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

1. 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

2. 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

3. 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

4. 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

5. 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

6. MMN design – roving paradigm introduction | scalp level analysis| DCM| conclusion Garrido et al., Neuroimage 2008, 2009

7. Scalp level analysis

8. 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

9. 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

10. 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

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

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

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

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

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

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

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

18. 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

19. Connectivity analysisusing DCM

20. 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?

21. MMN design – roving paradigm introduction | scalp level analysis| DCM | conclusion Garrido et al., Neuroimage 2008, 2009

22. 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

24. 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).

25. 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

26. 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

27. 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.

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

29. Alternative Models for Comparison

30. 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

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

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

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

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

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

36. DCM – quantitative connectivity analysis introduction | scalp level analysis| DCM | conclusion Boly, Garrido et al., 2011

37. 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

38. 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

39. 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

40. Conclusions

41. 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

42. 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

43. 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

44. 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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