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Bardel B, Ayache SS, Lefaucheur JP. The contribution of EEG to assess and treat motor disorders in multiple sclerosis. Clin Neurophysiol 2024; 162:174-200. [PMID: 38643612 DOI: 10.1016/j.clinph.2024.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/18/2024] [Accepted: 03/21/2024] [Indexed: 04/23/2024]
Abstract
OBJECTIVE Electroencephalography (EEG) can highlight significant changes in spontaneous electrical activity of the brain produced by altered brain network connectivity linked to inflammatory demyelinating lesions and neuronal loss occurring in multiple sclerosis (MS). In this review, we describe the main EEG findings reported in the literature to characterize motor network alteration in term of local activity or functional connectivity changes in patients with MS (pwMS). METHODS A comprehensive literature search was conducted to include articles with quantitative analyses of resting-state EEG recordings (spectrograms or advanced methods for assessing spatial and temporal dynamics, such as coherence, theory of graphs, recurrent quantification, microstates) or dynamic EEG recordings during a motor task, with or without connectivity analyses. RESULTS In this systematic review, we identified 26 original articles using EEG in the evaluation of MS-related motor disorders. Various resting or dynamic EEG parameters could serve as diagnostic biomarkers of motor control impairment to differentiate pwMS from healthy subjects or be related to a specific clinical condition (fatigue) or neuroradiological aspects (lesion load). CONCLUSIONS We highlight some key EEG patterns in pwMS at rest and during movement, both suggesting an alteration or disruption of brain connectivity, more specifically involving sensorimotor networks. SIGNIFICANCE Some of these EEG biomarkers of motor disturbance could be used to design future therapeutic strategies in MS based on neuromodulation approaches, or to predict the effects of motor training and rehabilitation in pwMS.
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Affiliation(s)
- Benjamin Bardel
- Univ Paris Est Creteil, Excitabilité Nerveuse et Thérapeutique (ENT), EA 4391, F-94010 Creteil, France; AP-HP, Henri Mondor University Hospital, Department of Clinical Neurophysiology, DMU FIxIT, F-94010 Creteil, France
| | - Samar S Ayache
- Univ Paris Est Creteil, Excitabilité Nerveuse et Thérapeutique (ENT), EA 4391, F-94010 Creteil, France; AP-HP, Henri Mondor University Hospital, Department of Clinical Neurophysiology, DMU FIxIT, F-94010 Creteil, France; Gilbert and Rose-Marie Chagoury School of Medicine, Department of Neurology, 4504 Byblos, Lebanon; Institut de la Colonne Vertébrale et des NeuroSciences (ICVNS), Centre Médico-Chirurgical Bizet, F-75116 Paris, France
| | - Jean-Pascal Lefaucheur
- Univ Paris Est Creteil, Excitabilité Nerveuse et Thérapeutique (ENT), EA 4391, F-94010 Creteil, France; AP-HP, Henri Mondor University Hospital, Department of Clinical Neurophysiology, DMU FIxIT, F-94010 Creteil, France.
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Meier TA, Refahi MS, Hearne G, Restifo DS, Munoz-Acuna R, Rosen GL, Woloszynek S. The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain. Curr Pain Headache Rep 2024:10.1007/s11916-024-01264-0. [PMID: 38822995 DOI: 10.1007/s11916-024-01264-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2024] [Indexed: 06/03/2024]
Abstract
PURPOSE OF REVIEW This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies. RECENT FINDINGS In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.
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Affiliation(s)
| | - Mohammad S Refahi
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Gavin Hearne
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | | | - Ricardo Munoz-Acuna
- Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Gail L Rosen
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Stephen Woloszynek
- Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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Saway BF, Palmer C, Hughes C, Triano M, Suresh RE, Gilmore J, George M, Kautz SA, Rowland NC. The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces. Neurotherapeutics 2024; 21:e00337. [PMID: 38377638 PMCID: PMC11103214 DOI: 10.1016/j.neurot.2024.e00337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/05/2024] [Accepted: 02/13/2024] [Indexed: 02/22/2024] Open
Abstract
Stroke is one of the most common and debilitating neurological conditions worldwide. Those who survive experience motor, sensory, speech, vision, and/or cognitive deficits that severely limit remaining quality of life. While rehabilitation programs can help improve patients' symptoms, recovery is often limited, and patients frequently continue to experience impairments in functional status. In this review, invasive neuromodulation techniques to augment the effects of conventional rehabilitation methods are described, including vagus nerve stimulation (VNS), deep brain stimulation (DBS) and brain-computer interfaces (BCIs). In addition, the evidence base for each of these techniques, pivotal trials, and future directions are explored. Finally, emerging technologies such as functional near-infrared spectroscopy (fNIRS) and the shift to artificial intelligence-enabled implants and wearables are examined. While the field of implantable devices for chronic stroke recovery is still in a nascent stage, the data reviewed are suggestive of immense potential for reducing the impact and impairment from this globally prevalent disorder.
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Affiliation(s)
- Brian F Saway
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA.
| | - Charles Palmer
- Department of Psychiatry, Medical University of South Carolina, SC 29425, USA
| | - Christopher Hughes
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Matthew Triano
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA
| | - Rishishankar E Suresh
- College of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Jordon Gilmore
- Department of Bioengineering, Clemson University, Clemson, SC 29634, USA
| | - Mark George
- Department of Psychiatry, Medical University of South Carolina, SC 29425, USA; Ralph H Johnson VA Health Care System, Charleston, SC 29425, USA
| | - Steven A Kautz
- Department of Health Science and Research, Medical University of South Carolina, SC 29425, USA; Ralph H Johnson VA Health Care System, Charleston, SC 29425, USA
| | - Nathan C Rowland
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA; MUSC Institute for Neuroscience Discovery (MIND), Medical University of South Carolina, SC 29425, USA
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Ille N, Nakao Y, Yano S, Taura T, Ebert A, Bornfleth H, Asagi S, Kozawa K, Itabashi I, Sato T, Sakuraba R, Tsuda R, Kakisaka Y, Jin K, Nakasato N. Ongoing EEG artifact correction using blind source separation. Clin Neurophysiol 2024; 158:149-158. [PMID: 38219404 DOI: 10.1016/j.clinph.2023.12.133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/23/2023] [Accepted: 12/15/2023] [Indexed: 01/16/2024]
Abstract
OBJECTIVE Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. METHODS The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. RESULTS The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. CONCLUSIONS Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. SIGNIFICANCE The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.
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Affiliation(s)
| | | | | | | | | | | | - Suguru Asagi
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Kanoko Kozawa
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Izumi Itabashi
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Takafumi Sato
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Rie Sakuraba
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Rie Tsuda
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Yosuke Kakisaka
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Nobukazu Nakasato
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan
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