exploiting temporal delays in interpreting eeg/meg data in terms of brain connectivity fraunhofer...
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![Page 1: Exploiting temporal delays in interpreting EEG/MEG data in terms of brain connectivity Fraunhofer FIRST, Berlin G. Nolte](https://reader036.vdocuments.site/reader036/viewer/2022081603/5697bfa01a28abf838c95321/html5/thumbnails/1.jpg)
Exploiting temporal delays in interpreting EEG/MEG data in terms of
brain connectivity
Fraunhofer FIRST, Berlin
G. Nolte
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Problem of volume conduction
)(1 tx
)(2 tx )(3 tx)(4 tx
?
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)(iz complex Fourier amplitude in the i.th channel
Cross-spectrum
)()()( jiij zzS Cross-spectrum
Coherency = normalized cross-spectrum
)()(
)()(
jjii
ijij
SS
SC
Coherence = absolute value of coherency
)()( ijij CCoh
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Power: Task-Rest
Coherence: C3-others Coherence: C4-others
C3 C4
EEG-simulation of ERD (two sources)
Rest: Real background + simulated dipolesTask: Real background
Fake!! Sources were indepent!!
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Rest Coherence
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EEG-simulation of ERD (1 source)
Rest: Real background + simulated dipoleTask: Real background
Inverse using beamformer (DICS) on cortex
Simulated dipole Estimated power ratio: Rest/Task
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Coh., signal+background Coh., background
Coh., difference
seed
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Coh., signal+background Coh., background
Coh., difference
seed
seedoriginal dipole location
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Observation:
Independent sources do not contribute to the imaginary part of the cross-spectrum
1 (non-interacting) source
Interaction with time delay
volume cond.
Many sources
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)()(
)()(
2
1
ii
ii
sbz
saz
Assumption:
sources are non-interacting
imaginary part of coherency must arise from interacting sources
Explicit derivation
)()()( 2112 zzS
real for instantaneous volume conduction(Stinstra and Peters, 1998)
Real !
2)(iii
i
sba )()(,
jijiji
ssba
=0 for ij
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Coherence
Imaginary coherency
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Imaginary coherency
Coherence
movement
Power
Selfpaced movement, C3-C4 relationships
Observations:
• coherence follows power
• imaginary part has onset 5secs before movement
• imaginary part not related to power
Nolte, et.al., Clinic. Neurophys., 2004
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Significance; False Discovery Rate (FDR)
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Simulated non-interacting sources
Imaginary coherency
Interaction exists!
Task-related activity exists!
Is task-related actvity interacting?
differences should be based on cross-spectra
2/12/1 Bjj
Bii
Bij
Ajj
Aii
Aij
SS
S
SS
S
2/12/1 Bjj
Bii
Ajj
Aii
Bij
Aij
SSSS
SS
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movement
2/12/1 Bjj
Bii
Bij
Ajj
Aii
Aij
SS
S
SS
S
2/12/1 Bjj
Bii
Ajj
Aii
Bij
Aij
SSSS
SS
Difference of normalized cross-spectra
Normalized difference of cross-spectra
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Imag, Cross-Spectrum, Left-Right
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MEG, Cross-Spectrum; imag, alpha
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Imaginary part, 5 dipoles
S1
S2
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“Philosophy”
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“Philosophy”
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Method A
interesting phenomena non-interesting phenomena
Data
Method B
“Philosophy”
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Pairwise Interacting Source Analysis (PISA)
2211 )()()( atsatstx
independent
ICA
spectrum
ICAwith temporal decorrelation(Sobi, TDSEP)
TT aafpaafpfC 222111 )()()(
spatial pattern
ISA
))((ˆ))(Im( 11111TT abbafpfC
“interaction spectrum” 2 spatial patterns
Nolte, et.al., Phys. Rev. E., 2006
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EEG, imagined foot movement
ISA1 ISA2
• finds systems blindly
• no 1/f spectrum
• clear higher harmonics
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Observation:
2D-subspace of channel space
Model for each grid-point:
3D-subspace
MUSIC
Angle
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A: measured field
B
MUSICchannel-subspaces for each voxel (here 2 dipole-directions)
field for dipole in x-direction
field for dipole in y-direction
AP BPProjector on A Projector on B
1cos1 show weplotsin -Φ-
ABA PPPΦ of eigenvaluelargest cos2
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MUSIC RAP-MUSIC
Example 1
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MUSIC RAP-MUSIC
Example 2
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ISA-pattern;
left mu-rhythm
RAP-MUSIC
first scan
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Conclusion
• Imaginary parts of cross-spectra is not affected by non-interacting sources valuable quantity to study interactions
• ICA-like decompositions finds and separates interacting systems blindly
• Localization with MUSIC and/or dipole fit