Crossfrequency coupling Charité University Medicine Berlin Department of Neurology University College London Wellcome Trust Centre for Neuroimaging Bernadette van Wijk SPM course for MEG amp EEG 2017 ID: 631973
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Slide1
Advanced applications of the GLM:
Cross-frequency coupling
Charité
- University Medicine Berlin Department of Neurology
University College London Wellcome Trust Centre for Neuroimaging
Bernadette van Wijk
SPM course for MEG & EEG 2017Slide2
Why care about cross-frequency coupling?
Brain
needs to integrate
information at different
frequencies and time scalesUnexplored mechanisms of information processing1Slide3
Axmacher
et al. (2009)
PNAS
Jensen & Colgin (2007) TiCS
Spatial navigation
Working memory
Canolty
et al. (2006)
Science
Florin
&
Baillet
(2015)
Neuroimage
Resting state
Modulation by behavioural task
Modulation by pathology
d
e
Hemptinne
et al.
(
2013)
PNAS
2Slide4
Jirsa & Müller, 2013; Frontiers in Comp Neurosci
Frequency
combinations Within a recorded
signal (brain region)Between
two different signals (
brain
regions
)
Many
combinations!
Various forms of cross-frequency coupling
delta-alpha
theta-beta
theta-gamma
alpha-gamma
beta-gamma
etc.
3Slide5
Amplitude
Amplitude
Envelope correlation
Dynamic causal modelling
Phase
Phase
Phase
A
mplitude
Phase
Frequency
n
:
m
phase locking index
Bispectrum
Bicoherence
Modulation
Index
Entropy
Vector
length
GLM
Phase locking envelope/
phase
Correlation envelope/
signal
Event-related PAC
…
Amplitude
Frequency
Frequency
Frequency
Cross-Frequency Coupling
Correlation
Low-frequency
phase modulates
High-frequency
amplitude
Optimization cross-frequency estimates
Instantaneous frequency tracking
Modelling non-sinusoidal waves
Selection of high-amplitude time bins
Spatial filters
4Slide6
PAC: How to detect it
How to detect PAC
Berman et al., 2012; Brain Connectivity
Bandpass filtering
Phase:
narrow bandAmplitude:
bandwidth
should
include
carrier
frequency
±
modulating
frequency
Extraction
of time series
for
phase
and
amplitude
5Slide7
How to detect PAC
Assessment of
distribution
amplitude as function of phaseStatistical evaluation (often bootstrapping)
6Slide8
Parametric
approach
No surrogate time series
Based on mean vector lengthPAC: How to detect it
General Linear Model
Penny et al. (2008)
J
Neurosci
Methods
Van Wijk et al. (2015)
J
Neurosci
Methods
7Slide9
Van Wijk et al. (2015)
J
Neurosci
Methods
Parametric
approach
No surrogate time series
Based
on
mean
vector
length
General Linear Model
8Slide10
Easy
to include other predictors:
amplitude correlations
non-linearitiesconfounding factorsPAC: How to detect it
General Linear Model
Van Wijk et al. (2015)
J
Neurosci
Methods
9Slide11
Noise level
ρ
GLM
permutations
Slightly lower statistical power for GLM
GLM vs permutation tests: simulations
van Wijk et al. (2015)
J
Neurosci
Methods
10Slide12
GLM 200
permutations
<7min 159minGLM ~24x faster to computevan Wijk et al. (2015) J Neurosci MethodsGLM vs permutation tests: real data11Slide13
PAC: How to detect it
GLM for PAC within SPM
12Slide14
Compute amplitude time series and phase time series for frequencies of interest
Create two separate files because of filter settings
First step: Time-frequency analysis
Amplitude
Phase
13Slide15
Data set with amplitude time series
Data set with phase time series
Add other time series as
regressors
Select phase or
amplitude as
regressors
Second step: Cross-frequency coupling
14
Select epoch size here (for stats) Slide16
Results
Figure is plotted + .
nii
images saved
Phase Frequency
Amplitude Frequency
Phase Frequency
15Slide17
Get in touch for
an example batch and/or script:
vanwijk.bernadette@gmail.com
Or ask for help during the practical session on Wednesday16