SENSOR LEVEL ANALYSIS AND SOURCE
LOCALISATION in M/EEG
METHODS FOR DUMMIES
2013-2014
Mrudul Bhatt & Wenjun Bai
M/EEG SO FAR
Source
of Signal
Dipoles
Preprocessing and Experimental design
Statistical Analysis
Source Reconstruction
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Statistical Analysis
1. Sensor level analysis in SPM
2. Scalp vs. Time Images
3. Time-frequency analysis
Data
is time varying modulation of EEG/MEG signal amplitude (or frequency specific
power) at each electrode or sensor.
Interested
in statistical significance of condition specific effects (observed at some
peri-stimulus time or at a particular sensor) at sensors
Need
to control FWER - the probability of making a false positive over the whole
search space - AKA multiple comparisons problem
FWER
scales with number of observations
Bonferroni
too conservative due to assumption of independence between
neighbouring samples
Can
circumvent issue if space/time of interest is specified a priori
Average
data over pre-specified sensors or time bins of interest - produces one
summary statistic per subject per condition
If
this is not possible can use topological inference
Topological inference
• Based on RFT
• RFT provides a way of adjusting p-values for the
fact that neighbouring sensors are not independent
due to continuity in the original data
• Provided data is smooth, RFT correction is more sensitive
than a bonferroni correction
• This is the method used in SPM
Steps in SPM
Data
transformed to image files (NifTI)
Procedurally
identical to 1st level analysis in PET or 2nd
level in fMRI after this
Analysis
assumes one summary statistic image per
subject per condition
Creating Summary Statistics: Conversion to
images
• Data converted to an image by generating a scalp map for each
time frame and stacking over peristimulus time
• Scalp maps are generated from using the 2D sensor layout
(specified in data set) and linear interpolation between sensors
(64 pixels each spatial direction suggested)
• 3D image files (space x space x time)
• If time-window of interest is known in advance we can average
over this are and create a 2D spatial image
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Time-Frequency Data
• In principle can apply topological inference for n dimensions
• In SPM 8 its limited to 3 dimensions
• If data has time-frequency components it must be reduced from
4D (space x space x time x frequency) to 3/2D
• Reduce data by averaging over frequency (3D) or spatial
channels (2D time-frequency image)
• When averaging over frequency, bandwidth must be specified and
a new data set is produced and is exported in the same way
images in the time domain are
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Smoothing
Smoothing: prior
to 2nd level/group analysis -multi dimensional convolution with
Gaussian kernel.
Important to accommodate spatial/temporal variability over subjects and ensure
images conform to assumptions.
Multi-dimensional convolution with
Gaussian kernel
EEG analysis steps
Epoching
D/A conversion
Digital filtering
Baseline correction
Artifact reduction
Single trial averaging
Re-referencing
Grand averaging
Plotting, spline and CSD maps
Quantification
Statistical evaluation
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EEG/MEG source localization
•The purpose of source localization
•The hurdle prevent us to accurately localize the
source : Inverse Problem
A little recap:
The advantage of EEG compare to fMRI:
Superior Temporal resolution, with the cost of
inferior spatial resolution
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Why it is so challenging?
Smearing and distortion
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Inverse Problem
Forward problem (well-posed)
Data Y
Inverse problem (ill-posed)
Current density J
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Analogy to understand the inverse problem
How We Deal with Inverse Problem
1.Setting up Assumptions(Constraints)
2.Two Basic Approaches
A. Discrete Source Analysis
B. Distributed Source Analysis
Anatomical
constraints
Final Product:
Reconstructed Source
ill-posed inverse problem
EEG/MEG
Data
Functional
constraints
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Constraints
Assumptions about the nature of the sources
Three Types of Constraints:
1. Mathematic Constraints( e.g., minimum norm, maximum
smoothness, optimal resolution, temporal independence)
2. Anatomical Constraints (e.g., Normally use the subject’s MRI scan, if
not, it is possible to use standardized MRI brain atlas (e.g., MNI) can be
be warped to optimally fit the subject's anatomy based on the subject's
digitized head shape.)
3. Functional Constraints (e.g.,
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Discrete vs. Distributed Source Model
Discrete source analysis
Distributed source analysis
Current dipole represents an extended brain
area
Each current dipole represents one small
brain segment
Number of sources < number of sensors
Number of sources > number of sensors
The leadfieldmatrix has more
rows (number of sensors)
than colums (number of sources)
The leadfieldmatrix has more colums than
rows
Result:
Source model
and source
waveforms
Result:
3D Volume image
for each time point
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Algorithms associated with each analysis
Discrete source analysis
Distributed source analysis
1. Parametric Dipole source fitting:
(a)Uncorrelated noise model
(b)Correlated noise model
(c) Global minimization
1.Spatial scanning and beamforming:
independently scan for dipoles within a grid
containing candidate locations (i.e., source points)
All (a)(b)(c) algorithms converges to a local minima in the
multidimensional space of parameters, the optimal
parameters (each corresponding to a dimension) are found.
The algorithms estimates five nonlinear parameters per
dipole: the x, y, and z dipole position values, and the two
angles necessary to define dipole orientations in 3D space.
2. Distributed MAP- based estimation
assume dipoles at all possible candidate locations of interest
within a grid and/or mesh called the sourcespace (e.g.,
source-points in grey matter) and then solve the
underdetermined linear system of equations
Take Home Message 1:
No cure-it-all Approach
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SPM Pipeline for source localization
One kind reminder:
Source Localization(source reconstruction) is a
computationally intense procedure. If you get “out of
Memory” error message, try more powerful computer
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Step 1: Mesh
MRI – individual
head meshes
(boundaries of different head
compartments) based
on the subject’s
structural scan
template
MRI
Template – SPM’s
template head
model based on
the MNI brain
Step 2: Coregister
Co-register
Step 3: Forward Model
Step 4: Invert (The most crucial)
WHAT DO WE GET
Comparison between fMRI and MEG on Temporal Resolution
Take Home Message 2:
Source Localization is not perfect, being cautious in drawing any inferences
related to location and strengthen of the source
REFERENCES
Tolga
Esat Ozkurt-High Temporal Resolution brain Imaging
with EEG/MEG Lecture 10: Statistics for M/EEG data
James Kilner and Karl Friston. 2010.Topological Inference
for EEG and MEG. Annals of Applied Statistics Vol 4:3 pp
1272-1290
Vladimir Litvak et al. 2011. EEG and MEG data analysis in
SPM 8. Computational Intelligence and Neuroscience Vol
2011
MFD 2012/13
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