estimation of large-scale network synchronization and ... · synchronization ”task condition,...

Satu Palva

Neuroscience Center, University of Helsinki, Helsinki, Finland

Estimation of large-scale network synchronization and cross-frequency

interactions from electrophysiological data

Overview and learning objectives:

The objectives are to understand:

1. The effects of signal mixing on connectivity

2. What are the different forms of Cross-Frequency

couplings (CFC)

3. How changes in SNR affect connectivity estimates

4. How stimulus-locking affects estimation of CFC

5. How non-sinusoidal signals create CFC

6. How graph analysis can be used in the connectivity

analysis

Understanding the effects of signal mixing on

connectivity

So

urc

e P

atch

True phase correlations among ongoing cortical

activity

Uncorrelated

Activity

”Control

Condition, C”

True Phase

Synchronization

”Task Condition,

T”C T

True Phase Synchronization

Network

”Task Condition, T”

Source Patch Source Patch

Palva & Palva 2012 TICS

Forward modeling ~ M/EEG recording

Uncorrelated

Activity

”Control

Condition, C”

True Phase

Synchronization

”Task Condition,

T”

Observed Phase

Synchronization Network

T-C

0.4

0.15

0

-0.15

-0.4

0.8

0.4

0

Left Right

Anterior

Posterior

MEG Sensor

ME

G S

enso

r

MEG Sensor

T-C

Im(T)

T-C

C T

Task-Control

T-C


Sensor-level connectivity is confounded by artificial

synchronization

Inverse modeling ~ reconstruction of sources of

M/EEG recording

T-C

C T

T-C

Im(T)

Uncorrelated

Activity

”Control

Condition, C”

True Phase

Synchronization

”Task Condition,

T”

Observed Phase


T-C

Task-Control

T-C

T-C

So

urc

e P

atch



Inverse modelling alleviates artificial synchronization

and reconstructs the “true” network

Inverse modeling ~ reconstruction of sources of M/EEG

recording

T-C

C T

T-C

Im(T)

Uncorrelated

Activity

”Control

Condition, C”

True Phase

Synchronization

”Task Condition,

T”

True Phase Synchronization

Network

Observed Phase


T-C

Task-Control

T-C

T-C

So

urc

e P

atch


..but some artificial connectivity remains also after the

source-reconstruction

The extent of artificial connectivity is dependent on

the accuracy of the source reconstruction approach

1

0

0.2

0.4

0.6

0.8

Inter-areal artificial synchrony

10 0.2 0.4 0.6 0.8

MNE

MEG a-gr.

MEG mag.

EEG

Original

Cum

ulat

ive

prob

abili

ty

Noise


The extent of artificial connectivity is dependent on the accuracy of the source

reconstruction approach

Different connectivity metrics

Metrics sensitive to 0-phase lagged interactions (linear mixing)

PLV

Coherence

Correlation coefficient

Metrics insensitive to 0-phase lagged interactions

Imaginary PLV (iPLV)

Phase-locking index (PLI)

Weighted phase-locking index (wPLI)

Imaginary coherence

Orthogonalized correlation coefficient

PLV iPLV

Artificial

c =

0

Metrics insensitive to 0-phase lagged interactionsremove direct artefactual synchronization

Palva, Wang, Palva, Zhigalov, Monto, Schoffelen, Jerbi provisionally accepted

Spurious connectivity

Spurious Artefactual

True

”Denied”

MFG

MTG

MFS

ArtificialSpurious

True

Spurious

True

c =

0.4

PLV iPLV

Spurious interactions are NOT removed by thesemetrics

Palva, Wang, Palva, Zhigalov, Monto, Schoffelen, Jerbi provisionally accepted

Area A

LFP

Spik

es

Area B

No synchrony No communication Communication!

Inter-areal phase synchronization

Coincident spikes

Spik

es

Coincidences

Area A

Area B

Synchronized assembly

Long-range 1:1 phase synchronizationcoordinates inter-areal processing

Theories:

Singer, 1999, Neuron

Fries, 2015, Neuron

How are networks at distinct frequenciesintegrated?

Slow network (q/a)Attention network

Fast network (g)Sensory representations

Cross-frequency phase synchrony (CFS) canbind networks at distinct frequencies

Spik

esSp

ikes

Coincidences

Coincident spikes

Cross-frequency phase synchrony!

Fastoscillation

Slowoscillation

CFS

Slow assembly

Fast assembly

Siebenhühner et al. 2016 Elife

Forms of cross-frequency coupling (CFC) among

oscillations

Phase-amplitude coupling

(PAC) / Nested oscillations

n:m cross-frequency phase

synchrony (n:m CFS)

1:1 phase synchrony

How to compute 1:1 synchrony

Within frequency synchronization is CFS where fx:fy ratio (n:m) = 1:1

1. Extract the phase of the signal

2. Use PLV, iPLV, PLI, wPLI etc. to estimate phase-consistency

between two signals

How to estimate cross-frequency synchrony (CFS)

Two oscillations at distinct frequencies fx and fy

1. Estimate the ratio nfhigh : mflow. In the example n:m = 4:1

2. Extract the phase of the signal

3. Multiply the signal m by the m:n ratio (here 4)

4. Use PLV to estimate phase-consistency between two signals either

locally or between brain areas


How to estimate phase-amplitude coupling (PAC)

1) Extract the phase of the signal (flow)

2) Compute the amplitude envelope of the signal fhigh

3) Extract the phase of the amplitude envelope of the signal fhigh

4) Use PLV to estimate phase-consistency between flow and fhigh

Many alternative options to compute PACSiebenhühner et al. 2016 Elife

Vanhatalo et al, 2004 PNAS

Two oscillations at distinct frequencies flow and fhigh

Pitfalls in the CFC analyses

Changes in CFC between conditions may be caused by changes in SNR caused by changes in signal power.

CFC may be caused by neuronal activity phase-locked to stimulus onset.

CFS may be caused by non-sinusoidal signals.

Low SNR affects the estimation of connectivity

C = true couplingO

bserv

ed

couplin

g

SNR

Low SNR

Increase in SNR in the low-SNR condition increase

observed PLVO

bse

rve

dP

LV

ch

an

ge

vs. S

NR

ch

an

ge

SNR

Low SNR

CFC with stimulus-locked activities

Ongoing activity is phase-locked to sensory stimulus onset over a wide range of frequencies.

If CFS is computed for such phase-locked signal, it is observed, but it is NOT true CFS caused by intrinsic dynamics coupling between two signals.

Solutions: Use data where such problem does not exist.

Always check the presence of phase-locking of ongoing oscillationsto stimulus onset.

Use surrogate analysis to remove the stimulus-locked component. Note that several difficulties are present because also stimulus-locked component have phase-jitter and because of signal mixing.

Signal sinusoidality and CFC

Signal harmonics and CFS

Graph analysis to estimate synchrony

d = 2 d = 1

K = 0.357

Graph analysis can be used estimate the graph / network properties

K = connection density =

proportion of connections from all

possible connections

Node = brain region

Edge = connection

Degree Hub = Node with many

connections

Centrality hub = Central for

communication

Hub

Node

Edge

Graph representation of a network

Visualize the most central or strongest connections and nodes

Node strenght = the strenght of synchronization in a given node.

Node degree = The centrality of a node. Hubs have high degree

and hence many connections.

Other centrality metrics for node centrality such eigen vector

centrality (EVC), betweennes centrality (BWC) etc.

Hub node

Non-hub node

Edge = significant connections

Lobier et al provisionally accepted

Graph representation of a network

Visualize the most central or strongest connections and nodes

Display them in an inflated (and flattened) cortical surface.

Co-localization and color coding with the fMRI based FC network aids

interpretability

Lobier et al provisionally accepted

One solution for spurious connectivity…


Bundling of artefactual interactions


Bundling artefactual connections

1 13

V1

V3

V2

V4

Signal mixing

neighbourhood of V1

Signal mixing

neighbourhood of V2

0.09

0.13

0.18

0.23

iPLV

1

13

Wang et al.,provisionally accepted

Linear signal mixing space Bundling according to signal mixing

Graph analysis to estimate CFC

d = 2 d = 1

K = 0.357

CFC graphs are bidirectional:

Most significant hubs and connections must be

estimated separately for low and high frequencies

CFS between alpha and gamma


Summary

Estimation of 1:1 synchronization and CFC from MEG / EEG data

Should be based on source-reconstructed data

Should take account the signal-mixing and spurious connectivity

Acknowledge SNR changes in oscillatory power

Take account the phase-locking of ongoing oscillation to stimulus onset

Understand how non-sinusoidal oscillations affect the CFC analyses

Use graph analyses to analyze and visualize your networks

estimation of large-scale network synchronization and ... · synchronization ”task condition,...

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