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Mpouzika M, Karanikola M, Blot S. The conundrum of predicting neurological outcomes in non-traumatic coma patients: True prediction or "Flipping a Coin"? Intensive Crit Care Nurs 2024; 83:103707. [PMID: 38636295 DOI: 10.1016/j.iccn.2024.103707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Affiliation(s)
- Meropi Mpouzika
- Nursing Department, Cyprus University of Technology, Limassol, Cyprus.
| | - Maria Karanikola
- Nursing Department, Cyprus University of Technology, Limassol, Cyprus
| | - Stijn Blot
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
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2
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Ordóñez-Rubiano EG, Castañeda-Duarte MA, Baeza-Antón L, Romo-Quebradas JA, Perilla-Estrada JP, Perilla-Cepeda TA, Enciso-Olivera CO, Rudas J, Marín-Muñoz JH, Pulido C, Gómez F, Martínez D, Zorro O, Garzón E, Patiño-Gómez JG. Resting state networks in patients with acute disorders of consciousness after severe traumatic brain injury. Clin Neurol Neurosurg 2024; 242:108353. [PMID: 38830290 DOI: 10.1016/j.clineuro.2024.108353] [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: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/05/2024]
Abstract
OBJECTIVES This study aims to describe resting state networks (RSN) in patients with disorders of consciousness (DOC)s after acute severe traumatic brain injury (TBI). METHODS Adult patients with TBI with a GCS score <8 who remained in a coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS), between 2017 and 2020 were included. Blood-oxygen-level dependent imaging was performed to compare their RSN with 10 healthy volunteers. RESULTS Of a total of 293 patients evaluated, only 13 patients were included according to inclusion criteria: 7 in coma (54%), 2 in MCS (15%), and 4 (31%) had an UWS. RSN analysis showed that the default mode network (DMN) was present and symmetric in 6 patients (46%), absent in 1 (8%), and asymmetric in 6 (46%). The executive control network (ECN) was present in all patients but was asymmetric in 3 (23%). The right ECN was absent in 2 patients (15%) and the left ECN in 1 (7%). The medial visual network was present in 11 (85%) patients. Finally, the cerebellar network was symmetric in 8 patients (62%), asymmetric in 1 (8%), and absent in 4 (30%). CONCLUSIONS A substantial impairment in activation of RSN is demonstrated in patients with DOC after severe TBI in comparison with healthy subjects. Three patterns of activation were found: normal/complete activation, 2) asymmetric activation or partially absent, and 3) absent activation.
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Affiliation(s)
- Edgar G Ordóñez-Rubiano
- Department of Neurosurgery, Hospital Universitario Fundación Santa Fe de Bogotá, Bogotá, Colombia; Department of Neurosurgery, Hospital de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Marcelo A Castañeda-Duarte
- Department of Neurosurgery, Hospital de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia; Department of Neurosurgery, Hospital Infantil Universitario de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Laura Baeza-Antón
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, USA.
| | - Jorge A Romo-Quebradas
- Department of Neurosurgery, Hospital de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia; Department of Neurosurgery, Hospital Infantil Universitario de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Juan P Perilla-Estrada
- Department of Neurosurgery, Hospital de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia; Department of Neurosurgery, Hospital Infantil Universitario de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Tito A Perilla-Cepeda
- Department of Neurosurgery, Hospital Infantil Universitario de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Cesar O Enciso-Olivera
- Department of Critical Care and Intensive Care Unit, Fundación Universitaria de Ciencias de la Salud (FUCS), Hospital Infantil Universitario de San José, Bogotá, Colombia
| | - Jorge Rudas
- Department of Biotechnology, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Jorge H Marín-Muñoz
- Department of Radiology, Fundación Universitaria de Ciencias de la Salud (FUCS), Hospital Infantil Universitario de San José, Bogotá, Colombia; Innovation and Research Division, Imaging Experts and Healthcare Services (ImexHS), Bogotá, Colombia
| | - Cristian Pulido
- Department of Mathematics, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Francisco Gómez
- Department of Computer Science, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Darwin Martínez
- Department of Computer Science, Universidad Sergio Arboleda, Bogotá, Colombia
| | - Oscar Zorro
- Department of Neurosurgery, Hospital de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Emilio Garzón
- Department of Neurosurgery, Hospital de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Javier G Patiño-Gómez
- Department of Neurosurgery, Hospital de San José, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
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Medina Carrion JP, Stanziano M, D'Incerti L, Sattin D, Palermo S, Ferraro S, Sebastiano DR, Leonardi M, Bruzzone MG, Rosazza C, Nigri A. Disorder of consciousness: Structural integrity of brain networks for the clinical assessment. Ann Clin Transl Neurol 2023; 10:384-396. [PMID: 36638220 PMCID: PMC10014003 DOI: 10.1002/acn3.51729] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/28/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
AIM When studying brain networks in patients with Disorders of Consciousness (DoC), it is important to evaluate the structural integrity of networks in addition to their functional activity. Here, we investigated whether structural MRI, together with clinical variables, can be useful for diagnostic purposes and whether a quantitative analysis is feasible in a group of chronic DoC patients. METHODS We studied 109 chronic patients with DoC and emerged from DoC with structural MRI: 65 in vegetative state/unresponsive wakefulness state (VS/UWS), 34 in minimally conscious state (MCS), and 10 with severe disability. MRI data were analyzed through qualitative and quantitative approaches. RESULTS The qualitative MRI analysis outperformed the quantitative one, which resulted to be hardly feasible in chronic DoC patients. The results of the qualitative approach showed that the structural integrity of HighOrder networks, altogether, had better diagnostic accuracy than LowOrder networks, particularly when the model included clinical variables (AUC = 0.83). Diagnostic differences between VS/UWS and MCS were stronger in anoxic etiology than vascular and traumatic etiology. MRI data of all LowOrder and HighOrder networks correlated with the clinical score. The integrity of the left hemisphere was associated with a better clinical status. CONCLUSIONS Structural integrity of brain networks is sensitive to clinical severity. When patients are chronic, the qualitative analysis of MRI data is indicated.
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Affiliation(s)
- Jean Paul Medina Carrion
- Diagnostic and Technology Department, Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Mario Stanziano
- Diagnostic and Technology Department, Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.,Neurosciences Department "Rita Levi Montalcini", University of Turin, Turin, Italy
| | - Ludovico D'Incerti
- Diagnostic and Technology Department, Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.,Radiology Unit, Children's Hospital A. Meyer-University of Florence, Florence, Italy
| | - Davide Sattin
- IRCCS Istituti Clinici Scientifici Maugeri di Milano, Milan, Italy
| | - Sara Palermo
- Diagnostic and Technology Department, Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.,Department of Psychology, University of Turin, Turin, Italy
| | - Stefania Ferraro
- Diagnostic and Technology Department, Neuroradiology Unit, 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
| | - Davide Rossi Sebastiano
- Department of Neurophysiology and Diagnostic, Epileptology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Matilde Leonardi
- Neurology, Public Health, Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta
| | - Maria Grazia Bruzzone
- Diagnostic and Technology Department, Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Cristina Rosazza
- Diagnostic and Technology Department, Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.,Department of Humanistic Studies, University of Urbino Carlo Bo, Urbino, Italy
| | - Anna Nigri
- Diagnostic and Technology Department, Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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Jarvis JM, Roy J, Schmithorst V, Lee V, Devine D, Meyers B, Munjal N, Clark RSB, Kochanek PM, Panigrahy A, Ceschin R, Fink EL. Limbic pathway vulnerability associates with neurologic outcome in children after cardiac arrest. Resuscitation 2023; 182:109634. [PMID: 36336196 PMCID: PMC10408582 DOI: 10.1016/j.resuscitation.2022.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/13/2022] [Accepted: 10/30/2022] [Indexed: 11/06/2022]
Abstract
AIM To analyze whether brain connectivity sequences including diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rsfMRI) identify vulnerable brain regions and networks associated with neurologic outcome after pediatric cardiac arrest. METHODS Children aged 2 d-17 y with cardiac arrest were enrolled in one of 2 parent studies at a single center. Clinically indicated brain MRI with DTI and rsfMRI and performed within 2 weeks after arrest were analyzed. Tract-wise fractional anisotropy (FA) and axial, radial, and mean diffusivity assessed DTI, and functional connectivity strength (FCS) assessed rsfMRI between outcome groups. Unfavorable neurologic outcome was defined as Pediatric Cerebral Performance Category score 4-6 or change > 1 between 6 months after arrest vs baseline. RESULTS Among children with DTI (n = 28), 57% had unfavorable outcome. Mean, radial, axial diffusivity and FA of varying direction of magnitude in the limbic tracts, including the right cingulum parolfactory, left cingulum parahippocampal, corpus callosum forceps major, and corpus callosum forceps minor tracts, were associated with unfavorable neurologic outcome (p < 0.05). Among children with rsfMRI (n = 12), 67% had unfavorable outcome. Decreased FCS in the ventromedial and dorsolateral prefrontal cortex, insula, precentral gyrus, anterior cingulate, and inferior parietal lobule were correlated regionally with unfavorable neurologic outcome (p < 0.05 Family-Wise Error corrected). CONCLUSION Decreased multimodal connectivity measures of paralimbic tracts were associated with unfavorable neurologic outcome after pediatric cardiac arrest. Longitudinal analysis correlating brain connectivity sequences with long term neuropsychological outcomes to identify the impact of pediatric cardiac arrest in vulnerable brain networks over time appears warranted.
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Affiliation(s)
- Jessica M Jarvis
- Department of Physical Medicine & Rehabilitation, University of Pittsburgh School of Medicine, United States
| | - Joy Roy
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, United States
| | - Vanessa Schmithorst
- Department of Pediatric Radiology, UPMC Children's Hospital of Pittsburgh, United States
| | - Vince Lee
- Department of Pediatric Radiology, UPMC Children's Hospital of Pittsburgh, United States; Department of Bioengineering, University of Pittsburgh, United States
| | - Danielle Devine
- Department of Critical Care Medicine, UPMC Children's Hospital of Pittsburgh, United States
| | - Benjamin Meyers
- Department of Pediatric Radiology, UPMC Children's Hospital of Pittsburgh, United States
| | - Neil Munjal
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, United States
| | - Robert S B Clark
- Department of Critical Care Medicine, UPMC Children's Hospital of Pittsburgh, United States; Safar Center for Resuscitation Research, University of Pittsburgh, United States
| | - Patrick M Kochanek
- Department of Critical Care Medicine, UPMC Children's Hospital of Pittsburgh, United States; Safar Center for Resuscitation Research, University of Pittsburgh, United States
| | - Ashok Panigrahy
- Department of Pediatric Radiology, UPMC Children's Hospital of Pittsburgh, United States
| | - Rafael Ceschin
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, United States; Department of Pediatric Radiology, UPMC Children's Hospital of Pittsburgh, United States
| | - Ericka L Fink
- Department of Critical Care Medicine, UPMC Children's Hospital of Pittsburgh, United States; Safar Center for Resuscitation Research, University of Pittsburgh, United States.
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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; 11:77462. [PMID: 35916363 PMCID: PMC9385205 DOI: 10.7554/elife.77462] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [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)
| | | | - Ane Lopez-Gonzalez
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gustavo Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | | | | | - Jitka Annen
- Coma Science Group, University of Liège, Liège, Belgium
| | - Prejaas Tewarie
- School of Physics, University of Nottingham, Nottingham, United Kingdom
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6
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Curley WH, Bodien YG, Zhou DW, Conte MM, Foulkes AS, Giacino JT, Victor JD, Schiff ND, Edlow BL. Electrophysiological correlates of thalamocortical function in acute severe traumatic brain injury. Cortex 2022; 152:136-152. [PMID: 35569326 PMCID: PMC9759728 DOI: 10.1016/j.cortex.2022.04.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/26/2022] [Accepted: 04/04/2022] [Indexed: 12/26/2022]
Abstract
Tools assaying the neural networks that modulate consciousness may facilitate tracking of recovery after acute severe brain injury. The ABCD framework classifies resting-state EEG into categories reflecting levels of thalamocortical network function that correlate with outcome in post-cardiac arrest coma. In this longitudinal cohort study, we applied the ABCD framework to 20 patients with acute severe traumatic brain injury requiring intensive care (12 of whom were also studied at ≥6-months post-injury) and 16 healthy controls. We tested four hypotheses: 1) EEG ABCD classifications are spatially heterogeneous and temporally variable; 2) ABCD classifications improve longitudinally, commensurate with the degree of behavioral recovery; 3) ABCD classifications correlate with behavioral level of consciousness; and 4) the Coma Recovery Scale-Revised arousal facilitation protocol yields improved ABCD classifications. Channel-level EEG power spectra were classified based on spectral peaks within pre-defined frequency bands: 'A' = no peaks above delta (<4 Hz) range (complete thalamocortical disruption); 'B' = theta (4-8 Hz) peak (severe thalamocortical disruption); 'C' = theta and beta (13-24 Hz) peaks (moderate thalamocortical disruption); or 'D' = alpha (8-13 Hz) and beta peaks (normal thalamocortical function). Acutely, 95% of patients demonstrated 'D' signals in at least one channel but exhibited within-session temporal variability and spatial heterogeneity in the proportion of different channel-level ABCD classifications. By contrast, healthy participants and patients at follow-up consistently demonstrated signals corresponding to intact thalamocortical network function. Patients demonstrated longitudinal improvement in ABCD classifications (p < .05) and ABCD classification distinguished patients with and without command-following in the subacute-to-chronic phase of recovery (p < .01). In patients studied acutely, ABCD classifications improved after the Coma Recovery Scale-Revised arousal facilitation protocol (p < .05) but did not correspond with behavioral level of consciousness. These findings support the use of the ABCD framework to characterize channel-level EEG dynamics and track fluctuations in functional thalamocortical network integrity in spatial detail.
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Affiliation(s)
- William H Curley
- Harvard Medical School, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Yelena G Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA, USA
| | - David W Zhou
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mary M Conte
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY, USA
| | - Andrea S Foulkes
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA, USA; Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jonathan D Victor
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY, USA; Department of Neurology, New York Presbyterian Hospital, New York, NY, USA
| | - Nicholas D Schiff
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY, USA; Department of Neurology, New York Presbyterian Hospital, New York, NY, USA
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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