Neural correlates of consciousness in patients who have emerged from a minimally conscious state: a cross-sectional multimodal imaging study

BACKGROUND Between pathologically impaired consciousness and normal consciousness exists a scarcely researched transition zone, referred to as emergence from minimally conscious state, in which patients regain the capacity for functional communication, object use, or both. We investigated neural correlates of consciousness in these patients compared with patients with disorders of consciousness and healthy controls, by multimodal imaging. METHODS In this cross-sectional, multimodal imaging study, patients with unresponsive wakefulness syndrome, patients in a minimally conscious state, and patients who had emerged from a minimally conscious state, diagnosed with the Coma Recovery Scale-Revised, were recruited from the neurology department of the Centre Hospitalier Universitaire de Liège, Belgium. Key exclusion criteria were neuroimaging examination in an acute state, sedation or anaesthesia during scanning, large focal brain damage, motion parameters of more than 3 mm in translation and 3° in rotation, and suboptimal segmentation and normalisation. We acquired resting state functional and structural MRI data and (18)F-fluorodeoxyglucose (FDG) PET data; we used seed-based functional MRI (fMRI) analysis to investigate positive default mode network connectivity (within-network correlations) and negative default mode network connectivity (between-network anticorrelations). We correlated FDG-PET brain metabolism with fMRI connectivity. We used voxel-based morphometry to test the effect of anatomical deformations on functional connectivity. FINDINGS We recruited a convenience sample of 58 patients (21 [36%] with unresponsive wakefulness syndrome, 24 [41%] in a minimally conscious state, and 13 [22%] who had emerged from a minimally conscious state) and 35 healthy controls between Oct 1, 2009, and Oct 31, 2014. We detected consciousness-level-dependent increases (from unresponsive wakefulness syndrome, minimally conscious state, emergence from minimally conscious state, to healthy controls) for positive and negative default mode network connectivity, brain metabolism, and grey matter volume (p<0·05 false discovery rate corrected for multiple comparisons). Positive default mode network connectivity differed between patients and controls but not among patient groups (F test p<0·0001). Negative default mode network connectivity was only detected in healthy controls and in those who had emerged from a minimally conscious state; patients with unresponsive wakefulness syndrome or in a minimally conscious state showed pathological between-network positive connectivity (hyperconnectivity; F test p<0·0001). Brain metabolism correlated with positive default mode network connectivity (Spearman's r=0·50 [95% CI 0·26 to 0·61]; p<0·0001) and negative default mode network connectivity (Spearman's r=-0·52 [-0·35 to -0·67); p<0·0001). Grey matter volume did not differ between the studied groups (F test p=0·06). INTERPRETATION Partial preservation of between-network anticorrelations, which are seemingly of neuronal origin and cannot be solely explained by morphological deformations, characterise patients who have emerged from a minimally conscious state. Conversely, patients with disorders of consciousness show pathological between-network correlations. Apart from a deeper understanding of the neural correlates of consciousness, these findings have clinical implications and might be particularly relevant for outcome prediction and could inspire new therapeutic options. FUNDING Belgian National Funds for Scientific Research (FNRS), European Commission, Natural Sciences and Engineering Research Council of Canada, James McDonnell Foundation, European Space Agency, Mind Science Foundation, French Speaking Community Concerted Research Action, Fondazione Europea di Ricerca Biomedica, University and University Hospital of Liège (Liège, Belgium), and University of Western Ontario (London, ON, Canada).

[1]  S. Hughes,et al.  The slow (<1 Hz) rhythm of non-REM sleep: a dialogue between three cardinal oscillators , 2010, Nature Neuroscience.

[2]  Justin L. Vincent,et al.  Intrinsic functional architecture in the anaesthetized monkey brain , 2007, Nature.

[3]  Steven Laureys,et al.  Brain function in coma, vegetative state, and related disorders , 2004, The Lancet Neurology.

[4]  R. Wise,et al.  PET scanning and neuronal loss in acute vegetative state , 2000, The Lancet.

[5]  Justin L. Vincent,et al.  Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.

[6]  Jonathan E. Peelle,et al.  Adjusting for global effects in voxel-based morphometry: Gray matter decline in normal aging , 2012, NeuroImage.

[7]  N. Schiff Recovery of consciousness after brain injury: a mesocircuit hypothesis , 2010, Trends in Neurosciences.

[8]  Athena Demertzi,et al.  Two Distinct Neuronal Networks Mediate the Awareness of Environment and of Self , 2011, Journal of Cognitive Neuroscience.

[9]  Thomas E. Nichols,et al.  Everything You Never Wanted to Know about Circular Analysis, but Were Afraid to Ask , 2010, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[10]  Todd W. Thompson,et al.  Resting-state anticorrelations between medial and lateral prefrontal cortex: Association with working memory, aging, and individual differences , 2014, Cortex.

[11]  M. Sigman,et al.  Signature of consciousness in the dynamics of resting-state brain activity , 2015, Proceedings of the National Academy of Sciences.

[12]  A. Luxen,et al.  PET scanning and neuronal loss in acute vegetative state , 2000, The Lancet.

[13]  James L Bernat,et al.  Chronic consciousness disorders. , 2009, Annual review of medicine.

[14]  P. Fransson Spontaneous low‐frequency BOLD signal fluctuations: An fMRI investigation of the resting‐state default mode of brain function hypothesis , 2005, Human brain mapping.

[15]  D K Menon,et al.  Neurometabolic coupling in the vegetative and minimally conscious states: preliminary findings , 2005, Journal of Neurology, Neurosurgery & Psychiatry.

[16]  Bharat B. Biswal,et al.  Metabolic Brain Covariant Networks as Revealed by FDG-PET with Reference to Resting-State fMRI Networks , 2012, Brain Connect..

[17]  Steve Majerus,et al.  Visual fixation in the vegetative state: an observational case series PET study , 2010, BMC neurology.

[18]  Athena Demertzi,et al.  Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations , 2014, Cortex.

[19]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[20]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Jonathan D. Power,et al.  Recent progress and outstanding issues in motion correction in resting state fMRI , 2015, NeuroImage.

[22]  Fabrice Wendling,et al.  Impaired consciousness during temporal lobe seizures is related to increased long-distance cortical-subcortical synchronization. , 2009, Brain : a journal of neurology.

[23]  J. Giacino,et al.  The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. , 2004, Archives of physical medicine and rehabilitation.

[24]  N. Schiff Central Thalamic Contributions to Arousal Regulation and Neurological Disorders of Consciousness , 2008, Annals of the New York Academy of Sciences.

[25]  M. Boly,et al.  Unresponsive wakefulness syndrome. , 2012, Archives italiennes de biologie.

[26]  K. M. Ropella,et al.  Graded defragmentation of cortical neuronal firing during recovery of consciousness in rats , 2014, Neuroscience.

[27]  A. Owen,et al.  Thalamo-frontal connectivity mediates top-down cognitive functions in disorders of consciousness , 2015, Neurology.

[28]  Steven Laureys The neural correlate of (un)awareness: lessons from the vegetative state , 2005, Trends in Cognitive Sciences.

[29]  K. Ishii,et al.  Measurement of Gray and White Matter Atrophy in Dementia with Lewy Bodies Using Diffeomorphic Anatomic Registration through Exponentiated Lie Algebra: A Comparison with Conventional Voxel-Based Morphometry , 2010, American Journal of Neuroradiology.

[30]  Steven Laureys,et al.  Disorders of consciousness after acquired brain injury: the state of the science , 2014, Nature Reviews Neurology.

[31]  Steve Majerus,et al.  A French validation study of the Coma Recovery Scale-Revised (CRS-R) , 2008, Brain injury.

[32]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[33]  Athena Demertzi,et al.  Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. , 2015, Brain : a journal of neurology.

[34]  G. Shulman,et al.  Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[35]  D. Sharp,et al.  Fractionating the Default Mode Network: Distinct Contributions of the Ventral and Dorsal Posterior Cingulate Cortex to Cognitive Control , 2011, The Journal of Neuroscience.

[36]  H. Blumenfeld Impaired consciousness in epilepsy , 2012, The Lancet Neurology.

[37]  Jeffrey S Anderson,et al.  Network anticorrelations, global regression, and phase‐shifted soft tissue correction , 2011, Human brain mapping.

[38]  M. Boly,et al.  Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient , 2009, Human brain mapping.

[39]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[40]  Steve Majerus,et al.  Functional neuroanatomy underlying the clinical subcategorization of minimally conscious state patients , 2012, Journal of Neurology.

[41]  J. Giacino,et al.  The minimally conscious state: Definition and diagnostic criteria , 2002, Neurology.

[42]  Athena Demertzi,et al.  Thalamus, Brainstem and Salience Network Connectivity Changes During Propofol-Induced Sedation and Unconsciousness , 2013, Brain Connect..

[43]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Steven Laureys,et al.  Limbic hyperconnectivity in the vegetative state , 2013, Neurology.

[45]  Dost Öngür,et al.  Anticorrelations in resting state networks without global signal regression , 2012, NeuroImage.

[46]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[47]  Yong He,et al.  Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury , 2015, The Journal of Neuroscience.

[48]  S Laureys,et al.  Restoration of thalamocortical connectivity after recovery from persistent vegetative state , 2000, The Lancet.

[49]  Karl J. Friston,et al.  Computing average shaped tissue probability templates , 2009, NeuroImage.

[50]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

[51]  Steven Laureys,et al.  Looking for the Self in Pathological Unconsciousness , 2013, Front. Hum. Neurosci..

[52]  Athena Demertzi,et al.  Metabolic activity in external and internal awareness networks in severely brain-damaged patients. , 2012, Journal of rehabilitation medicine.

[53]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[54]  M. Boly,et al.  Breakdown of within- and between-network Resting State Functional Magnetic Resonance Imaging Connectivity during Propofol-induced Loss of Consciousness , 2010, Anesthesiology.

[55]  Steven Laureys,et al.  Thalamic and extrathalamic mechanisms of consciousness after severe brain injury , 2015, Annals of neurology.

[56]  Marcus E. Raichle,et al.  The Restless Brain , 2011, Brain Connect..

[57]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[58]  Michelle Hampson,et al.  Functional connectivity between task-positive and task-negative brain areas and its relation to working memory performance. , 2010, Magnetic resonance imaging.

[59]  M. Boly,et al.  Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. , 2010, Brain : a journal of neurology.

[60]  M. Steriade Grouping of brain rhythms in corticothalamic systems , 2006, Neuroscience.

[61]  Athena Demertzi,et al.  Identifying the default‐mode component in spatial IC analyses of patients with disorders of consciousness , 2012, Human brain mapping.

[62]  Karl J. Friston,et al.  Metabolic connectivity mapping reveals effective connectivity in the resting human brain , 2015, Proceedings of the National Academy of Sciences.

[63]  Martin Biermann,et al.  Default‐mode network functional connectivity is closely related to metabolic activity , 2015, Human brain mapping.

[64]  J. Karp,et al.  Systematic and Distributed Time-of-Flight List Mode PET Reconstruction , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[65]  M Corbetta,et al.  Searching for activations that generalize over tasks , 1997, Human brain mapping.

[66]  Maurizio Corbetta,et al.  The role of impaired neuronal communication in neurological disorders , 2007, Current opinion in neurology.

[67]  George A. Mashour,et al.  Genuine and Spurious Phase Synchronization Strengths during Consciousness and General Anesthesia , 2012, PloS one.

[68]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[69]  Steven Laureys,et al.  Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study , 2014, The Lancet.