Simultaneous EEG-fMRI: from acquisition to

application.Karen Mullinger

Sir Peter Mansfield Magnetic Resonance Centre,School of Physics and Astronomy

University of Nottingham

Overview

• Introduction• Aspects of getting good quality data• Optimising experimental set-up

‒ General pointers‒ Facilitating good:

gradient artefact correction pulse artefact correction

‒ Summary

• Application‒ Neurovascular coupling.‒ Latest results (food for thought)

Why Simultaneous EEG –fMRI?

• Very powerful spatiotemporal tool • Same experimental environment • Same attention and awareness • Same brain activity Necessary when brain activity can’t be

predicted

O1

O2

100 µV

100 µV

fMRI

EEG

1. Gradient Artefact (GA): Switching of the gradient fields, causes large changes in magnetic flux

inducing electrical signals within the EEG.

EEG Artefact Sources

0

1000

2000

-1000

-2000

0 20 40 60 80 100

44

45

47

Vol

tage

µ

V

0

100

200

Vol

tage

µ

V

0

100

200

Vol

tage

µ

VV

olta

ge,

µV

Time, ms

B

D

E

C

EPI Block

Slice

PhaseReadout

A

0

20

-20Gra

dien

t Stre

ngth

, m

T 1 EPI cycle, 540s

46

0

1000

2000

-1000

-2000

0 20 40 60 80 1000 20 40 60 80 100

44

45

47

Vol

tage

µ

V

0

100

200

Vol

tage

µ

V

0

100

200

Vol

tage

µ

VV

olta

ge,

µV

Time, ms

B

D

E

C

EPI Block

Slice

PhaseReadout

Slice

PhaseReadout

Slice

PhaseReadoutPhaseReadout

A

0

20

-20Gra

dien

t Stre

ngth

, m

T 1 EPI cycle, 540s

1 EPI cycle, 540s

46

Average slice Artefact

0

1000

2000

-1000

-2000

0 20 40 60 80 100

44

45

47Vo

ltage

µ

V

0

100

200

Volta

ge

µV

0

100

200

Volta

ge

µV

Volta

ge,

µV

Time, ms

B

D

E

C

EPI Block

Slice

PhaseReadout

A

0

20

-20Grad

ient S

treng

th,

mT 1 EPI cycle,

540s

46

0

1000

2000

-1000

-2000

0 20 40 60 80 1000 20 40 60 80 100

44

45

47Vo

ltage

µ

V

0

100

200

Volta

ge

µV

0

100

200

Volta

ge

µV

Volta

ge,

µV

Time, ms

B

D

E

C

EPI Block

Slice

PhaseReadout

Slice

PhaseReadout

Slice

PhaseReadoutPhaseReadout

A

0

20

-20Grad

ient S

treng

th,

mT 1 EPI cycle,

540s1 EPI cycle,

540s

46

2. Pulse Artefact (PA): Precise source unclear but linked to the cardiac cycle.

EEG Artefact Sources

1) Pulsatile blood flow

effects (Hall effect).

2) Small head nod

3) Scalp expansion

The Result!

O1

O2

F7

F8

T7

T8

P7

P8

RWAV S128RWAV RWAV S128RWAV RWAV S128RWAV RWAV S128RWAV RWAV S128RWAV

200 µV

200 µV

200 µV

200 µV

200 µV

200 µV

200 µV

200 µV

200µV

Good quality EEG data

Two aspects to EEG-fMRI:

‒ Experimental set-up and data collection

‒ Best post-processing methods

Good quality EEG data

Experimental set-up and data collection

O1

O2

100 µV

100 µV

General advice

‒ Low impedances of EEG channels Less noisy EEG signals

‒ Subject comfort and paddingMinimise movement → reduced artefacts

General advice: Motion

Aim: • To investigate effect of motion artefacts on EEG-

BOLD correlates

Method: • 4 subjects• Standard 32 channel EEG recording.• EEG data were recorded during Dual Echo EPI:

• 40 slices, 84×84 matrix, 3×3×4 mm3 voxels• TR=3s TE1/TE2 =20/48ms

• Episodic memory task: required to move a cursor with a roller-ball to respond.

Jansen, M. et al, NeuroImage 59, 261-270 (2012)

General advice: MotionAnalysis: • EEG

– Gradient (AAS) and Pulse (OBS) artefact correction– ICA to remove residual artefacts– Noisy channels removed– Filtered 4-8Hz (Theta band)

• fMRI– Motion and physiological correction– Echoes combined– Regressors:

1. Continuous theta regressor

2. Head motion (from motion parameters)

3. Artefacts remaining after correction (from visual inspection)

Jansen, M. et al, NeuroImage 59, 261-270 (2012)

General advice: Motion

Jansen, M. et al, NeuroImage 59, 261-270 (2012)

Not convolved with HRF

Convolved with HRF

General advice: Motion

Not convolved with HRF

Convolved with HRF

CAREFUL how you interpret results!

Task: Foot motion

Jansen, M. et al, NeuroImage 59, 261-270 (2012)

General advice

‒ Low impedances of EEG channels Less noisy EEG signals

‒ Subject comfort and paddingMinimise movement → reduced artefacts

‒ Isolate amplifiers/cables from scanner bedMinimise vibration of equipment

General advice

0 50 100 150 200 2500

20

40

60

80

100

Frequency, (Hz)

Vol

tage

den

sity

(V

/Hz)

0 10 20 300

20

40

60

80

Frequency, (Hz)

Vol

tage

den

sity

(V

/Hz)

0 50 100 150 200 2500

20

40

60

80

100

Frequency, (Hz)

Vol

tage

den

sity

(V

/Hz)

0 10 20 300

20

40

60

80

Frequency, (Hz)

Vol

tage

den

sity

(V

/Hz)

7T, no scanning

Amplifier on the scanner bore

Amplifier suspended.

Mullinger, K.J. et al, MRI 26(7), 968-977 (2008)

General advice

‒ Low impedances of EEG channelsLess noisy EEG signals

‒ Subject comfort and paddingMinimise movement → reduced artefacts

‒ Isolate amplifiers/cables from scanner bedMinimise vibration of equipment

‒ Turn cyrocooler compression pumps offMinimise noise sources

0 50 100 150 200 2500

10

20

30

40

50

Frequency, (Hz)

Vol

tage

den

sity

(V

/Hz)

0 50 100 1500

5

10

15

0 50 100 150 200 2500

10

20

30

40

50

Frequency, (Hz)

Vol

tage

den

sity

(V

/Hz)

0 50 100 1500

5

10

15

7T, no scanning

Everything on

Cryopumps off..

...and room lights, gradient and patient airflow

Mullinger, K.J. et al, MRI 26(7), 968-977 (2008)

General advice

Gradient artefact

Fp1

Fp2

O1

O2

T7

T8

R128RWAV S 59RWAV R128RWAV RWAV RWAVR128 RWAV RWAVR128RWAV RWAV R128RWAV

2000 µV

2000 µV

2000 µV

2000 µV

2000 µV

2000 µV

Fp1

Fp2

O1

O2

T7

T8

R128RWAV S 59RWAV R128RWAV RWAV RWAVR128 RWAV RWAVR128RWAV RWAV R128RWAV

2000 µV

2000 µV

2000 µV

2000 µV

2000 µV

2000 µV

Average Artefact Subtraction (AAS)

Allen, P.J. et al. NeuroImage 12, 230-239 (2000)

Artefact Correction requirements

AAS Requires: – Artefact to be highly repeatable across cycles– Precisely recording the artefact waveform and the

beginning of each volume.

– These requirements must be closely adhered to as the unfiltered GA is at least 10,000 times larger than an evoked response

Residual artefacts are problematic

–Acquire EEG data at 5kHz–Ensure your slice TR is a

multiple of the scanner clock period (i.e. 200μs)

WARNING:–TR entered into console is

not always the TR outputted due to rounding issues!!

–Philips System for equidistant EPI: TR Calculator*

Precise sampling

*Need clinical science agreement for this

– Synchronise the MR Scanner and EEG clocks using the output from the MR scanner.

Philips system: use the 10MHz output from the MR scanner clock to drive the EEG clock

Precise sampling

Mandelkow, H. et al, NeuroImage 32(3)1120-1126 (2006)

Mullinger, K.J. et al, JMRI 27(3): p. 607-616 (2008)

Experimental Results

0

1000

2000

-1000

-2000

0 20 40 60 80 100

44

45

47

Vol

tage

µ

V

0

100

200

Vol

tage

µ

V

0

100

200

Vol

tage

µ

VV

olta

ge,

µV

Time, ms

B

D

E

C

EPI Block

Slice

PhaseReadout

A

0

20

-20Gra

dien

t Stre

ngth

, m

T 1 EPI cycle, 540s

46

0

1000

2000

-1000

-2000

0 20 40 60 80 1000 20 40 60 80 100

44

45

47

Vol

tage

µ

V

0

100

200

Vol

tage

µ

V

0

100

200

Vol

tage

µ

VV

olta

ge,

µV

Time, ms

B

D

E

C

EPI Block

Slice

PhaseReadout

Slice

PhaseReadout

Slice

PhaseReadoutPhaseReadout

A

0

20

-20Gra

dien

t Stre

ngth

, m

T 1 EPI cycle, 540s

1 EPI cycle, 540s

46180 dynamics, 20 slices, 3 subjects

Results from electrode F7 for a single subject

Average slice artifact

0

1000

2000

-1000

-2000

0 20 40 60 80 100

44

45

47

Volta

ge

µV

0

100

200

Volta

ge

µV

0

100

200

Volta

ge

µV

Volta

ge,

µV

Time, ms

B

D

E

C

EPI Block

Slice

PhaseReadout

A

0

20

-20Grad

ient S

treng

th,

mT 1 EPI cycle,

540s

46

0

1000

2000

-1000

-2000

0 20 40 60 80 1000 20 40 60 80 100

44

45

47

Volta

ge

µV

0

100

200

Volta

ge

µV

0

100

200

Volta

ge

µV

Volta

ge,

µV

Time, ms

B

D

E

C

EPI Block

Slice

PhaseReadout

Slice

PhaseReadout

Slice

PhaseReadoutPhaseReadout

A

0

20

-20Grad

ient S

treng

th,

mT 1 EPI cycle,

540s1 EPI cycle,

540s

46

TR = 2s, synchronised

TR = 2s, not synchronised

TR = 2.0001s, synchronised

Standard Deviation associated with average slice artifact

Mullinger, K.J. et al, JMRI 27(3): 607-616 (2008)

Minimising GA amplitude

• Why?– Prevent channel saturation– Allow higher EEG recording bandwidth– Improve artefact correction

• How?– Position subjects 4cm in foot direction

(naision at isocentre = 0cm). Approximately at Fp1&2.

Yan, W.X., et al. NeuroImage 46(2):459-471. (2009)Mullinger, K.J. et al, NeuroImage, 54(3):1942-1950 (2011)

Optimal Position: standard fMRI

Aim: • Compare GA produced by a multi-slice EPI sequence at

standard and optimal subject positions.

Method:• 6 subjects • Experiments were carried out with the nasion at:

- iso-centre - optimal (+4 cm) z-offset

• Standard 32 channel EEG recording, 250 Hz low pass filter.• EEG data were recorded during standard EPI:

• 32 slices, 84×84 matrix, 3×3×4 mm3 voxels• TR=2.5s TE =40ms; slice repetition frequency = 12.8 Hz

• Cued foot movement: 5s every 30s (total: 8 minutes): cumulative head movements of <1 mm.

Optimal position: Results

RMS of average artefact before correction

• 40% average reduction in RMS over all channels

STD across slices after correction

• 36% reduction in RMS at slice harmonics after correction

0 10 20 30 40 50 60 70 800

500

1000

1500

2000

Time(ms)

Vol

tage

( V

)

0 10 20 30 40 50 60 70 800

50

100

150

Time(ms)

Vol

tage

( V

)

Optimal positionIsocentre

Pulse artefact

Pulse Artefact Correction

‒ Many methods of PA correction

• Average artefact subtraction (AAS)1

• Optimal basis sets (OBS)2

• Independent component analysis (ICA)3

‒ Varying levels of success reported

‒ Most require correctly identifying the QRS complex

[1] Allen, P.J. et al, NeuroImage 8(3), 229-239 (1998)

[2] Srivastava, G. et al, NeuroImage 24, 50-60 (2005)

[3] Niazy, R.K. et al, NeuroImage 28, 720-737 (2005)

ECGwithin the ECG trace.

Pulse Artifact

Problems: • ECG is affected by

gradients as well. • Sometimes hard to get a

good ECG trace.• Trace is sometimes

saturated.

-15000

-10000

-5000

0

5000

10000

15000

20000

0 2.5 5

-1500-1000

-5000

5001000

15002000

25003000

3500

0.0 2.5 5.0

Vol

tage

µV

Vol

tage

µV

Vol

tage

(a.

u.)

A

B

C

-800

-600

-400

-200

0

200

400

600

800

0.0 2.5 5.0

-15000

-10000

-5000

0

5000

10000

15000

20000

0 2.5 5

-1500-1000

-5000

5001000

15002000

25003000

3500

0.0 2.5 5.0

Vol

tage

µV

Vol

tage

µV

Vol

tage

(a.

u.)

A

B

C

-800

-600

-400

-200

0

200

400

600

800

0.0 2.5 5.0

-15000

-10000

-5000

0

5000

10000

15000

20000

0 2.5 5

-1500-1000

-5000

5001000

15002000

25003000

3500

0.0 2.5 5.0

Vol

tage

µV

Vol

tage

µV

Vol

tage

(a.

u.)

A

B

C

-800

-600

-400

-200

0

200

400

600

800

0.0 2.5 5.0

-15000

-10000

-5000

0

5000

10000

15000

20000

0 2.5 5

-1500-1000

-5000

5001000

15002000

25003000

3500

0.0 2.5 5.0

Vol

tage

µV

Vol

tage

µV

Vol

tage

(a.

u.)

A

B

C

-800

-600

-400

-200

0

200

400

600

800

0.0 2.5 5.0

Solution on a Philips system*:

• Use vector cardiogram (VCG) from MR Scanner which is unaffected by gradients1.

• R peak markers are also placed automatically in the physlog file2 which can be used for pulse artefact correction directly.

-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

3500

0.0 2.5 5.0

Time (s)

Vol

tage

µV

Vol

tage

µV

Vol

tage

(a.

u.)

A

B

C

-800

-600

-400

-200

0

200

400

600

800

0.0 2.5 5.0

-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

3500

0.0 2.5 5.0

Time (s)

Vol

tage

µV

Vol

tage

µV

Vol

tage

(a.

u.)

A

B

C

-800

-600

-400

-200

0

200

400

600

800

0.0 2.5 5.0

-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

3500

0.0 2.5 5.0

Time (s)

Vol

tage

µV

Vol

tage

µV

Vol

tage

(a.

u.)

A

B

C

-800

-600

-400

-200

0

200

400

600

800

0.0 2.5 5.0

-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

3500

0.0 2.5 5.0

Time (s)

Vol

tage

µV

Vol

tage

µV

Vol

tage

(a.

u.)

A

B

C

-800

-600

-400

-200

0

200

400

600

800

0.0 2.5 5.0

[1] Chia et al. JMRI, 12:678-688 (2000)

[2] Fischer et al. MRM, 42:361-370 (1999)*Need research login to access physlog file

Results

0 0.25 0.5 0.75

0

20

40

0

100

-50

50

-100

0

-100

100

50

-50

Time, s

Vo

ltag

e, µV

Vo

ltag

e, µV

Vo

ltag

e, µV

A

B

C

-150

-20

-40

0 0.25 0.5 0.75

0

20

40

0

100

-50

50

-100

0

-100

100

50

-50

Time, s

Vo

ltag

e, µV

Vo

ltag

e, µV

Vo

ltag

e, µV

A

B

C

-150

-20

-40

No correction

Using ECG markers

Using VCG markers

• Data gradient-corrected and low-pass filtered at 70 Hz

• EEG trace from Tp10 averaged over all cardiac cycles in 2 minute period.

• 0 time=R peak marker from VCG

Mean Standard Deviation

• Precise source unclear but linked to the cardiac cycle.

Pulse Artefact

1) Pulsatile blood flow

effects 2) Small head nod 3) Scalp

expansion

T7

-100 0 100 200 300 400 500

-300

-200

-100

0

100

200

300T7

-100 0 100 200 300 400 500

-300

-200

-100

0

100

200

300

T7R-peak

Average pulse artefact

• Variation between cardiac cycles makes correction of difficult

• Problems increase with field strength

• Need a greater understanding of pulse artefact

Debener, S. et al, Int. J. Psychophys, 2008, 67(3), p.189-199

Measuring the PA constituents

• 6 subjects• Recorded EEG data in 3T MR scanner• 4 conditions:

1. Relaxed

2. Bite Bar and vacuum cushion (stop head nod)

3. Swimming cap (stop Hall effect)

4. 2&3 (left with scalp expansion).

Yan, W.X., et al., HBM, 2010. 31(4): p. 604-620.Mullinger, K.J. et al, #667 WTh HBM 2011. Quebec.

PA Experimental Results

0 0.2 0.4 0.60

20

40

60

80

100

Time(s)

Vol

tage

( V

)

0 0.2 0.4 0.60

20

40

60

80

100

Time(s)

Vol

tage

( V

)

0 0.2 0.4 0.60

20

40

60

80

100

Time(s)

Vol

tage

( V

)

0 0.2 0.4 0.60

20

40

60

80

100

Time(s)

Vol

tage

( V

)

Relax B

C D

Restrained

Insulated Restrained & insulated 0

10

20

30

40

50

Relaxed Restrained Insulated Insulated &Restrained

Condition

Am

pli

tud

e (

uV

)

Mean RMS std

Am

plit

ude

(µV

)

AEEG

Average RMS Subject RMS

Summary

‒ SNR of EEG data inside the MR scanner still lower than outside.

‒ Higher MR fields → increasing EEG artefact problems.

‒ Experimental set-up is important.

Data Acquisition Summary

‒ To improve gradient artefact correction:Chose TR and number of slices wiselySynchronise scanner clocksOptimally position the subject

‒ To improve pulse artefact correction:Use VCG to monitor cardiac trace

Application

Investigating origin of Negative BOLD

• Negative BOLD Response (NBR): Regions where there is a stimulus related decrease in BOLD signal.

• Reported in visual1, motor2 and somatosensory3 cortices.

[1] Shmuel et al. Neuron 36(6);2002. [3] Kastrup et al. Neuroimage 41(4);2008.

From: [2] Stefanovic et al. Neuroimage 22;2004.

Negative BOLD

• NBR origin unclear: – Neuronal basis– Haemodynamic artefact (blood steal)

• Invasive recordings in monkeys show a decrease in local field potentials (LFP) and spiking activity in regions of NBR, and suggest at least 60% of NBR is neuronal in origin1.

Clarification in humans is needed.

[1] Shmuel et al. Nat Neurosci. 9(4);2006.

Aim

To use simultaneous measurements of BOLD, ASL and EEG to investigate the

relationship between natural fluctuations in the NBR and somatosensory evoked potentials (SEPs) during median nerve

stimulation (MNS)1

[1] Mullinger et al Proc. ISMRM #109; 2011

Method

Simultaneous EEG-fMRI:– Philips Achieva 3T MR scanner; 8 channel SENSE head coil.– 64 channel Brain Products EEG system.

Localiser: GE-EPI BOLD sequence used for planning.

Experiment:– FAIR Double Acquisition Background Suppression1 sequence

used for simultaneous BOLD and background suppressed ASL data acquisition (TR=2.6s, TE=13/33ms (ASL/BOLD), label delay=1400ms, 3x3x5mm3 voxels, 212mm FOV, SENSE factor 2; background suppression TI1/TI2=340ms/560ms).

– Cardiac and respiration monitored.– MR and EEG scanner clocks synchronised.

• EEG electrode positions digitised (Polhemus system, Isotrack).

[1] Wesolowski et al. Proc. ISMRM, #6132;2009.

Paradigm

• 13 right handed subjects (8 males, 26±3 yrs)

• Stimulate median nerve of right wrist• Amplitude: just above motor threshold to

cause thumb distension • 2 Hz stimulation, 0.5ms pulses (Digitimer DS7A)

10s 20s 10s

40 blocks

20 pulses per block

Analysis

EEG pre-processing

• Gradient and pulse artefact correction using average artefact subtraction (Brain Vision Analyzer2)

• Data inspection:– 3 subjects excluded due to gross (>3mm) or stimulus-locked

movement.– Noisy channels and/or blocks rejected

• Down-sampled: 600Hz• Re-referenced: Average of non-noisy channels• Filtered: 2-40 Hz

-50 0 50 100 150 200 250 300 350 400-100

-50

0

50

100

Time (ms)

VE

am

plitu

de (

nAm

)

Analysis

EEG Beamformer1

Averaged over 20 responses in a block

Fitted2 basis set to SEP for each block to find peak-to-peak P100-N140 amplitude

VE timecourse for single block

T-stat map: active window: 0.01-0.16s passive window: 0.3-0.45s

0 5 10 15 20 25 30

-500

0

500

Time (s)

VE a

mpl

itude

(nAm

)

[1] Brookes et al. NeuroImage 40(3);2008 [2] Mayhew et al. Clin. Neurophysiol. 117(6);2006

-50 0 50 100 150 200 250 300 350 400-100

-50

0

50

100

Time (ms)

VE

am

plitu

de (n

Am

)

Analysis

fMRI pre-processing

• Motion corrected (FLIRT, FSL)• BOLD data physiologically corrected

(RETROICOR)• Interpolated to effective TR=2.6s• ASL: perfusion weighted image: Tag-Control• BOLD image pairs averaged• Normalised to MNI template• Smoothed: 5mm FWHM kernel

Analysis

fMRI General Linear Models

SEP amplitude modulator:

Boxcar:

• 2nd level fixed effects analysis on BOLD and ASL data

Group ROI defined for positive and negative correlation.

BOLD: P<0.05 FWE

ASL: P<0.001 uncorr

Timecourse for each region & subject obtained; averaged over subjects & blocks

0 20 40 60 80 100 120 140-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0 200 400 600 800 1000 1200 14007.06

7.08

7.1

7.12

7.14

7.16

7.18

7.2

7.22x 10

5

Time (s)

BO

LD

s

ign

al

Results

BOLD ASL

MNI peak co-ordinates(-42,-20,50) Positive

(36,-18,50) SEP

(34,-16,46) Negative

Positively correlated with Boxcar

Negatively correlated with Boxcar

Negatively correlated with SEP amplitude

• No positive correlation amplitude of SEP and fMRI in S1.

ResultsSolid line = BOLD, Dashed line= ASL

Results

-20 0 20 40-0.5

0

0.5

1

%CBF

%

BOLD

Constants: M = 7.2%1, α = 0.38, β = 1.2

Coupling ratio agrees with Stefanovic3

[1] Kastrup et al., Neuroimage 41(4);2008. [2] Davis et al., PNAS, 95;1998[3] Stefanovic et al., NeuroImage 22;2004

Isocontours of CMRO2 (Davis Model2)

[1] Kastrup et al., Neuroimage 41(4);2008. [2] Davis et al., PNAS, 95;1998

-20 0 20 40-15

-10

-5

0

5

10

15

20

%CBF

%

CM

RO

22

R = 0.9704, P<0.1*10-4 Gradient = 0.42

Discussion

• No positive correlation of fMRI and evoked potentials in S11.

• Ipsilateral NBR cannot be explained by blood steal2 as bilateral S1 regions are fed from different vascular territories.

• CMRO2 shown in NBR region - suggests a neuronal origin of the response.

[1] Klingner et al. Neuroimage 53(1); 2010[2] Wade et al. Neuron 36(6);2002

Discussion

• Show for first time correlation between ipsilateral S1 NBR and amplitude of concurrent EEG evoked response from contralateral S1/M1. Agrees with area identified by Klingner where NBR is modulated

by intensity of MNS1.

• Suggest that NBR-SEP relationship arises because NBR results from inhibition of task irrelevant processing in ipsilateral S1, with corresponding increase in excitability of contralateral S1, as indexed by increasing SEP amplitude.

[1] Klingner et al. Neuroimage 53(1); 2010

Why simultaneous recordings....

• Trial by trial natural fluctuations in the evoked response → simultaneous recordings are essential.

• Can also study changes in oscillatory activity and correlations with BOLD1 and also CBF........

[1] Mayhew et al, Proc ISMRM #1560, 2011

Positively correlated with Boxcar

Negatively correlated with Boxcar

Negatively correlated with Mu amplitude

p<0.05, FWE

Why Simultaneous recordings....food for thought

• Differences in oscillatory activity: providing evidence of a neuronal origin of the post-stimulus undershoot...

Acknowledgments

ColleaguesProfessor Richard Bowtell

Dr Susan FrancisWinston YanJade HavenhandDr Thomas WhiteDr Marjie JansenDr Elizabeth LiddleProf Peter Liddle

Birmingham UniversityDr Stephen MayhewDr Andrew Bagshaw

IndustryRobert Stormer (Brain Products)Dr Matthew Clemence (Philips)

FundingMRCEPSRCMansfield Fellowships