www.comascience.org

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 GroupCyclotron Research Centre & Neurology DepartmentCHU Sart Tilman, Liège, Belgium

Consciousness

Coma

General Anesthesia

Locked-in syndrome

Minimally Conscious State

Vegetative state

Conscious Wakefulness

Drowsiness

Light sleep

Deep Sleep

REM Sleep

Altered states of consciousness

Laureys & Boly, Current Opinion in Neurology 2007Laureys & Boly, Nature Clinical Practice 2008

SomnambulismEpilepsy

40 % misdiagnosis!

Schnakers et al., BMC Neurology 2009

introduction | scalp level analysis| DCM | conclusion

Consciousness

Coma

General Anesthesia

Locked-in syndrome

Minimally Conscious State

Vegetative state

Conscious Wakefulness

Drowsiness

Light sleep

Deep Sleep

REM Sleep

Diagnosing consciousness: the challenge

Boly, Massimini & Tononi, Progress in Brain Research 2009 Boly, Current Opinion in Neurology, in press

SomnambulismEpilepsy

Neural correlates of consciousness (NCC)

Functional neuroimaging

introduction | scalp level analysis| DCM | conclusion

Auditory NCC

Boly et al., Archives of Neurology 2004

Dehaene et al., TICS 2006

subliminal

conscious

preconscious

Diatz et al., JCognNsci 2007

Di et al., Neurology 2007

VS MCS

?

NCC in healthy volunteers

Del Cul et al., PLOS Biol 2007

Garrido et al., PNAS 2007

Garrido et al., Neuroimage 2008

Best correlate of conscious perception = long latency ERP componentsSuggested involvement of backward connections in their generation

introduction | scalp level analysis| DCM | conclusion

MMN design – roving paradigm

Garrido et al., Neuroimage 2008, 2009

introduction | scalp level analysis| DCM | conclusion

www.comascience.org

Scalp level analysis

ERP data analysis – Methods22 controls, 13 MCS and 8 VS patients

EEG data:60 electrodes EEG acquisition system (Nexstim) – 15 min acquisitionSampling rate 1450 Hz~200 standard, 200 deviants per subjectCT scan or structural MRI obtained for each subject

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., Science 2011 in press

ERP data analysis – Methods22 controls, 13 MCS and 8 VS patients

EEG data:60 electrodes EEG acquisition system (Nexstim) – 15 min acquisitionSampling rate 1450 Hz~200 standard, 200 deviants per subjectCT scan or structural MRI obtained for each subject

SPM data analysis:

High pass filtering 0.5 HzLow pass filtering 20 Hz (to decrease EMG-related noise in the signal)Downsampling at 200 HzCorrection for ocular artifacts (Berg method from SPM) on continuous signalEpoching -100 to 400 msAveraging data at the single subject level – standard & deviant (11th repetition) conditionsConvert to images in SPM

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., Science 2011 in press

ERP data analysis – Methods22 controls, 13 MCS and 8 VS patients

EEG data:60 electrodes EEG acquisition system (Nexstim) – 15 min acquisitionSampling rate 1450 Hz~200 standard, 200 deviants per subjectCT scan or structural MRI obtained for each subject

SPM data analysis:

High pass filtering 0.5 HzLow pass filtering 20 Hz (to decrease EMG-related noise in the signal)Downsampling at 200 HzCorrection for ocular artifacts (Berg method from SPM) on continuous signalEpoching -100 to 400 msAveraging data at the single subject level – standard & deviant (11th repetition) conditionsConvert to images in SPM

Random effects analysis – 3 groups x 2 conditionsPatient’s prognosis entered as a covariate of no interestF test for differential response to standard versus deviants in each groupF test for an effect of consciousness level on the amplitude of this responseThreshold FWE corrected p<0.05 at the voxel level

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., Science 2011 in press

MMN results – scalp levelRESPONSE TO DEVIANTS

Controls

introduction | scalp level analysis| DCM | conclusion

MMN results – scalp levelRESPONSE TO DEVIANTS

Controls MCS

introduction | scalp level analysis| DCM | conclusion

MMN results – scalp levelRESPONSE TO DEVIANTS

Controls MCS VS

introduction | scalp level analysis| DCM | conclusion

MMN results – scalp levelRESPONSE TO DEVIANTS

Controls MCS VS

introduction | scalp level analysis| DCM | conclusion

MMN results – scalp levelRESPONSE TO DEVIANTS

Controls MCS VS

introduction | scalp level analysis| DCM | conclusion

MMN results – scalp levelintroduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., Science 2011 in press

MMN results – scalp levelintroduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., Science 2011 in press

MMN results – scalp level

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

introduction | scalp level analysis| DCM | conclusion

www.comascience.org

Connectivity analysisusing DCM

DCM for EEG - principles

Which brain network creates this ERP?

And how?

Explain a given M/EEG signal at the neuronal level

introduction | scalp level analysis| DCM | conclusion

MMN design – roving paradigm

Garrido et al., Neuroimage 2008, 2009

introduction | scalp level analysis| DCM | conclusion

DCM for EEG - principles

Electromagnetic forward model for M/EEG

Depolarisation of

pyramidal cells

Forward model:lead field & gain

matrixScalp data

),,(0 uxfx LK 0),( LKxxgy

Forward model

introduction | scalp level analysis| DCM | conclusion

www.comascience.org

Spatial Forward Model

),( 00 xgxLy L

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

Depolarisation ofpyramidal cells

Sensor data

),,( uxfx LL

Spatial model

Neural mass model of a cortical macrocolumn =

ExcitatoryInterneurons

PyramidalCells

InhibitoryInterneurons

Extrinsic inputs

Excitatory connection

Inhibitory connection

MEG/EEGsignal

MEG/EEGsignal

mean firing rate

mean

postsynaptic potential

(PSP)

mean PSP

mean firing rate

Function P

Function S

CONNECTIVITY ORGANISATION POPULATION DYNAMICS

Excitatory IN

Inhibitory IN

Pyramidal cells

IntrinsicForward

BackwardLateral

Input u

1

32

Extrinsic

David et al., 2005David and Friston, 2003

Between-area connectivity

1 2

Model Inversion: fit the data

DataData

We need to estimate the extrinsic connectivity parameters and their

modulation from data.

We need to estimate the extrinsic connectivity parameters and their

modulation from data.input

0 50 100 150 200 250-8

-6

-4

-2

0

2

4

6

time (ms)

Observed (adjusted) 1

0 50 100 150 200 250-8

-6

-4

-2

0

2

4

6

time (ms)

Predicted

Predicted dataPredicted data

DCM for EEG – principlesintroduction | scalp level analysis| DCM | conclusion

DCM for EEG - principles

Balance betweenmodel fit &

model complexity

introduction | scalp level analysis| DCM | conclusion

Alternative Models for Comparison

DCM for EEG – group analysis

-35 -30 -25 -20 -15 -10 -5 0 5

Su

bje

cts

Log model evidence differences

MOG

LG LG

RVFstim.

LVFstim.

FGFG

LD|RVF

LD|LVF

LD LD

MOGMOG

LG LG

RVFstim.

LVFstim.

FGFG

LD

LD

LD|RVF LD|LVF

MOG

m2 m1

Stephan et al. 2009

Group level random effects BMS resistant to outliers

introduction | scalp level analysis| DCM | conclusion

Bayesian model comparisonintroduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

Bayesian model comparisonintroduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

Bayesian model comparisonintroduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

Bayesian model comparisonintroduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

Bayesian model comparisonintroduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

DCM – quantitative connectivity analysis

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

DCM – quantitative connectivity analysis

Impairment of BACKWARD connection from frontal to temporal corticesis the only significant difference between VS and controls

* (p = 0.012)

* (p = 0.006)ns

Ctrls VSMCS

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., 2011

DCM – quantitative connectivity analysis

Impairment of BACKWARD connection from frontal to temporal corticesis the only significant difference between VS and controls

introduction | scalp level analysis| DCM | conclusion

12

3

12

3

CONTROLS/MCS VS

DCM – quantitative connectivity analysis

Impairment of BACKWARD connection from frontal to temporal corticesis the only significant difference between VS and controls

Del Cul et al., PLOS Biol 2007

introduction | scalp level analysis| DCM | conclusion

12

3

VS

www.comascience.org

Conclusions

Conclusion

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., Science 2011 in press

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

Conclusion

introduction | scalp level analysis| DCM | conclusion

Boly, Garrido et al., Science 2011 in press

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 patientsFuture studies on a larger patient population to assess diagnostic utility and prognostic value

Conclusion

introduction | scalp level analysis| DCM | 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 patientsFuture studies on a larger patient population to assess diagnostic utility and prognostic value

Boly, Current Opinion in Neurology, in pressBuckner et al., J Neurosci 2009, Hagmann et al., PLOS Biology 2008

Impairment in unconsciousness

functional structural

Hierarchy ofbrain connectivity

?

www.comascience.org

We thank the participating patients and their families

University of LiègeSteven Laureys Olivia GosseriesCaroline SchnakersMarie-Aurélie BrunoPierre BoverouxAudrey VanhaudenhuyseDidier LedouxJean-Flory TshibandaQuentin NoirhommeRemy LehembreAndrea SodduAthena DemertziRémy LehembreChristophe PhillipsPierre Maquet

Stanford University Michael Greicius

University of Cambridge, UKAdrian OwenMartin ColemanJohn PickardMartin Monti

University of MilanMarcello MassiminiMario RosanovaAdenauer Casali Silvia Casarotto

University of Wisconsin - MadisonGiulio TononiBrady RiednerEric LandsnessMichael MurphyFabio Ferrarelli

Marie-Curie University, ParisLouis PuybassetHabib BenaliGiullaume MarrelecVincent PerlbargMelanie Pellegrini

Cornell University, NYNicholas Schiff

JFK Rehabilitation Center, NJJoseph Giacino

University College London, UKKarl FristonMarta GarridoVladimir LitvakRosalyn Moran

www.comascience.org

Any questions?..