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

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 2007 Laureys & Boly, Nature Clinical Practice 2008 Somnambulism Epilepsy 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 Somnambulism Epilepsy 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 components Suggested 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

Scalp level analysis

ERP data analysis – Methods 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 introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., Science 2011 in press

ERP data analysis – Methods 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 (11 th repetition) conditions Convert to images in SPM introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., Science 2011 in press

ERP data analysis – Methods 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 (11 th 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 introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., Science 2011 in press

MMN results – scalp level RESPONSE TO DEVIANTS Controls introduction | scalp level analysis| DCM | conclusion

MMN results – scalp level RESPONSE TO DEVIANTS Controls MCS introduction | scalp level analysis| DCM | conclusion

MMN results – scalp level RESPONSE TO DEVIANTS Controls MCS VS introduction | scalp level analysis| DCM | conclusion

MMN results – scalp level RESPONSE TO DEVIANTS Controls MCS VS introduction | scalp level analysis| DCM | conclusion

MMN results – scalp level RESPONSE TO DEVIANTS Controls MCS VS introduction | scalp level analysis| DCM | conclusion

MMN results – scalp level introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., Science 2011 in press

MMN results – scalp level introduction | 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

Connectivity analysis using 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 matrix Scalp data Forward model introduction | scalp level analysis| DCM | conclusion

Spatial Forward Model Default: Each area that is part of the model is modeled by one equivalent current dipole (ECD). Depolarisation of pyramidal cells Sensor data Spatial model

Neural mass model of a cortical macrocolumn = Excitatory Interneurons Pyramidal Cells Inhibitory Interneurons Extrinsic inputsExtrinsic inputs Excitatory connection Inhibitory connection MEG/EEG signal MEG/EEG signal mean firing rate  mean postsynapt ic potential (PSP) mean PSP  mean firing rate Function P Function S CONNECTIVITY ORGANISATION POPULATION DYNAMICS

Excitator y IN Inhibitory IN Pyramidal cells Intrinsic Forward Backward Lateral Input u Extrinsic David et al., 2005David and Friston, 2003 Between-area connectivity 1 2

Model Inversion: fit the data Data We need to estimate the extrinsic connectivity parameters and their modulation from data. Predicted data DCM for EEG – principles introduction | scalp level analysis| DCM | conclusion

DCM for EEG - principles Balance between model fit & model complexity introduction | scalp level analysis| DCM | conclusion

Alternative Models for Comparison

DCM for EEG – group analysis MOG LG RVF stim. LVF stim. FG LD|RVF LD|LVF LD MOG LG RVF stim. LVF stim. FG LD LD|RVFLD|LVF MOG m2m2 m1m1 Stephan et al Group level random effects BMS resistant to outliers introduction | scalp level analysis| DCM | conclusion

Bayesian model comparison introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., 2011

Bayesian model comparison introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., 2011

Bayesian model comparison introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., 2011

Bayesian model comparison introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., 2011

Bayesian model comparison introduction | 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 cortices is the only significant difference between VS and controls * (p = 0.012) * (p = 0.006) ns Ctrls VS MCS introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., 2011

DCM – quantitative connectivity analysis Impairment of BACKWARD connection from frontal to temporal cortices is the only significant difference between VS and controls introduction | scalp level analysis| DCM | conclusion CONTROLS/MCSVS

DCM – quantitative connectivity analysis Impairment of BACKWARD connection from frontal to temporal cortices is the only significant difference between VS and controls Del Cul et al., PLOS Biol 2007 introduction | scalp level analysis| DCM | conclusion VS

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 patients Future 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 patients Future studies on a larger patient population to assess diagnostic utility and prognostic value Boly, Current Opinion in Neurology, in press Buckner et al., J Neurosci 2009, Hagmann et al., PLOS Biology 2008 Impairment in unconsciousness functional structural Hierarchy of brain connectivity ?

We thank the participating patients and their families 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 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 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

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