang, He, et al., NeuroImage, 2011
Beyond EEGI
Increase temporal resolution of fMRI
Enhance spatial resolution of EEG/MEG Multimodal imaging combining fMRI
and electromagnetic imaging
Multimodal Neuroimaging
Electrophysiological and hemodynamic measurements represent complementaty responses of brain activation.
fMRI has high spatial resolution. EEG has high temporal resolution. Integration of fMRI with EEG represents a natural
approach to further improving spatio-temporal resolution, but a new challenge due to the very different time scales of these measurements.
EEG/fMRI & Brain Activity EEG
BOLD-fMRI activations Scalp potential maps
Neural activation
Neurovascular coupling
fMRI and EEG
He et al., IEEE TBME, 2011
Origin of BOLD
Logothetis et al., Nature, 2001
Simultaneous EEG-fMRI Recordings
Im, He et al., J Neurosci. Meth., 2006
Spatiotemporal Neuroimaging by fMRI-EEG Integration
EEG fMRI
fMRI-constrained cortical source imaging
Statistical Parametric Mapping
Cross-modal Relationship
Liu and He, NeuroImage, 2008
fMRI/EEG Multimodal Imaging
(Grill-Spector and Malach, Annu. Rev Neurosci, 2004)
Liu & He, NeuroImage, 2008
BOLD vs VEP Power
Liu, He, et al, NeuroImage, 2010
Functional Connectivity Imaging in Source Space
Babiloni, He, et al, NeuroImage, 2005
eConnectome
MATLAB-based, GUI Temporal Analysis Scalp Topographical Analysis Spectral Analysis Cortical Current Density Imaging Functional Connectivity Mapping EEG, V1.0, 2010 ECoG, V1.0, 2010 MEG V1.0, 2011 Open-source, GPL
Electrophysiological Connectome
http://econnectome.umn.edu
He et al., J Neurosci. Methods, 2011; Dai et al., Brain Topography, 2012
Thank you