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Errante A, Ferraro S, Demichelis G, Pinardi C, Stanziano M, Sattin D, Rossi Sebastiano D, Rozzi S, D’Incerti L, Catricalà E, Leonardi M, Bruzzone MG, Fogassi L, Nigri A. Brain activation during processing of mouth actions in patients with disorders of consciousness. Brain Commun 2024; 6:fcae045. [PMID: 38434219 PMCID: PMC10907975 DOI: 10.1093/braincomms/fcae045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 01/12/2024] [Accepted: 02/14/2024] [Indexed: 03/05/2024] Open
Abstract
In the past 2 decades, several attempts have been made to promote a correct diagnosis and possible restorative interventions in patients suffering from disorders of consciousness. Sensory stimulation has been proved to be useful in sustaining the level of arousal/awareness and to improve behavioural responsiveness with a significant effect on oro-motor functions. Recently, action observation has been proposed as a stimulation strategy in patients with disorders of consciousness, based on neurophysiological evidence that the motor cortex can be activated not only during action execution but also when actions are merely observed in the absence of motor output, or during listening to action sounds and speech. This mechanism is provided by the activity of mirror neurons. In the present study, a group of patients with disorders of consciousness (11 males, 4 females; median age: 55 years; age range: 19-74 years) underwent task-based functional MRI in which they had, in one condition, to observe and listen to the sound of mouth actions, and in another condition, to listen to verbs with motor or abstract content. In order to verify the presence of residual activation of the mirror neuron system, the brain activations of patients were compared with that of a group of healthy individuals (seven males, eight females; median age: 33.4 years; age range: 24-65 years) performing the same tasks. The results show that brain activations were lower in patients with disorders of consciousness compared with controls, except for primary auditory areas. During the audiovisual task, 5 out of 15 patients with disorders of consciousness showed only residual activation of low-level visual and auditory areas. Activation of high-level parieto-premotor areas was present in six patients. During the listening task, three patients showed only low-level activations, and six patients activated also high-level areas. Interestingly, in both tasks, one patient with a clinical diagnosis of vegetative state showed activations of high-level areas. Region of interest analysis on blood oxygen level dependent signal change in temporal, parietal and premotor cortex revealed a significant linear relation with the level of clinical functioning, assessed with coma recovery scale-revised. We propose a classification of the patient's response based on the presence of low-level and high-level activations, combined with the patient's functional level. These findings support the use of action observation and listening as possible stimulation strategies in patients with disorders of consciousness and highlight the relevance of combined methods based on functional assessment and brain imaging to provide more detailed neuroanatomical specificity about residual activated areas at both cortical and subcortical levels.
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Affiliation(s)
- Antonino Errante
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - Stefania Ferraro
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Greta Demichelis
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Chiara Pinardi
- Health Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Mario Stanziano
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
- Neurosciences Department ‘Rita Levi Montalcini’, University of Turin, 10126 Turin, Italy
| | - Davide Sattin
- Istituti Clinici Scientifici Maugeri IRCCS, 20138 Milan, Italy
| | - Davide Rossi Sebastiano
- Neurophysiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Stefano Rozzi
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - Ludovico D’Incerti
- Neuroradiology Unit, Children’s Hospital A. Meyer—University of Florence, 50139 Florence, Italy
| | - Eleonora Catricalà
- ICoN Cognitive Neuroscience Center, IUSS, Institute for Advances Studies, 27100 Pavia, Italy
| | - Matilde Leonardi
- Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Leonardo Fogassi
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - Anna Nigri
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
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Mammone N, Ieracitano C, Spataro R, Guger C, Cho W, Morabito FC. A Few-Shot Transfer Learning Approach for Motion Intention Decoding from Electroencephalographic Signals. Int J Neural Syst 2024; 34:2350068. [PMID: 38073546 DOI: 10.1142/s0129065723500685] [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] [Indexed: 01/27/2024]
Abstract
In this study, a few-shot transfer learning approach was introduced to decode movement intention from electroencephalographic (EEG) signals, allowing to recognize new tasks with minimal adaptation. To this end, a dataset of EEG signals recorded during the preparation of complex sub-movements was created from a publicly available data collection. The dataset was divided into two parts: the source domain dataset (including 5 classes) and the support (target domain) dataset, (including 2 classes) with no overlap between the two datasets in terms of classes. The proposed methodology consists in projecting EEG signals into the space-frequency-time domain, in processing such projections (rearranged in channels × frequency frames) by means of a custom EEG-based deep neural network (denoted as EEGframeNET5), and then adapting the system to recognize new tasks through a few-shot transfer learning approach. The proposed method achieved an average accuracy of 72.45 ± 4.19% in the 5-way classification of samples from the source domain dataset, outperforming comparable studies in the literature. In the second phase of the study, a few-shot transfer learning approach was proposed to adapt the neural system and make it able to recognize new tasks in the support dataset. The results demonstrated the system's ability to adapt and recognize new tasks with an average accuracy of 80 ± 0.12% in discriminating hand opening/closing preparation and outperforming reported results in the literature. This study suggests the effectiveness of EEG in capturing information related to the motor preparation of complex movements, potentially paving the way for BCI systems based on motion planning decoding. The proposed methodology could be straightforwardly extended to advanced EEG signal processing in other scenarios, such as motor imagery or neural disorder classification.
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Affiliation(s)
- Nadia Mammone
- DICEAM, University Mediterranea of Reggio Calabria Via Zehender, Loc. Feo di Vito, Reggio Calabria, 89122, Italy
| | - Cosimo Ieracitano
- DICEAM, University Mediterranea of Reggio Calabria Via Zehender, Loc. Feo di Vito, Reggio Calabria, 89122, Italy
| | - Rossella Spataro
- ALS Clinical Research Center, BiND, University of Palermo, Palermo, Italy
- Intensive Rehabilitation Unit, Villa delle Ginestre Hospital, Palermo, Italy
| | | | - Woosang Cho
- g.tec Medical Engineering GmbH, 4521, Schiedlberg, Austria
| | - Francesco Carlo Morabito
- DICEAM, University Mediterranea of Reggio Calabria Via Zehender, Loc. Feo di Vito, Reggio Calabria, 89122, Italy
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Abstract
Covert consciousness is a state of residual awareness following severe brain injury or neurological disorder that evades routine bedside behavioral detection. Patients with covert consciousness have preserved awareness but are incapable of self-expression through ordinary means of behavior or communication. Growing recognition of the limitations of bedside neurobehavioral examination in reliably detecting consciousness, along with advances in neurotechnologies capable of detecting brain states or subtle signs indicative of consciousness not discernible by routine examination, carry promise to transform approaches to classifying, diagnosing, prognosticating and treating disorders of consciousness. Here we describe and critically evaluate the evolving clinical category of covert consciousness, including approaches to its diagnosis through neuroimaging, electrophysiology, and novel behavioral tools, its prognostic relevance, and open questions pertaining to optimal clinical management of patients with covert consciousness recovering from severe brain injury.
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Affiliation(s)
- Michael J. Young
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Brian L. Edlow
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Yelena G. Bodien
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
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Lewis A, Young MJ, Rohaut B, Jox RJ, Claassen J, Creutzfeldt CJ, Illes J, Kirschen M, Trevick S, Fins JJ. Ethics Along the Continuum of Research Involving Persons with Disorders of Consciousness. Neurocrit Care 2023; 39:565-577. [PMID: 36977963 PMCID: PMC11023737 DOI: 10.1007/s12028-023-01708-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/23/2023] [Indexed: 03/30/2023]
Abstract
Interest in disorders of consciousness (DoC) has grown substantially over the past decade and has illuminated the importance of improving understanding of DoC biology; care needs (use of monitoring, performance of interventions, and provision of emotional support); treatment options to promote recovery; and outcome prediction. Exploration of these topics requires awareness of numerous ethics considerations related to rights and resources. The Curing Coma Campaign Ethics Working Group used its expertise in neurocritical care, neuropalliative care, neuroethics, neuroscience, philosophy, and research to formulate an informal review of ethics considerations along the continuum of research involving persons with DoC related to the following: (1) study design; (2) comparison of risks versus benefits; (3) selection of inclusion and exclusion criteria; (4) screening, recruitment, and enrollment; (5) consent; (6) data protection; (7) disclosure of results to surrogates and/or legally authorized representatives; (8) translation of research into practice; (9) identification and management of conflicts of interest; (10) equity and resource availability; and (11) inclusion of minors with DoC in research. Awareness of these ethics considerations when planning and performing research involving persons with DoC will ensure that the participant rights are respected while maximizing the impact and meaningfulness of the research, interpretation of outcomes, and communication of results.
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Affiliation(s)
- Ariane Lewis
- NYU Langone Medical Center, 530 First Avenue, Skirball-7R, New York, NY, 10016, USA.
| | - Michael J Young
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Benjamin Rohaut
- Inserm, CNRS, APHP - Hôpital de la Pitié Salpêtrière, Paris Brain Institute - ICM, DMU Neuroscience, Sorbonne University, Paris, France
| | - Ralf J Jox
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jan Claassen
- New York Presbyterian Hospital, Columbia University, New York, NY, USA
| | - Claire J Creutzfeldt
- Harborview Medical Center, Seattle, WA, USA
- University of Washington, Seattle, WA, USA
- Cambia Palliative Care Center of Excellence, Seattle, WA, USA
| | - Judy Illes
- University of British Columbia, Vancouver, BC, Canada
| | | | | | - Joseph J Fins
- Weill Cornell Medical College, New York, NY, USA
- Yale Law School, New Haven, CT, USA
- Rockefeller University, New York, NY, USA
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Galiotta V, Quattrociocchi I, D'Ippolito M, Schettini F, Aricò P, Sdoia S, Formisano R, Cincotti F, Mattia D, Riccio A. EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review. Front Hum Neurosci 2022; 16:1040816. [PMID: 36545350 PMCID: PMC9760911 DOI: 10.3389/fnhum.2022.1040816] [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: 09/09/2022] [Accepted: 11/17/2022] [Indexed: 12/11/2022] Open
Abstract
Background Disorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess "covert" awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs). Objectives The aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI. Methods The search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient. Results Among the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients. Conclusion Despite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy.
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Affiliation(s)
- Valentina Galiotta
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Ilaria Quattrociocchi
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy
| | - Mariagrazia D'Ippolito
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,*Correspondence: Mariagrazia D'Ippolito
| | - Francesca Schettini
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Servizio di Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Pietro Aricò
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy,Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy,BrainSigns srl, Rome, Italy
| | - Stefano Sdoia
- Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Rita Formisano
- Neurorehabilitation 2 and Post-Coma Unit, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Febo Cincotti
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy
| | - Donatella Mattia
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Servizio di Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Angela Riccio
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Servizio di Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
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Liuzzi P, Grippo A, Campagnini S, Scarpino M, Draghi F, Romoli A, Bahia H, Sterpu R, Maiorelli A, Macchi C, Cecchi F, Carrozza MC, Mannini A. Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1504-1513. [PMID: 35635833 DOI: 10.1109/tnsre.2022.3178801] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission. One dataset was completely EEG-based, one solely clinical data-based and the last was composed by the union of the two. Each model was optimized, validated and tested with a robust nested cross-validation pipeline. The best models resulted in a median absolute test error of 4.54 [IQR = 4.56], 3.39 [IQR = 4.36], 3.16 [IQR = 4.13] for respectively the EEG, clinical and hybrid model. Furthermore, the hybrid model for what concerns overcoming an unresponsive wakefulness state and exiting a DoC results in an AUC of 0.91 and 0.88 respectively. Small but useful improvements are added by the EEG dataset to the clinical model for what concerns overcoming an unresponsive wakefulness state. Data-driven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decision-making process the practitioners must face.
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Xiao J, He Y, Yu T, Pan J, Xie Q, Cao C, Zheng H, Huang W, Gu Z, Yu Z, Li Y. Towards Assessment of Sound Localization in Disorders of Consciousness Using a Hybrid Audiovisual Brain-Computer Interface. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1422-1432. [PMID: 35584066 DOI: 10.1109/tnsre.2022.3176354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients.
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