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Impaired top-down processes in the vegetative state revealed by SPM analysis of EEG data. Mélanie Boly, MD, PhD. Wellcome Trust Centre for Neuroimaging, Functional Imaging Laboratory, University College London Coma Science Group Cyclotron Research Centre & Neurology Department

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impaired top down processes in the vegetative state revealed by spm analysis of eeg data

Impaired top-down processes in the vegetative state revealed by SPM analysis of EEG data

Mélanie Boly, MD, PhD

Wellcome Trust Centre for Neuroimaging,

Functional Imaging Laboratory,

University College London

Coma Science Group

Cyclotron Research Centre

& Neurology Department

CHU Sart Tilman, Liège, Belgium

slide2

Consciousness

Altered states of consciousness

Locked-in syndrome

Drowsiness

REM Sleep

Light sleep

Minimally Conscious State

Deep Sleep

40 % misdiagnosis!

Vegetative state

Coma

Schnakers et al., BMC Neurology 2009

introduction | scalp level analysis| DCM| conclusion

Conscious

Wakefulness

General Anesthesia

Somnambulism

Epilepsy

Laureys & Boly, Current Opinion in Neurology 2007

Laureys & Boly, Nature Clinical Practice 2008

slide3

Consciousness

Diagnosing consciousness: the challenge

Locked-in syndrome

Drowsiness

REM Sleep

Light sleep

Minimally Conscious State

Deep Sleep

Vegetative state

Coma

introduction | scalp level analysis| DCM| conclusion

Conscious

Wakefulness

Functional neuroimaging

Neural correlates of consciousness (NCC)

General Anesthesia

Somnambulism

Epilepsy

Boly, Massimini & Tononi, Progress in Brain Research 2009

Boly, Current Opinion in Neurology, in press

auditory ncc
Auditory NCC

Boly et al., Archives of Neurology 2004

subliminal

conscious

preconscious

Dehaene et al., TICS 2006

VS

MCS

?

Diatz et al., JCognNsci 2007

Di et al., Neurology 2007

ncc in healthy volunteers
NCC in healthy volunteers

Garrido et al., Neuroimage 2008

introduction | scalp level analysis| DCM| conclusion

Garrido et al., PNAS 2007

Del Cul et al., PLOS Biol 2007

Best correlate of conscious perception = long latency ERP components

Suggested involvement of backward connections in their generation

mmn design roving paradigm
MMN design – roving paradigm

introduction | scalp level analysis| DCM| conclusion

Garrido et al., Neuroimage 2008, 2009

erp data analysis methods
ERP data analysis – Methods

introduction | scalp level analysis| DCM| conclusion

22 controls, 13 MCS and 8 VS patients

EEG data:

60 electrodes EEG acquisition system (Nexstim) – 15 min acquisition

Sampling rate 1450 Hz

~200 standard, 200 deviants per subject

CT scan or structural MRI obtained for each subject

Boly, Garrido et al., Science 2011 in press

erp data analysis methods1
ERP data analysis – Methods

introduction | scalp level analysis| DCM| conclusion

22 controls, 13 MCS and 8 VS patients

EEG data:

60 electrodes EEG acquisition system (Nexstim) – 15 min acquisition

Sampling rate 1450 Hz

~200 standard, 200 deviants per subject

CT scan or structural MRI obtained for each subject

SPM data analysis:

High pass filtering 0.5 Hz

Low pass filtering 20 Hz (to decrease EMG-related noise in the signal)

Downsampling at 200 Hz

Correction for ocular artifacts (Berg method from SPM) on continuous signal

Epoching -100 to 400 ms

Averaging data at the single subject level – standard & deviant (11th repetition) conditions

Convert to images in SPM

Boly, Garrido et al., Science 2011 in press

erp data analysis methods2
ERP data analysis – Methods

introduction | scalp level analysis| DCM| conclusion

22 controls, 13 MCS and 8 VS patients

EEG data:

60 electrodes EEG acquisition system (Nexstim) – 15 min acquisition

Sampling rate 1450 Hz

~200 standard, 200 deviants per subject

CT scan or structural MRI obtained for each subject

SPM data analysis:

High pass filtering 0.5 Hz

Low pass filtering 20 Hz (to decrease EMG-related noise in the signal)

Downsampling at 200 Hz

Correction for ocular artifacts (Berg method from SPM) on continuous signal

Epoching -100 to 400 ms

Averaging data at the single subject level – standard & deviant (11th repetition) conditions

Convert to images in SPM

Random effects analysis – 3 groups x 2 conditions

Patient’s prognosis entered as a covariate of no interest

F test for differential response to standard versus deviants in each group

F test for an effect of consciousness level on the amplitude of this response

Threshold FWE corrected p<0.05 at the voxel level

Boly, Garrido et al., Science 2011 in press

mmn results scalp level
MMN results – scalp level

introduction | scalp level analysis| DCM| conclusion

RESPONSE TO DEVIANTS

Controls

mmn results scalp level1
MMN results – scalp level

introduction | scalp level analysis| DCM| conclusion

RESPONSE TO DEVIANTS

MCS

Controls

mmn results scalp level2
MMN results – scalp level

introduction | scalp level analysis| DCM| conclusion

RESPONSE TO DEVIANTS

VS

MCS

Controls

mmn results scalp level3
MMN results – scalp level

introduction | scalp level analysis| DCM| conclusion

RESPONSE TO DEVIANTS

VS

MCS

Controls

mmn results scalp level4
MMN results – scalp level

introduction | scalp level analysis| DCM| conclusion

RESPONSE TO DEVIANTS

VS

MCS

Controls

mmn results scalp level5
MMN results – scalp level

introduction | scalp level analysis| DCM| conclusion

Boly, Garrido et al., Science 2011 in press

mmn results scalp level6
MMN results – scalp level

introduction | scalp level analysis| DCM| conclusion

Boly, Garrido et al., Science 2011 in press

mmn results scalp level7
MMN results – scalp level

introduction | scalp level analysis| DCM| conclusion

RESPONSE TO DEVIANTS

  • Correlation between the level of consciousness and:
  • Global amplitude of the ERP response
  • Predominant late components in latency of ERP
  • Involvement of frontal topography at the scalp level
dcm for eeg principles
DCM for EEG - principles

introduction | scalp level analysis| DCM | conclusion

Explain a given M/EEG signal at the neuronal level

Which brain network

creates this ERP?

And how?

mmn design roving paradigm1
MMN design – roving paradigm

introduction | scalp level analysis| DCM | conclusion

Garrido et al., Neuroimage 2008, 2009

dcm for eeg principles1
DCM for EEG - principles

introduction | scalp level analysis| DCM | conclusion

Electromagnetic forward model for M/EEG

Forward model:

lead field & gain matrix

Depolarisation of

pyramidal cells

Scalp data

Forward model

spatial forward model
Spatial Forward Model

Depolarisation of

pyramidal cells

Spatial model

Sensor data

Default: Each area that is part of the model is modeled by one equivalent current dipole (ECD).

slide25

Neural mass model of a cortical macrocolumn =

POPULATION DYNAMICS

CONNECTIVITY ORGANISATION

E

x

t

r

i

n

s

i

c

i

n

p

u

t

s

Excitatory

Interneurons

Function P

mean firing rate  mean postsynaptic potential (PSP)

Pyramidal

Cells

MEG/EEG

signal

Function S

mean PSP mean firing rate

Inhibitory

Interneurons

Excitatory connection

Inhibitory connection

slide26

Between-area connectivity

1

2

Inhibitory

IN

2

3

Excitatory IN

1

Pyramidal

cells

Intrinsic

Forward

Backward

Lateral

Extrinsic

Input u

David and Friston, 2003

David et al., 2005

model inversion fit the data

DCM for EEG – principles

Model Inversion: fit the data

Observed (adjusted) 1

Predicted

6

6

4

4

2

2

0

0

-2

-2

-4

-4

input

-6

-6

-8

-8

0

50

100

150

200

250

0

50

100

150

200

250

time (ms)

time (ms)

introduction | scalp level analysis| DCM | conclusion

Data

Predicted data

We need to estimate the extrinsic connectivity parameters and their modulation from data.

dcm for eeg principles2
DCM for EEG - principles

introduction | scalp level analysis| DCM | conclusion

Balance between

model fit &

model complexity

dcm for eeg group analysis
DCM for EEG – group analysis

LD

LD|LVF

LD|RVF

LD|LVF

LD

LD

RVF

stim.

LD

LVF

stim.

RVF

stim.

LD|RVF

LVF

stim.

LG

LG

MOG

MOG

MOG

MOG

LG

FG

FG

FG

FG

LG

introduction | scalp level analysis| DCM | conclusion

m2

m1

Group level random effects BMS

resistant to outliers

Stephan et al. 2009

bayesian model comparison
Bayesian model comparison

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

bayesian model comparison1
Bayesian model comparison

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

bayesian model comparison2
Bayesian model comparison

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

bayesian model comparison3
Bayesian model comparison

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

bayesian model comparison4
Bayesian model comparison

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

dcm quantitative connectivity analysis
DCM – quantitative connectivity analysis

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

dcm quantitative connectivity analysis1
DCM – quantitative connectivity analysis

introduction | scalp level analysis| DCM | conclusion

Impairment of BACKWARD connection from frontal to temporal cortices

is the only significant difference between VS and controls

*

(p = 0.012)

ns

* (p = 0.006)

VS

Ctrls

MCS

Boly, Garrido et al., 2011

dcm quantitative connectivity analysis2
DCM – quantitative connectivity analysis

introduction | scalp level analysis| DCM | conclusion

Impairment of BACKWARD connection from frontal to temporal cortices

is the only significant difference between VS and controls

CONTROLS/MCS

VS

3

3

1

1

2

2

dcm quantitative connectivity analysis3
DCM – quantitative connectivity analysis

introduction | scalp level analysis| DCM | conclusion

Impairment of BACKWARD connection from frontal to temporal cortices

is the only significant difference between VS and controls

VS

3

1

2

Del Cul et al., PLOS Biol 2007

slide41

introduction | scalp level analysis| DCM | conclusion

Conclusion

  • SCALP LEVEL:
  • Correlation between response amplitude (latency >100 ms, involving frontal component) with the level of consciousness

Boly, Garrido et al., Science 2011 in press

slide42

introduction | scalp level analysis| DCM | conclusion

Conclusion

  • SCALP LEVEL:
  • Correlation between response amplitude (latency >100 ms, involving frontal component) with the level of consciousness
  • DCM ANALYSIS:
  • Selective impairment in backward connectivity from frontal to temporal cortices in VS
  • MCS patients show a pattern similar to controls
  • Fits very well with NCC in healthy volunteers (though only indirect evidence there for backward processes being important beforehand)
  • First direct demonstration of a link between preserved top-down processes and the level of consciousness in these patients
  • Future studies on a larger patient population to assess diagnostic utility and prognostic value

Boly, Garrido et al., Science 2011 in press

slide43

introduction | scalp level analysis| DCM | conclusion

Conclusion

  • SCALP LEVEL:
  • Correlation between response amplitude (latency >100 ms, involving frontal component) with the level of consciousness
  • DCM ANALYSIS:
  • Selective impairment in backward connectivity from frontal to temporal cortices in VS
  • MCS patients show a pattern similar to controls
  • Fits very well with NCC in healthy volunteers (though only indirect evidence there for backward processes being important beforehand)
  • First direct demonstration of a link between preserved top-down processes and the level of consciousness in these patients
  • Future studies on a larger patient population to assess diagnostic utility and prognostic value

Impairment in

unconsciousness

Hierarchy of

brain connectivity

?

functional

structural

Boly, Current Opinion in Neurology, in press

Buckner et al., J Neurosci 2009, Hagmann et al., PLOS Biology 2008

we thank the participating patients and their families

Marie-Curie University, Paris

Louis Puybasset

Habib Benali

Giullaume Marrelec

Vincent Perlbarg

Melanie Pellegrini

Cornell University, NY

Nicholas Schiff

JFK Rehabilitation Center, NJ

Joseph Giacino

University College London, UK

Karl Friston

Marta Garrido

Vladimir Litvak

Rosalyn Moran

University of Cambridge, UK

Adrian Owen

Martin Coleman

John Pickard

Martin Monti

University of Milan

Marcello Massimini

Mario Rosanova

Adenauer Casali

Silvia Casarotto

University of Wisconsin - Madison

Giulio Tononi

Brady Riedner

Eric Landsness

Michael Murphy

Fabio Ferrarelli

University of Liège

Steven Laureys

Olivia Gosseries

Caroline Schnakers

Marie-Aurélie Bruno

Pierre Boveroux

Audrey Vanhaudenhuyse

Didier Ledoux

Jean-Flory Tshibanda

Quentin Noirhomme

Remy Lehembre

Andrea Soddu

Athena Demertzi

Rémy Lehembre

Christophe Phillips

Pierre Maquet

Stanford University

Michael Greicius

We thank the participating patients and their families

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