Simultaneous eeg and fmri for the localisation of spontaneous alpha rhythm
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Simultaneous EEG and fMRI for the localisation of spontaneous alpha-rhythm. J.C. de Munck S.I. Gonçalves P.J.W. Pouwels R. Schoonhoven J.P.A. Kuijer E.J.W. Van Someren P. Anderson N.M. Maurits J.M. Hoogduin R.M. Heethaar F.H. Lopes da Silva

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Simultaneous EEG and fMRI for the localisation of spontaneous alpha-rhythm

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Simultaneous eeg and fmri for the localisation of spontaneous alpha rhythm

Simultaneous EEG and fMRI for the localisation of spontaneous alpha-rhythm

J.C. de Munck

S.I. Gonçalves

P.J.W. Pouwels

R. Schoonhoven

J.P.A. Kuijer

E.J.W. Van Someren

P. Anderson

N.M. Maurits

J.M. Hoogduin

R.M. Heethaar

F.H. Lopes da Silva

VU Medical Centre, Amsterdam

AZG, Groningen

Institute of Neurobiology, UvA, Amsterdam


Outline

Outline

  • Introduction

  • Methodology

  • Results

  • Discussion and Future Work


Introduction

Introduction

  • The alpha rhythm is the hallmark of the resting state, therefore related to all fMRI studies.

  • Different types of alpha activity: posterior alpha, mu rhythm, midtemporal third rhythm.

  • Many open questions related to the nature and origin of this type of activity still remain.


Introduction source localisation with eeg

Find Solution to the EEG Inverse Problem

specify

specify

Source Model:

Current Dipole

Volume Conductor:

Realistic

Spherical

IntroductionSource localisation with EEG


Introduction fmri

active

active

active

active

A

rest

rest

rest

rest

voxel intensity

R/P

L

4 minutes

: each point represents intensity of voxel during one 3 second MRI - EPI scan

IntroductionfMRI


Introduction1

Average alpha power time series

4 minutes

Introduction


Introduction eeg fmri

Average alpha power time series

Simultaneous EEG/fMRI

possible with

voxel intensity

4 minutes

: each point represents intensity of voxel during one 3 second MRI - EPI scan

IntroductionEEG/fMRI


Methodology artifacts on the eeg

Gradient

artifacts

RF pulse

artifact

1 slice

MethodologyArtifacts on the EEG

The MR greatly disturbs the EEG signal


Methodology artifacts on the eeg1

Methodology Artifacts on the EEG

There are also artefact that are related to the heart beat.


Methodology artefacts on the eeg

Methodology Artefacts on the EEG

These artefacts can be removed by an averaging procedure.


Methodology induced alpha rhythm

eyes

closed

eyes

closed

eyes

closed

eyes

closed

eyes

closed

eyes

closed

eyes

closed

eyes

closed

eyes

closed

eyes

closed

eyes

open

eyes

open

eyes

open

f (0-100Hz)

eyes

open

eyes

open

eyes

open

eyes

open

eyes

open

eyes

open

eyes

open

10 Hz

t (0-600s)

Methodology: Induced alpha rhythm


Results experiment description

ResultsExperiment description

  • Data recorded from 8 healthy subjects (4 males, 4 females, mean age 34±8), 2 discarded.

  • Subjects instructed to lie still inside the scanner, keeping the eyes closed.

  • EEG acquired with MR compatible EEG amplifier (SD MRI, Micromed, Treviso, Italy) and cap with 19 Aq/AgCl electrodes positioned in 10/20 system, Bipolar montage.

  • Functional images acquired on 1.5 T MR scanner (Magnetom Sonata, Siemens, Erlangen, Germany) using T2* weighted EPI (TR=3000ms) consisting of 24 transversal slices.

  • High resolution MPRAGE sequence consisting of 160 slices to provide anatomical reference.

  • For each subject, 400 volumes (in a total of 20 mins of data) were acquired per subject. For 3 subjects, data was acquired in two series of 10 mins each.


Results spontaneous alpha rhythm

f (0-100 Hz)

10 Hz

t (0-1200s)

Results: Spontaneous alpha rhythm


Results subjects 1 2 and 3

Results - Subjects 1, 2 and 3

Subject 1

FDR=10-7

all derivations

Subject 2

FDR=0.05

0.15290.4000

all derivations

Subject 3

FDR=0.05

0.20000.4000

all derivations


Results subject 4

Results - Subject 4

Subject 4

spectrogram


Results subject 41

Results - Subject 4

Subject 4

spectrogram


Results subject 42

Alpha period (darker blue)

Beta period (lighter blue)

-0.2706-0.1647

P3-O1, P4-O2

-0.3176-0.2353

C3-P3, C4-P4

Results - Subject 4

Subject 4

FDR=0.2


Results subject 5

Results - Subject 5

Subject 5

FDR=0.05

-0.4000-0.1529

C3-P3, C4-P4, T5-T3, T6-T4


Results subjects 5 and 6

Results - Subjects 5 and 6

Subject 5

FDR=0.05

-0.4000-0.1529

C3-P3, C4-P4, T5-T3, T6-T4

Subject 6

FDR=0.05

0.20000.4000

C3-P3, C4-P4


Discussion and future work

Discussion and Future work

  • Our results suggest that inter-subject variability is important and should be taken into account. (e.g. subjects 1, 5 and 6).

  • Furthermore, the results show that even within one subject (e.g. subject 4), different states correspond to different correlation patterns.

  • Since the resting state is the reference state in most fMRI studies, our results show that variability in resting state may be an important cause of the variability of fMRI results.


Discussion and future work1

Discussion and Future work

EEG

EEG2

Spectrogram

ECG

Correlation pattern

fMRI

The analysis of simultaneous EEG-ECG-fMRI data is quit complex.


Discussion and future work2

Discussion and Future work

Heart beat

Volumes

3 s.

3 s.

There is a correlation between BOLD and the heartbeat signal...


Discussion and future work3

Discussion and Future work

Heart beat

Volumes

3 s.

3 s.

There is a correlation between BOLD and the heartbeat signal...


Discussion and future work4

Discussion and Future work

There are many fMRI points that correlate well with the heart beats. Therefore

the heart beat should be accounted for in the correlation analysis.


Discussion and future work5

Discussion and Future work

EEG

EEG2

Spectrogram

ECG

SSP

Correlation pattern

fMRI

MRI

Source model

But in the future it will be even more complex...


Discussion and future work6

Discussion and Future work

  • The complexity of the problem put demands on the software for the data analysis:

  • High performance

  • Good visualisation tool

  • Efforts to keep track of raw data, intermediate results and end product.


Results subjects 5 and 61

Results - Subjects 5 and 6


Results temporal modulation of the regressor

Subject 2

Subject 5

disc. sub

Results - Temporal modulation of the regressor


Results temporal modulation of the regressor all derivations

N is the number of samples;

Pi is the power value at time sample i;

is the average power.

Results - Temporal modulation of the regressor (all derivations)


Results temporal modulation of the regressor and within subject variation

Results - Temporal modulation of the regressor and within subject variation


Discussion and conclusions

Discussion and Conclusions

  • Results suggest that the resting state is not comparable amongst subjects and sometimes, not even within one subject.

  • As the resting state plays an important role in fMRI analysis where the paradigms are of the type “rest-task”, the abovementioned variability should be considered when questioning how comparable are fMRI results from different subjects .

  • The question raised previously could be ultimately addressed by recording the simultaneous EEG and using the average alpha power time series as a distractor in the fMRI analysis.


Future work

Future work

  • Technical improvements- Signal Space Projection methods;- Dipole fitting on simultaneous EEG;- Non-linear correlation measures;- Variability of hemodynamic response.

  • Scientific questions- Can the abovementioned findings be confirmed in a more systematic study?- Does the alpha rhythm variability decrease when the state of the subject is more well defined?- What is the relation between the first and second harmonics of the alpha rhythm?


Methodology false detection rate fdr

MethodologyFalse Detection Rate (FDR)

In this procedure, where N null hypothesis are being tested simultaneously, the goal is to control the goal of FDR (Benjamin and Hochberg (1995)):

where

E(.) stands for the expected value;

F is the number of false detections;

T is the number of true detections;

FDR = 0 if T+F=0.


Methodology false detection rate fdr1

1. Select desired FDR bound q;

2. Order p-values from smallest to largest

p1p2 …. pN;

3. Determine largest i such as:

4. Declare voxels v(1) to v(i) as active.

MethodologyFalse Detection Rate (FDR)


Technical requirements safety issues

E.g.

Presence of metal wires that can

act as antennas;

Existence of wire loops generating

induced currents;

Technical requirementsSafety issues


Technical requirements hardware solutions

Technical requirementsHardware solutions

Degradation of MR signal: RF contamination, ferromagnetic materials

Safety: limitation of induced currents and closed loops

EEG artifact caused by the MR

  • DC amplifiers of large dynamic range and high resolution (22/24 bits)

  • High sampling frequency (> 1 KHz)

  • current limiting resistors close to electrodes

  • use of carbon wires

  • careful wire placement avoiding loops

  • fiber optic connection between subject + EEG Amp. and the remaining system.

  • Shielding of EEG system

  • Use of appropriate materials


First experiments biosemi 24 bits 16 khz

Raw signals

Remaining

RF pulse

Signals after removing average over slices and volumes

First ExperimentsBioSemi (24 bits, 16 KHz)


First experiments

Linear interpolation of remaining RF artifact

First Experiments

Unfiltered data


First experiments1

First Experiments

Average Ballist. Art.

Corrected (black) vs. uncorrected

(gray) data


Methodology artifacts on the eeg2

Ballistocardiogram artifact on the EEG

(time locked to the ECG)

Hall Effect

wire displacement due

to pulsate vessel movement

F+

v

VH

B

F-

Methodology Artifacts on the EEG


Methodology artifacts on the mr

MethodologyArtifacts on the MR

RF contamination of the MR signal by the EEG hardware.


Methodology artifacts on the mr1

MethodologyArtifacts on the MR

Degradation of the MR signal by the presence of ferromagnetic materials.


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