MEG/EEG source imaging using
VBMEG
2019/11/27 Yusuke Takeda
ATR Neural Information Analysis Laboratories

Contents• Overview• Theory• Usage• FAQ and News

Contents• Overview• Theory• Usage• FAQ and News

What is VBMEG?
• VBMEG = Variational Bayesian Multimodal EncephaloGraphy• A MATLAB toolbox for MEG/EEG source imaging

Main functions(Takeda et al., 2019)

Source current estimation(Yoshioka et al., 2008)

Retinotopic map(Paulun et al., 2018)

Source current estimation

Connectome dynamics estimation

HistorySato et al., 2004 NeuroImageVBMEG 1 (2011)(Takeda et al., 2019)

Fukushima et al., 2015 NeuroImageVBMEG 2 (2017)

Advantage
Ability to integrate multimodal measurements(Takeda et al., 2019)

Why is integrating multimodal
measurements important?
MEG/EEGfMRI

Current sourcesHigh spatialresolution

High spatiotemporal
resolution+High temporal
resolution

Why is integrating multimodal
measurements important?
Estimating source current is ill-posed problem.

Why is integrating multimodal
measurements important?
Prior information is necessary to reduce solution space.

Uniform prior
Low spatial resolution

Why is integrating multimodal
measurements important?
By using fMRI as prior information, we can obtainsource current with high spatial resolution.

fMRI priorHigh spatial resolutionReliability of VBMEG
8 method studies on VBMEG(Sato et al., NeuroImage 2004; Yoshioka et al., NeuroImage 2008; Fujiwara et al.,NeuroImage 2009; Aihara et al., NeuroImage 2012; Morishige et al., NeuroImage
2014; Fukushima et al., NeuroImage 2015; Sato et al., PLoS One 2018; Takeda et al.,Front Neurosci 2019)14 neuroscience studies using VBMEG(Shibata et al., Cereb Cortex 2008; Callan et al., NeuroImage 2010; Toda et al.,NeuroImage 2011; Yoshimura et al., NeuroImage 2012; Yamagishi et al., PLoSOne 2013; Morioka et al., NeuroImage 2014; Takeda et al., PLoS One 2014;Callan et al., Front Hum Neurosci 2016; Ohata et al., Sci Rep 2016; Yanagisawa etal., Nat Commun 2016; Yoshimura et al., Sci Rep 2017; Fukuma et al., Front
Neurosci 2018; Mejia et al., Front Neurosci 2018; Filatova et al., Front NeuralCirc 2018)

Contents• Overview• Theory• Usage• FAQ and News

References• Source current estimation
Sato M, Yoshioka T, Kajihara S, Toyama K, Goda N, Doya K, Kawato M.
Hierarchical Bayesian estimation for MEG inverse problem. Neuroimage.
2004 Nov;23(3):806-26.
吉岡 琢、佐藤 雅昭. 階層変分ベイズ推定法（VBMEG）の原理と応用. 日本神経回路学会誌 2011 Vol. 18, No. 4:214-223.
https://www.jstage.jst.go.jp/article/jnns/18/4/18_4_214/_pdf• Connectome dynamics estimation
Fukushima M, Yamashita O, Knösche TR, Sato M. MEG sourcereconstruction based on identification of directed source interactions onwhole-brain anatomical networks. Neuroimage. 2015 Jan 15;105:408-27.
https://www.sciencedirect.com/science/article/pii/S1053811914008088?
via%3DihubSource current estimation
Forward model of VBMEG
MEG
EEG

B

Current

JCurrent
inversevarianceα

A = diag(α1,...,αI)

fMRI

fMRI

Difference from dSPM (Dale et al., 2000)
VBMEG

dSPM

B

B

JJ

α

fMRI

fMRI

VBMEG is robust to inaccurate fMRI
activities.

Source current estimation
Forward model of VBMEG
MEG
EEG

B

Current

J

Current
inversevariance

αfMRI

Localized currents tend to be estimated.

AutomaticRelevance
Determination(ARD)Source current estimation
Estimation algorithm

BJαSource current estimation
Estimation algorithm

BdSPM

Jα

Source current estimation
Estimation algorithm

BJαα-step fMRI

Source current estimation
Estimation algorithm

BJ
αJ-step

α-step
DatafMRI
Currentvariance
Prior weight(Hyperparameter)

Prior weight(0 ≤ w ≤ 1)

Current variance

SmallFar from fMRI activity

SparseLarge

Close to fMRI activity

Dense

Connectome dynamics estimation
Generative model

Anatomical connectivity
derived from dMRI

Node nRelation with DCM
VBMEG

Dynamic Causal Modeling(DCM)• Linear• Whole-brain• Nonlinear• Few ROIs

Connectome dynamics estimation
Estimation algorithm• Fukushima et al.’s method (2015)
Current and connectivity are jointly estimated.
CurrentConnectivity• Two-step method
After estimating current, connectivity is estimated by
the l2-regularized least squares method.
Current

Connectivity

Contents• Overview• Theory• Usage• FAQ and NewsHow to learn VBMEG’s usage
https://vbmeg.atr.jp/document/

ScriptGUI• Users manual

General manual of VBMEG(with function references and tips)• Basic tutorial

MEG → Source current• Advanced tutorial

MEG/EEG → Source current→ Connectome dynamics• Easy VBMEG tutorial

EEG → Source current(without T1 image and sensor location)• Neuromag tutorial

MEG/EEG → Source current→ Group analysis→ Connectome dynamics

Research topic in Front. Neurosci.
https://www.frontiersin.org/research-topics/5158/from-raw-megeeg-to-publicationhow-to-perform-megeeg-group-analysis-with-free-academic-software#articles• VBMEG• SPM12• Brainstrom• FieldTrip• MNE• etc.

Neuromag tutorial
https://www.frontiersin.org/articles/10.3389/fnins.2019.00241/full

https://vbmeg.atr.jp/docs/v2/static/vbmeg2_tutorial_neuromag.html

https://openneuro.org/datasets/ds000117/versions/1.0.1

Neuromag tutorialNeuromag MEG/EEG• Open dataset recorded by Wakeman and Henson (2015)
https://openneuro.org/datasets/ds000117/versions/1.0.1• MEG and EEG were simultaneously recorded during aface recognition task by Neuromag system.

Face

Scrambled

Starting VBMEG
https://vbmeg.atr.jp/download2/

System requirements
OS : Linux
Software : MATLAB (R14–R2014a)
FreeSurfer4.2 or newer
SPM8

Modeling brain

Signal processing toolbox

M/EEG processing

MRtrix 0.2.1x
FSL 4.1 or newer

Connectomedynamics
estimation

Procedure• Brain modeling• Import data
• Estimate source current from MEG• Group analysis• Estimate source current from EEG• Estimate source current from MEG and EEG• Estimate connectome dynamics

Procedure• Brain modeling• Import data
• Estimate source current from MEG• Group analysis• Estimate source current from EEG• Estimate source current from MEG and EEG• Estimate connectome dynamics

Brain modeling
1. Import T1 image (convert coordinate system to that of VBMEG)
2. Extract cortical surface by FreeSurfer
3. Define sources on cortical surface

VBMEG’s coordinate system
RAS, origin = center of T1+X = Right+Y = Anterior+Z = Superior

How to define sources
FreeSurfer’ssphere coordinate
Standard brain(MNI-ICBM152)

Subject brain

MappingSource locations are consistent acrosssubjects
Standardi-th vertex

Subj. 1i-th vertex

Subj. 2

Subj. Ni-th vertex

i-th vertex

We can proceed to group analyses without anytransformation.

Procedure• Brain modeling• Import data
• Estimate source current from MEG• Group analysis• Estimate source current from EEG• Estimate source current from MEG and EEG• Estimate connectome dynamics

Importing data
Supported format: Yokogawa and Neuromag MEG
Biosemi and Brainamp EEG

If you have any requests for the supportedformat, please inform us.

Preprocessed MEG
Stimulus-triggered averagePreprocessed EEG
Stimulus-triggered averageProcedure• Brain modeling• Import data
• Estimate source current from MEG• Group analysis• Estimate source current from EEG• Estimate source current from MEG and EEG• Estimate connectome dynamics

Estimate source current from MEG
Important parameters• bayes_parm.prior_weight• bayes_parm.twin_meg• bayes_parm.twin_noise• bayes_parm.trial_averagebayes_parm.prior_weight
α-stepPrior weight(Hyperparameter)

Data

fMRI

(0 ≤ w ≤ 1)

In Neuromag tutorial, bayes_parm.prior_weight = 0.3

bayes_parm.twin_meg
α-step

Data

fMRI

Prior weight(0 ≤ w ≤ 1)

(Hyperparameter)

In Neuromag tutorial
00.5 sbayes_parm.twin_noise
MEGEEGB

Current

JObservation noise covariance

In Neuromag tutorial-0.3 s-0.3–0 sec0

bayes_parm.trial_average
This parameter determines whether noise covarianceand current variances are calculated from trialaveraged data.
We highly recommend
bayes_parm.trial_average = OFF(default)

Estimated source current from MEG
Stimulus-triggered average (Sub. 8)

LRComparison with other methods
VBMEG

L

R

wMNEdSPMLCMVThanks to fMRI and ARD priors, VBMEG’s source
current is localized around visual processing areas.

Procedure• Brain modeling• Import data
• Estimate source current from MEG• Group analysis• Estimate source current from EEG• Estimate source current from MEG and EEG• Estimate connectome dynamics

Group analysis
Detect face-selective activity
FaceScrambled–No transformation is necessary

Caution
Absolute values (or powers) of currents should be used.

In Neuromag tutorial•••

Calculate stimulus-triggered average of currents
Normalize it so that baseline period (-0.3–0 s) has mean 0 and SD 1
Calculate amplitude•

For each source and time, compared 16 subjects’ amplitudes
between face and scrambled conditions
Multiple comparison problem is controlled by False Discovery Rate(FDR)•Group analysis result
Face - Scrambled

Fusiform Face
Area (FFA)

LRComparison with other methods
VBMEGL

RwMNE

dSPM

LCMV

VBMEG exhibited localized activity around right FFA.
This might be because integrating fMRI suppressedsignal leakage.

Procedure• Brain modeling• Import data
• Estimate source current from MEG• Group analysis• Estimate source current from EEG• Estimate source current from MEG and EEG• Estimate connectome dynamics

Estimating source current from EEGCaution
We need to take a reference from EEG and its leadfield.
In Neuromag tutorial% Take common average> basis_file = fullfile(p.proj_root, basis_parm.basis_file);> load(basis_file, 'basis’)> for v = 1:size(basis, 1);basis(v, :) = basis(v, :)-mean(basis(v, :), 2);> end> vb_save(basis_file, 'basis');Estimating source current from EEG
Brain, Skull, Scalp
Default conductivities are set to0.62, 0.03, 0.62 S/mFrom Fujiwara-san’sslide

Estimated source current from EEG
Stimulus-triggered average (Sub. 8)

LRProcedure• Brain modeling• Import data• Estimate source current from MEG• Group analysis• Estimate source current from EEG• Estimate source current from MEG and EEG• Estimate connectome dynamics

Estimating source current from MEG
and EEGMEG and EEG have different sensitivities.

From Fujiwara-san’sslide

Estimating source current from MEG
and EEG
Combining MEG and EEG improves the estimation
accuracy of source current.
MEG+EEGMEG

EEGAccurate
From Fujiwara-san’sslide

Estimated source current from MEG
and EEG
Stimulus-triggered average (Sub. 8)

LRProcedure• Brain modeling• Import data
• Estimate source current from MEG• Group analysis• Estimate source current from EEG• Estimate source current from MEG and EEG• Estimate connectome dynamics

Estimate connectome dynamics
1. Estimate anatomical connectivity

2. Estimate connectome dynamics

Estimate anatomical connectivity
Necessary software:

Procedure

MRtrix 0.2.1x
FSL 4.1 or newer

1. Parcellation
2. Fiber tracking
3. ThresholdingEstimate connectome dynamics
1. Estimate connectome dynamics
by l2-regularized least squares method(default) p = 2, c = 6 m/s, D = 27 ms

1. test2. Generate a movie displaying signal flows

Generated movie

Contents• Overview• Theory• Usage• FAQ and News

FAQ and News• I don’t have fMRI
• How to determine prior weight• Brodmann area labels are strange• Optically-pumped magnetometerFAQ and News• I don’t have fMRI
• How to determine prior weight• Brodmann area labels are strange• Optically-pumped magnetometerI don’t have fMRI
• Use uniform prior by setting
bayes_parm.act_key = ‘Uniform’bayes_parm.prior_weight = very small value(e.g. 0.0001)

Or• Use meta-analysis results of fMRI studies
Suzuki-kun’s talkI don’t have any data〜
Please use our dataset (n=5)!

https://bicr-resource.atr.jp/mulds/

FAQ and News• I don’t have fMRI
• How to determine prior weight• Brodmann area labels are strange• Optically-pumped magnetometer

How to determine prior weight
Simulation test conducted by Suzuki-kun
SNR of MEG = 0

SNR of MEG = 10
Correlation coefficient

prior weight = 0.3 is good

How to determine prior weight

Practically, try prior weights from 0.01 to 0.3
by few subjects’ data and determine the priorweight which provide reasonable result.

FAQ and News• I don’t have fMRI
• How to determine prior weight• Brodmann area labels are strange• Optically-pumped magnetometer

Brodmann area labels are strange
BA4 in*_brodmann.area.mat

BA4(https://en.wikipedia.org/wiki/Brodmann_area_4)• Left-right asymmetry• Bad location

Causes• Removing island made scattered area small

BA6BA4

BA6BA4• Bad matching between voxel and surface

Solution
Import FreeSurfer’s label file

FreeSurfer’s label file(aparc.annot)
ImportVBMEG brain model(80000 vertices)New area files in new VBMEG*_Destrieux.area.mat*_Desikan-Killany.area.mat*_Yeo2011_7Networks_N1000.area.mat*_Yeo2011_17Networks_N1000.area.mat

To be removed area file

FAQ and News• I don’t have fMRI
• How to determine prior weight• Brodmann area labels are strange• Optically-pumped magnetometer

Optically-Pumped Magnetometer
LaseremitterSensor
MagneticfieldGasDetector• Closer to the brain• No maintenance cost
• Subject can moveFuture plan
Estimate source current from simultaneous recordingsof OPM and EEGCap image

Summary of VBMEG’ advantagesHigh
spatiotemporal

Whole-brain

Limitation
VBMEG does not support source imaging fromresting-state MEG/EEG.
Ito-kun’s talkThank you for your attention!