Brain network motif topography may predict emergence from disorders of consciousness: a case series

Abstract Neuroimaging methods have improved the accuracy of diagnosis in patients with disorders of consciousness (DOC), but novel, clinically translatable methods for prognosticating this population are still needed. In this case series, we explored the association between topographic and global brain network properties and prognosis in patients with DOC. We recorded high-density electroencephalograms in three patients with acute or chronic DOC, two of whom also underwent an anesthetic protocol. In these two cases, we compared functional network motifs, network hubs and power topography (i.e. topographic network properties), as well as relative power and graph theoretical measures (i.e. global network properties), at baseline, during exposure to anesthesia and after recovery from anesthesia. We also compared these properties to a group of healthy, conscious controls. At baseline, the topographic distribution of nodes participating in alpha motifs resembled conscious controls in patients who later recovered consciousness and high relative power in the delta band was associated with a negative outcome. Strikingly, the reorganization of network motifs, network hubs and power topography under anesthesia followed by their return to a baseline patterns upon recovery from anesthesia, was associated with recovery of consciousness. Our findings suggest that topographic network properties measured at the single-electrode level might provide more prognostic information than global network properties that are averaged across the brain network. In addition, we propose that the brain network’s capacity to reorganize in response to a perturbation is a precursor to the recovery of consciousness in DOC patients.

[1]  B. de Gelder,et al.  Mismatch negativity predicts recovery from the vegetative state , 2007, Clinical Neurophysiology.

[2]  Srivas Chennu,et al.  Bedside detection of awareness in the vegetative state: a cohort study , 2011, The Lancet.

[3]  N. Morton,et al.  Pharmacokinetic model driven infusion of propofol in children. , 1991, British journal of anaesthesia.

[4]  M. Sigman,et al.  Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state. , 2014, Brain : a journal of neurology.

[5]  Daniel Kondziella,et al.  Preserved consciousness in vegetative and minimal conscious states: systematic review and meta-analysis , 2015, Journal of Neurology, Neurosurgery & Psychiatry.

[6]  Anthony G. Hudetz,et al.  Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness , 2016, Front. Comput. Neurosci..

[7]  Leonardo L. Gollo,et al.  Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[8]  M. Boly,et al.  Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment , 2009, BMC neurology.

[9]  Steven Laureys,et al.  Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness , 2017, Brain : a journal of neurology.

[10]  B. Jennett The vegetative state , 2002, Journal of neurology, neurosurgery, and psychiatry.

[11]  J. Changeux,et al.  Experimental and Theoretical Approaches to Conscious Processing , 2011, Neuron.

[12]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[13]  Jack W. Tsao,et al.  Observed brain dynamics, P.P. Mitra, H. Bokil. Oxford University Press (2008), ISBN-13: 978-0-19-517808-1, 381 pages, $65.00 , 2009 .

[14]  Athena Demertzi,et al.  Sedation of Patients With Disorders of Consciousness During Neuroimaging: Effects on Resting State Functional Brain Connectivity , 2017, Anesthesia and analgesia.

[15]  Rüdiger Ilg,et al.  Simultaneous EEG–PET–fMRI measurements in disorders of consciousness: an exploratory study on diagnosis and prognosis , 2017, Journal of Neurology.

[16]  Andrew J. King,et al.  Measuring the Performance of Neural Models , 2016, Front. Comput. Neurosci..

[17]  M. Boly,et al.  Willful modulation of brain activity in disorders of consciousness. , 2010, The New England journal of medicine.

[18]  Steven Laureys,et al.  Human consciousness is supported by dynamic complex patterns of brain signal coordination , 2019, Science Advances.

[19]  Scott A Lewis,et al.  Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues , 2017, bioRxiv.

[20]  G. Tononi,et al.  Stratification of unresponsive patients by an independently validated index of brain complexity , 2016, Annals of neurology.

[21]  Anthony G. Hudetz,et al.  Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans , 2018, Front. Hum. Neurosci..

[22]  S. Dehaene,et al.  Probing consciousness with event-related potentials in the vegetative state , 2011, Neurology.

[23]  Steven Laureys,et al.  The Minimal Energetic Requirement of Sustained Awareness after Brain Injury , 2016, Current Biology.

[24]  Matthäus Staniek,et al.  Symbolic transfer entropy. , 2008, Physical review letters.

[25]  George A. Mashour,et al.  Normal Brain Response to Propofol in Advance of Recovery from Unresponsive Wakefulness Syndrome , 2016, Front. Hum. Neurosci..

[26]  O. Sporns,et al.  Motifs in Brain Networks , 2004, PLoS biology.

[27]  Cornelis J. Stam,et al.  Go with the flow: Use of a directed phase lag index (dPLI) to characterize patterns of phase relations in a large-scale model of brain dynamics , 2012, NeuroImage.

[28]  Jürgen Kurths,et al.  Modification of Brain Oscillations via Rhythmic Light Stimulation Provides Evidence for Entrainment but Not for Superposition of Event-Related Responses , 2016, Front. Hum. Neurosci..

[29]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[30]  G. Tononi An information integration theory of consciousness , 2004, BMC Neuroscience.

[31]  S. Eickhoff,et al.  N400 predicts recovery from disorders of consciousness , 2013, Annals of neurology.

[32]  Fabrice Wendling,et al.  Decreased integration of EEG source-space networks in disorders of consciousness , 2018 .

[33]  Fang Sun,et al.  Willful modulation of brain activity in disorders of consciousness. , 2010, The New England journal of medicine.

[34]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[35]  Athena Demertzi,et al.  Measuring states of pathological (un)consciousness: research dimensions, clinical applications, and ethics† , 2017, Neuroscience of consciousness.

[36]  M. Kennard,et al.  Measuring benefits of protected area management: trends across realms and research gaps for freshwater systems , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[37]  S. Dehaene,et al.  Event related potentials elicited by violations of auditory regularities in patients with impaired consciousness , 2012, Neuropsychologia.

[38]  Marcello Massimini,et al.  Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients , 2012, Brain : a journal of neurology.

[39]  Robert Oostenveld,et al.  An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias , 2011, NeuroImage.

[40]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[41]  A. Fingelkurts,et al.  Changes in Standard Electroencephalograms Parallel Consciousness Improvements in Patients With Unresponsive Wakefulness Syndrome. , 2017, Archives of physical medicine and rehabilitation.

[42]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[43]  F W Sharbrough,et al.  Anterior shift of the dominant EEG rhytham during anesthesia in the Java monkey: correlation with anesthetic potency. , 1977, Anesthesiology.

[44]  Tianzi Jiang,et al.  Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics , 2018, eLife.

[45]  Lorina Naci,et al.  Risk, diagnostic error, and the clinical science of consciousness , 2015, NeuroImage: Clinical.

[46]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[47]  Fabrice Wendling,et al.  Decreased integration of EEG source-space networks in disorders of consciousness , 2018, NeuroImage: Clinical.

[48]  George A. Mashour,et al.  Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia , 2013, PloS one.

[49]  Andreas Bender,et al.  Consciousness Indexing and Outcome Prediction with Resting-State EEG in Severe Disorders of Consciousness , 2018, Brain Topography.

[50]  E. John,et al.  Invariant Reversible QEEG Effects of Anesthetics , 2001, Consciousness and Cognition.

[51]  G. Tononi,et al.  A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior , 2013, Science Translational Medicine.

[52]  Matthew H. Davis,et al.  Detecting Awareness in the Vegetative State , 2006, Science.

[53]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[54]  ShiNung Ching,et al.  Sevoflurane Alters Spatiotemporal Functional Connectivity Motifs That Link Resting-State Networks during Wakefulness , 2016, Front. Neural Circuits.

[55]  Yong Wang,et al.  Electroencephalography quadratic phase self-coupling correlates with consciousness states and restoration in patients with disorders of consciousness , 2019, Clinical Neurophysiology.

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

[57]  UnCheol Lee,et al.  Reconfiguration of Network Hub Structure after Propofol-induced Unconsciousness , 2013, Anesthesiology.

[58]  G. Mashour,et al.  Brain network motifs are markers of loss and recovery of consciousness , 2020, bioRxiv.

[59]  Mathias Basner,et al.  Protocol for the Reconstructing Consciousness and Cognition (ReCCognition) Study , 2017, Front. Hum. Neurosci..