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Wu M, Concolato M, Sorger B, Yu Y, Li X, Luo B, Riecke L. Acoustic-electric trigeminal-nerve stimulation enhances functional connectivity in patients with disorders of consciousness. CNS Neurosci Ther 2024; 30:e14385. [PMID: 37525451 PMCID: PMC10928333 DOI: 10.1111/cns.14385] [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: 12/19/2022] [Revised: 06/29/2023] [Accepted: 07/16/2023] [Indexed: 08/02/2023] Open
Abstract
AIM Disruption of functional brain connectivity is thought to underlie disorders of consciousness (DOC) and recovery of impaired connectivity is suggested as an indicator of consciousness restoration. We recently found that rhythmic acoustic-electric trigeminal-nerve stimulation (i.e., musical stimulation synchronized to electrical stimulation of the trigeminal nerve) in the gamma band can improve consciousness in patients with DOC. Here, we investigated whether these beneficial stimulation effects are mediated by alterations in functional connectivity. METHODS Sixty-three patients with DOC underwent 5 days of gamma, beta, or sham acoustic-electric trigeminal-nerve stimulation. Resting-state electroencephalography was measured before and after the stimulation and functional connectivity was assessed using phase-lag index (PLI). RESULTS We found that gamma stimulation induces an increase in gamma-band PLI. Further characterization revealed that the enhancing effect is (i) specific to the gamma band (as we observed no comparable change in beta-band PLI and no effect of beta-band acoustic-electric stimulation or sham stimulation), (ii) widely spread across the cortex, and (iii) accompanied by improvements in patients' auditory abilities. CONCLUSION These findings show that gamma acoustic-electric trigeminal-nerve stimulation can improve resting-state functional connectivity in the gamma band, which in turn may be linked to auditory abilities and/or consciousness restoration in DOC patients.
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Affiliation(s)
- Min Wu
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Marta Concolato
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
- Department of Developmental Psychology and SocializationUniversity of PadovaPadovaItaly
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Yamei Yu
- Department of Neurology and Brain Medical Centre, First Affiliated Hospital, School of MedicineZhejiang UniversityHangzhouChina
| | - Xiaoxia Li
- Department of Neurology and Brain Medical Centre, First Affiliated Hospital, School of MedicineZhejiang UniversityHangzhouChina
| | - Benyan Luo
- Department of Neurology and Brain Medical Centre, First Affiliated Hospital, School of MedicineZhejiang UniversityHangzhouChina
| | - Lars Riecke
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
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2
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Xu LB, Hampton S, Fischer D. Neuroimaging in Disorders of Consciousness and Recovery. Phys Med Rehabil Clin N Am 2024; 35:51-64. [PMID: 37993193 DOI: 10.1016/j.pmr.2023.06.017] [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] [Indexed: 11/24/2023]
Abstract
There is a clinical need for more accurate diagnosis and prognostication in patients with disorders of consciousness (DoC). There are several neuroimaging modalities that enable detailed, quantitative assessment of structural and functional brain injury, with demonstrated diagnostic and prognostic value. Additionally, longitudinal neuroimaging studies have hinted at quantifiable structural and functional neuroimaging biomarkers of recovery, with potential implications for the management of DoC.
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Affiliation(s)
- Linda B Xu
- Department of Neurology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
| | - Stephen Hampton
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, 1800 Lombard Street, Philadelphia, PA 19146, USA
| | - David Fischer
- Department of Neurology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
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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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Plosnić G, Raguž M, Deletis V, Chudy D. Dysfunctional connectivity as a neurophysiologic mechanism of disorders of consciousness: a systematic review. Front Neurosci 2023; 17:1166187. [PMID: 37539385 PMCID: PMC10394244 DOI: 10.3389/fnins.2023.1166187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
Introduction Disorders of consciousness (DOC) has been an object of numbers of research regarding the diagnosis, treatment and prognosis in last few decades. We believe that the DOC could be considered as a disconnection syndrome, although the exact mechanisms are not entirely understood. Moreover, different conceptual frameworks highly influence results interpretation. The aim of this systematic review is to assess the current knowledge regarding neurophysiological mechanisms of DOC and to establish possible influence on future clinical implications and usage. Methods We have conducted a systematic review according to PRISMA guidelines through PubMed and Cochrane databases, with studies being selected for inclusion via a set inclusion and exclusion criteria. Results Eighty-nine studies were included in this systematic review according to the selected criteria. This includes case studies, randomized controlled trials, controlled clinical trials, and observational studies with no control arms. The total number of DOC patients encompassed in the studies cited in this review is 1,533. Conclusion Connectomics and network neuroscience offer quantitative frameworks for analysing dynamic brain connectivity. Functional MRI studies show evidence of abnormal connectivity patterns and whole-brain topological reorganization, primarily affecting sensory-related resting state networks (RSNs), confirmed by EEG studies. As previously described, DOC patients are identified by diminished global information processing, i.e., network integration and increased local information processing, i.e., network segregation. Further studies using effective connectivity measurement tools instead of functional connectivity as well as the standardization of the study process are needed.
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Affiliation(s)
- Gabriela Plosnić
- Department of Pediatrics, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Marina Raguž
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
- School of Medicine, Catholic University of Croatia, Zagreb, Croatia
| | - Vedran Deletis
- Albert Einstein College of Medicine, New York, NY, United States
| | - Darko Chudy
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
- School of Medicine, University of Zagreb, Zagreb, Croatia
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Williams GB, Craig MM, Finoia P, Peattie ARD, Coppola P, Menon DK, Bor D, Stamatakis EA. Reduced emergent character of neural dynamics in patients with a disrupted connectome. Neuroimage 2023; 269:119926. [PMID: 36740030 PMCID: PMC9989666 DOI: 10.1016/j.neuroimage.2023.119926] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Leverhulme Centre for the Future of Intelligence, Cambridge, UK; The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Brain Science, Center for Psychedelic Research, Imperial College London, London, UK; Data Science Institute, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Center for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Department of Neurosciences, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Paola Finoia
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK; Department of Psychology, Queen Mary University of London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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6
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Chen H, Miao G, Wang S, Zheng J, Zhang X, Lin J, Hao C, Huang H, Jiang T, Gong Y, Liao W. Disturbed functional connectivity and topological properties of the frontal lobe in minimally conscious state based on resting-state fNIRS. Front Neurosci 2023; 17:1118395. [PMID: 36845431 PMCID: PMC9950516 DOI: 10.3389/fnins.2023.1118395] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
Background Patients in minimally conscious state (MCS) exist measurable evidence of consciousness. The frontal lobe is a crucial part of the brain that encodes abstract information and is closely related to the conscious state. We hypothesized that the disturbance of the frontal functional network exists in MCS patients. Methods We collected the resting-state functional near-infrared spectroscopy (fNIRS) data of fifteen MCS patients and sixteen age- and gender-matched healthy controls (HC). The Coma Recovery Scale-Revised (CRS-R) scale of MCS patients was also composed. The topology of the frontal functional network was analyzed in two groups. Results Compared with HC, the MCS patients showed widely disrupted functional connectivity in the frontal lobe, especially in the frontopolar area and right dorsolateral prefrontal cortex. Moreover, the MCS patients displayed lower clustering coefficient, global efficiency, local efficiency, and higher characteristic path length. In addition, the nodal clustering coefficient and nodal local efficiency in the left frontopolar area and right dorsolateral prefrontal cortex were significantly reduced in MCS patients. Furthermore, the nodal clustering coefficient and nodal local efficiency in the right dorsolateral prefrontal cortex were positively correlated to auditory subscale scores. Conclusion This study reveals that MCS patients' frontal functional network is synergistically dysfunctional. And the balance between information separation and integration in the frontal lobe is broken, especially the local information transmission in the prefrontal cortex. These findings help us to understand the pathological mechanism of MCS patients better.
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Affiliation(s)
| | | | - Sirui Wang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jun Zheng
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Xin Zhang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Junbin Lin
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chizi Hao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Hailong Huang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Ting Jiang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
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7
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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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8
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Zheng RZ, Qi ZX, Wang Z, Xu ZY, Wu XH, Mao Y. Clinical Decision on Disorders of Consciousness After Acquired Brain Injury: Stepping Forward. Neurosci Bull 2023; 39:138-162. [PMID: 35804219 PMCID: PMC9849546 DOI: 10.1007/s12264-022-00909-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 04/10/2022] [Indexed: 01/22/2023] Open
Abstract
Major advances have been made over the past few decades in identifying and managing disorders of consciousness (DOC) in patients with acquired brain injury (ABI), bringing the transformation from a conceptualized definition to a complex clinical scenario worthy of scientific exploration. Given the continuously-evolving framework of precision medicine that integrates valuable behavioral assessment tools, sophisticated neuroimaging, and electrophysiological techniques, a considerably higher diagnostic accuracy rate of DOC may now be reached. During the treatment of patients with DOC, a variety of intervention methods are available, including amantadine and transcranial direct current stimulation, which have both provided class II evidence, zolpidem, which is also of high quality, and non-invasive stimulation, which appears to be more encouraging than pharmacological therapy. However, heterogeneity is profoundly ingrained in study designs, and only rare schemes have been recommended by authoritative institutions. There is still a lack of an effective clinical protocol for managing patients with DOC following ABI. To advance future clinical studies on DOC, we present a comprehensive review of the progress in clinical identification and management as well as some challenges in the pathophysiology of DOC. We propose a preliminary clinical decision protocol, which could serve as an ideal reference tool for many medical institutions.
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Affiliation(s)
- Rui-Zhe Zheng
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200032, China
| | - Zeng-Xin Qi
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200032, China
| | - Zhe Wang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200032, China
| | - Ze-Yu Xu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200032, China
| | - Xue-Hai Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
- National Center for Neurological Disorders, Shanghai, 200040, China.
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China.
- Neurosurgical Institute of Fudan University, Shanghai, 200040, China.
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040, China.
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200032, China.
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
- National Center for Neurological Disorders, Shanghai, 200040, China.
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China.
- Neurosurgical Institute of Fudan University, Shanghai, 200040, China.
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040, China.
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200032, China.
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9
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Yu J, Wu Y, Wu B, Xu C, Cai J, Wen X, Meng F, Zhang L, He F, Hong L, Gao J, Li J, Yu J, Luo B. Sleep patterns correlates with the efficacy of tDCS on post-stroke patients with prolonged disorders of consciousness. J Transl Med 2022; 20:601. [PMID: 36522680 PMCID: PMC9756665 DOI: 10.1186/s12967-022-03710-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The subclassification of prolonged disorders of consciousness (DoC) based on sleep patterns is important for the evaluation and treatment of the disease. This study evaluates the correlation between polysomnographic patterns and the efficacy of transcranial direct current stimulation (tDCS) in patients with prolonged DoC due to stroke. METHODS In total, 33 patients in the vegetative state (VS) with sleep cycles or without sleep cycles were randomly assigned to either active or sham tDCS groups. Polysomnography was used to monitor sleep changes before and after intervention. Additionally, clinical scale scores and electroencephalogram (EEG) analysis were performed before and after intervention to evaluate the efficacy of tDCS on the patients subclassified according to their sleep patterns. RESULTS The results suggest that tDCS improved the sleep structure, significantly prolonged total sleep time (TST) (95%CI: 14.387-283.527, P = 0.013) and NREM sleep stage 2 (95%CI: 3.157-246.165, P = 0.040) of the VS patients with sleep cycles. It also significantly enhanced brain function of patients with sleep cycles, which were reflected by the increased clinical scores (95%CI: 0.340-3.440, P < 0.001), the EEG powers and functional connectivity in the brain and the 6-month prognosis. Moreover, the changes in NREM sleep stage 2 had a significant positive correlation with each index of the β band. CONCLUSION This study reveals the importance of sleep patterns in the prognosis and treatment of prolonged DoC and provides new evidence for the efficacy of tDCS in post-stroke patients with VS patients subclassified by sleep pattern. Trial registration URL: https://www. CLINICALTRIALS gov . Unique identifier: NCT03809936. Registered 18 January 2019.
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Affiliation(s)
- Jie Yu
- grid.452661.20000 0004 1803 6319Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 Zhejiang China
| | - Yuehao Wu
- grid.452661.20000 0004 1803 6319Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 Zhejiang China ,Department of Neurology, First People’s Hospital of Linping District, Hangzhou, 310003 Zhejiang China
| | - Biwen Wu
- grid.415999.90000 0004 1798 9361Center for Sleep Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 China
| | - Chuan Xu
- grid.452661.20000 0004 1803 6319Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 Zhejiang China
| | - Jiaye Cai
- grid.415999.90000 0004 1798 9361Center for Sleep Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 China
| | - Xinrui Wen
- grid.452661.20000 0004 1803 6319Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 Zhejiang China
| | - Fanxia Meng
- grid.452661.20000 0004 1803 6319Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 Zhejiang China
| | - Li Zhang
- grid.417401.70000 0004 1798 6507Rehabilitation Medicine Center, Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou, Zhejiang China
| | - Fangping He
- grid.452661.20000 0004 1803 6319Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 Zhejiang China
| | - Lirong Hong
- Department of Rehabilitation, Hangzhou Hospital of Zhejiang Armed Police Corps, Hangzhou, 310051 China
| | - Jian Gao
- Department of Rehabilitation, Hangzhou Mingzhou Brain Rehabilitation Hospital, Hangzhou, 311215 China
| | - Jingqi Li
- Department of Rehabilitation, Hangzhou Mingzhou Brain Rehabilitation Hospital, Hangzhou, 311215 China
| | - Jintai Yu
- grid.411405.50000 0004 1757 8861Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200031 China
| | - Benyan Luo
- grid.452661.20000 0004 1803 6319Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 Zhejiang China
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10
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White matter connectometry in patients with disorders of consciousness revealed by 7-Tesla magnetic resonance imaging. Brain Imaging Behav 2022; 16:1983-1991. [DOI: 10.1007/s11682-022-00668-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 03/01/2022] [Accepted: 03/21/2022] [Indexed: 11/02/2022]
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11
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Williams GB, Craig MM, Finoia P, Peattie ARD, Coppola P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness. Commun Biol 2022; 5:384. [PMID: 35444252 PMCID: PMC9021270 DOI: 10.1038/s42003-022-03330-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 03/30/2022] [Indexed: 12/02/2022] Open
Abstract
The human brain entertains rich spatiotemporal dynamics, which are drastically reconfigured when consciousness is lost due to anaesthesia or disorders of consciousness (DOC). Here, we sought to identify the neurobiological mechanisms that explain how transient pharmacological intervention and chronic neuroanatomical injury can lead to common reconfigurations of neural activity. We developed and systematically perturbed a neurobiologically realistic model of whole-brain haemodynamic signals. By incorporating PET data about the cortical distribution of GABA receptors, our computational model reveals a key role of spatially-specific local inhibition for reproducing the functional MRI activity observed during anaesthesia with the GABA-ergic agent propofol. Additionally, incorporating diffusion MRI data obtained from DOC patients reveals that the dynamics that characterise loss of consciousness can also emerge from randomised neuroanatomical connectivity. Our results generalise between anaesthesia and DOC datasets, demonstrating how increased inhibition and connectome perturbation represent distinct neurobiological paths towards the characteristic activity of the unconscious brain. Perturbations in a large-scale whole-brain model suggest that anesthesia and injury may be imparting functionally similar effects in terms of brain dynamics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK. .,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. .,Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK. .,The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, UK.,Department of Psychology, Queen Mary University of London, London, UK
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK.,Data Science Institute, Imperial College London, London, UK.,Centre for Complexity Science, Imperial College London, London, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Paola Finoia
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- The Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK.,Department of Psychology, Queen Mary University of London, London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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12
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Coppola P, Spindler LRB, Luppi AI, Adapa R, Naci L, Allanson J, Finoia P, Williams GB, Pickard JD, Owen AM, Menon DK, Stamatakis EA. Network dynamics scale with levels of awareness. Neuroimage 2022; 254:119128. [PMID: 35331869 DOI: 10.1016/j.neuroimage.2022.119128] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 02/10/2022] [Accepted: 03/20/2022] [Indexed: 02/04/2023] Open
Abstract
Small world topologies are thought to provide a valuable insight into human brain organisation and consciousness. However, functional magnetic resonance imaging studies in consciousness have not yielded consistent results. Given the importance of dynamics for both consciousness and cognition, here we investigate how the diversity of small world dynamics (quantified by sample entropy; dSW-E1) scales with decreasing levels of awareness (i.e., sedation and disorders of consciousness). Paying particular attention to result reproducibility, we show that dSW-E is a consistent predictor of levels of awareness even when controlling for the underlying functional connectivity dynamics. We find that dSW-E of subcortical and cortical areas are predictive, with the former showing higher and more robust effect sizes across analyses. We find that the network dynamics of intermodular communication in the cerebellum also have unique predictive power for levels of awareness. Consequently, we propose that the dynamic reorganisation of the functional information architecture, in particular of the subcortex, is a characteristic that emerges with awareness and has explanatory power beyond that of the complexity of dynamic functional connectivity.
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Affiliation(s)
- Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Lennart R B Spindler
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Ram Adapa
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Division of Neurosurgery, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Judith Allanson
- Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Neurosciences, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation, Hills Rd., Cambridge, CB2 0QQ, UK
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Division of Neurosurgery, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, UK
| | - John D Pickard
- Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Division of Neurosurgery, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, UK
| | - Adrian M Owen
- The Brain and Mind Institute, Western Interdisciplinary Research Building, University of Western Ontario, London, ON N6A 5B7, Canada
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK.
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13
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Wang S, Zheng M, Lou C, Chen S, Guo H, Gao Y, Lv H, Yuan X, Zhang X, Shang P. Evaluating the biological safety on mice at 16 T static magnetic field with 700 MHz radio-frequency electromagnetic field. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 230:113125. [PMID: 34971997 DOI: 10.1016/j.ecoenv.2021.113125] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/22/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVES This study evaluated the associated biological effects of radio-frequency (RF) exposure at 16 T magnetic resonance imaging (MRI) on mice health. MATERIAL AND METHODS A total of 48 healthy 8-week-old male C57BL/6 mice were investigated. A 16 T high static magnetic field (HiSMF) was generated by a superconducting magnet, and a radiofrequency (RF) electromagnetic field for hydrogen resonance at 16 T (700 MHz) was transmitted via a homemade RF system. The mice were exposed inside the 16 T HiSMF with the 700 MHz RF field for 60 min, and the body weight, organ coefficients, histomorphology of major organs, and blood indices were analyzed for the basal state of the mice on day 0 and day 14. The Heat Shock Protein 70 (HSP70), cyclooxygenase 2 (COX2), and interleukin- 6 (IL-6) were used to evaluate the thermal effects on the brain. Locomotor activity, the open field test, tail suspension test, forced swimming test, and grip strength test were used to assess the behavioral characteristics of the mice. RESULTS The 16 T HiSMF with 700 MHz RF electromagnetic field exposure had no significant effects on body weight, organ coefficients, or histomorphology of major organs in the mice. On day 0, the expressions of HSP70 and COX2 in the brain were increased by 16 T HiSMF with 700 MHz RF electromagnetic field exposure. However, the expression of HSP70, COX2, and IL-6 had no significant difference compared with the sham group on day 14. Compared with the sham groups, the meancorpuscularvolume (MCV) on day 0 and the total protein (TP) on day 14 were increased significantly, whereas the other blood indices did not change significantly. The 16 T HiSMF with 700 MHz RF electromagnetic field exposure caused the mice to briefly circle tightly but had no effect on other behavioral indicators. CONCLUSIONS In summary, 16 T HiSMF with 700 MHz RF electromagnetic field exposure for 60 min did not have severe effects on mice.
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Affiliation(s)
- Shenghang Wang
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China; School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Key Laboratory for Space Biosciences and Biotechnology, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Mengxuan Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Chenge Lou
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China; School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Key Laboratory for Space Biosciences and Biotechnology, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Shuai Chen
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China; School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Key Laboratory for Space Biosciences and Biotechnology, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Huijie Guo
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China; School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Key Laboratory for Space Biosciences and Biotechnology, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Yang Gao
- School of Medicine, Zhejiang University, Hangzhou, China
| | - Huanhuan Lv
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China; School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Key Laboratory for Space Biosciences and Biotechnology, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Xichen Yuan
- Key Laboratory for Space Biosciences and Biotechnology, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Ministry of Education Key Laboratory of Micro/Nano Systems for Aerospace Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Xiaotong Zhang
- College of Electrical Engineering, Zhejiang University, Hangzhou, China.
| | - Peng Shang
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China; Key Laboratory for Space Biosciences and Biotechnology, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
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14
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Enciso-Olivera CO, Ordóñez-Rubiano EG, Casanova-Libreros R, Rivera D, Zarate-Ardila CJ, Rudas J, Pulido C, Gómez F, Martínez D, Guerrero N, Hurtado MA, Aguilera-Bustos N, Hernández-Torres CP, Hernandez J, Marín-Muñoz JH. Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness. Sci Rep 2021; 11:22952. [PMID: 34824383 PMCID: PMC8617304 DOI: 10.1038/s41598-021-98506-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 07/20/2021] [Indexed: 11/24/2022] Open
Abstract
To determine the role of early acquisition of blood oxygen level-dependent (BOLD) signals and diffusion tensor imaging (DTI) for analysis of the connectivity of the ascending arousal network (AAN) in predicting neurological outcomes after acute traumatic brain injury (TBI), cardiopulmonary arrest (CPA), or stroke. A prospective analysis of 50 comatose patients was performed during their ICU stay. Image processing was conducted to assess structural and functional connectivity of the AAN. Outcomes were evaluated after 3 and 6 months. Nineteen patients (38%) had stroke, 18 (36%) CPA, and 13 (26%) TBI. Twenty-three patients were comatose (44%), 11 were in a minimally conscious state (20%), and 16 had unresponsive wakefulness syndrome (32%). Univariate analysis demonstrated that measurements of diffusivity, functional connectivity, and numbers of fibers in the gray matter, white matter, whole brain, midbrain reticular formation, and pontis oralis nucleus may serve as predictive biomarkers of outcome depending on the diagnosis. Multivariate analysis demonstrated a correlation of the predicted value and the real outcome for each separate diagnosis and for all the etiologies together. Findings suggest that the above imaging biomarkers may have a predictive role for the outcome of comatose patients after acute TBI, CPA, or stroke.
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Affiliation(s)
- 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
| | - Edgar G Ordóñez-Rubiano
- Department of Neurological Surgery, Fundación Universitaria de Ciencias de La Salud (FUCS), Hospital de San José, Bogotá, Colombia
| | - Rosángela Casanova-Libreros
- Division of Clinical Research, Fundación Universitaria de Ciencias de La Salud (FUCS), Hospital de San José, Hospital Infantil Universitario de San José, Bogotá, Colombia
| | - Diana Rivera
- Division of Clinical Research, Fundación Universitaria de Ciencias de La Salud (FUCS), Hospital de San José, Hospital Infantil Universitario de San José, Bogotá, Colombia
| | - Carol J Zarate-Ardila
- Division of Clinical Research, Fundación Universitaria de Ciencias de La Salud (FUCS), Hospital de San José, Hospital Infantil Universitario de San José, Bogotá, Colombia
| | - Jorge Rudas
- Department of Biotechnology, Universidad Nacional de Colombia, 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 Central, Bogotá, Colombia
| | - Natalia Guerrero
- Department of Radiology, Fundación Universitaria de Ciencias de La Salud (FUCS), Hospital Infantil Universitario de San José, Bogotá, Colombia
| | - Mayra A Hurtado
- Department of Critical Care and Intensive Care Unit, Fundación Universitaria de Ciencias de La Salud (FUCS), Hospital Infantil Universitario de San José, Bogotá, Colombia
| | - Natalia Aguilera-Bustos
- Division of Clinical Research, Fundación Universitaria de Ciencias de La Salud (FUCS), Hospital de San José, Hospital Infantil Universitario de San José, Bogotá, Colombia
| | - Clara P Hernández-Torres
- Department of Psychology, Fundación Universitaria de Ciencias de La Salud (FUCS), Hospital Infantil Universitario de San José, Bogotá, Colombia
| | - José Hernandez
- Department of Neurology, Fundación Universitaria de Ciencias de La Salud (FUCS), Hospital Infantil Universitario de San José, 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), Street 92 # 11-51, Of 202, Bogotá, Colombia.
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15
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Luppi AI, Craig MM, Coppola P, Peattie ARD, Finoia P, Williams GB, Allanson J, Pickard JD, Menon DK, Stamatakis EA. Preserved fractal character of structural brain networks is associated with covert consciousness after severe brain injury. NEUROIMAGE-CLINICAL 2021; 30:102682. [PMID: 34215152 PMCID: PMC8102619 DOI: 10.1016/j.nicl.2021.102682] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 03/30/2021] [Accepted: 04/18/2021] [Indexed: 12/24/2022]
Abstract
We study structural brain networks in patients with disorders of consciousness (DOC) Structural brain networks are less fractal (self-similar) in patients than controls. Preserved fractal dimension is associated with covert consciousness in DOC patients.
Self-similarity is ubiquitous throughout natural phenomena, including the human brain. Recent evidence indicates that fractal dimension of functional brain networks, a measure of self-similarity, is diminished in patients diagnosed with disorders of consciousness arising from severe brain injury. Here, we set out to investigate whether loss of self-similarity is observed in the structural connectome of patients with disorders of consciousness. Using diffusion MRI tractography from N = 11 patients in a minimally conscious state (MCS), N = 10 patients diagnosed with unresponsive wakefulness syndrome (UWS), and N = 20 healthy controls, we show that fractal dimension of structural brain networks is diminished in DOC patients. Remarkably, we also show that fractal dimension of structural brain networks is preserved in patients who exhibit evidence of covert consciousness by performing mental imagery tasks during functional MRI scanning. These results demonstrate that differences in fractal dimension of structural brain networks are quantitatively associated with chronic loss of consciousness induced by severe brain injury, highlighting the close connection between structural organisation of the human brain and its ability to support cognitive function.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom.
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - Guy B Williams
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, United Kingdom
| | - Judith Allanson
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
| | - John D Pickard
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, United Kingdom
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, United Kingdom
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, United Kingdom
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16
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Cao B, Guo Y, Guo Y, Xie Q, Chen L, Huang H, Yu R, Huang R. Time-delay structure predicts clinical scores for patients with disorders of consciousness using resting-state fMRI. NEUROIMAGE: CLINICAL 2021; 32:102797. [DOI: 10.1016/j.nicl.2021.102797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 08/08/2021] [Accepted: 08/17/2021] [Indexed: 01/22/2023] Open
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17
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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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18
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Zhou Z, Chen X, Zhang Y, Hu D, Qiao L, Yu R, Yap P, Pan G, Zhang H, Shen D. A toolbox for brain network construction and classification (BrainNetClass). Hum Brain Mapp 2020; 41:2808-2826. [PMID: 32163221 PMCID: PMC7294070 DOI: 10.1002/hbm.24979] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 02/09/2020] [Accepted: 02/25/2020] [Indexed: 12/12/2022] Open
Abstract
Brain functional network has been increasingly used in understanding brain functions and diseases. While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation-based functional network and group-level comparisons. We introduce a "Brain Network Construction and Classification (BrainNetClass)" toolbox to promote more advanced brain network construction methods to the filed, including some state-of-the-art methods that were recently developed to capture complex and high-order interactions among brain regions. The toolbox also integrates a well-accepted and rigorous classification framework based on brain connectome features toward individualized disease diagnosis in a hope that the advanced network modeling could boost the subsequent classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with both graphical user-friendly interfaces and a command line mode targeting cognitive neuroscientists and clinicians for promoting reliability, reproducibility, and interpretability of connectome-based, computer-aided diagnosis. It generates abundant classification-related results from network presentations to contributing features that have been largely ignored by most studies to grant users the ability of evaluating the disease diagnostic model and its robustness and generalizability. We demonstrate the effectiveness of the toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) is available at https://github.com/zzstefan/BrainNetClass.
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Affiliation(s)
- Zhen Zhou
- College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Xiaobo Chen
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Automotive Engineering Research InstituteJiangsu UniversityZhenjiangChina
| | - Yu Zhang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Department of Psychiatry and Behavior SciencesStanford UniversityStanfordCaliforniaUSA
| | - Dan Hu
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Lishan Qiao
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- School of Mathematics ScienceLiaocheng UniversityLiaochengChina
| | - Renping Yu
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- School of Electric EngineeringZhengzhou UniversityZhengzhouChina
| | - Pew‐Thian Yap
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Gang Pan
- College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
| | - Han Zhang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Dinggang Shen
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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19
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Machado C, Rodríguez-Rojas R, Leisman G. Partial recovery of vegetative state after a massive ischaemic stroke in a child with sickle cell anaemia. BMJ Case Rep 2020; 13:13/5/e233737. [PMID: 32376659 DOI: 10.1136/bcr-2019-233737] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
A 15-year-old patient with sickle cell disease with recessive homozygous haemoglobin S/HbSS suffered several crises developmentally after the last of which the patient fell into coma. CT scan then revealed a large infarct of the right cerebral hemisphere. Three weeks after the event, the patient began to demonstrate spontaneous eye opening and spastic quadriparesis with no evidence of command-following, gestural or verbal communication, visual pursuit or purposeful motor behaviour. Our case was in an 'unresponsive wakefulness syndrome' with atrophy of lateral and frontal regions of both hemispheres, demonstrated by MRI and preservation of circulation in the posterior arterial system, documented by MR angiography. Currently observed are spontaneous eye opening, preserved visual and auditory startle reflexes, normal brainstem reflexes, and grasp, palmomental and sucking reflexes. Our case demonstrates partial recovery of awareness with significant brain lesions, reflecting preserved brain activity as an indication of the modular nature of functional networks.
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Affiliation(s)
- Calixto Machado
- Department of Clinical Neurophysiology, Instituto de Neurología y Neurocirugía, La Habana, Cuba
| | - Rafael Rodríguez-Rojas
- CINAC (Centro Integral de Neurociencias), University Hospital CEU-San Pablo University, Madrid, Spain
| | - Gerry Leisman
- Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel .,Department of Clinical Elelctrophysiology, Universidad de Ciencias Médicas, Instituto de Neurología y Neurocirugía, Neurofisiología Cliníca, Ciudad de La Habana, Cuba
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20
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Wu M, Li F, Wu Y, Zhang T, Gao J, Xu P, Luo B. Impaired Frontoparietal Connectivity in Traumatic Individuals with Disorders of Consciousness: A Dynamic Brain Network Analysis. Aging Dis 2020; 11:301-314. [PMID: 32257543 PMCID: PMC7069467 DOI: 10.14336/ad.2019.0606] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 06/06/2019] [Indexed: 12/25/2022] Open
Abstract
Recent advances in neuroimaging have demonstrated that patients with disorders of consciousness (DOC) may retain residual consciousness through activation of a complex functional brain network. However, an understanding of the hierarchy of residual consciousness and dynamic network connectivity in DOC patients is lacking. This study aimed to investigate residual consciousness and the dynamics of neural processing in DOC patients. We included 42 patients with DOC, categorized by aetiology. Event-related potentials combined with time-varying electroencephalography networks were used to probe affective consciousness in DOC and examine the related network mechanisms. The results showed an obvious frontal P3a component among patients in minimally conscious state (MCS), while a prominent N1 was observed in unresponsive wakefulness syndrome (UWS). No late positive potential (LPP) was detected in these patients. Next, we divided the results by aetiology. Patients with nontraumatic injury presented an obvious frontal P3a response compared to those with traumatic injury. With respect to the dynamic network mechanism, patients with UWS, both with and without trauma, exhibited impaired frontoparietal network connectivity during the middle to late emotion processing period (P3a and LPP). Surprisingly, unconscious post-traumatic patients had an evident deficit in top-down connectivity. This, it appears that early automatic sensory identification is preserved in UWS and that exogenous attention was preserved even in MCS. However, high-level cognitive abilities were severely attenuated in unconscious patients. We also speculate that reduced frontoparietal connectivity may be useful as a biomarker to distinguish patients in an MCS from those with UWS given the same aetiology.
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Affiliation(s)
- Min Wu
- 1Department of Neurology & Brain Medical Centre, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Fali Li
- 2The Clinical Hospital of Chengdu Brain Science Institute, Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuehao Wu
- 1Department of Neurology & Brain Medical Centre, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Tieying Zhang
- 1Department of Neurology & Brain Medical Centre, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jian Gao
- 3Department of Rehabilitation, Hangzhou Hospital of Zhejiang Armed Police Corps, Hangzhou, China
| | - Peng Xu
- 2The Clinical Hospital of Chengdu Brain Science Institute, Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Benyan Luo
- 1Department of Neurology & Brain Medical Centre, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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21
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Corchs S, Chioma G, Dondi R, Gasparini F, Manzoni S, Markowska-Kacznar U, Mauri G, Zoppis I, Morreale A. Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness. Front Neurosci 2019; 13:807. [PMID: 31447631 PMCID: PMC6691089 DOI: 10.3389/fnins.2019.00807] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Accepted: 07/19/2019] [Indexed: 12/16/2022] Open
Abstract
Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application.
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Affiliation(s)
- Silvia Corchs
- Department of Computer Science, University Milano-Bicocca, Milan, Italy
| | - Giovanni Chioma
- Behavioral Neurology, Montecatone Rehabilitation Institute, Imola, Italy
| | - Riccardo Dondi
- Department of Letter and Communication, University of Bergamo, Bergamo, Italy
| | | | - Sara Manzoni
- Department of Computer Science, University Milano-Bicocca, Milan, Italy
| | - Urszula Markowska-Kacznar
- Department of Computational Intelligence, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wroclaw, Poland
| | - Giancarlo Mauri
- Department of Computer Science, University Milano-Bicocca, Milan, Italy
| | - Italo Zoppis
- Department of Computer Science, University Milano-Bicocca, Milan, Italy
| | - Angela Morreale
- Behavioral Neurology, Montecatone Rehabilitation Institute, Imola, Italy
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