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Wang J, Lai Q, Han J, Qin P, Wu H. Neuroimaging biomarkers for the diagnosis and prognosis of patients with disorders of consciousness. Brain Res 2024; 1843:149133. [PMID: 39084451 DOI: 10.1016/j.brainres.2024.149133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 05/29/2024] [Accepted: 07/25/2024] [Indexed: 08/02/2024]
Abstract
The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches.
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Affiliation(s)
- Jiaying Wang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Qiantu Lai
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China; Pazhou Lab, Guangzhou 510330, China.
| | - Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China.
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2
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Riganello F, Vatrano M, Cortese MD, Tonin P, Soddu A. Central autonomic network and early prognosis in patients with disorders of consciousness. Sci Rep 2024; 14:1610. [PMID: 38238457 PMCID: PMC10796939 DOI: 10.1038/s41598-024-51457-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/05/2024] [Indexed: 01/22/2024] Open
Abstract
The central autonomic network (CAN) plays a crucial role in modulating the autonomic nervous system. Heart rate variability (HRV) is a valuable marker for assessing CAN function in disorders of consciousness (DOC) patients. We used HRV analysis for early prognosis in 58 DOC patients enrolled within ten days of hospitalization. They underwent a five-minute electrocardiogram during baseline and acoustic/visual stimulation. The coma recovery scale-revised (CRS-R) was used to define the patient's consciousness level and categorize the good/bad outcome at three months. The high-frequency Power Spectrum Density and the standard deviation of normal-to-normal peaks in baseline, the sample entropy during the stimulation, and the time from injury features were used in the support vector machine analysis (SVM) for outcome prediction. The SVM predicted the patients' outcome with an accuracy of 96% in the training test and 100% in the validation test, underscoring its potential to provide crucial clinical information about prognosis.
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Affiliation(s)
- Francesco Riganello
- Reseach in Advanced Neurorehabilitation, S. Anna Institute, 88900, Crotone, Italy.
| | - Martina Vatrano
- Reseach in Advanced Neurorehabilitation, S. Anna Institute, 88900, Crotone, Italy
| | | | - Paolo Tonin
- Reseach in Advanced Neurorehabilitation, S. Anna Institute, 88900, Crotone, Italy
| | - Andrea Soddu
- Physics & Astronomy Department and Western Institute for Neuroscience, University of Western Ontario, London, ON, Canada
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3
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Oujamaa L, Delon-Martin C, Jaroszynski C, Termenon M, Silva S, Payen JF, Achard S. Functional hub disruption emphasizes consciousness recovery in severe traumatic brain injury. Brain Commun 2023; 5:fcad319. [PMID: 38757093 PMCID: PMC11098044 DOI: 10.1093/braincomms/fcad319] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 08/20/2023] [Accepted: 11/21/2023] [Indexed: 05/18/2024] Open
Abstract
Severe traumatic brain injury can lead to transient or even chronic disorder of consciousness. To increase diagnosis and prognosis accuracy of disorder of consciousness, functional neuroimaging is recommended 1 month post-injury. Here, we investigated brain networks remodelling on longitudinal data between 1 and 3 months post severe traumatic brain injury related to change of consciousness. Thirty-four severe traumatic brain-injured patients were included in a cross-sectional and longitudinal clinical study, and their MRI data were compared to those of 20 healthy subjects. Long duration resting-state functional MRI were acquired in minimally conscious and conscious patients at two time points after their brain injury. The first time corresponds to the exit from intensive care unit and the second one to the discharge from post-intensive care rehabilitation ward. Brain networks data were extracted using graph analysis and metrics at each node quantifying local (clustering) and global (degree) connectivity characteristics. Comparison with brain networks of healthy subjects revealed patterns of hyper- and hypo-connectivity that characterize brain networks reorganization through the hub disruption index, a value quantifying the functional disruption in each individual severe traumatic brain injury graph. At discharge from intensive care unit, 24 patients' graphs (9 minimally conscious and 15 conscious) were fully analysed and demonstrated significant network disruption. Clustering and degree nodal metrics, respectively, related to segregation and integration properties of the network, were relevant to distinguish minimally conscious and conscious groups. At discharge from post-intensive care rehabilitation unit, 15 patients' graphs (2 minimally conscious, 13 conscious) were fully analysed. The conscious group still presented a significant difference with healthy subjects. Using mixed effects models, we showed that consciousness state, rather than time, explained the hub disruption index differences between minimally conscious and conscious groups. While severe traumatic brain-injured patients recovered full consciousness, regional functional connectivity evolved towards a healthy pattern. More specifically, the restoration of a healthy brain functional segregation could be necessary for consciousness recovery after severe traumatic brain injury. For the first time, extracting the hub disruption index directly from each patient's graph, we were able to track the clinical alteration and subsequent recovery of consciousness during the first 3 months following a severe traumatic brain injury.
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Affiliation(s)
- Lydia Oujamaa
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Chantal Delon-Martin
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Chloé Jaroszynski
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Maite Termenon
- Faculty of Engineering, Biomedical Engineering Department, Mondragon Unibertsitatea (MU-ENG), 20500 Mondragon, Spain
| | - Stein Silva
- Toulouse NeuroImaging Center, Toulouse III Paul Sabatier University, Inserm, 31062 Toulouse, France
- Critical Care Unit, University Teaching Hospital of Purpan, 31059 Toulouse, France
| | - Jean-François Payen
- University Grenoble Alpes, Inserm U1216, Grenoble Institut Neurosciences, CHU Grenoble Alpes, 38000 Grenoble, France
| | - Sophie Achard
- University Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France
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4
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Pozeg P, Alemán-Goméz Y, Jöhr J, Muresanu D, Pincherle A, Ryvlin P, Hagmann P, Diserens K, Dunet V. Structural connectivity in recovery after coma: Connectome atlas approach. Neuroimage Clin 2023; 37:103358. [PMID: 36868043 PMCID: PMC9996111 DOI: 10.1016/j.nicl.2023.103358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 02/06/2023] [Accepted: 02/20/2023] [Indexed: 03/05/2023]
Abstract
AIM Pathological states of recovery after coma as a result of a severe brain injury are marked with changes in structural connectivity of the brain. This study aimed to identify a topological correlation between white matter integrity and the level of functional and cognitive impairment in patients recovering after coma. METHODS Structural connectomes were computed based on fractional anisotropy maps from 40 patients using a probabilistic human connectome atlas. We used a network based statistics approach to identify potential brain networks associated with a more favorable outcome, assessed with clinical neurobehavioral scores at the patient's discharge from the acute neurorehabilitation unit. RESULTS We identified a subnetwork whose strength of connectivity correlated with a more favorable outcome as measured with the Disability Rating Scale (network based statistics: t >3.5, P =.010). The subnetwork predominated in the left hemisphere and included the thalamic nuclei, putamen, precentral and postcentral gyri, and medial parietal regions. Spearman correlation between the mean fractional anisotropy value of the subnetwork and the score was ρ = -0.60 (P <.0001). A less extensive overlapping subnetwork correlated with the Coma Recovery Scale Revised score, consisting mostly of the left hemisphere connectivity between the thalamic nuclei and pre- and post-central gyri (network based statistics: t >3.5, P =.033; Spearman's ρ = 0.58, P <.0001). CONCLUSION The present findings suggest an important role of structural connectivity between the thalamus, putamen and somatomotor cortex in the recovery from coma as evaluated with neurobehavioral scores. These structures are part of the motor circuit involved in the generation and modulation of voluntary movement, as well as the forebrain mesocircuit supposedly underlying the maintenance of consciousness. As behavioural assessment of consciousness depends heavily on the signs of voluntary motor behaviour, further work will elucidate whether the identified subnetwork reflects the structural architecture underlying the recovery of consciousness or rather the ability to communicate its content.
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Affiliation(s)
- Polona Pozeg
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland; Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland
| | - Yasser Alemán-Goméz
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland; Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland
| | - Jane Jöhr
- Neurology and Acute Neurorehabilitation Unit, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland
| | - Dafin Muresanu
- Department of Neuroscience, Luliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca 400347, Romania
| | - Alessandro Pincherle
- Neurology Unit, Department of Medicine, Hôpitaux Robert Schuman, Luxembourg 2540, Luxembourg
| | - Philippe Ryvlin
- Laboratory of Cortical Excitability and Arousal Disorders, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland; Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland
| | - Karin Diserens
- Neurology and Acute Neurorehabilitation Unit, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland
| | - Vincent Dunet
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland.
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Di Gregorio F, La Porta F, Petrone V, Battaglia S, Orlandi S, Ippolito G, Romei V, Piperno R, Lullini G. Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach. Biomedicines 2022; 10:biomedicines10081897. [PMID: 36009445 PMCID: PMC9405912 DOI: 10.3390/biomedicines10081897] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/04/2022] [Accepted: 07/29/2022] [Indexed: 11/18/2022] Open
Abstract
Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.
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Affiliation(s)
- Francesco Di Gregorio
- UO Medicina Riabilitativa e Neuroriabilitazione, Azienda Unità Sanitaria Locale, 40133 Bologna, Italy
| | - Fabio La Porta
- IRCCS Istituto delle Scienze Neurologiche di Bologna
- Correspondence:
| | | | - Simone Battaglia
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
- Dipartimento di Psicologia, Università di Torino, 10124 Torino, Italy
| | - Silvia Orlandi
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Viale Risorgimento, 2, 40136 Bologna, Italy
| | - Giuseppe Ippolito
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | | | - Giada Lullini
- IRCCS Istituto delle Scienze Neurologiche di Bologna
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Panda R, Thibaut A, Lopez-Gonzalez A, Escrichs A, Bahri MA, Hillebrand A, Deco G, Laureys S, Gosseries O, Annen J, Tewarie P. Disruption in structural-functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness. eLife 2022; 11:e77462. [PMID: 35916363 PMCID: PMC9385205 DOI: 10.7554/elife.77462] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.
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Affiliation(s)
- Rajanikant Panda
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Aurore Thibaut
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Ane Lopez-Gonzalez
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu FabraBracelonaSpain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu FabraBracelonaSpain
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of LiègeLiègeBelgium
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam NeuroscienceAmsterdamNetherlands
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu FabraBracelonaSpain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA)BarcelonaSpain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
- CERVO Research Center, Laval UniversityQuébecCanada
| | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Prejaas Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam NeuroscienceAmsterdamNetherlands
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of NottinghamNottinghamUnited Kingdom
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7
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Lutkenhoff ES, Nigri A, Rossi Sebastiano D, Sattin D, Visani E, Rosazza C, D'Incerti L, Bruzzone MG, Franceschetti S, Leonardi M, Ferraro S, Monti MM. EEG Power spectra and subcortical pathology in chronic disorders of consciousness. Psychol Med 2022; 52:1491-1500. [PMID: 32962777 DOI: 10.1017/s003329172000330x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Despite a growing understanding of disorders of consciousness following severe brain injury, the association between long-term impairment of consciousness, spontaneous brain oscillations, and underlying subcortical damage, and the ability of such information to aid patient diagnosis, remains incomplete. METHODS Cross-sectional observational sample of 116 patients with a disorder of consciousness secondary to brain injury, collected prospectively at a tertiary center between 2011 and 2013. Multimodal analyses relating clinical measures of impairment, electroencephalographic measures of spontaneous brain activity, and magnetic resonance imaging data of subcortical atrophy were conducted in 2018. RESULTS In the final analyzed sample of 61 patients, systematic associations were found between electroencephalographic power spectra and subcortical damage. Specifically, the ratio of beta-to-delta relative power was negatively associated with greater atrophy in regions of the bilateral thalamus and globus pallidus (both left > right) previously shown to be preferentially atrophied in chronic disorders of consciousness. Power spectrum total density was also negatively associated with widespread atrophy in regions of the left globus pallidus, right caudate, and in the brainstem. Furthermore, we showed that the combination of demographics, encephalographic, and imaging data in an analytic framework can be employed to aid behavioral diagnosis. CONCLUSIONS These results ground, for the first time, electroencephalographic presentation detected with routine clinical techniques in the underlying brain pathology of disorders of consciousness and demonstrate how multimodal combination of clinical, electroencephalographic, and imaging data can be employed in potentially mitigating the high rates of misdiagnosis typical of this patient cohort.
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Affiliation(s)
- Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Davide Rossi Sebastiano
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Davide Sattin
- Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Elisa Visani
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Cristina Rosazza
- Scientific Direction, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Ludovico D'Incerti
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Silvana Franceschetti
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Matilde Leonardi
- Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Stefania Ferraro
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
- School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China: On the behalf of the Coma Research Center, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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8
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Papadimitriou C, Weaver JA, Guernon A, Walsh E, Mallinson T, Pape TLB. "Fluctuation is the norm": Rehabilitation practitioner perspectives on ambiguity and uncertainty in their work with persons in disordered states of consciousness after traumatic brain injury. PLoS One 2022; 17:e0267194. [PMID: 35446897 PMCID: PMC9022828 DOI: 10.1371/journal.pone.0267194] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 04/04/2022] [Indexed: 11/19/2022] Open
Abstract
The purpose of this study is to describe the clinical lifeworld of rehabilitation practitioners who work with patients in disordered states of consciousness (DoC) after severe traumatic brain injury (TBI). We interviewed 21 practitioners using narrative interviewing methods from two specialty health systems that admit patients in DoC to inpatient rehabilitation. The overarching theme arising from the interview data is "Experiencing ambiguity and uncertainty in clinical reasoning about consciousness" when treating persons in DoC. We describe practitioners' practices of looking for consistency, making sense of ambiguous and hard to explain patient responses, and using trial and error or "tinkering" to care for patients. Due to scientific uncertainty about diagnosis and prognosis in DoC and ambiguity about interpretation of patient responses, working in the field of DoC disrupts the canonical meaning-making processes that practitioners have been trained in. Studying the lifeworld of rehabilitation practitioners through their story-making and story-telling uncovers taken-for-granted assumptions and normative structures that may exist in rehabilitation medical and scientific culture, including practitioner training. We are interested in understanding these canonical breaches in order to make visible how practitioners make meaning while treating patients.
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Affiliation(s)
- Christina Papadimitriou
- Departments of Interdisciplinary Health Sciences, and Sociology, Oakland University, Rochester, MI, United States of America
| | - Jennifer A. Weaver
- Department of Occupational Therapy, Colorado State University, Fort Collins, CO, United States of America
| | - Ann Guernon
- Speech-Language Pathology Department, Lewis University, Romeoville, IL, United States of America
| | - Elyse Walsh
- Research Service and Center for Innovation in Complex Chronic Healthcare, Edward Hines Jr. VA, Hines, IL, United States of America
| | - Trudy Mallinson
- Department of Clinical Research & Leadership, George Washington University, Washington, DC, United States of America
| | - Theresa L. Bender Pape
- Research Service and Center for Innovation in Complex Chronic Healthcare, Edward Hines Jr. VA, Hines, IL, United States of America
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States of America
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9
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Toker D, Pappas I, Lendner JD, Frohlich J, Mateos DM, Muthukumaraswamy S, Carhart-Harris R, Paff M, Vespa PM, Monti MM, Sommer FT, Knight RT, D'Esposito M. Consciousness is supported by near-critical slow cortical electrodynamics. Proc Natl Acad Sci U S A 2022; 119:e2024455119. [PMID: 35145021 PMCID: PMC8851554 DOI: 10.1073/pnas.2024455119] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 12/20/2021] [Indexed: 12/21/2022] Open
Abstract
Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, we empirically identify a mathematically specific critical point near which waking cortical oscillatory dynamics operate, which is known as the edge-of-chaos critical point, or the boundary between stability and chaos. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magnetoencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of low-frequency cortical electric oscillations away from this critical point; conversely, we show that psychedelics may increase the information richness of cortical activity by tuning low-frequency cortical oscillations closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC) and show that assessing the proximity of slow cortical oscillatory electrodynamics to the edge-of-chaos critical point may be useful as an index of consciousness in the clinical setting.
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Affiliation(s)
- Daniel Toker
- Department of Psychology, University of California, Los Angeles, CA 90095;
| | - Ioannis Pappas
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Department of Psychology, University of California, Berkeley, CA 94704
- Laboratory of Neuro Imaging, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Janna D Lendner
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Department of Anesthesiology and Intensive Care, University Medical Center, 72076 Tübingen, Germany
| | - Joel Frohlich
- Department of Psychology, University of California, Los Angeles, CA 90095
| | - Diego M Mateos
- Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina, C1425 Buenos Aires, Argentina
- Facultad de Ciencia y Tecnología, Universidad Autónoma de Entre Ríos, E3202 Paraná, Entre Ríos, Argentina
- Grupo de Análisis de Neuroimágenes, Instituo de Matemática Aplicada del Litoral, S3000 Santa Fe, Argentina
| | - Suresh Muthukumaraswamy
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, 1010 Auckland, New Zealand
| | - Robin Carhart-Harris
- Neuropsychopharmacology Unit, Centre for Psychiatry, Imperial College London, London SW7 2AZ, United Kingdom
- Centre for Psychedelic Research, Department of Psychiatry, Imperial College London, London SW7 2AZ, United Kingdom
| | - Michelle Paff
- Department of Neurological Surgery, University of California, Irvine, CA 92697
| | - Paul M Vespa
- Brain Injury Research Center, Department of Neurosurgery, University of California, Los Angeles, CA 90095
| | - Martin M Monti
- Department of Psychology, University of California, Los Angeles, CA 90095
- Brain Injury Research Center, Department of Neurosurgery, University of California, Los Angeles, CA 90095
| | - Friedrich T Sommer
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA 94704
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Department of Psychology, University of California, Berkeley, CA 94704
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Department of Psychology, University of California, Berkeley, CA 94704
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10
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Pain Perception in Disorder of Consciousness: A Scoping Review on Current Knowledge, Clinical Applications, and Future Perspective. Brain Sci 2021; 11:brainsci11050665. [PMID: 34065349 PMCID: PMC8161058 DOI: 10.3390/brainsci11050665] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/05/2021] [Accepted: 05/19/2021] [Indexed: 01/18/2023] Open
Abstract
Pain perception in individuals with prolonged disorders of consciousness (PDOC) is still a matter of debate. Advanced neuroimaging studies suggest some cortical activations even in patients with unresponsive wakefulness syndrome (UWS) compared to those with a minimally conscious state (MCS). Therefore, pain perception has to be considered even in individuals with UWS. However, advanced neuroimaging assessment can be challenging to conduct, and its findings are sometimes difficult to be interpreted. Conversely, multichannel electroencephalography (EEG) and laser-evoked potentials (LEPs) can be carried out quickly and are more adaptable to the clinical needs. In this scoping review, we dealt with the neurophysiological basis underpinning pain in PDOC, pointing out how pain perception assessment in these individuals might help in reducing the misdiagnosis rate. The available literature data suggest that patients with UWS show a more severe functional connectivity breakdown among the pain-related brain areas compared to individuals in MCS, pointing out that pain perception increases with the level of consciousness. However, there are noteworthy exceptions, because some UWS patients show pain-related cortical activations that partially overlap those observed in MCS individuals. This suggests that some patients with UWS may have residual brain functional connectivity supporting the somatosensory, affective, and cognitive aspects of pain processing (i.e., a conscious experience of the unpleasantness of pain), rather than only being able to show autonomic responses to potentially harmful stimuli. Therefore, the significance of the neurophysiological approach to pain perception in PDOC seems to be clear, and despite some methodological caveats (including intensity of stimulation, multimodal paradigms, and active vs. passive stimulation protocols), remain to be solved. To summarize, an accurate clinical and neurophysiological assessment should always be performed for a better understanding of pain perception neurophysiological underpinnings, a more precise differential diagnosis at the level of individual cases as well as group comparisons, and patient-tailored management.
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11
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Zheng W, Tan X, Liu T, Li X, Gao J, Hong L, Zhang X, Zhao Z, Yu Y, Zhang Y, Luo B, Wu D. Individualized Thalamic Parcellation Reveals Alterations in Shape and Microstructure of Thalamic Nuclei in Patients with Disorder of Consciousness. Cereb Cortex Commun 2021; 2:tgab024. [PMID: 34296169 PMCID: PMC8152869 DOI: 10.1093/texcom/tgab024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/25/2021] [Accepted: 03/28/2021] [Indexed: 12/02/2022] Open
Abstract
The thalamus plays crucial roles in consciousness generation and information processing. Previous evidence suggests that disorder of consciousness (DOC) caused by severe brain injury, is potentially related to thalamic abnormalities. However, how the morphology and microstructure change in thalamic subfields and thalamocortical fiber pathways in patients with DOC, and the relationships between these changes and the consciousness status remain unclear. Here, we generated the individual-specific thalamic parcellation in 10 DOC patients and 10 healthy controls (HC) via a novel thalamic segmentation framework based on the fiber orientation distribution (FOD) derived from 7-Tesla diffusion MRI, and investigated the shape deformation of thalamic nuclei as well as the microstructural changes associated with thalamic nuclei and thalamocortical pathways in patients with DOC. Enlargement of dorsal posterior nucleus and atrophy of anterior nucleus in the right thalamus were observed in DOC cohort relative to the HCs, and the former was closely linked to the consciousness level of the patients. We also found significant reductions of fiber density, but not fiber bundle cross-section, within several thalamic nuclei and most of the thalamocortical fiber pathways, suggesting that loss of axons might take primary responsibility for the impaired thalamocortical connections in patients with DOC rather than the change in fiber-bundle morphology. Furthermore, the individual-specific thalamic parcellation achieved 80% accuracy in classifying patients at the minimally conscious state from the vegetative state, compared with ~60% accuracy based on group-level parcellations. Our findings provide the first evidence for the shape deformation of thalamic nuclei in DOC patients and the microstructural basis of the disrupted thalamocortical connections.
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Affiliation(s)
- Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P.R. China
| | - Xufei Tan
- Department of Clinical Medicine, School of Medicine, Zhejiang University City College, Hangzhou, 310015, P.R. China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P.R. China
| | - Xiaoxia Li
- Department of Neurology and Brain Medical Centre, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P.R. China
| | - Jian Gao
- Department of Rehabilitation, Hospital of Zhejiang Armed Police Corps, Hangzhou, 310051, P.R. China
| | - Lirong Hong
- Department of Rehabilitation, Hospital of Zhejiang Armed Police Corps, Hangzhou, 310051, P.R. China
| | - Xiaotong Zhang
- Interdisciplinary Institute of Neuroscience and Technology, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310029, P.R. China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P.R. China
| | - Yamei Yu
- Department of Neurology and Brain Medical Centre, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P.R. China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P.R. China
| | - Benyan Luo
- Department of Neurology and Brain Medical Centre, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P.R. China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P.R. China
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12
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Olsen A, Babikian T, Bigler ED, Caeyenberghs K, Conde V, Dams-O'Connor K, Dobryakova E, Genova H, Grafman J, Håberg AK, Heggland I, Hellstrøm T, Hodges CB, Irimia A, Jha RM, Johnson PK, Koliatsos VE, Levin H, Li LM, Lindsey HM, Livny A, Løvstad M, Medaglia J, Menon DK, Mondello S, Monti MM, Newcombe VFJ, Petroni A, Ponsford J, Sharp D, Spitz G, Westlye LT, Thompson PM, Dennis EL, Tate DF, Wilde EA, Hillary FG. Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group. Brain Imaging Behav 2021; 15:526-554. [PMID: 32797398 PMCID: PMC8032647 DOI: 10.1007/s11682-020-00313-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group's short-term, intermediate, and long-term goals.
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Affiliation(s)
- Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Erin D Bigler
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology and Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia
| | - Virginia Conde
- Department of Psychology, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Kristen Dams-O'Connor
- Department of Rehabilitation Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ekaterina Dobryakova
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ, USA
- Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Helen Genova
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ, USA
| | - Jordan Grafman
- Cognitive Neuroscience Laboratory, Shirley Ryan AbilityLab, Chicago, IL, USA
- Department of Physical Medicine & Rehabilitation, Neurology, Department of Psychiatry & Department of Psychology, Cognitive Neurology and Alzheimer's, Center, Feinberg School of Medicine, Weinberg, Chicago, IL, USA
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hopsital, Trondheim University Hospital, Trondheim, Norway
| | - Ingrid Heggland
- Section for Collections and Digital Services, NTNU University Library, Norwegian University of Science and Technology, Trondheim, Norway
| | - Torgeir Hellstrøm
- Department of Physical Medicine and Rehabilitation, Oslo University Hospital, Oslo, Norway
| | - Cooper B Hodges
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Andrei Irimia
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Ruchira M Jha
- Departments of Critical Care Medicine, Neurology, Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
- Safar Center for Resuscitation Research, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, Pittsburgh, PA, USA
| | - Paula K Johnson
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Vassilis E Koliatsos
- Departments of Pathology(Neuropathology), Neurology, and Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Neuropsychiatry Program, Sheppard and Enoch Pratt Hospital, Baltimore, MD, USA
| | - Harvey Levin
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Lucia M Li
- C3NL, Imperial College London, London, UK
- UK DRI Centre for Health Care and Technology, Imperial College London, London, UK
| | - Hannah M Lindsey
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Abigail Livny
- Department of Diagnostic Imaging, Sheba Medical Center, Tel-Hashomer, Ramat Gan, Israel
- Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Ramat Gan, Israel
| | - Marianne Løvstad
- Sunnaas Rehabilitation Hospital, Nesodden, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - John Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA, USA
- Department of Neurology, Drexel University, Philadelphia, PA, USA
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stefania Mondello
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Department of Neurosurgery, Brain Injury Research Center (BIRC), UCLA, Los Angeles, CA, USA
| | | | - Agustin Petroni
- Department of Psychology, Norwegian University of Science and Technology, 7491, Trondheim, Norway
- Department of Computer Science, Faculty of Exact & Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina
- National Scientific & Technical Research Council, Institute of Research in Computer Science, Buenos Aires, Argentina
| | - Jennie Ponsford
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Epworth Rehabilitation Research Centre, Epworth Healthcare, Melbourne, Australia
| | - David Sharp
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research & Technology Centre, UK Dementia Research Institute, London, UK
| | - Gershon Spitz
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, USC, Los Angeles, CA, USA
| | - Emily L Dennis
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - David F Tate
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Frank G Hillary
- Department of Neurology, Hershey Medical Center, State College, PA, USA.
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13
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Naro A, Maggio MG, Leo A, Calabrò RS. Multiplex and Multilayer Network EEG Analyses: A Novel Strategy in the Differential Diagnosis of Patients with Chronic Disorders of Consciousness. Int J Neural Syst 2020; 31:2050052. [PMID: 33034532 DOI: 10.1142/s0129065720500525] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The deterioration of specific topological network measures that quantify different features of whole-brain functional network organization can be considered a marker for awareness impairment. Such topological measures reflect the functional interactions of multiple brain structures, which support the integration of different sensorimotor information subtending awareness. However, conventional, single-layer, graph theoretical analysis (GTA)-based approaches cannot always reliably differentiate patients with Disorders of Consciousness (DoC). Using multiplex and multilayer network analyses of frequency-specific and area-specific networks, we investigated functional connectivity during resting-state EEG in 17 patients with Unresponsive Wakefulness Syndrome (UWS) and 15 with Minimally Conscious State (MCS). Multiplex and multilayer network metrics indicated the deterioration and heterogeneity of functional networks and, particularly, the frontal-parietal (FP), as the discriminant between patients with MCS and UWS. These data were not appreciable when considering each individual frequency-specific network. The distinctive properties of multiplex/multilayer network metrics and individual frequency-specific network metrics further suggest the value of integrating the networks as opposed to analyzing frequency-specific network metrics one at a time. The hub vulnerability of these regions was positively correlated with the behavioral responsiveness, thus strengthening the clinically-based differential diagnosis. Therefore, it may be beneficial to adopt both multiplex and multilayer network analyses when expanding the conventional GTA-based analyses in the differential diagnosis of patients with DoC. Multiplex analysis differentiated patients at a group level, whereas the multilayer analysis offered complementary information to differentiate patients with DoC individually. Although further studies are necessary to confirm our preliminary findings, these results contribute to the issue of DoC differential diagnosis and may help in guiding patient-tailored management.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
| | - Maria Grazia Maggio
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
| | - Antonino Leo
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
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14
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Bender Pape TL, Livengood SL, Kletzel SL, Blabas B, Guernon A, Bhaumik DK, Bhaumik R, Mallinson T, Weaver JA, Higgins JP, Wang X, Herrold AA, Rosenow JM, Parrish T. Neural Connectivity Changes Facilitated by Familiar Auditory Sensory Training in Disordered Consciousness: A TBI Pilot Study. Front Neurol 2020; 11:1027. [PMID: 33132997 PMCID: PMC7578344 DOI: 10.3389/fneur.2020.01027] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 08/06/2020] [Indexed: 12/19/2022] Open
Abstract
For people with disordered consciousness (DoC) after traumatic brain injury (TBI), relationships between treatment-induced changes in neural connectivity and neurobehavioral recovery have not been explored. To begin building a body of evidence regarding the unique contributions of treatments to changes in neural network connectivity relative to neurobehavioral recovery, we conducted a pilot study to identify relationships meriting additional examination in future research. To address this objective, we examined previously unpublished neural connectivity data derived from a randomized clinical trial (RCT). We leveraged these data because treatment efficacy, in the RCT, was based on a comparison of a placebo control with a specific intervention, the familiar auditory sensory training (FAST) intervention, consisting of autobiographical auditory-linguistic stimuli. We selected a subgroup of RCT participants with high-quality imaging data (FAST n = 4 and placebo n = 4) to examine treatment-related changes in brain network connectivity and how and if these changes relate to neurobehavioral recovery. To discover promising relationships among the FAST intervention, changes in neural connectivity, and neurobehavioral recovery, we examined 26 brain regions and 19 white matter tracts associated with default mode, salience, attention, and language networks, as well as three neurobehavioral measures. Of the relationships discovered, the systematic filtering process yielded evidence supporting further investigation of the relationship among the FAST intervention, connectivity of the left inferior longitudinal fasciculus, and auditory-language skills. Evidence also suggests that future mechanistic research should focus on examining the possibility that the FAST supports connectivity changes by facilitating redistribution of brain resources. For a patient population with limited treatment options, the reported findings suggest that a simple, yet targeted, passive sensory stimulation treatment may have altered functional and structural connectivity. If replicated in future research, then these findings provide the foundation for characterizing the unique contributions of the FAST intervention and could inform development of new treatment strategies. For persons with severely damaged brain networks, this report represents a first step toward advancing understanding of the unique contributions of treatments to changing brain network connectivity and how these changes relate to neurobehavioral recovery for persons with DoC after TBI. Clinical Trial Registry: NCT00557076, The Efficacy of Familiar Voice Stimulation During Coma Recovery (http://www.clinicaltrials.gov).
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Affiliation(s)
- Theresa L Bender Pape
- The Department of Veterans Affairs (VA), Center for Innovation in Complex Chronic Healthcare & Research Service, Edward Hines Jr. VA Hospital, Hines, IL, United States.,Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Sherri L Livengood
- The Department of Veterans Affairs (VA), Center for Innovation in Complex Chronic Healthcare & Research Service, Edward Hines Jr. VA Hospital, Hines, IL, United States.,Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Sandra L Kletzel
- The Department of Veterans Affairs (VA), Center for Innovation in Complex Chronic Healthcare & Research Service, Edward Hines Jr. VA Hospital, Hines, IL, United States
| | - Brett Blabas
- The Department of Veterans Affairs (VA), Center for Innovation in Complex Chronic Healthcare & Research Service, Edward Hines Jr. VA Hospital, Hines, IL, United States
| | - Ann Guernon
- The Department of Veterans Affairs (VA), Center for Innovation in Complex Chronic Healthcare & Research Service, Edward Hines Jr. VA Hospital, Hines, IL, United States.,Marianjoy Rehabilitation Hospital Part of Northwestern Medicine, Wheaton, IL, United States
| | - Dulal K Bhaumik
- Division of Epidemiology and Biostatistics, Department of Psychiatry, Biostatistical Research Center, University of Illinois at Chicago, Chicago, IL, United States.,Research Service, Cooperative Studies Program Coordinating Center, Edward Hines Jr. VA Hospital, Hines, IL, United States
| | - Runa Bhaumik
- Division of Epidemiology and Biostatistics, Department of Psychiatry, Biostatistical Research Center, University of Illinois at Chicago, Chicago, IL, United States
| | - Trudy Mallinson
- Department of Clinical Research and Leadership, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - Jennifer A Weaver
- Department of Clinical Research and Leadership, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - James P Higgins
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Xue Wang
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Amy A Herrold
- The Department of Veterans Affairs (VA), Center for Innovation in Complex Chronic Healthcare & Research Service, Edward Hines Jr. VA Hospital, Hines, IL, United States.,Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Joshua M Rosenow
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.,Northwestern Memorial Hospital, Chicago, IL, United States
| | - Todd Parrish
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
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15
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Gabrieli D, Schumm SN, Vigilante NF, Parvesse B, Meaney DF. Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks. PLoS One 2020; 15:e0234749. [PMID: 32966291 PMCID: PMC7510994 DOI: 10.1371/journal.pone.0234749] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 06/01/2020] [Indexed: 12/26/2022] Open
Abstract
Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seen in vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.
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Affiliation(s)
- David Gabrieli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Samantha N. Schumm
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Nicholas F. Vigilante
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Brandon Parvesse
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - David F. Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
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16
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Dell'Italia J, Johnson MA, Vespa PM, Monti MM. Accounting for Changing Structure in Functional Network Analysis of TBI Patients. Front Syst Neurosci 2020; 14:42. [PMID: 32848638 PMCID: PMC7427444 DOI: 10.3389/fnsys.2020.00042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 06/05/2020] [Indexed: 12/05/2022] Open
Abstract
Over the last 15 years, network analysis approaches based on MR data have allowed a renewed understanding of the relationship between brain function architecture and consciousness. Application of this approach to Disorders of Consciousness (DOC) highlights the relationship between specific aspects of network topology and levels of consciousness. Nonetheless, such applications do not acknowledge that DOC patients present with a dramatic level of heterogeneity in structural connectivity (SC) across groups (e.g., etiology, diagnostic categories) and within individual patients (e.g., over time), which possibly affects the level and quality of functional connectivity (FC) patterns that can be expressed. In addition, it is rarely acknowledged that the most frequently employed outcome metrics in the study of brain connectivity (e.g., degree distribution, inter- or intra-resting state network connectivity, and clustering coefficient) are interrelated and cannot be assumed to be independent of each other. We present empirical data showing that, when the two points above are not taken into consideration with an appropriate analytic model, it can lead to a misinterpretation of the role of each outcome metric in the graph's structure and thus misinterpretation of FC results. We show that failing to account for either SC or the inter-relation between outcome measures can lead to inflated false positives (FP) and/or false negatives (FN) in inter- or intra-resting state network connectivity results (defined, respectively, as a positive or negative result in network connectivity that is present when not accounting for SC and/or outcome measure inter-relation, but becomes not significant when accounting for all variables). Overall, we find that unconscious patients have lower rates of FP and FN for within cortical connectivity, lower rates of FN for cortico-subcortical connectivity, and lower rates of FP for within subcortical connectivity. These lower rates in unconscious patients may reflect differences in their triadic closure and SC metrics, which bias the interpretations of the inter- or intra-resting state network connectivity if the SC metrics and triadic closure are not modeled. We suggest that future studies of functional connectivity in DOC patients (i) incorporate where possible SC metrics and (ii) properly account for the intercorrelated nature of outcome variables.
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Affiliation(s)
- John Dell'Italia
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Micah A. Johnson
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Paul M. Vespa
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Martin M. Monti
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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17
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Subcortical atrophy correlates with the perturbational complexity index in patients with disorders of consciousness. Brain Stimul 2020; 13:1426-1435. [PMID: 32717393 DOI: 10.1016/j.brs.2020.07.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 05/26/2020] [Accepted: 07/21/2020] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND The complexity of neurophysiological brain responses to direct cortical stimulation, referred to as the perturbational complexity index (PCI), has been shown able to discriminate between consciousness and unconsciousness in patients surviving severe brain injury as well as several other conditions (e.g., wake, dreamless sleep, sleep and ketamine dreaming, anesthesia). OBJECTIVE This study asks whether, in patients with a disorder of consciousness (DOC), the complexity of the neurophysiological response to cortical stimulation is preferentially associated with atrophy within specific brain structures. METHODS We perform a retrospective analysis of 40 DOC patients and correlate their maximal PCI to MR-based measurements of cortical thinning and subcortical atrophy. RESULTS PCI was systematically and inversely associated with the degree of local atrophy within the globus pallidus, a region previously linked to electrocortical and behavioral arousal. Conversely, we fail to detect any association between variance in cortical ribbon thickness and PCI. CONCLUSION These findings corroborate the previously reported association between pallidal atrophy and low behavioral arousal and suggest that this region's role in maintaining the overall balance of excitation and inhibition may critically affect the emergence of complex cortical interactions in chronic disorders of consciousness. This finding thus also suggests a target for potential neuromodulatory intervention in DOC patients.
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18
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Crone JS, Lutkenhoff ES, Vespa PM, Monti MM. A systematic investigation of the association between network dynamics in the human brain and the state of consciousness. Neurosci Conscious 2020; 2020:niaa008. [PMID: 32551138 PMCID: PMC7293819 DOI: 10.1093/nc/niaa008] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 02/17/2020] [Accepted: 03/09/2020] [Indexed: 12/29/2022] Open
Abstract
An increasing amount of studies suggest that brain dynamics measured with resting-state functional magnetic resonance imaging (fMRI) are related to the state of consciousness. However, the challenge of investigating neuronal correlates of consciousness is the confounding interference between (recovery of) consciousness and behavioral responsiveness. To address this issue, and validate the interpretation of prior work linking brain dynamics and consciousness, we performed a longitudinal fMRI study in patients recovering from coma. Patients were assessed twice, 6 months apart, and assigned to one of two groups. One group included patients who were unconscious at the first assessment but regained consciousness and improved behavioral responsiveness by the second assessment. The other group included patients who were already conscious and improved only behavioral responsiveness. While the two groups were matched in terms of the average increase in behavioral responsiveness, only one group experienced a categorical change in their state of consciousness allowing us to partially dissociate consciousness and behavioral responsiveness. We find the variance in network metrics to be systematically different across states of consciousness, both within and across groups. Specifically, at the first assessment, conscious patients exhibited significantly greater variance in network metrics than unconscious patients, a difference that disappeared once all patients had recovered consciousness. Furthermore, we find a significant increase in dynamics for patients who regained consciousness over time, but not for patients who only improved responsiveness. These findings suggest that changes in brain dynamics are indeed linked to the state of consciousness and not just to a general level of behavioral responsiveness.
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Affiliation(s)
- Julia S Crone
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Paul M Vespa
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA.,Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA 90095, USA
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19
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Garner R, La Rocca M, Vespa P, Jones N, Monti MM, Toga AW, Duncan D. Imaging biomarkers of posttraumatic epileptogenesis. Epilepsia 2019; 60:2151-2162. [PMID: 31595501 PMCID: PMC6842410 DOI: 10.1111/epi.16357] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 09/10/2019] [Accepted: 09/10/2019] [Indexed: 12/14/2022]
Abstract
Traumatic brain injury (TBI) affects 2.5 million people annually within the United States alone, with over 300 000 severe injuries resulting in emergency room visits and hospital admissions. Severe TBI can result in long-term disability. Posttraumatic epilepsy (PTE) is one of the most debilitating consequences of TBI, with an estimated incidence that ranges from 2% to 50% based on severity of injury. Conducting studies of PTE poses many challenges, because many subjects with TBI never develop epilepsy, and it can be more than 10 years after TBI before seizures begin. One of the unmet needs in the study of PTE is an accurate biomarker of epileptogenesis, or a panel of biomarkers, which could provide early insights into which TBI patients are most susceptible to PTE, providing an opportunity for prophylactic anticonvulsant therapy and enabling more efficient large-scale PTE studies. Several recent reviews have provided a comprehensive overview of this subject (Neurobiol Dis, 123, 2019, 3; Neurotherapeutics, 11, 2014, 231). In this review, we describe acute and chronic imaging methods that detect biomarkers for PTE and potential mechanisms of epileptogenesis. We also describe shortcomings in current acquisition methods, analysis, and interpretation that limit ongoing investigations that may be mitigated with advancements in imaging techniques and analysis.
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Affiliation(s)
- Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Marianna La Rocca
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Paul Vespa
- Division of Neurosurgery, Department of Neurology, University of California Los Angeles School of Medicine, Los Angeles, CA, United States
| | - Nigel Jones
- Van Cleef Centre for Nervous Diseases, Department of Neuroscience, Monash University, Clayton, VIC, Australia
| | - Martin M. Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, United States
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
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20
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Prefrontal neural dynamics in consciousness. Neuropsychologia 2019; 131:25-41. [DOI: 10.1016/j.neuropsychologia.2019.05.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 05/17/2019] [Accepted: 05/20/2019] [Indexed: 12/11/2022]
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21
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Riganello F, Larroque SK, Di Perri C, Prada V, Sannita WG, Laureys S. Measures of CNS-Autonomic Interaction and Responsiveness in Disorder of Consciousness. Front Neurosci 2019; 13:530. [PMID: 31293365 PMCID: PMC6598458 DOI: 10.3389/fnins.2019.00530] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 05/08/2019] [Indexed: 12/25/2022] Open
Abstract
Neuroimaging studies have demonstrated functional interactions between autonomic (ANS) and brain (CNS) structures involved in higher brain functions, including attention and conscious processes. These interactions have been described by the Central Autonomic Network (CAN), a concept model based on the brain-heart two-way integrated interaction. Heart rate variability (HRV) measures proved reliable as non-invasive descriptors of the ANS-CNS function setup and are thought to reflect higher brain functions. Autonomic function, ANS-mediated responsiveness and the ANS-CNS interaction qualify as possible independent indicators for clinical functional assessment and prognosis in Disorders of Consciousness (DoC). HRV has proved helpful to investigate residual responsiveness in DoC and predict clinical recovery. Variability due to internal (e.g., homeostatic and circadian processes) and environmental factors remains a key independent variable and systematic research with this regard is warranted. The interest in bidirectional ANS-CNS interactions in a variety of physiopathological conditions is growing, however, these interactions have not been extensively investigated in DoC. In this brief review we illustrate the potentiality of brain-heart investigation by means of HRV analysis in assessing patients with DoC. The authors' opinion is that this easy, inexpensive and non-invasive approach may provide useful information in the clinical assessment of this challenging patient population.
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Affiliation(s)
- Francesco Riganello
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University Hospital of Liège, Liège, Belgium
- S. Anna Institute, Research in Advanced Neurorehabilitation, Crotone, Italy
| | - Stephen Karl Larroque
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University Hospital of Liège, Liège, Belgium
| | - Carol Di Perri
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University Hospital of Liège, Liège, Belgium
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, United Kingdom
| | - Valeria Prada
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal/Child Sciences, Polyclinic Hospital San Martino IRCCS, University of Genoa, Genoa, Italy
| | - Walter G. Sannita
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal/Child Sciences, Polyclinic Hospital San Martino IRCCS, University of Genoa, Genoa, Italy
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University Hospital of Liège, Liège, Belgium
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22
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Stimulation of the Angular Gyrus Improves the Level of Consciousness. Brain Sci 2019; 9:brainsci9050103. [PMID: 31064138 PMCID: PMC6562708 DOI: 10.3390/brainsci9050103] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/02/2019] [Accepted: 05/05/2019] [Indexed: 12/11/2022] Open
Abstract
Background: Navigated repetitive transcranial magnetic stimulation (rTMS) is a promising tool for neuromodulation. In previous studies it has been shown that the activity of the default mode network (DMN) areas, particularly of its key region—the angular gyrus—is positively correlated with the level of consciousness. Our study aimed to explore the effect of rTMS of the angular gyrus as a new approach for disorders of consciousness (DOC) treatment; Methods: A 10-session 2-week high-frequency rTMS protocol was delivered over the left angular gyrus in 38 DOC patients with repeated neurobehavioral assessments obtained at baseline and in 2 days after the stimulation course was complete; Results: 20 Hz-rTMS over left angular gyrus improved the coma recovery scale revised (CRS-R) total score in minimally conscious state (MCS) patients. We observed no effects in vegetative state (VS) patients; and Conclusions: The left angular gyrus is likely to be effective target for rTMS in patients with present signs of consciousness.
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23
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Cortical networks are disturbed in people with cirrhosis even in the absence of neuropsychometric impairment. Clin Neurophysiol 2018; 130:419-427. [PMID: 30552046 DOI: 10.1016/j.clinph.2018.11.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 11/08/2018] [Accepted: 11/22/2018] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Hepatic encephalopathy is a common complication of cirrhosis; it is characterised by neuropsychometric/neurophysiological abnormalities. Its pathophysiology is complex but glial neuronal communication is likely to be disrupted and to impact on oscillatory networks and cortical connectivity. The aim of this study was to use multichannel electroencephalography (EEG) to investigate functional connectivity, as a surrogate for cortical networks, in patients with cirrhosis. METHODS Resting EEGs were recorded in 98 healthy controls and in 264 patients with cirrhosis characterised psychometrically using the Psychometric Hepatic Encephalopathy Score (PHES). Functional connectivity was calculated using the phase-lag index with stratification into standard EEG frequency bands. The findings were validated in a further cohort of 39 healthy controls and 106 patients with cirrhosis. RESULTS Widespread disruption in functional connectivity was observed in the patients compared with the controls; connectivity was increased in the theta (4-8 Hz) band and decreased in the delta (1-3.5 Hz), alpha (8.5-13 Hz) and beta (13.5-26.5 Hz) bands. Changes were apparent even in patients who were psychometrically unimpaired compared with healthy controls viz mean ± SEM theta 0.107 ± 0.001 vs. 0.103 ± 0.002 (p < 0.05) and alpha 0.139 ± 0.003 vs. 0.154 ± 0.003 (p < 0.01); more pronounced changes were observed with increasing neuropsychometric impairment. The findings were replicated in the second cohort. CONCLUSIONS Cortical networks are disturbed in patients with cirrhosis even in the absence of psychometric impairment. SIGNIFICANCE These findings will facilitate further exploration of the pathophysiology of this condition and provide a robust means for assessing treatment effects in research settings.
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24
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Conventional Structural Magnetic Resonance Imaging in Differentiating Chronic Disorders of Consciousness. Brain Sci 2018; 8:brainsci8080144. [PMID: 30081605 PMCID: PMC6120007 DOI: 10.3390/brainsci8080144] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 07/26/2018] [Accepted: 08/03/2018] [Indexed: 01/14/2023] Open
Abstract
Differential diagnosis of unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) is one of the most challenging problems for specialists who deal with chronic disorders of consciousness (DOC). The aim of the current study was to develop a conventional MRI-based scale and to evaluate its role in distinguishing chronic disorders of consciousness (Disorders of Consciousness MRI-based Distinguishing Scale, DOC-MRIDS). Data were acquired from 30 patients with clinically diagnosed chronic disorders of consciousness. All patients underwent conventional MRI using a Siemens Verio 3.0 T scanner, which included T2 and T1 sequences for patient assessment. Diffuse cortical atrophy, ventricular enlargement, sulcal widening, leukoaraiosis, brainstem and/or thalamus degeneration, corpus callosum degeneration, and corpus callosum lesions were assessed according to DOC-MRIDS criteria, with a total score calculation. The ROC-analysis showed that a reasonable threshold DOC-MRIDS total score was 5.5, that is, patients with DOC-MRIDS total score of 6 and above were classified as UWS and 5 and below as MCS, with sensitivity of 82.4% and specificity of 92.3%. The novel structural MRI-based scale for the assessment of typical brain lesions in patients with chronic DOC is relatively easy to apply, and provides good specificity and sensitivity values for discrimination between UWS and MCS.
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25
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Dell'Italia J, Johnson MA, Vespa PM, Monti MM. Network Analysis in Disorders of Consciousness: Four Problems and One Proposed Solution (Exponential Random Graph Models). Front Neurol 2018; 9:439. [PMID: 29946293 PMCID: PMC6005847 DOI: 10.3389/fneur.2018.00439] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 05/24/2018] [Indexed: 12/24/2022] Open
Abstract
In recent years, the study of the neural basis of consciousness, particularly in the context of patients recovering from severe brain injury, has greatly benefited from the application of sophisticated network analysis techniques to functional brain data. Yet, current graph theoretic approaches, as employed in the neuroimaging literature, suffer from four important shortcomings. First, they require arbitrary fixing of the number of connections (i.e., density) across networks which are likely to have different "natural" (i.e., stable) density (e.g., patients vs. controls, vegetative state vs. minimally conscious state patients). Second, when describing networks, they do not control for the fact that many characteristics are interrelated, particularly some of the most popular metrics employed (e.g., nodal degree, clustering coefficient)-which can lead to spurious results. Third, in the clinical domain of disorders of consciousness, there currently are no methods for incorporating structural connectivity in the characterization of functional networks which clouds the interpretation of functional differences across groups with different underlying pathology as well as in longitudinal approaches where structural reorganization processes might be operating. Finally, current methods do not allow assessing the dynamics of network change over time. We present a different framework for network analysis, based on Exponential Random Graph Models, which overcomes the above limitations and is thus particularly well suited for clinical populations with disorders of consciousness. We demonstrate this approach in the context of the longitudinal study of recovery from coma. First, our data show that throughout recovery from coma, brain graphs vary in their natural level of connectivity (from 10.4 to 14.5%), which conflicts with the standard approach of imposing arbitrary and equal density thresholds across networks (e.g., time-points, subjects, groups). Second, we show that failure to consider the interrelation between network measures does lead to spurious characterization of both inter- and intra-regional brain connectivity. Finally, we show that Separable Temporal ERGM can be employed to describe network dynamics over time revealing the specific pattern of formation and dissolution of connectivity that accompany recovery from coma.
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Affiliation(s)
- John Dell'Italia
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Micah A. Johnson
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Paul M. Vespa
- Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Martin M. Monti
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
- Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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26
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Juliano SL, Perez-Polo R. Reflections on traumatic brain injury research in 2018. J Neurosci Res 2018; 96:485-486. [PMID: 29415328 DOI: 10.1002/jnr.24219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Accepted: 01/08/2018] [Indexed: 11/10/2022]
Affiliation(s)
- Sharon L Juliano
- Neuroscience, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Regino Perez-Polo
- Departments of Biochemistry and Molecular Biology, University of Texas Medical Branch at Galveston, Galveston, Texas
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