Functional connectivity and network analysis during hypoactive delirium and recovery from anesthesia

OBJECTIVE To gain insight in the underlying mechanism of reduced levels of consciousness due to hypoactive delirium versus recovery from anesthesia, we studied functional connectivity and network topology using electroencephalography (EEG). METHODS EEG recordings were performed in age and sex-matched patients with hypoactive delirium (n=18), patients recovering from anesthesia (n=20), and non-delirious control patients (n=20), all after cardiac surgery. Functional and directed connectivity were studied with phase lag index and directed phase transfer entropy. Network topology was characterized using the minimum spanning tree (MST). A random forest classifier was calculated based on all measures to obtain discriminative ability between the three groups. RESULTS Non-delirious control subjects showed a back-to-front information flow, which was lost during hypoactive delirium (p=0.01) and recovery from anesthesia (p<0.01). The recovery from anesthesia group had more integrated network in the delta band compared to non-delirious controls. In contrast, hypoactive delirium showed a less integrated network in the alpha band. High accuracy for discrimination between hypoactive delirious patients and controls (86%) and recovery from anesthesia and controls (95%) were found. Accuracy for discrimination between hypoactive delirium and recovery from anesthesia was 73%. CONCLUSION Loss of functional and directed connectivity were observed in both hypoactive delirium and recovery from anesthesia, which might be related to the reduced level of consciousness in both states. These states could be distinguished in topology, which was a less integrated network during hypoactive delirium. SIGNIFICANCE Functional and directed connectivity are similarly disturbed during a reduced level of consciousness due to hypoactive delirium and sedatives, however topology was differently affected.

[1]  George A Mashour,et al.  Integrating the Science of Consciousness and Anesthesia , 2006, Anesthesia and analgesia.

[2]  Theodore Speroff,et al.  Evaluation of delirium in critically ill patients: Validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) , 2001, Critical care medicine.

[3]  T. Gotō,et al.  Cerebral dysfunction after coronary artery bypass surgery , 2014, Journal of Anesthesia.

[4]  Linda Douw,et al.  Epilepsy surgery outcome and functional network alterations in longitudinal MEG: A minimum spanning tree analysis , 2014, NeuroImage.

[5]  K. Müller,et al.  Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.

[6]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[7]  Cornelis J. Stam,et al.  Declining functional connectivity and changing hub locations in Alzheimer’s disease: an EEG study , 2015, BMC Neurology.

[8]  J. H. Fallon,et al.  Toward a Unified Theory of Narcosis: Brain Imaging Evidence for a Thalamocortical Switch as the Neurophysiologic Basis of Anesthetic-Induced Unconsciousness , 2000, Consciousness and Cognition.

[9]  Steven Laureys,et al.  Resting-state EEG study of comatose patients: a connectivity and frequency analysis to find differences between vegetative and minimally conscious states. , 2012, Functional neurology.

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

[11]  B. W. van Dijk,et al.  Opportunities and methodological challenges in EEG and MEG resting state functional brain network research , 2015, Clinical Neurophysiology.

[12]  Edwin van Dellen,et al.  Being Conscious of Methodological Pitfalls in Functional Brain Network Analysis. , 2015, Anesthesiology.

[13]  Manuel S. Schröter,et al.  Spatiotemporal Reconfiguration of Large-Scale Brain Functional Networks during Propofol-Induced Loss of Consciousness , 2012, The Journal of Neuroscience.

[14]  G. Sandini,et al.  Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. , 2009, Brain : a journal of neurology.

[15]  B. Caffo,et al.  Resting brain activity in disorders of consciousness , 2015, Neurology.

[16]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[17]  M. Paluš,et al.  Directionality of coupling from bivariate time series: how to avoid false causalities and missed connections. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Andreas Daffertshofer,et al.  Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.

[19]  George A. Mashour,et al.  General Relationship of Global Topology, Local Dynamics, and Directionality in Large-Scale Brain Networks , 2015, PLoS Comput. Biol..

[20]  G. L. Engel,et al.  Delirium, a syndrome of cerebral insufficiency. , 1959, Journal of chronic diseases.

[21]  Janet B W Williams,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[22]  S. Georgiadis,et al.  Directional Connectivity between Frontal and Posterior Brain Regions Is Altered with Increasing Concentrations of Propofol , 2014, PloS one.

[23]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[24]  J. O'Brien,et al.  Delirium and dementia with Lewy bodies: distinct diagnoses or part of the same spectrum? , 2014, Journal of Neurology, Neurosurgery & Psychiatry.

[25]  Andreas K. Engel,et al.  Temporal Binding, Binocular Rivalry, and Consciousness , 1999, Consciousness and Cognition.

[26]  Bernhard Hemmer,et al.  Simultaneous Electroencephalographic and Functional Magnetic Resonance Imaging Indicate Impaired Cortical Top–Down Processing in Association with Anesthetic-induced Unconsciousness , 2013, Anesthesiology.

[27]  C. Stam,et al.  Improved Diagnosis in Children with Partial Epilepsy Using a Multivariable Prediction Model Based on EEG Network Characteristics , 2013, PloS one.

[28]  Cornelis J. Stam,et al.  Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease , 2016, Alzheimer's & dementia.

[29]  Edwin van Dellen,et al.  The minimum spanning tree: An unbiased method for brain network analysis , 2015, NeuroImage.

[30]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[31]  S Minoshima,et al.  Diagnosis and management of dementia with Lewy bodies , 2005, Neurology.

[32]  F. Leijten,et al.  Original Research: Critical CareDelirium Detection Using EEG , 2015 .

[33]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

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

[35]  R. Jaeschke,et al.  Clinical Practice Guidelines for the Management of Pain, Agitation, and Delirium in Adult Patients in the Intensive Care Unit , 2013, Critical care medicine.

[36]  C. Stam,et al.  Decreased Functional Connectivity and Disturbed Directionality of Information Flow in the Electroencephalography of Intensive Care Unit Patients with Delirium after Cardiac Surgery , 2014, Anesthesiology.

[37]  G. Mashour,et al.  Disconnecting Consciousness: Is There a Common Anesthetic End Point? , 2016, Anesthesia and analgesia.

[38]  Philip Scheltens,et al.  Loss of EEG Network Efficiency Is Related to Cognitive Impairment in Dementia With Lewy Bodies , 2015, Movement disorders : official journal of the Movement Disorder Society.

[39]  F. de Leeuw,et al.  Routine use of the confusion assessment method for the intensive care unit: a multicenter study. , 2011, American journal of respiratory and critical care medicine.

[40]  C. Sessler,et al.  The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients. , 2002, American journal of respiratory and critical care medicine.

[41]  Anthony G. Hudetz,et al.  Suppressing consciousness: Mechanisms of general anesthesia , 2006 .

[42]  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.

[43]  Cornelis J Stam,et al.  Structural Brain Network Disturbances in the Psychosis Spectrum. , 2016, Schizophrenia bulletin.

[44]  G. Tononi,et al.  Consciousness and Anesthesia , 2008, Science.

[45]  Russell R. Miller,et al.  The association between brain volumes, delirium duration, and cognitive outcomes in intensive care unit survivors: The VISIONS cohort magnetic resonance imaging study* , 2012, Critical care medicine.

[46]  D. van Dijk,et al.  Long-term outcome of delirium during intensive care unit stay in survivors of critical illness: a prospective cohort study , 2014, Critical Care.

[47]  A. Slooter,et al.  Light levels of sedation and DSM-5 criteria for delirium , 2014, Intensive Care Medicine.

[48]  UnCheol Lee,et al.  The directionality and functional organization of frontoparietal connectivity during consciousness and anesthesia in humans , 2009, Consciousness and Cognition.

[49]  C. Kalkman,et al.  Electroencephalographic Characteristics of Emergence from Propofol/Sufentanil Total Intravenous Anesthesia , 1995, Anesthesia and analgesia.

[50]  C. Stam,et al.  Direction of information flow in large-scale resting-state networks is frequency-dependent , 2016, Proceedings of the National Academy of Sciences.

[51]  S. Marino,et al.  Functional connectivity in disorders of consciousness: methodological aspects and clinical relevance , 2015, Brain Imaging and Behavior.

[52]  Mark W. Woolrich,et al.  Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach , 2016, NeuroImage.

[53]  Steven Laureys,et al.  Functional neuroanatomy of disorders of consciousness , 2014, Epilepsy & Behavior.

[54]  F. Leijten,et al.  Delirium detection using EEG: what and how to measure. , 2015, Chest.

[55]  J. Matias Palva,et al.  Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions , 2014, NeuroImage.

[56]  E. Whitham,et al.  Scalp electrical recording during paralysis: Quantitative evidence that EEG frequencies above 20Hz are contaminated by EMG , 2007, Clinical Neurophysiology.

[57]  B. Hjorth An on-line transformation of EEG scalp potentials into orthogonal source derivations. , 1975, Electroencephalography and clinical neurophysiology.

[58]  Arjan Hillebrand,et al.  Disrupted brain network topology in Parkinson's disease: a longitudinal magnetoencephalography study. , 2014, Brain : a journal of neurology.