Fictional or Functional Connectivity? Validating and ... · analyses of EEG and MEG data. •Major...
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Fictional or Functional Connectivity? Validating and improving functional connectivity analyses for EEG Anthony Herdman 1 and Alexander Moiseev 2 1 University of British Columbia, Canada, 2 Simon Fraser University, Canada • Functional Connectivity (FC) has become prominent in neuroimaging analyses of EEG and MEG data. • Major problem with FC analyses is source mixing (a.k.a. leakage) due to electrical volume conduction and mathematical constraints in most single- source modeling procedures (e.g., SPA, LCMV , eLORETA, and MNE). • Source mixing generates false (a.k.a fictional) connectivity patterns. • Multi-source null-constraints in beamforming methods can significantly reduce or eliminate source mixing. (e.g., MIA=multiple-iterative step approach; Herdman et al., 2018) • Multi-source beamformers can; therefore, provide better estimates of functional connectivity from EEG data. Objective • To verify inverse-source solutions for FC analyses using simulated data (known truths) so that we feel more confident when interpreting FC results obtained from source modeling of real EEG & MEG data (unknown truths) Introduction Functional Connectivity Results Methods Conclusions 1) Use Multi-Source Beamformers (e.g., MIA) for functional connectivity analyses of EEG in order to improve: Finding TRUE connections Finding few, if any, false connections source waveform reconstruction and localization 2) Using Phase-Lag Index (PLI) can help but many false connections are still found for single-source inverse methods 3) Because single-source inverse methods (SPA, LCMV , eLORETA, and MNE) create large source leakages and find many false connections, these methods should be used with caution when conducting functional connectivity analyses of EEG. Acknowledgements Support and funding for this project were provided by: Available @ www.audiospeech.ubc.ca/research/branelab/ Multi-source beamformer (MIA) outperformed single-source methods (SPA, LCMV , ELOR, MNE) PLI improved performances but multi-source method still significantly better than single-source method Six sources largely reduced performances for single-source methods, but not for multi-source beamformer (MIA) Multi-source beamformers (MIA) found all TRUE connections and no false connections Single-source inverse methods (SPA, LCMV , ELOR, MNE) found many FALSE connections multiply ERPs by inverse wts Functional Connectivity Analyses black line = True PLV /PLI color lines = Seed PLV /PLI grey lines = other voxels’ PLV/PLI Phase-Locking Value (PLV) Analyses Phase-Lag Index (PLI) Analyses LH Aud Seed LH Aud Seed + = MIA SPA LCMV ELOR MNE Source Analyses EEG Simulation Inverse Solutions MIA wts SPA wts LCMV wts ELOR wts MNE wts Inverse Weights chans voxels wts wts wts wts wts Source Waveforms * time amp * * * * Inverse source waveforms by trial True source waveforms SNR = 0.6 (-4 dB) + = Time (sec) 3 (Aud) Sources Signal + Noise ERPs Source Configurations = 3 (Aud), 3 (Vis), 6 (Aud + Vis) Signal-to-Noise Ratio (SNR) = 0.2 (-14 dB), 0.4 (-8 dB), 0.6 (-4 dB) PLV/PLI = 0.4 for all possible connections among simulated sources Inverse Source Analyses: • multi-source (MIA) & single-source (SPA) scalar beamformer (Moiseev et al., 2011; Moiseev & Herdman, 2013; Herdman et al., 2018) • single-source inverse solutions (LCMV , eLORETA, and MNE) (www.fieldtriptoolbox.org) Head Model & Leadfields = Boundary-Element Model; 590 voxels (15 mm 3 ) x 64 EEG Channels Receiver Operating Characteristics (ROC) 3 (Aud) 3 (Vis) MIA significance (p<.05) LCMV significance ELOR significance SNR=0.2 significance 3(Aud) significance 3(Vis) significance Performances for PLV Functional Connectivity Statistics Performances for PLI MIA significance (p<.05) SPA significance LCMV significance ELOR significance SNR=0.2 significance 3(Aud) significance 3(Vis) significance Inverse Solutions Signal-to-Noise Ratio Source Configurations Inverse Solutions Signal-to-Noise Ratio Source Configurations real EEG noise Inverse Solutions x SNR Inverse Solutions x Configurations Inverse Solutions x SNR Inverse Solutions x Configurations SNR 3 (Aud) 3 (Vis) 6 (Aud+Vis) PLV: Receiver Operating Characteristic (ROC) Curves SNR 3 (Aud) 3 (Vis) 6 (Aud+Vis) PLI: Receiver Operating Characteristic (ROC) Curves Left AUD Seed Right AUD Seed Frontal Seed All Other Voxels grey = noise color = signal + noise black = true signal color = localizer Time (sec) Evoked Waveforms for 3 AUD sources (SNR=0.6, run=10) Event-Related Power Waveforms for 3 AUD sources (SNR=0.6, run=10) Time (sec) Better source waveform reconstructions for multi-source beamformer (MIA) than single-source methods (SPA, LCMV , ELOR, MNE) grey = noise color = signal + noise black = true signal color = inverse soln LH AUD Seed RH AUD Seed single-source inverse solutions multi-source inverse solution Frontal Seed true source connectivity color lines = True Positives grey lines = False Positives Grand-Averaged PLI Maps for 6 sources (Aud+Vis) at SNR=0.6
Transcript of Fictional or Functional Connectivity? Validating and ... · analyses of EEG and MEG data. •Major...
Fictional or Functional Connectivity? Validating and improving functional connectivity analyses for EEG
Anthony Herdman1 and Alexander Moiseev2 1University of British Columbia, Canada, 2Simon Fraser University, Canada
• Functional Connectivity (FC) has become prominent in neuroimaging analyses of EEG and MEG data.
• Major problem with FC analyses is source mixing (a.k.a. leakage) due to electrical volume conduction and mathematical constraints in most single-source modeling procedures (e.g., SPA, LCMV, eLORETA, and MNE).
• Source mixing generates false (a.k.a fictional) connectivity patterns.
• Multi-source null-constraints in beamforming methods can significantly reduce or eliminate source mixing. (e.g., MIA=multiple-iterative step approach; Herdman et al., 2018)
• Multi-source beamformers can; therefore, provide better estimates of functional connectivity from EEG data.
Objective
• To verify inverse-source solutions for FC analyses using simulated data (known truths) so that we feel more confident when interpreting FC results obtained from source modeling of real EEG & MEG data (unknown truths)
Introduction Functional Connectivity Results
Methods
Conclusions 1) Use Multi-Source Beamformers (e.g., MIA) for functional connectivity analyses
of EEG in order to improve: Finding TRUE connections Finding few, if any, false connections source waveform reconstruction and localization
2) Using Phase-Lag Index (PLI) can help but many false connections are still found for single-source inverse methods
3) Because single-source inverse methods (SPA, LCMV, eLORETA, and MNE) create large source leakages and find many false connections, these methods should be used with caution when conducting functional connectivity analyses of EEG.
Acknowledgements Support and funding for this project were provided by:
Available @ www.audiospeech.ubc.ca/research/branelab/
Multi-source beamformer (MIA) outperformed single-source methods (SPA, LCMV, ELOR, MNE)
PLI improved performances but multi-source method still significantly better than single-source method Six sources largely reduced performances for single-source methods, but not for multi-source beamformer (MIA)
Multi-source beamformers (MIA) found all TRUE connections and no false connections Single-source inverse methods (SPA, LCMV, ELOR, MNE) found many FALSE connections
multiply ERPs by
inverse wts
Functional Connectivity Analyses
black line = True PLV /PLI color lines = Seed PLV /PLI grey lines = other voxels’ PLV/PLI
Phase-Locking Value (PLV) Analyses
Phase-Lag Index (PLI) Analyses
LH A
ud
See
d
LH A
ud
See
d
+ = MIA
SPA
LCMV
ELOR
MNE
Source Analyses EEG Simulation
Inverse Solutions
MIA wts
SPA wts
LCMV wts
ELOR wts
MNE wts
Inverse Weights
chan
s
voxels
wts wts wts wts wts
Source Waveforms
*
time
amp
* * * *
Inverse source waveforms by trial
True source waveforms
SNR = 0.6 (-4 dB)
+ =
Time (sec)
3 (Aud) Sources
Signal + Noise ERPs
Source Configurations = 3 (Aud), 3 (Vis), 6 (Aud + Vis) Signal-to-Noise Ratio (SNR) = 0.2 (-14 dB), 0.4 (-8 dB), 0.6 (-4 dB)
PLV/PLI = 0.4 for all possible connections among simulated sources Inverse Source Analyses: • multi-source (MIA) & single-source (SPA) scalar beamformer (Moiseev et al., 2011; Moiseev & Herdman, 2013; Herdman et al., 2018)
• single-source inverse solutions (LCMV, eLORETA, and MNE) (www.fieldtriptoolbox.org)
Head Model & Leadfields = Boundary-Element Model; 590 voxels (15 mm3) x 64 EEG Channels
Receiver Operating Characteristics (ROC)
3 (Aud)
3 (Vis)
MIA significance (p<.05)
LCMV significance
ELOR significance
SNR=0.2 significance 3(Aud) significance
3(Vis) significance
Performances for PLV
Functional Connectivity Statistics Performances for PLI
MIA significance (p<.05)
SPA significance
LCMV significance
ELOR significance
SNR=0.2 significance 3(Aud) significance
3(Vis) significance
Inverse Solutions Signal-to-Noise Ratio Source Configurations Inverse Solutions Signal-to-Noise Ratio Source Configurations
real EEG noise
Inverse Solutions x SNR Inverse Solutions x Configurations Inverse Solutions x SNR Inverse Solutions x Configurations
SNR
3 (
Au
d)
3 (
Vis
) 6
(A
ud
+Vis
)
PLV: Receiver Operating Characteristic (ROC) Curves
SNR
3 (
Au
d)
3 (
Vis
) 6
(A
ud
+Vis
)
PLI: Receiver Operating Characteristic (ROC) Curves
Left AUD Seed
Right AUD Seed
Frontal Seed
All Other Voxels
grey = noise color = signal + noise
black = true signal color = localizer
Time (sec)
Evoked Waveforms for 3 AUD sources (SNR=0.6, run=10) Event-Related Power Waveforms for 3 AUD sources (SNR=0.6, run=10)
Time (sec)
Better source waveform reconstructions for multi-source beamformer (MIA) than single-source methods (SPA, LCMV, ELOR, MNE)
grey = noise color = signal + noise
black = true signal color = inverse soln
LH A
UD
Se
ed
RH
AU
D S
eed
single-source inverse solutions multi-source
inverse solution
Fro
nta
l Se
ed
true source connectivity
color lines = True Positives grey lines = False Positives
Grand-Averaged PLI Maps for 6 sources (Aud+Vis) at SNR=0.6
