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Shen Y, You H, Yang Y, Tang R, Ji Z, Liu H, Du M, Zhou M. Predicting brain edema and outcomes after thrombectomy in stroke: Frontal delta/alpha ratio as an optimal quantitative EEG index. Clin Neurophysiol 2024; 164:149-160. [PMID: 38896932 DOI: 10.1016/j.clinph.2024.05.009] [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: 11/09/2023] [Revised: 04/26/2024] [Accepted: 05/17/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVE We aimed to determine whether quantitative electroencephalography (QEEG) measures have predictive value for cerebral edema (CED) and clinical outcomes in acute ischemic stroke (AIS) patients with anterior circulation large vessel occlusion who underwent mechanical thrombectomy (MT). METHODS A total of 105 patients with AIS in the anterior circulation were enrolled in this prospective study. The occurrence and severity of CED were assessed through computed tomography conducted 24 h after MT. Clinical outcomes were evaluated based on early neurological deterioration (END) and 3-month functional status, as measured by the modified Rankin scale (mRS). Electroencephalography (EEG) recordings were performed 24 h after MT, and QEEG indices were calculated from the standard 16 electrodes and 2 frontal channels (F3-C3, F4-C4). The delta/alpha ratio (DAR), the (delta + theta) / (alpha + beta) ratio (DTABR), and relative delta power were averaged over all electrodes (global) and the F3-C3 and F4-C4 channels (frontal). The predictive effect and value of QEEG indices for CED and clinical outcomes were assessed using ordinal and logistic regression models, as well as receiver operating characteristic (ROC) curves. RESULTS Significantly, both global and frontal DAR were found to be associated with the severity of CED, END, and poor functional outcomes at 90 days, while global and frontal DTABR and relative delta power were not associated with outcomes. In ROC analysis, the best predictive effect was observed in frontal DAR, with an area under the curve of approximately 0.80. It exhibited approximately 75% sensitivity and 71% specificity for radiological and clinical outcomes when a threshold of 3.3 was used. CONCLUSIONS QEEG techniques may be considered an efficient bedside monitoring method for assessing treatment efficacy, identifying patients at higher risk of severe CED and END, and predicting long-term functional outcomes. SIGNIFICANCE QEEG can help identify patients at risk of severe neurological complications that can impact long-term functional recovery in AIS patients who underwent MT.
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Affiliation(s)
- Yeru Shen
- Department of Critical Care Medicine, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Heyang You
- Department of Critical Care Medicine, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yanyan Yang
- Department of Critical Care Medicine, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Rui Tang
- Department of Critical Care Medicine, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Zongshu Ji
- Department of Critical Care Medicine, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Haiyan Liu
- Department of Critical Care Medicine, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Min Du
- Department of Critical Care Medicine, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Min Zhou
- Department of Critical Care Medicine, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
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Peterson W, Ramakrishnan N, Browder K, Sanossian N, Nguyen P, Fink E. Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods. J Stroke Cerebrovasc Dis 2024; 33:107714. [PMID: 38636829 DOI: 10.1016/j.jstrokecerebrovasdis.2024.107714] [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: 02/08/2024] [Revised: 03/15/2024] [Accepted: 04/06/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVES We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which features can be computed. MATERIALS AND METHODS Using a large-scale, retrospective database of EEG recordings and matching clinical reports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke). RESULTS Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an AUC of 0.95, with a sensitivity and specificity of 93% and 86%, respectively. Allowing for multiple recordings per subject in the training set boosted sensitivity by 7%, attributable to a more balanced dataset. CONCLUSIONS Our work demonstrates strong potential for the use of EEG in conjunction with machine learning methods to distinguish stroke patients from healthy subjects. Our approach provides a solution that is not only timely (3-minutes recording time) but also highly precise and accurate (AUC: 0.95).
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Affiliation(s)
| | | | | | - Nerses Sanossian
- Roxanna Todd Hodges Stroke Program, United States; Keck School of Medicine of the University of Southern California, United States
| | - Peggy Nguyen
- Keck School of Medicine of the University of Southern California, United States
| | - Ezekiel Fink
- Houston Hospital, Houston, TX, United States; Weill Cornell School of Medicine Sciences, New York, NY, United States
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Juárez Martínez EL, Kimchi E. Investigating delirium in stroke with an EEG lens: Focal lesions with global impact? Clin Neurophysiol 2024; 162:219-221. [PMID: 38631924 DOI: 10.1016/j.clinph.2024.03.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 03/30/2024] [Indexed: 04/19/2024]
Affiliation(s)
- Erika L Juárez Martínez
- Ken & Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Eyal Kimchi
- Ken & Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
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Liuzzi P, Grippo A, Sodero A, Castagnoli C, Pellegrini I, Burali R, Toci T, Barretta T, Mannini A, Hakiki B, Macchi C, Lolli F, Cecchi F. Quantitative EEG and prognosis for recovery in post-stroke patients: The effect of lesion laterality. Neurophysiol Clin 2024; 54:102952. [PMID: 38422721 DOI: 10.1016/j.neucli.2024.102952] [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: 07/18/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
OBJECTIVE There is emerging confidence that quantitative EEG (qEEG) has the potential to inform clinical decision-making and guide individualized rehabilitation after stroke, but consensus on the best EEG biomarkers is needed for translation to clinical practice. This study investigates the spatial qEEG spectral and symmetry distribution in patients with a left/right hemispheric stroke, to evaluate their side-specific prognostic power in post-acute rehabilitation outcome. METHODS Resting-state 19-channel EEG recordings were collected with clinical information on admission to intensive inpatient rehabilitation (within 30 days post stroke), and six months post stroke. After preprocessing, spectral (Delta-to-Alpha Ratio, DAR) and symmetry (pairwise and hemispheric Brain Symmetry Index) features were extracted. Patients were divided into Affected Right and Left (AR/AL) groups, according to the location of their lesion. Within each group, DAR was compared between homologous electrode pairs and the pairwise difference between pairs was compared across pairs in the scalp. Then, the prognostic power of qEEG admission metrics was evaluated by performing correlations between admission metrics and discharge mBI values. RESULTS Fifty-two patients with hemorrhagic or ischemic stroke (20 females, 38.5 %, median age 76 years [IQR = 22]) were included in the study. DAR was significantly higher in the affected hemisphere for both AR and AL groups, and, a higher frontal (to posterior) asymmetry was found independent of the side of the lesion. DAR was found to be a prognostic marker of 6-months modified Barthel Index (mBI) only for the AL group, while hemispheric asymmetry did not correlate with follow-up outcomes in either group. DISCUSSION While the presence of EEG abnormalities in the affected hemisphere of a stroke is well recognized, we have shown that the extent of DAR abnormalities seen correlates with disability at 6 months post stroke, but only for left hemispheric lesions. Routine prognostic evaluation, in addition to motor and functional scales, can add information concerning neuro-prognostication and reveal neurophysiological abnormalities to be assessed during rehabilitation.
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Affiliation(s)
- Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy; Scuola Superiore Sant'Anna, Istituto di BioRobotica, Viale Rinaldo Piaggio 34, Pontedera, Italy.
| | - Antonello Grippo
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy
| | - Alessandro Sodero
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy
| | - Chiara Castagnoli
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy
| | - Ilaria Pellegrini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy
| | - Rachele Burali
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy
| | - Tanita Toci
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy
| | - Teresa Barretta
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy
| | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy
| | - Claudio Macchi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy; Università di Firenze, Dipartimento di Medicina Sperimentale e Clinica, Largo Brambilla 3, Firenze, Italy
| | - Francesco Lolli
- Università degli Studi di Firenze, Dipartimento di Scienze Biomediche Sperimentali e Cliniche, Viale Morgagni 50, Firenze, Italy
| | - Francesca Cecchi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Di Scandicci 269, Firenze, Italy; Università degli Studi di Firenze, Dipartimento di Scienze Biomediche Sperimentali e Cliniche, Viale Morgagni 50, Firenze, Italy
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Wang F, Yang X, Zhang X, Hu F. Monitoring the after-effects of ischemic stroke through EEG microstates. PLoS One 2024; 19:e0300806. [PMID: 38517874 PMCID: PMC10959352 DOI: 10.1371/journal.pone.0300806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 03/05/2024] [Indexed: 03/24/2024] Open
Abstract
BACKGROUND AND PURPOSE Stroke may cause extensive after-effects such as motor function impairments and disorder of consciousness (DoC). Detecting these after-effects of stroke and monitoring their changes are challenging jobs currently undertaken via traditional clinical examinations. These behavioural examinations often take a great deal of manpower and time, thus consuming significant resources. Computer-aided examinations of the electroencephalogram (EEG) microstates derived from bedside EEG monitoring may provide an alternative way to assist medical practitioners in a quick assessment of the after-effects of stroke. METHODS In this study, we designed a framework to extract microstate maps and calculate their statistical parameters to input to classifiers to identify DoC in ischemic stroke patients automatically. As the dataset is imbalanced with the minority of patients being DoC, an ensemble of support vector machines (EOSVM) is designed to solve the problem that classifiers always tend to be the majority classes in the classification on an imbalanced dataset. RESULTS The experimental results show EOSVM get better performance (with accuracy and F1-Score both higher than 89%), improving sensitivity the most, from lower than 60% (SVM and AdaBoost) to higher than 80%. This highlighted the usefulness of the EOSVM-aided DoC detection based on microstates parameters. CONCLUSION Therefore, the classifier EOSVM classification based on features of EEG microstates is helpful to medical practitioners in DoC detection with saved resources that would otherwise be consumed in traditional clinic checks.
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Affiliation(s)
- Fang Wang
- West China Biomedical Big Data Center of West China Hospital, Sichuan University, Chengdu, China
| | - Xue Yang
- West China Biomedical Big Data Center of West China Hospital, Sichuan University, Chengdu, China
| | - Xueying Zhang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Fengyun Hu
- Department of Neurology, Shanxi Provincial People’s Hospital Affiliated with Shanxi Medical University, Taiyuan, China
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Shamsi F, Aligholi H, Karimi MT, Borhani-Haghighi A, Nami M. Quantitative EEG for the Monitoring of Walking Recovery in Chronic Stroke Patients Receiving Action Observation Training. J Mot Behav 2024; 56:428-438. [PMID: 38408745 DOI: 10.1080/00222895.2024.2320904] [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: 04/13/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024]
Abstract
The current study aimed to evaluate the effects of action observation on the walking ability and oscillatory brain activity of chronic stroke patients. Fourteen chronic stroke patients were allocated randomly to the action observation (AO) or sham observation (SO) groups. Both groups received 12 sessions of intervention. Each session composed of 12 min of observational training, which depicted exercises for the experimental group but nature pictures for the sham group and 40 min of occupational therapy, which was the same for the both groups. Walking ability was assessed by a motion analysis system and brain activity was monitored using quantitative electroencephalography (QEEG) before and after the intervention. Brain asymmetry at alpha frequency, the percentage of stance phase, and step length showed significant changes in the AO group. Only the change in global alpha power was significantly correlated with the change in velocity after the intervention in AO group. Despite more improvements in walking and brain activity of patients in the AO group, our study failed to show significant correlations between the brain activity changes and functional improvements after the intervention, which might be mainly due to the small sample size in our study. Trial registration: IRCT20181014041333N1.
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Affiliation(s)
- Fatemeh Shamsi
- Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
- Neuroscience Laboratory (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hadi Aligholi
- Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
- Neuroscience Laboratory (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Taghi Karimi
- Rehabilitation Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Mohammad Nami
- Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
- Neuroscience Laboratory (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
- Neuroscience Center, Instituto de Investigaciones Científicas Servicios de Alta Tecnología (INDICASAT AIP), City of Knowledge, Panama City, Panama
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Rösch J, Emanuel Vetter D, Baldassarre A, Souza VH, Lioumis P, Roine T, Jooß A, Baur D, Kozák G, Blair Jovellar D, Vaalto S, Romani GL, Ilmoniemi RJ, Ziemann U. Individualized treatment of motor stroke: A perspective on open-loop, closed-loop and adaptive closed-loop brain state-dependent TMS. Clin Neurophysiol 2024; 158:204-211. [PMID: 37945452 DOI: 10.1016/j.clinph.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/11/2023] [Accepted: 10/18/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Johanna Rösch
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - David Emanuel Vetter
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Andreas Jooß
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - David Baur
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Gábor Kozák
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - D Blair Jovellar
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Selja Vaalto
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; HUS Diagnostic Center, Clinical Neurophysiology, Clinical Neurosciences, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Ulf Ziemann
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany.
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Lassi M, Dalise S, Bandini A, Spina V, Azzollini V, Vissani M, Micera S, Mazzoni A, Chisari C. Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients. Eur J Phys Rehabil Med 2024; 60:13-26. [PMID: 37987741 DOI: 10.23736/s1973-9087.23.07922-4] [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: 11/22/2023]
Abstract
BACKGROUND Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanisms depend on the time after stroke is yet to be assessed. AIM We investigated the response to an intensive protocol of robot-assisted rehabilitation in sub-acute and chronic stroke patients, by analyzing the underlying changes in clinical scores, electroencephalography (EEG) and end-effector kinematics. We aimed at identifying neural correlates of the participants' upper limb motor function recovery, following an intensive 2-week rehabilitation protocol. DESIGN Prospective cohort study. SETTING Inpatients and outpatients from the Neurorehabilitation Unit of Pisa University Hospital, Italy. POPULATION Sub-acute and chronic stroke survivors. METHODS Thirty-one stroke survivors (14 sub-acute, 17 chronic) with mild-to-moderate UL paresis were enrolled. All participants underwent ten rehabilitative sessions of task-oriented exercises with a planar end-effector robotic device. All patients were evaluated with the Fugl-Meyer Assessment Scale and the Wolf Motor Function Test, at recruitment (T0), end-of-treatment (T1), and one-month follow-up (T2). Along with clinical scales, kinematic parameters and quantitative EEG were collected for each patient. Kinematics metrics were related to velocity, acceleration and smoothness of the movement. Relative power in four frequency bands was extracted from the EEG signals. The evolution over time of kinematic and EEG features was analyzed, in correlation with motor recovery. RESULTS Both groups displayed significant gains in motility after treatment. Sub-acute patients displayed more pronounced clinical improvements, significant changes in kinematic parameters, and a larger increase in Beta-band in the motor area of the affected hemisphere. In both groups these improvements were associated to a decrease in the Delta-band of both hemispheres. Improvements were retained at T2. CONCLUSIONS The intensive two-week rehabilitation protocol was effective in both chronic and sub-acute patients, and improvements in the two groups shared similar dynamics. However, stronger cortical and behavioral changes were observed in sub-acute patients suggesting different reorganizational patterns. CLINICAL REHABILITATION IMPACT This study paves the way to personalized approaches to UL motor rehabilitation after stroke, as highlighted by different neurophysiological modifications following recovery in subacute and chronic stroke patients.
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Affiliation(s)
- Michael Lassi
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Stefania Dalise
- Neurorehabilitation Unit, Pisa University Hospital, Pisa, Italy
| | - Andrea Bandini
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
- Health Science Interdisciplinary Research Center, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Vincenzo Spina
- Neurorehabilitation Unit, Pisa University Hospital, Pisa, Italy
| | | | - Matteo Vissani
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
- Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Silvestro Micera
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
- Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fèdèrale de Lausanne, Lausanne, Switzerland
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Carmelo Chisari
- Neurorehabilitation Unit, Pisa University Hospital, Pisa, Italy -
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Vimolratana O, Aneksan B, Siripornpanich V, Hiengkaew V, Prathum T, Jeungprasopsuk W, Khaokhiew T, Vachalathiti R, Klomjai W. Effects of anodal tDCS on resting state eeg power and motor function in acute stroke: a randomized controlled trial. J Neuroeng Rehabil 2024; 21:6. [PMID: 38172973 PMCID: PMC10765911 DOI: 10.1186/s12984-023-01300-x] [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: 05/04/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Anodal transcranial direct current stimulation (tDCS) is a beneficial adjunctive tool in stroke rehabilitation. However, only a few studies have investigated its effects on acute stroke and recruited only individuals with mild motor deficits. This study investigated the effect of five consecutive sessions of anodal tDCS and conventional physical therapy on brain activity and motor outcomes in individuals with acute stroke, with low and high motor impairments. METHODS Thirty participants were recruited and randomly allocated to either the anodal or sham tDCS group. Five consecutive sessions of tDCS (1.5 mA anodal or sham tDCS for 20 min) were administered, followed by conventional physical therapy. Electroencephalography (EEG), Fugl-Meyer Motor Assessment (FMA), and Wolf Motor Function Test (WMFT) were performed at pre-, post-intervention (day 5), and 1-month follow-up. Sub-analyses were performed on participants with low and high motor impairments. The relationship between EEG power and changes in motor functions was assessed. RESULTS Linear regression showed a significant positive correlation between beta bands and the FMA score in the anodal group. Elevated high frequency bands (alpha and beta) were observed at post-intervention and follow-up in all areas of both hemispheres in the anodal group, while only in the posterior area of the non-lesioned hemisphere in the sham group; however, such elevation induced by tDCS was not greater than sham. Lower limb function assessed by FMA was improved in the anodal group compared with the sham group at post-intervention and follow-up only in those with low motor impairment. For the upper limb outcomes, no difference between groups was found. CONCLUSIONS Five consecutive days of anodal tDCS and physical therapy in acute stroke did not result in a superior improvement of beta bands that commonly related to stroke recovery over sham, but improved lower extremity functions with a post-effect at 1-month follow-up in low motor impairment participants. The increase of beta bands in the lesioned brain in the anodal group was associated with improvement in lower limb function. TRIAL REGISTRATION NCT04578080, date of first registration 10/01/2020.
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Affiliation(s)
- O Vimolratana
- Faculty of Physical Therapy, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom, 73170, Thailand
- Neuro Electrical Stimulation Laboratory, Faculty of Physical Therapy, Mahidol University, Nakhon Pathom, 73170, Thailand
- School of Integrative Medicine, Mae Fah Luang University, Chiang Rai, 57100, Thailand
| | - B Aneksan
- Faculty of Physical Therapy, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom, 73170, Thailand
- Neuro Electrical Stimulation Laboratory, Faculty of Physical Therapy, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - V Siripornpanich
- Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - V Hiengkaew
- Faculty of Physical Therapy, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom, 73170, Thailand
| | - T Prathum
- Faculty of Physical Therapy, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom, 73170, Thailand
- Neuro Electrical Stimulation Laboratory, Faculty of Physical Therapy, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - W Jeungprasopsuk
- Faculty of Medical Technology, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - T Khaokhiew
- Faculty of Medical Technology, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - R Vachalathiti
- Faculty of Physical Therapy, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom, 73170, Thailand
| | - W Klomjai
- Faculty of Physical Therapy, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom, 73170, Thailand.
- Neuro Electrical Stimulation Laboratory, Faculty of Physical Therapy, Mahidol University, Nakhon Pathom, 73170, Thailand.
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10
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Yue Z, Xiao P, Wang J, Tong RKY. Brain oscillations in reflecting motor status and recovery induced by action observation-driven robotic hand intervention in chronic stroke. Front Neurosci 2023; 17:1241772. [PMID: 38146541 PMCID: PMC10749335 DOI: 10.3389/fnins.2023.1241772] [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: 06/17/2023] [Accepted: 11/14/2023] [Indexed: 12/27/2023] Open
Abstract
Hand rehabilitation in chronic stroke remains challenging, and finding markers that could reflect motor function would help to understand and evaluate the therapy and recovery. The present study explored whether brain oscillations in different electroencephalogram (EEG) bands could indicate the motor status and recovery induced by action observation-driven brain-computer interface (AO-BCI) robotic therapy in chronic stroke. The neurophysiological data of 16 chronic stroke patients who received 20-session BCI hand training is the basis of the study presented here. Resting-state EEG was recorded during the observation of non-biological movements, while task-stage EEG was recorded during the observation of biological movements in training. The motor performance was evaluated using the Action Research Arm Test (ARAT) and upper extremity Fugl-Meyer Assessment (FMA), and significant improvements (p < 0.05) on both scales were found in patients after the intervention. Averaged EEG band power in the affected hemisphere presented negative correlations with scales pre-training; however, no significant correlations (p > 0.01) were found both in the pre-training and post-training stages. After comparing the variation of oscillations over training, we found patients with good and poor recovery presented different trends in delta, low-beta, and high-beta variations, and only patients with good recovery presented significant changes in EEG band power after training (delta band, p < 0.01). Importantly, motor improvements in ARAT correlate significantly with task EEG power changes (low-beta, c.c = 0.71, p = 0.005; high-beta, c.c = 0.71, p = 0.004) and task/rest EEG power ratio changes (delta, c.c = -0.738, p = 0.003; low-beta, c.c = 0.67, p = 0.009; high-beta, c.c = 0.839, p = 0.000). These results suggest that, in chronic stroke, EEG band power may not be a good indicator of motor status. However, ipsilesional oscillation changes in the delta and beta bands provide potential biomarkers related to the therapeutic-induced improvement of motor function in effective BCI intervention, which may be useful in understanding the brain plasticity changes and contribute to evaluating therapy and recovery in chronic-stage motor rehabilitation.
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Affiliation(s)
- Zan Yue
- Institute of Robotics and Intelligent Systems, Xi’an Jiaotong University, Xi’an, China
- Neurorehabilitation Robotics Research Institute, Xi’an Jiaotong University, Xi’an, China
| | - Peng Xiao
- Institute of Robotics and Intelligent Systems, Xi’an Jiaotong University, Xi’an, China
- Neurorehabilitation Robotics Research Institute, Xi’an Jiaotong University, Xi’an, China
| | - Jing Wang
- Institute of Robotics and Intelligent Systems, Xi’an Jiaotong University, Xi’an, China
- Neurorehabilitation Robotics Research Institute, Xi’an Jiaotong University, Xi’an, China
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Raymond Kai-yu Tong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
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11
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Lanzone J, Motolese F, Ricci L, Tecchio F, Tombini M, Zappasodi F, Cruciani A, Capone F, Di Lazzaro V, Assenza G. Quantitative measures of the resting EEG in stroke: a systematic review on clinical correlation and prognostic value. Neurol Sci 2023; 44:4247-4261. [PMID: 37542545 DOI: 10.1007/s10072-023-06981-9] [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: 10/27/2022] [Accepted: 07/26/2023] [Indexed: 08/07/2023]
Abstract
OBJECT Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. We systematically reviewed published papers that focus on qEEG metrics in the resting EEG of patients with mono-hemispheric stroke, to summarize current knowledge and pave the way for future research. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched the literature for papers that fitted our inclusion criteria. Rayyan QCRR was used to allow deduplication and collaborative blinded paper review. Due to multiple outcomes and non-homogeneous literature, a scoping review approach was used to address the topic. RESULTS Or initial search (PubMed, Embase, Google scholar) yielded 3200 papers. After proper screening, we selected 71 papers that fitted our inclusion criteria and we developed a scoping review thar describes the current state of the art of qEEG in stroke. Notably, among selected papers 53 (74.3%) focused on spectral power; 11 (15.7%) focused on symmetry indexes, 17 (24.3%) on connectivity metrics, while 5 (7.1%) were about other metrics (e.g. detrended fluctuation analysis). Moreover, 42 (58.6%) studies were performed with standard 19 electrodes EEG caps and only a minority used high-definition EEG. CONCLUSIONS We systematically assessed major findings on qEEG and stroke, evidencing strengths and potential pitfalls of this promising branch of research.
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Affiliation(s)
- J Lanzone
- Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation Department of the Milano Institute, Milan, Italy.
| | - F Motolese
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - L Ricci
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - F Tecchio
- Laboratory of Electrophysiology for Translational Neuroscience LET'S, Institute of Cognitive Sciences and Technologies ISTC, Consiglio Nazionale Delle Ricerche CNR, Rome, Italy
| | - M Tombini
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - F Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences and Institute for Advanced Biomedical Technologies, 'Gabriele D'Annunzio' University, Chieti, Italy
| | - A Cruciani
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - F Capone
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - V Di Lazzaro
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - G Assenza
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
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12
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Liu G, Tian F, Zhu Y, Jiang M, Cui L, Zhang Y, Wang Y, Su Y. The predictive value of EEG reactivity by electrical stimulation and quantitative analysis in critically ill patients with large hemispheric infarction. J Crit Care 2023; 78:154358. [PMID: 37329762 DOI: 10.1016/j.jcrc.2023.154358] [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: 11/03/2022] [Revised: 05/05/2023] [Accepted: 06/02/2023] [Indexed: 06/19/2023]
Abstract
PURPOSE The intensive care of critically ill patients with large hemispheric infarction improves the survival rate. However, established prognostic markers for neurological outcome show variable accuracy. We aimed to assess the value of electrical stimulation and quantitative analysis of EEG reactivity for early prognostication in this critically ill population. MATERIALS AND METHODS We prospectively enrolled consecutive patients between January 2018 and December 2021. EEG reactivity was randomly performed by pain or electrical stimulation via visual and quantitative analysis. Neurological outcome within 6-month was dichotomized as good (modified Rankin Scale, mRS 0-3) or poor (mRS 4-6). RESULTS Ninety-four patients were admitted, and 56 were included in the final analysis. EEG reactivity using electrical stimulation was superior to pain stimulation for good outcome prediction (visual analysis: AUC 0.825 vs. 0.763, P = 0.143; quantitative analysis: AUC 0.931 vs. 0.844, P = 0.058). The AUC of EEG reactivity by pain stimulation with visual analysis was 0.763, which increased to 0.931 by electrical stimulation with quantitative analysis (P = 0.006). When using quantitative analysis, the AUC of EEG reactivity increased (pain stimulation 0.763 vs. 0.844, P = 0.118; electrical stimulation 0.825 vs. 0.931, P = 0.041). CONCLUSION EEG reactivity by electrical stimulation and quantitative analysis seems a promising prognostic factor in these critical patients.
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Affiliation(s)
- Gang Liu
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Fei Tian
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Yu Zhu
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Mengdi Jiang
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Lili Cui
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Yan Zhang
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Yuan Wang
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China.
| | - Yingying Su
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China.
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13
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Benedetti GM, Guerriero RM, Press CA. Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG. Neurocrit Care 2023; 39:618-638. [PMID: 36949358 PMCID: PMC10033183 DOI: 10.1007/s12028-023-01686-5] [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: 07/28/2022] [Accepted: 01/30/2023] [Indexed: 03/24/2023]
Abstract
Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement.
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Affiliation(s)
- Giulia M Benedetti
- Division of Pediatric Neurology, Department of Neurology, Seattle Children's Hospital and the University of Washington School of Medicine, Seattle, WA, USA.
- Division of Pediatric Neurology, Department of Pediatrics, C.S. Mott Children's Hospital and the University of Michigan, 1540 E Hospital Drive, Ann Arbor, MI, 48109-4279, USA.
| | - Rejéan M Guerriero
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Craig A Press
- Departments of Neurology and Pediatric, Children's Hospital of Philadelphia and Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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14
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Antonioni A, Galluccio M, Toselli R, Baroni A, Fregna G, Schincaglia N, Milani G, Cosma M, Ferraresi G, Morelli M, Casetta I, De Vito A, Masiero S, Basaglia N, Malerba P, Severini G, Straudi S. A Multimodal Analysis to Explore Upper Limb Motor Recovery at 4 Weeks After Stroke: Insights From EEG and Kinematics Measures. Clin EEG Neurosci 2023:15500594231209397. [PMID: 37859431 DOI: 10.1177/15500594231209397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Background. Stroke is a leading cause of death and disability worldwide and there is a very short period of increased synaptic plasticity, fundamental in motor recovery. Thus, it is crucial to acquire data to guide the rehabilitation treatment. Promising results have been achieved with kinematics and neurophysiological data, but currently, few studies integrate these different modalities. Objectives. We explored the correlations between standardized clinical scales, kinematic data, and EEG measures 4 weeks after stroke. Methods. 26 patients were considered. Among them, 20 patients also performed the EEG study, beyond the kinematic analysis, at 4 weeks. Results. We found correlations between the Fugl-Meyer Assessment-Upper Extremity, movement duration, smoothness measures, and velocity peaks. Moreover, EEG measures showed a tendency for the healthy hemisphere to vicariate the affected one in patients characterized by better clinical conditions. Conclusions. These results suggest the relevance of kinematic (in particular movement duration and smoothness) and EEG biomarkers to evaluate post-stroke recovery. We emphasize the importance of integrating clinical data with kinematic and EEG analyses from the early stroke stages, in order to guide rehabilitation strategies to best leverage the short period of increased synaptic plasticity.
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Affiliation(s)
- Annibale Antonioni
- Department of Neuroscience and Rehabilitation, Ferrara University, Ferrara, Italy
- Doctoral Program in Translational Neurosciences and Neurotechnologies, Ferrara University, Ferrara, Italy
| | - Martina Galluccio
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Riccardo Toselli
- Department of Neuroscience, Section of Rehabilitation, University of Padua, Padua, Italy
| | - Andrea Baroni
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Giulia Fregna
- Doctoral Program in Translational Neurosciences and Neurotechnologies, Ferrara University, Ferrara, Italy
| | - Nicola Schincaglia
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Giada Milani
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Michela Cosma
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Giovanni Ferraresi
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Monica Morelli
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Ilaria Casetta
- Department of Neuroscience and Rehabilitation, Ferrara University, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Alessandro De Vito
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Stefano Masiero
- Department of Neuroscience, Section of Rehabilitation, University of Padua, Padua, Italy
| | - Nino Basaglia
- Department of Neuroscience and Rehabilitation, Ferrara University, Ferrara, Italy
| | - Paola Malerba
- Center for Biobehavioral Health, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
- School of Medicine, The Ohio State University, Columbus, OH, USA
| | - Giacomo Severini
- School of Electrical and Electronic Engineering, University College Dublin, Dulin, Ireland
| | - Sofia Straudi
- Department of Neuroscience and Rehabilitation, Ferrara University, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
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15
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Yu F, Gao Y, Li F, Zhang X, Hu F, Jia W, Li X. Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness. Front Neurosci 2023; 17:1257511. [PMID: 37849891 PMCID: PMC10577186 DOI: 10.3389/fnins.2023.1257511] [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: 07/12/2023] [Accepted: 09/18/2023] [Indexed: 10/19/2023] Open
Abstract
Introduction Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology. Methods In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC. Results Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%). Discussion Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.
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Affiliation(s)
- Fang Yu
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Yanzhe Gao
- College of Life Sciences, Nankai University, Tianjin, China
| | - Fenglian Li
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Xueying Zhang
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Fengyun Hu
- The Fifth Clinical Medical College of Shanxi Medical University, Department of Neurology, Shanxi Provincial People’s Hospital, Taiyuan, China
| | - Wenhui Jia
- The Fifth Clinical Medical College of Shanxi Medical University, Department of Neurology, Shanxi Provincial People’s Hospital, Taiyuan, China
| | - Xiaohui Li
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China
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16
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Lee M, Hong Y, An S, Park U, Shin J, Lee J, Oh MS, Lee BC, Yu KH, Lim JS, Kang SW. Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes. Front Aging Neurosci 2023; 15:1238274. [PMID: 37842126 PMCID: PMC10568623 DOI: 10.3389/fnagi.2023.1238274] [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: 06/11/2023] [Accepted: 09/11/2023] [Indexed: 10/17/2023] Open
Abstract
Objectives More than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach. Methods We enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups. Results Eighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively. Conclusion Estimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.
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Affiliation(s)
- Minwoo Lee
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
| | | | - Sungsik An
- Department of Neurology, Hwahong Hospital, Suwon, Republic of Korea
| | - Ukeob Park
- iMedisync, Inc., Seoul, Republic of Korea
| | | | - Jeongjae Lee
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Mi Sun Oh
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Byung-Chul Lee
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Kyung-Ho Yu
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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17
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Vidaurre C, Irastorza-Landa N, Sarasola-Sanz A, Insausti-Delgado A, Ray AM, Bibián C, Helmhold F, Mahmoud WJ, Ortego-Isasa I, López-Larraz E, Lozano Peiteado H, Ramos-Murguialday A. Challenges of neural interfaces for stroke motor rehabilitation. Front Hum Neurosci 2023; 17:1070404. [PMID: 37789905 PMCID: PMC10543821 DOI: 10.3389/fnhum.2023.1070404] [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: 10/14/2022] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
More than 85% of stroke survivors suffer from different degrees of disability for the rest of their lives. They will require support that can vary from occasional to full time assistance. These conditions are also associated to an enormous economic impact for their families and health care systems. Current rehabilitation treatments have limited efficacy and their long-term effect is controversial. Here we review different challenges related to the design and development of neural interfaces for rehabilitative purposes. We analyze current bibliographic evidence of the effect of neuro-feedback in functional motor rehabilitation of stroke patients. We highlight the potential of these systems to reconnect brain and muscles. We also describe all aspects that should be taken into account to restore motor control. Our aim with this work is to help researchers designing interfaces that demonstrate and validate neuromodulation strategies to enforce a contingent and functional neural linkage between the central and the peripheral nervous system. We thus give clues to design systems that can improve or/and re-activate neuroplastic mechanisms and open a new recovery window for stroke patients.
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Affiliation(s)
- Carmen Vidaurre
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Ikerbasque Science Foundation, Bilbao, Spain
| | | | | | | | - Andreas M. Ray
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Carlos Bibián
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Florian Helmhold
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Wala J. Mahmoud
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Iñaki Ortego-Isasa
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
| | - Eduardo López-Larraz
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Bitbrain, Zaragoza, Spain
| | | | - Ander Ramos-Murguialday
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
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18
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García-Peña P, Ramos M, López JM, Martinez-Murillo R, de Arcas G, Gonzalez-Nieto D. Preclinical examination of early-onset thalamic-cortical seizures after hemispheric stroke. Epilepsia 2023; 64:2499-2514. [PMID: 37277947 DOI: 10.1111/epi.17675] [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: 11/29/2022] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 06/07/2023]
Abstract
OBJECTIVE Ischemic stroke is one of the main causes of death and disability worldwide and currently has limited treatment options. Electroencephalography (EEG) signals are significantly affected in stroke patients during the acute stage. In this study, we preclinically characterized the brain electrical rhythms and seizure activity during the hyperacute and late acute phases in a hemispheric stroke model with no reperfusion. METHODS EEG signals and seizures were studied in a model of hemispheric infarction induced by permanent occlusion of the middle cerebral artery (pMCAO), which mimics the clinical condition of stroke patients with permanent ischemia. Electrical brain activity was also examined using a photothrombotic (PT) stroke model. In the PT model, we induced a similar (PT group-1) or smaller (PT group-2) cortical lesion than in the pMCAO model. For all models, we used a nonconsanguineous mouse strain that mimics human diversity and genetic variation. RESULTS The pMCAO hemispheric stroke model exhibited thalamic-origin nonconvulsive seizures during the hyperacute stage that propagated to the thalamus and cortex. The seizures were also accompanied by progressive slowing of the EEG signal during the acute phase, with elevated delta/theta, delta/alpha, and delta/beta ratios. Cortical seizures were also confirmed in the PT stroke model of similar lesions as in the pMCAO model, but not in the PT model of smaller injuries. SIGNIFICANCE In the clinically relevant pMCAO model, poststroke seizures and EEG abnormalities were inferred from recordings of the contralateral hemisphere (noninfarcted hemisphere), emphasizing the reciprocity of interhemispheric connections and that injuries affecting one hemisphere had consequences for the other. Our results recapitulate many of the EEG signal hallmarks seen in stroke patients, thereby validating this specific mouse model for the examination of the mechanistic aspects of brain function and for the exploration of the reversion or suppression of EEG abnormalities in response to neuroprotective and anti-epileptic therapies.
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Affiliation(s)
- Pablo García-Peña
- Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Madrid, Spain
| | - Milagros Ramos
- Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Madrid, Spain
- Departamento de Tecnología Fotónica y Bioingeniería, ETSI Telecomunicaciones, Universidad Politécnica de Madrid, Madrid, Spain
- Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Juan M López
- Instrumentation and Applied Acoustics Research Group (I2A2), Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Guillermo de Arcas
- Instrumentation and Applied Acoustics Research Group (I2A2), Universidad Politécnica de Madrid, Madrid, Spain
- Departamento de Ingeniería Mecánica, ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
- Laboratorio de Neuroacústica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Daniel Gonzalez-Nieto
- Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Madrid, Spain
- Departamento de Tecnología Fotónica y Bioingeniería, ETSI Telecomunicaciones, Universidad Politécnica de Madrid, Madrid, Spain
- Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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19
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Ferreira LO, de Souza RD, Teixeira LL, Pinto LC, Rodrigues JCM, Martins-Filho AJ, da Costa ET, Hamoy M, Lopes DCF. The GPER1 agonist G1 reduces brain injury and improves the qEEG and behavioral outcome of experimental ischemic stroke. J Neuropathol Exp Neurol 2023; 82:787-797. [PMID: 37558387 DOI: 10.1093/jnen/nlad061] [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: 08/11/2023] Open
Abstract
Stroke is one of the principal cerebrovascular diseases in human populations and contributes to a majority of the functional impairments in the elderly. Recent discoveries have led to the inclusion of electroencephalography (EEG) in the complementary prognostic evaluation of patients. The present study describes the EEG, behavioral, and histological changes that occur following cerebral ischemia associated with treatment by G1, a potent and selective G protein-coupled estrogen receptor 1 (GPER1) agonist in a rat model. Treatment with G1 attenuated the neurological deficits induced by ischemic stroke from the second day onward, and reduced areas of infarction. Treatment with G1 also improved the total brainwave power, as well as the theta and alpha wave activity, specifically, and restored the delta band power to levels similar to those observed in the controls. Treatment with G1 also attenuated the peaks of harmful activity observed in the EEG indices. These improvements in brainwave activity indicate that GPER1 plays a fundamental role in the mediation of cerebral injury and in the behavioral outcome of ischemic brain injuries, which points to treatment with G1 as a potential pharmacological strategy for the therapy of stroke.
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Affiliation(s)
- Luan Oliveira Ferreira
- Laboratory of Experimental Neuropathology, Joao de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | - Rafael Dias de Souza
- Laboratory of Experimental Neuropathology, Joao de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | - Leonan Lima Teixeira
- Laboratory of Experimental Neuropathology, Joao de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | - Laine Celestino Pinto
- Laboratory of Experimental Neuropathology, Joao de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | - Joao Cleiton Martins Rodrigues
- Laboratory of Experimental Neuropathology, Joao de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | | | - Edmar Tavares da Costa
- Laboratory of Experimental Neuropathology, Joao de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | - Moisés Hamoy
- Laboratory of Pharmacology and Toxicology of Natural Products, Biological Sciences Institute, Federal University of Pará, Belém, Brazil
| | - Dielly Catrina Favacho Lopes
- Laboratory of Experimental Neuropathology, Joao de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
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20
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Su D, Hu Z, Wu J, Shang P, Luo Z. Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition. Front Neurorobot 2023; 17:1186175. [PMID: 37465413 PMCID: PMC10350518 DOI: 10.3389/fnbot.2023.1186175] [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: 03/14/2023] [Accepted: 06/13/2023] [Indexed: 07/20/2023] Open
Abstract
Stroke is a significant cause of disability worldwide, and stroke survivors often experience severe motor impairments. Lower limb rehabilitation exoskeleton robots provide support and balance for stroke survivors and assist them in performing rehabilitation training tasks, which can effectively improve their quality of life during the later stages of stroke recovery. Lower limb rehabilitation exoskeleton robots have become a hot topic in rehabilitation therapy research. This review introduces traditional rehabilitation assessment methods, explores the possibility of lower limb exoskeleton robots combining sensors and electrophysiological signals to assess stroke survivors' rehabilitation objectively, summarizes standard human-robot coupling models of lower limb rehabilitation exoskeleton robots in recent years, and critically introduces adaptive control models based on motion intent recognition for lower limb exoskeleton robots. This provides new design ideas for the future combination of lower limb rehabilitation exoskeleton robots with rehabilitation assessment, motion assistance, rehabilitation treatment, and adaptive control, making the rehabilitation assessment process more objective and addressing the shortage of rehabilitation therapists to some extent. Finally, the article discusses the current limitations of adaptive control of lower limb rehabilitation exoskeleton robots for stroke survivors and proposes new research directions.
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Affiliation(s)
- Dongnan Su
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhigang Hu
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
- Henan Intelligent Rehabilitation Medical Robot Engineering Research Center, Henan University of Science and Technology, Luoyang, China
| | - Jipeng Wu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Peng Shang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhaohui Luo
- State-Owned Changhong Machinery Factory, Guilin, China
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21
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Ag Lamat MSN, Abd Rahman MSH, Wan Zaidi WA, Yahya WNNW, Khoo CS, Hod R, Tan HJ. Qualitative electroencephalogram and its predictors in the diagnosis of stroke. Front Neurol 2023; 14:1118903. [PMID: 37377856 PMCID: PMC10291181 DOI: 10.3389/fneur.2023.1118903] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 05/16/2023] [Indexed: 06/29/2023] Open
Abstract
Introduction Stroke is a typical medical emergency that carries significant disability and morbidity. The diagnosis of stroke relies predominantly on the use of neuroimaging. Accurate diagnosis is pertinent for management decisions of thrombolysis and/or thrombectomy. Early identification of stroke using electroencephalogram (EEG) in the clinical assessment of stroke has been underutilized. This study was conducted to determine the relevance of EEG and its predictors with the clinical and stroke features. Methods A cross-sectional study was carried out where routine EEG assessment was performed in 206 consecutive acute stroke patients without seizures. The demographic data and clinical stroke assessment were collated using the National Institutes of Health Stroke Scale (NIHSS) score with neuroimaging. Associations between EEG abnormalities and clinical features, stroke characteristics, and NIHSS scores were evaluated. Results The mean age of the study population was 64.32 ± 12 years old, with 57.28% consisting of men. The median NIHSS score on admission was 6 (IQR 3-13). EEG was abnormal in more than half of the patients (106, 51.5%), which consisted of focal slowing (58, 28.2%) followed by generalized slowing (39, 18.9%) and epileptiform changes (9, 4.4%). NIHSS score was significantly associated with focal slowing (13 vs. 5, p < 0.05). Type of stroke and imaging characteristics were significantly associated with EEG abnormalities (p < 0.05). For every increment in NIHSS score, there are 1.08 times likely for focal slowing (OR 1.089; 95% CI 1.033, 1.147, p = 0.002). Anterior circulation stroke has 3.6 times more likely to have abnormal EEG (OR 3.628; 95% CI 1.615, 8.150, p = 0.002) and 4.55 times higher to exhibit focal slowing (OR 4.554; 95% CI 1.922, 10.789, p = 0.01). Conclusion The type of stroke and imaging characteristics are associated with EEG abnormalities. Predictors of focal EEG slowing are NIHSS score and anterior circulation stroke. The study emphasized that EEG is a simple yet feasible investigational tool, and further plans for advancing stroke evaluation should consider the inclusion of this functional modality.
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Affiliation(s)
- Mohd Syahrul Nizam Ag Lamat
- Department of Medicine, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia
- Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia
| | - Muhammad Samir Haziq Abd Rahman
- Department of Medicine, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia
- Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia
| | - Wan Asyraf Wan Zaidi
- Department of Medicine, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia
- Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia
| | - Wan Nur Nafisah Wan Yahya
- Department of Medicine, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia
- Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia
| | - Ching Soong Khoo
- Department of Medicine, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia
- Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia
| | - Rozita Hod
- Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia
- Department of Community Health, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia
| | - Hui Jan Tan
- Department of Medicine, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia
- Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia
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22
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Hu Y, Wang Y, Zhang R, Hu Y, Fang M, Li Z, Shi L, Zhang Y, Zhang Z, Gao J, Zhang L. Assessing stroke rehabilitation degree based on quantitative EEG index and nonlinear parameters. Cogn Neurodyn 2023; 17:661-669. [PMID: 37265653 PMCID: PMC10229519 DOI: 10.1007/s11571-022-09849-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 06/03/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022] Open
Abstract
The assessment of motor function is critical to the rehabilitation of stroke patients. However, commonly used evaluation methods are based on behavior scoring, which lacks neurological indicators that directly reflect the motor function of the brain. The objective of this study was to investigate whether resting-state EEG indicators could improve stroke rehabilitation evaluation. We recruited 68 participants and recorded their resting-state EEG data. According to Brunnstrom stage, the participants were divided into three groups: severe, moderate, and mild. Ten quantitative electroencephalographic (QEEG) and five non-linear parameters of resting-state EEG were calculated for further analysis. Statistical tests were performed, and the genetic algorithm-support vector machine was used to select the best feature combination for classification. We found the QEEG parameters show significant differences in Delta, Alpha1, Alpha2, DAR, and DTABR (P < 0.05) among the three groups. Regarding nonlinear parameters, ApEn, SampEn, Lz, and C0 showed significant differences (P < 0.05). The optimal feature classification combination accuracy rate reached 85.3%. Our research shows that resting-state EEG indicators could be used for stroke rehabilitation evaluation.
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Affiliation(s)
- Yuxia Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Yufei Wang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| | - Rui Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Yubo Hu
- Shenqiu County People’s Hospital, Henan Province, China
| | - Mingzhu Fang
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhe Li
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, China
| | - Yankun Zhang
- Zhengzhou Boone Technology Company, Zhengzhou, China
| | - Zhong Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Jinfeng Gao
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| | - Lipeng Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
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23
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Mo L, Nie Y, Wan G, Zhang Y, Zhao M, Wu J, Wang H, Li Q, Liu A. Application of Transcranial Magnetic Stimulation with Electroencephalography in the Evaluation of Brain Function Changes after Stroke. Int J Clin Pract 2023; 2023:3051175. [PMID: 37265838 PMCID: PMC10232191 DOI: 10.1155/2023/3051175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 03/13/2023] [Accepted: 05/09/2023] [Indexed: 06/03/2023] Open
Abstract
Objective Based on transcranial magnetic stimulation (TMS) with electroencephalography technology, this study analyzed the rehabilitation mechanism of patients' motor function reconstruction and nerve remodeling after stroke. It revealed the function of the cerebral cortex network at a deeper level and established a set of prognostic marker evaluation indicators for the reconstruction of motor function after stroke. Methods Twenty-one patients treated at the Beijing Rehabilitation Hospital of Capital Medical University because of ischemic stroke in the territory supplied by the middle cerebral artery were selected as the experimental group. Neurophysiological evaluation, motor function evaluation, and clinical evaluation were performed 30 and 180 d after the onset of ischemic stroke. In the control group, neurophysiological evaluation was also performed as a reference index to evaluate the changes in cortical patterns after stroke. Results The brain topographic map showed the changes in energy or power spectral density (PSD) at 1,000 ms after stimulation as compared with before stimulation, but no difference was detected in these patients. The time-frequency analysis showed that when the left primary motor cortex (M1) area was stimulated using TMS, the PSD values of the left and right M1 and posterior occipital cortex areas produced an 8-40 Hz wave band in patients S1-S11. There was no significant energy change in patients S12-S16. Conclusions For patients with different injury types, degrees of injury, and different onset periods, individualized intervention methods should be adopted. The evaluation methods should be as diverse as possible, and the rehabilitation effects of patients should be assessed from multiple perspectives to avoid the limitations of single factors. Possible mechanism: After brain injury, the nervous system can change its structure and function through different ways and maintain it for a certain period of time. This plasticity change will change with the course of the disease.
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Affiliation(s)
- Linhong Mo
- Neuro-Rehabilitation Center, Capital Medical University, Beijing Rehabilitation Hospital, Beijing 100144, China
| | - Yiqiu Nie
- Neuro-Rehabilitation Center, Capital Medical University, Beijing Rehabilitation Hospital, Beijing 100144, China
| | - Guiling Wan
- Neuro-Rehabilitation Center, Capital Medical University, Beijing Rehabilitation Hospital, Beijing 100144, China
| | - Yingbin Zhang
- Neuro-Rehabilitation Center, Capital Medical University, Beijing Rehabilitation Hospital, Beijing 100144, China
| | - Man Zhao
- Neuro-Rehabilitation Center, Capital Medical University, Beijing Rehabilitation Hospital, Beijing 100144, China
| | - Jiaojiao Wu
- Neuro-Rehabilitation Center, Capital Medical University, Beijing Rehabilitation Hospital, Beijing 100144, China
| | - Huiqi Wang
- Neuro-Rehabilitation Center, Capital Medical University, Beijing Rehabilitation Hospital, Beijing 100144, China
| | - Qing Li
- Neuro-Rehabilitation Center, Capital Medical University, Beijing Rehabilitation Hospital, Beijing 100144, China
| | - Aixian Liu
- Neuro-Rehabilitation Center, Capital Medical University, Beijing Rehabilitation Hospital, Beijing 100144, China
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24
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Yadav H, Maini S. Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-45. [PMID: 37362726 PMCID: PMC10157593 DOI: 10.1007/s11042-023-15653-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 07/17/2022] [Accepted: 04/22/2023] [Indexed: 06/28/2023]
Abstract
Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.
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Affiliation(s)
- Hitesh Yadav
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, Punjab India
| | - Surita Maini
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, Punjab India
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Hua X, Li J, Wang T, Wang J, Pi S, Li H, Xi X. Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10530-10551. [PMID: 37322947 DOI: 10.3934/mbe.2023465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Changes in the functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation. To quantify changes in functional connections between the cerebral cortex and muscles, we combined corticomuscular coupling and graph theory to propose dynamic time warped (DTW) distances for electroencephalogram (EEG) and electromyography (EMG) signals as well as two new symmetry metrics. EEG and EMG data from 18 stroke patients and 16 healthy individuals, as well as Brunnstrom scores from stroke patients, were recorded in this paper. First, calculate DTW-EEG, DTW-EMG, BNDSI and CMCSI. Then, the random forest algorithm was used to calculate the feature importance of these biological indicators. Finally, based on the results of feature importance, different features were combined and validated for classification. The results showed that the feature importance was from high to low as CMCSI/BNDSI/DTW-EEG/DTW-EMG, while the feature combination with the highest accuracy was CMCSI+BNDSI+DTW-EEG. Compared to previous studies, combining the CMCSI+BNDSI+DTW-EEG features of EEG and EMG achieved better results in the prediction of motor function rehabilitation at different levels of stroke. Our work implies that the establishment of a symmetry index based on graph theory and cortical muscle coupling has great potential in predicting stroke recovery and promises to have an impact on clinical research applications.
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Affiliation(s)
- Xian Hua
- Jinhua People's Hospital, Jinhua 321000, China
| | - Jing Li
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
| | - Ting Wang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
| | - Junhong Wang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
| | - Shaojun Pi
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
| | - Hangcheng Li
- Hangzhou Mingzhou Naokang Rehabilitation Hospital, Hangzhou 311215, China
| | - Xugang Xi
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
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Maura RM, Rueda Parra S, Stevens RE, Weeks DL, Wolbrecht ET, Perry JC. Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability. J Neuroeng Rehabil 2023; 20:21. [PMID: 36793077 PMCID: PMC9930366 DOI: 10.1186/s12984-023-01142-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 01/19/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Significant clinician training is required to mitigate the subjective nature and achieve useful reliability between measurement occasions and therapists. Previous research supports that robotic instruments can improve quantitative biomechanical assessments of the upper limb, offering reliable and more sensitive measures. Furthermore, combining kinematic and kinetic measurements with electrophysiological measurements offers new insights to unlock targeted impairment-specific therapy. This review presents common methods for analyzing biomechanical and neuromuscular data by describing their validity and reporting their reliability measures. METHODS This paper reviews literature (2000-2021) on sensor-based measures and metrics for upper-limb biomechanical and electrophysiological (neurological) assessment, which have been shown to correlate with clinical test outcomes for motor assessment. The search terms targeted robotic and passive devices developed for movement therapy. Journal and conference papers on stroke assessment metrics were selected using PRISMA guidelines. Intra-class correlation values of some of the metrics are recorded, along with model, type of agreement, and confidence intervals, when reported. RESULTS A total of 60 articles are identified. The sensor-based metrics assess various aspects of movement performance, such as smoothness, spasticity, efficiency, planning, efficacy, accuracy, coordination, range of motion, and strength. Additional metrics assess abnormal activation patterns of cortical activity and interconnections between brain regions and muscle groups; aiming to characterize differences between the population who had a stroke and the healthy population. CONCLUSION Range of motion, mean speed, mean distance, normal path length, spectral arc length, number of peaks, and task time metrics have all demonstrated good to excellent reliability, as well as provide a finer resolution compared to discrete clinical assessment tests. EEG power features for multiple frequency bands of interest, specifically the bands relating to slow and fast frequencies comparing affected and non-affected hemispheres, demonstrate good to excellent reliability for populations at various stages of stroke recovery. Further investigation is needed to evaluate the metrics missing reliability information. In the few studies combining biomechanical measures with neuroelectric signals, the multi-domain approaches demonstrated agreement with clinical assessments and provide further information during the relearning phase. Combining the reliable sensor-based metrics in the clinical assessment process will provide a more objective approach, relying less on therapist expertise. This paper suggests future work on analyzing the reliability of metrics to prevent biasedness and selecting the appropriate analysis.
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Affiliation(s)
- Rene M. Maura
- Mechanical Engineering Department, University of Idaho, Moscow, ID USA
| | | | - Richard E. Stevens
- Engineering and Physics Department, Whitworth University, Spokane, WA USA
| | - Douglas L. Weeks
- College of Medicine, Washington State University, Spokane, WA USA
| | - Eric T. Wolbrecht
- Mechanical Engineering Department, University of Idaho, Moscow, ID USA
| | - Joel C. Perry
- Mechanical Engineering Department, University of Idaho, Moscow, ID USA
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27
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Motolese F, Lanzone J, Todisco A, Rossi M, Santoro F, Cruciani A, Capone F, Di Lazzaro V, Pilato F. The role of neurophysiological tools in the evaluation of ischemic stroke evolution: a narrative review. Front Neurol 2023; 14:1178408. [PMID: 37181549 PMCID: PMC10172480 DOI: 10.3389/fneur.2023.1178408] [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: 03/02/2023] [Accepted: 03/23/2023] [Indexed: 05/16/2023] Open
Abstract
Ischemic stroke is characterized by a complex cascade of events starting from vessel occlusion. The term "penumbra" denotes the area of severely hypo-perfused brain tissue surrounding the ischemic core that can be potentially recovered if blood flow is reestablished. From the neurophysiological perspective, there are local alterations-reflecting the loss of function of the core and the penumbra-and widespread changes in neural networks functioning, since structural and functional connectivity is disrupted. These dynamic changes are closely related to blood flow in the affected area. However, the pathological process of stroke does not end after the acute phase, but it determines a long-term cascade of events, including changes of cortical excitability, that are quite precocious and might precede clinical evolution. Neurophysiological tools-such as Transcranial Magnetic Stimulation (TMS) or Electroencephalography (EEG)-have enough time resolution to efficiently reflect the pathological changes occurring after stroke. Even if they do not have a role in acute stroke management, EEG and TMS might be helpful for monitoring ischemia evolution-also in the sub-acute and chronic stages. The present review aims to describe the changes occurring in the infarcted area after stroke from the neurophysiological perspective, starting from the acute to the chronic phase.
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Affiliation(s)
- Francesco Motolese
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
- *Correspondence: Francesco Motolese,
| | - Jacopo Lanzone
- Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation Unit of Milan Institute, Milan, Italy
| | - Antonio Todisco
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Mariagrazia Rossi
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Francesca Santoro
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Alessandro Cruciani
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Fioravante Capone
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Vincenzo Di Lazzaro
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Fabio Pilato
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
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28
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Xu J, Wang J, Wu H, Han F, Wang Q, Jiang Y, Chen R. Effects of severe obstructive sleep apnea on functional prognosis in the acute phase of ischemic stroke and quantitative electroencephalographic markers. Sleep Med 2023; 101:452-460. [PMID: 36516602 DOI: 10.1016/j.sleep.2022.11.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/07/2022]
Abstract
OBJECTIVE To investigate the effect of severe obstructive sleep apnea (OSA) on functional prognosis in the acute phase and quantitative electroencephalography (EEG) markers during sleep in ischemic stroke patients. METHODS This study included 125 mild-to-moderate acute ischemic stroke patients with OSA who underwent polysomnography (PSG) within one week of stroke onset between January 2015 and June 2020. Patients were grouped according to their apnea-hypopnea index (</≥ 30/h). Poor functional prognosis was defined as modified Rankin Scale score ≥3. The EEG recorded by PSG was extracted during different sleep stages for power spectrum analysis. The delta/alpha power ratio (DAR), (delta + theta)/(alpha + beta) ratio (the slowing ratio, TSR), and the relative power (RP) of each frequency band were calculated. Differences in clinical, PSG, and quantitative EEG characteristics were compared between the groups. Additionally, we explored predictors of poor functional prognosis. RESULTS Patients with severe OSA had a higher proportion of hypertension, lower relative power of high-frequency bands, and higher delta RP, TSR, and DAR (p < 0.05). Severe OSA was associated with a 3.6-fold increase in risk of poor prognosis (p < 0.05). Increased delta RP and TSR, as well as decreased alpha, beta, and sigma RP, may be independent predictors of a poor functional prognosis. CONCLUSIONS Severe OSA is an independent risk factor for a poor functional prognosis in patients with acute ischemic stroke, and quantitative EEG during sleep showed a significant slow wave enhancement, suggesting more severe brain dysfunction. The treatment of severe OSA may improve functional prognosis.
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Affiliation(s)
- Juan Xu
- Department of Respiratory Medicine, Sleep Center, The Second Affiliated Hospital of Soochow University, Soochow University, Suzhou, China; Department of Respiratory Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng City, Yancheng, China
| | - Jianhua Wang
- Department of Respiratory Medicine, Sleep Center, The Second Affiliated Hospital of Soochow University, Soochow University, Suzhou, China; Department of Respiratory Medicine, Zigong Third People's Hospital, Zigong, China
| | - Huaman Wu
- Department of Respiratory Medicine, Sleep Center, The Second Affiliated Hospital of Soochow University, Soochow University, Suzhou, China
| | - Fei Han
- Department of Respiratory Medicine, Sleep Center, The Second Affiliated Hospital of Soochow University, Soochow University, Suzhou, China
| | - Qiaojun Wang
- Department of Respiratory Medicine, Sleep Center, The Second Affiliated Hospital of Soochow University, Soochow University, Suzhou, China
| | - Yongqian Jiang
- Department of Respiratory Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng City, Yancheng, China.
| | - Rui Chen
- Department of Respiratory Medicine, Sleep Center, The Second Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
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Johnston PR, McIntosh AR, Meltzer JA. Spectral slowing in chronic stroke reflects abnormalities in both periodic and aperiodic neural dynamics. Neuroimage Clin 2023; 37:103277. [PMID: 36495856 PMCID: PMC9758570 DOI: 10.1016/j.nicl.2022.103277] [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: 09/16/2022] [Revised: 11/21/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022]
Abstract
Decades of electrophysiological work have demonstrated the presence of "spectral slowing" in stroke patients - a prominent shift in the power spectrum towards lower frequencies, most evident in the vicinity of the lesion itself. Despite the reliability of this slowing as a marker of dysfunctional tissue across patient groups as well as animal models, it has yet to be explained in terms of the pathophysiological processes of stroke. To do so requires clear understanding of the neural dynamics that these differences represent, acknowledging the often overlooked fact that spectral power reflects more than just the amplitude of neural oscillations. To accomplish this, we used a combination of frequency domain and time domain measures to disambiguate and quantify periodic (oscillatory) and aperiodic (non-oscillatory) neural dynamics in resting state magnetoencephalography (MEG) recordings from chronic stroke patients. We found that abnormally elevated low frequency power in these patients was best explained by a steepening of the aperiodic component of the power spectrum, rather than an enhancement of low frequency oscillations, as is often assumed. However, genuine oscillatory activity at higher frequencies was also found to be abnormal, with patients showing alpha slowing and diminished oscillatory activity in the beta band. These aperiodic and periodic abnormalities were found to covary, and could be detected even in the un-lesioned hemisphere, however they were most prominent in perilesional tissue, where their magnitude was predictive of cognitive impairment. This work redefines spectral slowing as a pattern of changes involving both aperiodic and periodic neural dynamics and narrows the gap in understanding between non-invasive markers of dysfunctional tissue and disease processes responsible for altered neural dynamics.
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Affiliation(s)
- Phillip R Johnston
- Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada; Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada.
| | - Anthony R McIntosh
- Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Drive E K9625, Burnaby, BC V5A 1S6, Canada; Institute for Neuroscience and Neurotechnology, Simon Fraser University, 8888 University Drive E K9625, Burnaby, BC V5A 1S6, Canada
| | - Jed A Meltzer
- Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada; Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada; Department of Speech-Language Pathology, University of Toronto, 500 University Avenue, Toronto, ON M5G 1V7, Canada
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Livinț Popa L, Chira D, Dăbală V, Hapca E, Popescu BO, Dina C, Cherecheș R, Strilciuc Ș, Mureșanu DF. Quantitative EEG as a Biomarker in Evaluating Post-Stroke Depression. Diagnostics (Basel) 2022; 13:diagnostics13010049. [PMID: 36611341 PMCID: PMC9818970 DOI: 10.3390/diagnostics13010049] [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: 11/07/2022] [Revised: 12/14/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Introduction: Post-stroke depression (PSD) has complex pathophysiology determined by various biological and psychological factors. Although it is a long-term complication of stroke, PSD is often underdiagnosed. Given the diagnostic role of quantitative electroencephalography (qEEG) in depression, it was investigated whether a possible marker of PSD could be identified by observing the evolution of the (Delta + Theta)/(Alpha + Beta) Ratio (DTABR), respectively the Delta/Alpha Ratio (DAR) values in post-stroke depressed patients (evaluated through the HADS-D subscale). Methods: The current paper analyzed the data of 57 patients initially selected from a randomized control trial (RCT) that assessed the role of N-Pep 12 in stroke rehabilitation. EEG recordings from the original trial database were analyzed using signal processing techniques, respecting the conditions (eyes open, eyes closed), and several cognitive tasks. Results: We observed two significant associations between the DTABR values and the HADS-D scores of post-stroke depressed patients for each of the two visits (V1 and V2) of the N-Pep 12 trial. We recorded the relationships in the Global (V1 = 30 to 120 days after stroke) and Frontal Extended (V2 = 90 days after stroke) regions during cognitive tasks that trained attention and working memory. For the second visit, the association between the analyzed variables was negative. Conclusions: As both our relationships were described during the cognitive condition, we can state that the neural networks involved in processing attention and working memory might go through a reorganization process one to four months after the stroke onset. After a period longer than six months, the process could localize itself at the level of frontal regions, highlighting a possible divergence between the local frontal dynamics and the subjective well-being of stroke survivors. QEEG parameters linked to stroke progression evolution (like DAR or DTABR) can facilitate the identification of the most common neuropsychiatric complication in stroke survivors.
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Affiliation(s)
- Livia Livinț Popa
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Diana Chira
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Correspondence:
| | - Victor Dăbală
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Elian Hapca
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Bogdan Ovidiu Popescu
- Department of Neuroscience, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania
| | - Constantin Dina
- Faculty of Medicine, Ovidius University, 900527 Constanta, Romania
| | - Răzvan Cherecheș
- Department of Public Health, Babes-Bolyai University, 400294 Cluj-Napoca, Romania
| | - Ștefan Strilciuc
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Dafin F. Mureșanu
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
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Islam MS, Hussain I, Rahman MM, Park SJ, Hossain MA. Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249859. [PMID: 36560227 PMCID: PMC9782764 DOI: 10.3390/s22249859] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/30/2022] [Accepted: 12/14/2022] [Indexed: 05/07/2023]
Abstract
State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are considered "black boxes," because there is no explanation for the decisions made by these models. Users may find it challenging to comprehend and interpret the results. Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. Electroencephalography (EEG) is a potential predictive tool for understanding cortical impairment caused by an ischemic stroke and can be utilized for acute stroke prediction, neurologic prognosis, and post-stroke treatment. This study aims to utilize ML models to classify the ischemic stroke group and the healthy control group for acute stroke prediction in active states. Moreover, XAI tools (Eli5 and LIME) were utilized to explain the behavior of the model and determine the significant features that contribute to stroke prediction models. In this work, we studied 48 patients admitted to a hospital with acute ischemic stroke and 75 healthy adults who had no history of identified other neurological illnesses. EEG was obtained within three months following the onset of ischemic stroke symptoms using frontal, central, temporal, and occipital cortical electrodes (Fz, C1, T7, Oz). EEG data were collected in an active state (walking, working, and reading tasks). In the results of the ML approach, the Adaptive Gradient Boosting models showed around 80% accuracy for the classification of the control group and the stroke group. Eli5 and LIME were utilized to explain the behavior of the stroke prediction model and interpret the model locally around the prediction. The Eli5 and LIME interpretable models emphasized the spectral delta and theta features as local contributors to stroke prediction. From the findings of this explainable AI research, it is expected that the stroke-prediction XAI model will help with post-stroke treatment and recovery, as well as help healthcare professionals, make their diagnostic decisions more explainable.
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Affiliation(s)
- Mohammed Saidul Islam
- Network and Data Analysis Group, Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh
| | - Iqram Hussain
- Department of Biomedical Engineering, Medical Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
- Data Mind Ltd., Dhaka 1230, Bangladesh
- Correspondence: or (I.H.); (M.A.H.)
| | - Md Mezbaur Rahman
- Network and Data Analysis Group, Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh
| | - Se Jin Park
- AI-Based Healthcare Research Group, Sewon Intelligence Ltd., Seoul 04512, Republic of Korea
| | - Md Azam Hossain
- Network and Data Analysis Group, Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh
- Correspondence: or (I.H.); (M.A.H.)
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Cui Z, Li Y, Huang S, Wu X, Fu X, Liu F, Wan X, Wang X, Zhang Y, Qiu H, Chen F, Yang P, Zhu S, Li J, Chen W. BCI system with lower-limb robot improves rehabilitation in spinal cord injury patients through short-term training: a pilot study. Cogn Neurodyn 2022; 16:1283-1301. [PMID: 36408074 PMCID: PMC9666612 DOI: 10.1007/s11571-022-09801-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 07/27/2021] [Accepted: 11/04/2021] [Indexed: 12/27/2022] Open
Abstract
In the recent years, the increasing applications of brain-computer interface (BCI) in rehabilitation programs have enhanced the chances of functional recovery for patients with neurological disorders. We presented and validated a BCI system with a lower-limb robot for short-term training of patients with spinal cord injury (SCI). The cores of this system included: (1) electroencephalogram (EEG) features related to motor intention reported through experiments and used to drive the robot; (2) a decision tree to determine the training mode provided for patients with different degrees of injuries. Seven SCI patients (one American Spinal Injury Association Impairment Scale (AIS) A, three AIS B, and three AIS C) participated in the short-term training with this system. All patients could learn to use the system rapidly and maintained a high intensity during the training program. The strength of the lower limb key muscles of the patients was improved. Four AIS A/B patients were elevated to AIS C. The cumulative results indicate that clinical application of the BCI system with lower-limb robot is feasible and safe, and has potentially positive effects on SCI patients. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09801-6.
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Affiliation(s)
- Zhengzhe Cui
- School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Yongqiang Li
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Sisi Huang
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xixi Wu
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangxiang Fu
- School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Fei Liu
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaojiao Wan
- School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Xue Wang
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yuting Zhang
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Huaide Qiu
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fang Chen
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Peijin Yang
- School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Shiqiang Zhu
- School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Jianan Li
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Weidong Chen
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
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Kopańska M, Dejnowicz-Velitchkov A, Bartman P, Szczygielski J. MiniQEEG and Neurofeedback in Diagnosis and Treatment of COVID-19-Related Panic Attacks: A Case Report. Brain Sci 2022; 12:1541. [PMID: 36421865 PMCID: PMC9688264 DOI: 10.3390/brainsci12111541] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Both the global COVID-19 pandemic situation, as well as the current political situation in Eastern Europe may exacerbate anxiety and contribute to stress-related disorders such as panic disorder. Electroencephalography (EEG)-based neurofeedback provides both assessment of the subject's brainwave activity as well as the possibility of its therapeutic correction. It is possible that it can be implemented as an auxiliary treatment in panic disorders of different origin. The aim of this feasibility study was to demonstrate (both short- and long-term) effectiveness of neurofeedback therapy in a patient with previously diagnosed panic attacks, related to fear of COVID-19 infection. METHODS We report the case study of a 47-year-old man affected by panic attacks, related to his profound, constant fear of COVID-19 infection and its sequelae. For the initial diagnostic workup, several clinical and research tools were used: 1. Baseline psychological exam; 2. Anxiety-targeted interview performed by miniQEEG therapist; 3. Analysis of previous clinical test results (EEG record/lab blood test); and 4. The miniQEEG exam (central strip recording Cz-C3-C4), The patient was subjected to regular EEG Neurofeedback sessions for two consecutive months. After completing the treatment, follow-up tests, as listed above were repeated immediately after completing the whole treatment program, as well as 1 and 2 years later. MiniQEEG results were compared with healthy control (age-matched male subject not affected with panic attacks) and evaluated over the time that the subject was involved in the study. RESULTS Initially, the patient was suffering from severe panic attacks accompanied by vegetative symptoms and from destructive and negative thoughts. After 8 consecutive weeks of treatment encompassing sixteen QEEG neurofeedback training sessions (each lasting 30 min), a subjective improvement of his complaints was reported. More importantly, QEEG records of the patient also improved, approximating the pattern of QEEG recorded in the healthy control. CONCLUSION In this single case-based feasibility analysis, we demonstrate that systematic application of QEEG-Neurofeedback may result in manifest and durable therapeutic effect. Of note, use of this treatment may be a valuable option for patients with panic attack/panic disorder, especially if related to the psychological burden of the COVID-19/war era. Future studies on a larger patient population, especially with a longitudinal/prospective design, are warranted.
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Affiliation(s)
- Marta Kopańska
- Department of Pathophysiology, Institute of Medical Sciences, University of Rzeszow, 35-959 Rzeszow, Poland
| | | | - Paulina Bartman
- Students Science Club “Reh-Tech”, Institute of Medical Sciences, University of Rzeszow, 35-959 Rzeszow, Poland
| | - Jacek Szczygielski
- Department of Neurosurgery, Institute of Medical Sciences, University of Rzeszow, 35-959 Rzeszow, Poland
- Department of Neurosurgery, Faculty of Medicine, Saarland University, 66123 Saarbrücken, Germany
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Sato Y, Schmitt O, Ip Z, Rabiller G, Omodaka S, Tominaga T, Yazdan-Shahmorad A, Liu J. Pathological changes of brain oscillations following ischemic stroke. J Cereb Blood Flow Metab 2022; 42:1753-1776. [PMID: 35754347 PMCID: PMC9536122 DOI: 10.1177/0271678x221105677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 04/01/2022] [Accepted: 05/17/2022] [Indexed: 11/16/2022]
Abstract
Brain oscillations recorded in the extracellular space are among the most important aspects of neurophysiology data reflecting the activity and function of neurons in a population or a network. The signal strength and patterns of brain oscillations can be powerful biomarkers used for disease detection and prediction of the recovery of function. Electrophysiological signals can also serve as an index for many cutting-edge technologies aiming to interface between the nervous system and neuroprosthetic devices and to monitor the efficacy of boosting neural activity. In this review, we provided an overview of the basic knowledge regarding local field potential, electro- or magneto- encephalography signals, and their biological relevance, followed by a summary of the findings reported in various clinical and experimental stroke studies. We reviewed evidence of stroke-induced changes in hippocampal oscillations and disruption of communication between brain networks as potential mechanisms underlying post-stroke cognitive dysfunction. We also discussed the promise of brain stimulation in promoting post stroke functional recovery via restoring neural activity and enhancing brain plasticity.
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Affiliation(s)
- Yoshimichi Sato
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurological Surgery, SFVAMC, San Francisco, CA, USA
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Oliver Schmitt
- Department of Anatomy, Medical School Hamburg, University of Applied Sciences and Medical University, Hamburg, Germany
| | - Zachary Ip
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Gratianne Rabiller
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurological Surgery, SFVAMC, San Francisco, CA, USA
| | - Shunsuke Omodaka
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurological Surgery, SFVAMC, San Francisco, CA, USA
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Teiji Tominaga
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Azadeh Yazdan-Shahmorad
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Jialing Liu
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurological Surgery, SFVAMC, San Francisco, CA, USA
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Corominas-Teruel X, Mozo RMSS, Simó MF, Colomina Fosch MT, Valero-Cabré A. Transcranial direct current stimulation for gait recovery following stroke: A systematic review of current literature and beyond. Front Neurol 2022; 13:953939. [PMID: 36158971 PMCID: PMC9490093 DOI: 10.3389/fneur.2022.953939] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/02/2022] [Indexed: 11/21/2022] Open
Abstract
Background Over the last decade, transcranial direct current stimulation (tDCS) has set promise contributing to post-stroke gait rehabilitation. Even so, results are still inconsistent due to low sample size, heterogeneity of samples, and tDCS design differences preventing comparability. Nonetheless, updated knowledge in post-stroke neurophysiology and stimulation technologies opens up opportunities to massively improve treatments. Objective The current systematic review aims to summarize the current state-of-the-art on the effects of tDCS applied to stroke subjects for gait rehabilitation, discuss tDCS strategies factoring individual subject profiles, and highlight new promising strategies. Methods MEDLINE, SCOPUS, CENTRAL, and CINAHL were searched for stroke randomized clinical trials using tDCS for the recovery of gait before 7 February 2022. In order to provide statistical support to the current review, we analyzed the achieved effect sizes and performed statistical comparisons. Results A total of 24 records were finally included in our review, totaling n = 651 subjects. Detailed analyses revealed n = 4 (17%) studies with large effect sizes (≥0.8), n = 6 (25%) studies with medium ones (≥0.5), and n = 6 (25%) studies yielding low effects sizes (≤ 0.2). Statistically significant negative correlations (rho = −0.65, p = 0.04) and differences (p = 0.03) argued in favor of tDCS interventions in the sub-acute phase. Finally, significant differences (p = 0.03) were argued in favor of a bifocal stimulation montage (anodal M1 ipsilesional and cathodal M1 contralesional) with respect to anodal ipsilesional M1. Conclusion Our systematic review highlights the potential of tDCS to contribute to gait recovery following stroke, although also the urgent need to improve current stimulation strategies and subject-customized interventions considering stroke severity, type or time-course, and the use of network-based multifocal stimulation approaches guided by computational biophysical modeling. Systematic review registration PROSPERO: CRD42021256347.
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Affiliation(s)
- Xavier Corominas-Teruel
- Department of Psychology, Neurobehavior and Health Research Group (NEUROLAB), Universitat Rovira i Virgili, Tarragona, Spain
- Cerebral Dynamics, Plasticity and Rehabilitation Group, Institut du Cerveau et de la Moelle Epinière, CNRS UMR 7225, Paris, France
| | | | - Montserrat Fibla Simó
- Rehabilitation and Physical Medicine Department, Hospital Universitari Joan XXIII, Tarragona, Spain
| | - Maria Teresa Colomina Fosch
- Department of Psychology, Neurobehavior and Health Research Group (NEUROLAB), Universitat Rovira i Virgili, Tarragona, Spain
- *Correspondence: Antoni Valero-Cabré
| | - Antoni Valero-Cabré
- Cerebral Dynamics, Plasticity and Rehabilitation Group, Institut du Cerveau et de la Moelle Epinière, CNRS UMR 7225, Paris, France
- Cognitive Neuroscience and Information Tech. Research Program, Open University of Catalonia (UOC), Barcelona, Spain
- Department of Anatomy and Neurobiology, Laboratory of Cerebral Dynamics, Boston University School of Medicine, Boston, MA, United States
- Maria Teresa Colomina Fosch
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Zhang N, Chen F, Xie X, Xie Z, Hong D, Li J, Ouyang T. Application of quantitative EEG in acute ischemic stroke patients who underwent thrombectomy: A comparison with CT perfusion. Clin Neurophysiol 2022; 141:24-33. [PMID: 35809546 DOI: 10.1016/j.clinph.2022.06.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 05/22/2022] [Accepted: 06/02/2022] [Indexed: 12/30/2022]
Abstract
OBJECTIVE This study aimed to evaluate the predictive value of quantitative electroencephalography (QEEG) in the outcome of patients with acute ischemic stroke (AIS) who underwent mechanical thrombectomy (MT) and to assess the correlation between clinical outcome and QEEG and CT perfusion (CTP) data. METHODS Twenty-nine MT patients were included in this prospective study. Continuous electroencephalography (EEG) monitoring was performed, in which delta power, the δ/α ratio (DAR), and the (θ + δ)/(α + β) ratio (DTABR) were calculated. The clinical scores at different points were recorded. Based on the modified Ranking scale, the patients were divided into good and poor outcome groups. Several CTP parameters were recorded before MT. The correlation between QEEG, CTP parameters, and clinical scores was analyzed using the Spearman correlation analysis. The predictive value of QEEG indices and CTP parameters for the 3-month outcome was compared using the receiver operating characteristic (ROC) curve. RESULTS Delta power except for 7 days after MT, DAR, DATBR, and several CTP parameters were all significantly associated with the clinical scores. Although some CTP parameters were associated with the clinical scores, they were less powerful than QEEG in predicting a good or poor outcome at 3 months. Among the different explored EEG indicators, the predictive value of delta 24 h after MT was the highest. CONCLUSIONS QEEG indices may have a certain predictive value for the outcome of AIS patients who underwent MT. SIGNIFICANCE QEEG may become a new prognostic tool in AIS patients who underwent MT, facilitating the planning and management of related rehabilitation plans.
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Affiliation(s)
- Na Zhang
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Jiangxi Province, China
| | - Fangmei Chen
- Department of the First People's Hospital of Jingdezhen, Jiangxi Province, China
| | - Xufang Xie
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Jiangxi Province, China
| | - Zunchun Xie
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Jiangxi Province, China
| | - Daojun Hong
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Jiangxi Province, China
| | - Jun Li
- Department of the Second Clinical Medical College of Nanchang University, Jiangxi Province, China.
| | - Taohui Ouyang
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Jiangxi Province, China.
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Effect of transcranial direct-current stimulation on executive function and resting EEG after stroke: A pilot randomized controlled study. J Clin Neurosci 2022; 103:141-147. [PMID: 35872448 DOI: 10.1016/j.jocn.2022.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 05/28/2022] [Accepted: 07/12/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND The effects of transcranial direct current stimulation (tDCS) on post-stroke executive impairment (PSEI) remain controversial. Resting stateelectroencephalogram (EEG) can assist in the diagnosis and assessment of executive dysfunction. OBJECTIVES We aimed to use EEG to explore the effect of tDCS on executive function among stroke patients. METHODS Twenty-four patients with PSEI were randomly divided into experimental and control groups, which received real and sham stimulation, respectively. Anodal electrical stimulation was applied to the left dorsolateral prefrontal lobe (F3). The stimulation intensity was 2 mA for 20 min once daily for 7 days. Executive function was monitored using neuropsychological scales. RESULTS The experimental group outperformed the control group in clinical scale results, with significant differences in the following scores: symbol digital modalities test, TMT-A, TMT-B, and digital span test. In the left central zone, theta band power was significantly higher after anodal electrical stimulation than before. Analysis of the correlation between EEG power and psychometric scores revealed that the power change was positively correlated with the scores on the symbol digital modality test (r = 0.435, p < 0.05). CONCLUSION Anodal tDCS can enhance executive function in patients with PSEI, and tDCS-related improvements are related to the enhancement of theta power in the affected region.
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Chen Y, Wang C, Song P, Sun C, Zhang Y, Zhao X, Du J. Alpha rhythm of electroencephalography was modulated differently by three transcranial direct current stimulation protocols in patients with ischemic stroke. Front Hum Neurosci 2022; 16:887849. [PMID: 35911595 PMCID: PMC9334563 DOI: 10.3389/fnhum.2022.887849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
The heterogeneity of transcranial direct current stimulation (tDCS) protocols and clinical profiles may explain variable results in modulating excitability in the motor cortex after stroke. However, the cortical electrical effects induced by different tDCS protocols remain unclear. Here, we aimed to compare rhythm changes in electroencephalography (EEG) induced by three tDCS position protocols and the association between tDCS effects and clinical factors in stroke. Nineteen patients with chronic ischemic stroke underwent four experimental sessions with three tDCS protocols [anodal (atDCS), cathodal (ctDCS), and bilateral (bi-tDCS)] and a sham protocol, according to a single-blind randomized crossover design. Resting-state EEG was acquired before and after each protocol. First, a paired-sample t-test was used to examine the difference in spectral power between pre- and post-stimulation. Then, linear and quadratic regression models were used separately to describe the association between the clinical factors of stroke and changes in spectral power which was significantly different between pre- and post-tDCS. Finally, repeated measures analysis of variance with lesion hemisphere, stimulation protocol, and the location was performed to investigate the effects of tDCS over time. The induced effect of tDCS was mainly reflected in the alpha rhythms. The alpha power was increased by atDCS, especially low-alpha (8–10 Hz), in localized areas of the central and distant areas of the frontal and parietal lobes. Bi-tDCS also affected alpha power but in a smaller area that mainly focused on high-alpha rhythms (10–13 Hz). However, ctDCS and sham had no significant effects on any EEG rhythm. The clinical factors of time since stroke and motor impairment level were related to the change in high-alpha induced by atDCS and bi-tDCS following quadratic regression models. The above-mentioned modulation effect lasted for 20 min without attenuation. In conclusion, our findings provide evidence that the alpha rhythm of EEG is modulated differently by different tDCS protocols and that high alpha is affected by clinical characteristics such as post-stroke time and motor deficits, which is of great significance for understanding the modulation effect of different tDCS protocols on stroke and the guidance of protocols to promote motor recovery following stroke.
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Affiliation(s)
- Yuanyuan Chen
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Chunfang Wang
- Department of Rehabilitation Medicine, Tianjin Union Medical Centre, Rehabilitation Medical Research Center of Tianjin, Tianjin, China
| | - Peiqing Song
- Department of Rehabilitation Medicine, Tianjin Union Medical Centre, Rehabilitation Medical Research Center of Tianjin, Tianjin, China
| | - Changcheng Sun
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Rehabilitation Medicine, Tianjin Union Medical Centre, Rehabilitation Medical Research Center of Tianjin, Tianjin, China
| | - Ying Zhang
- Department of Rehabilitation Medicine, Tianjin Union Medical Centre, Rehabilitation Medical Research Center of Tianjin, Tianjin, China
- *Correspondence: Ying Zhang,
| | - Xin Zhao
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Xin Zhao,
| | - Jingang Du
- Department of Rehabilitation Medicine, Tianjin Union Medical Centre, Rehabilitation Medical Research Center of Tianjin, Tianjin, China
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Biskamp J, Isla Cainzos S, Higgen FL, Gerloff C, Magnus T. Normalization of Aperiodic Electrocorticography Components Indicates Fine Motor Recovery After Sensory Cortical Stroke in Mice. Stroke 2022; 53:2945-2953. [PMID: 35770668 DOI: 10.1161/strokeaha.122.039335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Electrophysiological signatures of ischemic stroke might help to develop a deeper understanding of the mechanisms of recovery. However, to identify critical windows for novel treatment approaches, suitable readout parameters in vivo with the potential to close the gap between functional modifications within the peri-infarct cortex and behavioral outcome on the systems-level are still lacking. METHODS Wild-type mice were trained in a skilled reaching task and underwent permanent distal medial cerebral artery occlusion or sham intervention. Functional deficits and their recovery were monitored both behaviorally and electrophysiologically recording multichannel electrocorticography from both hemispheres. RESULTS Ischemic strokes are located in sensory cortical areas. Affected mice presented fine motor deficits of their contralateral forepaw. Analyses of electrocorticography signals from awake animals demonstrated a modulation of the shape of power spectral density in the vicinity of the infarct. While power spectral density consists of both rhythmic oscillatory and nonrhythmic, aperiodic components, the alteration of spectrum shape was reflected in a transient increase of aperiodic exponents in the peri-infarct cortex. The relative power and frequency of slow oscillations remained unchanged. Exponents derived from motor areas significantly correlated with fine motor recovery, thus indicating functional modifications of neuronal activity. CONCLUSIONS Aperiodic spectral exponents exhibited a unique spatiotemporal profile in the mouse cortex after stroke and might complement future translational studies providing a dynamic link from pathophysiology to behavior.
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Affiliation(s)
- Jonatan Biskamp
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Sara Isla Cainzos
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Focko L Higgen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Tim Magnus
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
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Blum C, Baur D, Achauer LC, Berens P, Biergans S, Erb M, Hömberg V, Huang Z, Kohlbacher O, Liepert J, Lindig T, Lohmann G, Macke JH, Römhild J, Rösinger-Hein C, Zrenner B, Ziemann U. Personalized neurorehabilitative precision medicine: from data to therapies (MWKNeuroReha) - a multi-centre prospective observational clinical trial to predict long-term outcome of patients with acute motor stroke. BMC Neurol 2022; 22:238. [PMID: 35773640 PMCID: PMC9245298 DOI: 10.1186/s12883-022-02759-2] [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: 03/06/2022] [Accepted: 06/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Stroke is one of the most frequent diseases, and half of the stroke survivors are left with permanent impairment. Prediction of individual outcome is still difficult. Many but not all patients with stroke improve by approximately 1.7 times the initial impairment, that has been termed proportional recovery rule. The present study aims at identifying factors predicting motor outcome after stroke more accurately than before, and observe associations of rehabilitation treatment with outcome. METHODS The study is designed as a multi-centre prospective clinical observational trial. An extensive primary data set of clinical, neuroimaging, electrophysiological, and laboratory data will be collected within 96 h of stroke onset from patients with relevant upper extremity deficit, as indexed by a Fugl-Meyer-Upper Extremity (FM-UE) score ≤ 50. At least 200 patients will be recruited. Clinical scores will include the FM-UE score (range 0-66, unimpaired function is indicated by a score of 66), Action Research Arm Test, modified Rankin Scale, Barthel Index and Stroke-Specific Quality of Life Scale. Follow-up clinical scores and applied types and amount of rehabilitation treatment will be documented in the rehabilitation hospitals. Final follow-up clinical scoring will be performed 90 days after the stroke event. The primary endpoint is the change in FM-UE defined as 90 days FM-UE minus initial FM-UE, divided by initial FM-UE impairment. Changes in the other clinical scores serve as secondary endpoints. Machine learning methods will be employed to analyze the data and predict primary and secondary endpoints based on the primary data set and the different rehabilitation treatments. DISCUSSION If successful, outcome and relation to rehabilitation treatment in patients with acute motor stroke will be predictable more reliably than currently possible, leading to personalized neurorehabilitation. An important regulatory aspect of this trial is the first-time implementation of systematic patient data transfer between emergency and rehabilitation hospitals, which are divided institutions in Germany. TRIAL REGISTRATION This study was registered at ClinicalTrials.gov ( NCT04688970 ) on 30 December 2020.
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Affiliation(s)
- Corinna Blum
- Department for Neurology & Stroke, University Hospital of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, Ottfried-Müller-Straße 25, 72076, Tübingen, Germany
| | - David Baur
- Department for Neurology & Stroke, University Hospital of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, Ottfried-Müller-Straße 25, 72076, Tübingen, Germany
| | - Lars-Christian Achauer
- medical Data Integration Centre (meDIC), University Hospital of Tübingen, Schaffhausenstr. 77, 72072, Tübingen, Germany
| | - Philipp Berens
- University Hospital of Tübingen, Institute for Ophthalmic Research, Elfriede-Aulhorn-Str. 7, 72076, Tübingen, Germany.,Cluster of Excellence Machine Learning, University of Tübingen, Maria-von-Linden-Str. 6, 72076, Tübingen, Germany
| | - Stephanie Biergans
- medical Data Integration Centre (meDIC), University Hospital of Tübingen, Schaffhausenstr. 77, 72072, Tübingen, Germany
| | - Michael Erb
- Department for Biomedical Magnetic Resonance, University Hospital of Tübingen, Ottfried-Müller-Str. 51, 72076, Tübingen, Germany.,Max Planck Institute for Biological Cybernetics, Max-Planck-Ring 8-14, 72076, Tübingen, Germany
| | - Volker Hömberg
- SRH Gesundheitszentrum Bad Wimpfen GmbH, Bei der alten Saline 2, 74206, Bad Wimpfen, Germany
| | - Ziwei Huang
- University Hospital of Tübingen, Institute for Ophthalmic Research, Elfriede-Aulhorn-Str. 7, 72076, Tübingen, Germany
| | - Oliver Kohlbacher
- medical Data Integration Centre (meDIC), University Hospital of Tübingen, Schaffhausenstr. 77, 72072, Tübingen, Germany.,University hospital of Tübingen, Institute for translational Bioinformation (TBI), Schaffhausenstr. 77, 72072, Tübingen, Germany.,University of Tübingen, Interfaculty Institute for Biomedical Informatics (IBMI), Sand 14, 72076, Tübingen, Germany.,Department of Computer Science, Applied Bioinformatics (ABI), University of Tübingen, Sand 14, 72076, Tübingen, Germany
| | - Joachim Liepert
- Schmieder Clinic Allensbach, Zum Tafelholz 8, 78476, Allensbach, Germany
| | - Tobias Lindig
- Department for Diagnostic and Interventional Neuroradiology, University Hospital of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany
| | - Gabriele Lohmann
- Department for High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076, Tübingen, Germany
| | - Jakob H Macke
- Cluster of Excellence Machine Learning, University of Tübingen, Maria-von-Linden-Str. 6, 72076, Tübingen, Germany
| | - Jörg Römhild
- medical Data Integration Centre (meDIC), University Hospital of Tübingen, Schaffhausenstr. 77, 72072, Tübingen, Germany
| | - Christine Rösinger-Hein
- Hertie Institute for Clinical Brain Research, Ottfried-Müller-Straße 25, 72076, Tübingen, Germany
| | - Brigitte Zrenner
- Department for Neurology & Stroke, University Hospital of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, Ottfried-Müller-Straße 25, 72076, Tübingen, Germany
| | - Ulf Ziemann
- Department for Neurology & Stroke, University Hospital of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany. .,Hertie Institute for Clinical Brain Research, Ottfried-Müller-Straße 25, 72076, Tübingen, Germany.
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Guo X, Zhang X, Sun M, Yu L, Qian C, Zhang J, Xu W, Xie Y, Xu T, Jin Z. Modulation of Brain Rhythm Oscillations by Xingnao Kaiqiao Acupuncture Correlates with Stroke Recovery: A Randomized Control Trial. JOURNAL OF INTEGRATIVE AND COMPLEMENTARY MEDICINE 2022; 28:436-444. [PMID: 35275751 DOI: 10.1089/jicm.2021.0264] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Objectives: In China, Xingnao Kaiqiao (XNKQ) acupuncture has been widely used for stroke treatment. However, its electrophysiological mechanism remains unclear. Hence, this study aims to study how XNKQ acupuncture modulates brain rhythm oscillations of stroke patients, and investigate its correlation with stroke recovery. Design: Randomized control trial. Subjects: Twenty (sub)acute ischemic stroke patients were enrolled and randomly assigned to two groups (an acupuncture group [AG] [n = 10] and a control group [CG] [n = 10]), and four patients (two patients in each group) dropped out of the study. Interventions: All patients received conventional treatments, and the patients in AG received additional XNKQ acupuncture treatment once a day for 10 consecutive days. Outcome measures: Before treatment, 14 days after, and 30 days after treatment onset, their movement impairments and neurologic deficits were measured using the National Institute of Health Stroke Scale (NIHSS), the Fugl-Meyer (FM) Scale, the Modified Rankin Scale (mRS), and the Modified Barthel Index (MBI), and their electroencephalogram data were recorded. Results: Compared with the CG, the AG showed more improvement in FM scores (p = 0.02), as well as decreased relative delta power and increased relative alpha power after 2 weeks' treatment. The decrease of the relative delta power and the increase of the relative alpha power in the ipsilesional frontal area were significantly correlated with the FM improvement (F5, F7, FC1, and Fz electrodes, all |r| > 0.517, p < 0.040). Conclusions: The curative effect of XNKQ acupuncture related to its electrophysiological modulation. This study was registered at the Chinese Clinical Trial Registry (ChiCTR2000038560).
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Affiliation(s)
- Xiaoli Guo
- The School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | | | - Meng Sun
- Shanghai Minhang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Lingxiao Yu
- The School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chuan Qian
- Shanghai Minhang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Jidan Zhang
- Wujing Community Health Service Center, Shanghai, China
| | - Wenli Xu
- Shanghai Minhang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Yu Xie
- Shanghai Minhang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Tao Xu
- Department of Anesthesiology, Affiliated Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Zheng Jin
- Shanghai Minhang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
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42
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Sun R, Wong WW, Wang J, Wang X, Tong RKY. Functional brain networks assessed with surface electroencephalography for predicting motor recovery in a neural guided intervention for chronic stroke. Brain Commun 2022; 3:fcab214. [PMID: 35350709 PMCID: PMC8936428 DOI: 10.1093/braincomms/fcab214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 06/04/2021] [Accepted: 07/28/2021] [Indexed: 12/12/2022] Open
Abstract
Predicting whether a chronic stroke patient is likely to benefit from a specific intervention can help patients establish reasonable expectations. It also provides the basis for candidates selecting for the intervention. Recent convergent evidence supports the value of network-based approach for understanding the relationship between dysfunctional neural activity and motor deficits after stroke. In this study, we applied resting-state brain connectivity networks to investigate intervention-specific predictive biomarkers of motor improvement in 22 chronic stroke participants who received either combined action observation with EEG-guided robot-hand training (Neural Guided-Action Observation Group, n = 12, age: 34–68 years) or robot-hand training without action observation and EEG guidance (non-Neural Guided-text group, n = 10, age: 42–57 years). The robot hand in Neural Guided-Action Observation training was activated only when significant mu suppression (8–12 Hz) was detected from participant’s EEG signals in ipsilesional hemisphere while it was randomly activated in non-Neural Guided-text training. Only the Neural Guided-Action Observation group showed a significant long-term improvement in their upper-limb motor functions (P < 0.5). In contrast, no significant training effect on the paretic motor functions was found in the non-Neural Guided-text group (P > 0.5). The results of brain connectivity estimated via EEG coherence showed that the pre-training interhemispheric connectivity of delta, theta, alpha and contralesional connectivity of beta were motor improvement related in the Neural Guided-Action Observation group. They can not only differentiate participants with good and poor recovery (interhemispheric delta: P = 0.047, Hedges’ g = 1.409; interhemispheric theta: P = 0.046, Hedges’ g = 1.333; interhemispheric alpha: P = 0.038, Hedges’ g = 1.536; contralesional beta: P = 0.027, Hedges’ g = 1.613) but also significantly correlated with post-training intervention gains (interhemispheric delta: r = −0.901, P < 0.05; interhemispheric theta: r = −0.702, P < 0.05; interhemispheric alpha: r = −0.641, P < 0.05; contralesional beta: r = −0.729, P < 0.05). In contrast, no EEG coherence was significantly correlated with intervention gains in the non-Neural Guided-text group (all Ps>0.05). Partial least square regression showed that the combination of pre-training interhemispheric and contralesional local connectivity could precisely predict intervention gains in the Neural Guided-Action Observation group with a strong correlation between predicted and observed intervention gains (r = 0.82r=0.82) and between predicted and observed intervention outcomes (r = 0.90r=0.90). In summary, EEG-based resting-state brain connectivity networks may serve clinical decision-making by offering an approach to predicting Neural Guided-Action Observation training-induced motor improvement.
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Affiliation(s)
- Rui Sun
- The Laboratory of Neuroscience for Education, Faculty of Education, the University of Hong Kong, Pokfulam, Hong Kong, China
| | - Wan-Wa Wong
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Jing Wang
- School of Mechanical Engineering, Xi'an Jiaotong University, Shaanxi, China
| | - Xin Wang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Raymond K Y Tong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China
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Hakiki B, Donnini I, Romoli AM, Draghi F, Maccanti D, Grippo A, Scarpino M, Maiorelli A, Sterpu R, Atzori T, Mannini A, Campagnini S, Bagnoli S, Ingannato A, Nacmias B, De Bellis F, Estraneo A, Carli V, Pasqualone E, Comanducci A, Navarro J, Carrozza MC, Macchi C, Cecchi F. Clinical, Neurophysiological, and Genetic Predictors of Recovery in Patients With Severe Acquired Brain Injuries (PRABI): A Study Protocol for a Longitudinal Observational Study. Front Neurol 2022; 13:711312. [PMID: 35295839 PMCID: PMC8919857 DOI: 10.3389/fneur.2022.711312] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 01/13/2022] [Indexed: 01/03/2023] Open
Abstract
Background Due to continuous advances in intensive care technology and neurosurgical procedures, the number of survivors from severe acquired brain injuries (sABIs) has increased considerably, raising several delicate ethical issues. The heterogeneity and complex nature of the neurological damage of sABIs make the detection of predictive factors of a better outcome very challenging. Identifying the profile of those patients with better prospects of recovery will facilitate clinical and family choices and allow to personalize rehabilitation. This paper describes a multicenter prospective study protocol, to investigate outcomes and baseline predictors or biomarkers of functional recovery, on a large Italian cohort of sABI survivors undergoing postacute rehabilitation. Methods All patients with a diagnosis of sABI admitted to four intensive rehabilitation units (IRUs) within 4 months from the acute event, aged above 18, and providing informed consent, will be enrolled. No additional exclusion criteria will be considered. Measures will be taken at admission (T0), at three (T1) and 6 months (T2) from T0, and follow-up at 12 and 24 months from onset, including clinical and functional data, neurophysiological results, and analysis of neurogenetic biomarkers. Statistics Advanced machine learning algorithms will be cross validated to achieve data-driven prediction models. To assess the clinical applicability of the solutions obtained, the prediction of recovery milestones will be compared to the evaluation of a multiprofessional, interdisciplinary rehabilitation team, performed within 2 weeks from admission. Discussion Identifying the profiles of patients with a favorable prognosis would allow customization of rehabilitation strategies, to provide accurate information to the caregivers and, possibly, to optimize rehabilitation outcomes. Conclusions The application and validation of machine learning algorithms on a comprehensive pool of clinical, genetic, and neurophysiological data can pave the way toward the implementation of tools in support of the clinical prognosis for the rehabilitation pathways of patients after sABI.
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Affiliation(s)
- Bahia Hakiki
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Ida Donnini
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Anna Maria Romoli
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Francesca Draghi
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Daniela Maccanti
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Antonello Grippo
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Maenia Scarpino
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Antonio Maiorelli
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Raisa Sterpu
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Tiziana Atzori
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Andrea Mannini
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy.,The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Silvia Campagnini
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy.,The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Silvia Bagnoli
- Neuroscience Section, Department of Neurofarba, University of Florence, Firenze, Italy
| | - Assunta Ingannato
- Neuroscience Section, Department of Neurofarba, University of Florence, Firenze, Italy
| | - Benedetta Nacmias
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy.,Neuroscience Section, Department of Neurofarba, University of Florence, Firenze, Italy
| | - Francesco De Bellis
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Anna Estraneo
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Valentina Carli
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Eugenia Pasqualone
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Angela Comanducci
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Milano, Italy
| | - Jorghe Navarro
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Milano, Italy
| | | | - Claudio Macchi
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy.,Department of Experimental and Clinical Medicine, University of Florence, Firenze, Italy
| | - Francesca Cecchi
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Don Carlo Gnocchi, Firenze, Italy.,Department of Experimental and Clinical Medicine, University of Florence, Firenze, Italy
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Manquat E, Ravaux H, Kindermans M, Joachim J, Serrano J, Touchard C, Mateo J, Mebazaa A, Gayat E, Vallée F, Cartailler J. Impact of impaired cerebral blood flow autoregulation on electroencephalogram signals in adults undergoing propofol anaesthesia: a pilot study. BJA OPEN 2022; 1:100004. [PMID: 37588691 PMCID: PMC10430849 DOI: 10.1016/j.bjao.2022.100004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/26/2022] [Indexed: 08/18/2023]
Abstract
Background Cerebral autoregulation actively maintains cerebral blood flow over a range of MAPs. During general anaesthesia, this mechanism may not compensate for reductions in MAP leading to brain hypoperfusion. Cerebral autoregulation can be assessed using the mean flow index derived from Doppler measurements of average blood velocity in the middle cerebral artery, but this is impractical for routine monitoring within the operating room. Here, we investigate the possibility of using the EEG as a proxy measure for a loss of cerebral autoregulation, determined by the mean flow index. Methods Thirty-six patients (57.5 [44.25; 66.5] yr; 38.9% women, non-emergency neuroradiology surgery) anaesthetised using propofol were prospectively studied. Continuous recordings of MAP, average blood velocity in the middle cerebral artery, EEG, and regional cerebral oxygen saturation were made. Poor cerebral autoregulation was defined as a mean flow index greater than 0.3. Results Eighteen patients had preserved cerebral autoregulation, and 18 had altered cerebral autoregulation. The two groups had similar ages, MAPs, and average blood velocities in the middle cerebral artery. Patients with altered cerebral autoregulation exhibited a significantly slower alpha peak frequency (9.4 [9.0, 9.9] Hz vs 10.5 [10.1, 10.9] Hz, P<0.001), which persisted after adjusting for age, norepinephrine infusion rate, and ASA class (odds ratio=0.038 [confidence interval, 0.004, 0.409]; P=0.007). Conclusion In this pilot study, we found that loss of cerebral autoregulation was associated with a slower alpha peak frequency, independent of age. This work suggests that impaired cerebral autoregulation could be monitored in the operating room using the existing EEG setup. Clinical trial registration NCT03769142.
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Affiliation(s)
- Elsa Manquat
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- AP-HP-Inria, Laboratoire Daniel Bernoulli, Paris, France
| | - Hugues Ravaux
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Manuel Kindermans
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Jona Joachim
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - José Serrano
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Cyril Touchard
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Joaquim Mateo
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Alexandre Mebazaa
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- INSERM, UMR-942, Paris, France
| | - Etienne Gayat
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- INSERM, UMR-942, Paris, France
| | - Fabrice Vallée
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- Laboratoire de Mécanique des Solides (LMS), Ecole Polytechnique/CNRS/Institut Polytechnique de Paris, France
- INSERM, UMR-942, Paris, France
| | - Jérôme Cartailler
- Department of Anesthesiology, Burn and Critical Care, St-Louis-Lariboisiere University Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
- INSERM, UMR-942, Paris, France
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Kumari R, Janković M, Costa A, Savić A, Konstantinović L, Djordjević O, Vucković A. Short term priming effect of brain-actuated muscle stimulation using bimanual movements in stroke. Clin Neurophysiol 2022; 138:108-121. [DOI: 10.1016/j.clinph.2022.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/26/2022] [Accepted: 03/01/2022] [Indexed: 11/03/2022]
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EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery. Clin Neurophysiol 2022; 137:92-101. [PMID: 35303540 PMCID: PMC9038588 DOI: 10.1016/j.clinph.2022.02.022] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/02/2022] [Accepted: 02/22/2022] [Indexed: 12/20/2022]
Abstract
The Spectral Exponent (SE) indexes power-law features of the resting EEG in stroke patients. SE is consistently steeper in the affected hemisphere of patients after middle cerebral artery stroke. SE is linked to clinical status and seems to be a good predictor of clinical outcome.
Objective Quantitative Electroencephalography (qEEG) can capture changes in brain activity following stroke. qEEG metrics traditionally focus on oscillatory activity, however recent findings highlight the importance of aperiodic (power-law) structure in characterizing pathological brain states. We assessed neurophysiological alterations and recovery after mono-hemispheric stroke by means of the Spectral Exponent (SE), a metric that reflects EEG slowing and quantifies the power-law decay of the EEG Power Spectral Density (PSD). Methods Eighteen patients (n = 18) with mild to moderate mono-hemispheric Middle Cerebral Artery (MCA) ischaemic stroke were retrospectively enrolled for this study. Patients underwent EEG recording in the sub-acute phase (T0) and after 2 months of physical rehabilitation (T1). Sixteen healthy controls (HC; n = 16) matched by age and sex were enrolled as a normative group. SE values and narrow-band PSD were estimated for each recording. We compared SE and band-power between patients and HC, and between the affected (AH) and unaffected hemisphere (UH) at T0 and T1 in patients. Results At T0, stroke patients showed significantly more negative SE values than HC (p = 0.003), reflecting broad-band EEG slowing. Most important, in patients SE over the AH was consistently more negative compared to the UH and showed a renormalization at T1. This SE renormalization significantly correlated with National Institute of Health Stroke Scale (NIHSS) improvement (R = 0.63, p = 0.005). Conclusions SE is a reliable readout of the neurophysiological and clinical alterations occurring after an ischaemic cortical lesion. Significance SE promise to be a robust method to monitor and predict patients’ functional outcome.
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Sutcliffe L, Lumley H, Shaw L, Francis R, Price CI. Surface electroencephalography (EEG) during the acute phase of stroke to assist with diagnosis and prediction of prognosis: a scoping review. BMC Emerg Med 2022; 22:29. [PMID: 35227206 PMCID: PMC8883639 DOI: 10.1186/s12873-022-00585-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 02/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Stroke is a common medical emergency responsible for significant mortality and disability. Early identification improves outcomes by promoting access to time-critical treatments such as thrombectomy for large vessel occlusion (LVO), whilst accurate prognosis could inform many acute management decisions. Surface electroencephalography (EEG) shows promise for stroke identification and outcome prediction, but evaluations have varied in technology, setting, population and purpose. This scoping review aimed to summarise published literature addressing the following questions: 1. Can EEG during acute clinical assessment identify: a) Stroke versus non-stroke mimic conditions. b) Ischaemic versus haemorrhagic stroke. c) Ischaemic stroke due to LVO. 2. Can these states be identified if EEG is applied < 6 h since onset. 3. Does EEG during acute assessment predict clinical recovery following confirmed stroke. METHODS We performed a systematic search of five bibliographic databases ending 19/10/2020. Two reviewers assessed eligibility of articles describing diagnostic and/or prognostic EEG application < 72 h since suspected or confirmed stroke. RESULTS From 5892 abstracts, 210 full text articles were screened and 39 retained. Studies were small and heterogeneous. Amongst 21 reports of diagnostic data, consistent associations were reported between stroke, greater delta power, reduced alpha/beta power, corresponding ratios and greater brain asymmetry. When reported, the area under the curve (AUC) was at least good (0.81-1.00). Only one study combined clinical and EEG data (AUC 0.88). There was little data found describing whether EEG could identify ischaemic versus haemorrhagic stroke. Radiological changes suggestive of LVO were also associated with increased slow and decreased fast waves. The only study with angiographic proof of LVO reported AUC 0.86 for detection < 24 h since onset. Amongst 26 reports of prognostic data, increased slow and reduced fast wave EEG changes were associated with future dependency, neurological impairment, mortality and poor cognition, but there was little evidence that EEG enhanced outcome prediction relative to clinical and/or radiological variables. Only one study focussed solely on patients < 6 h since onset for predicting neurological prognosis post-thrombolysis, with more favourable outcomes associated with greater hemispheric symmetry and a greater ratio of fast to slow waves. CONCLUSIONS Although studies report important associations with EEG biomarkers, further technological development and adequately powered real-world studies are required before recommendations can be made regarding application during acute stroke assessment.
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Affiliation(s)
- Lou Sutcliffe
- Stroke Research Group, Population Health Science Institute, Newcastle University, Newcastle-Upon-Tyne, UK
| | - Hannah Lumley
- Stroke Research Group, Population Health Science Institute, Newcastle University, Newcastle-Upon-Tyne, UK.
| | - Lisa Shaw
- Stroke Research Group, Population Health Science Institute, Newcastle University, Newcastle-Upon-Tyne, UK
| | - Richard Francis
- Stroke Research Group, Population Health Science Institute, Newcastle University, Newcastle-Upon-Tyne, UK
| | - Christopher I Price
- Stroke Research Group, Population Health Science Institute, Newcastle University, Newcastle-Upon-Tyne, UK
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Keser Z, Buchl SC, Seven NA, Markota M, Clark HM, Jones DT, Lanzino G, Brown RD, Worrell GA, Lundstrom BN. Electroencephalogram (EEG) With or Without Transcranial Magnetic Stimulation (TMS) as Biomarkers for Post-stroke Recovery: A Narrative Review. Front Neurol 2022; 13:827866. [PMID: 35273559 PMCID: PMC8902309 DOI: 10.3389/fneur.2022.827866] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/31/2022] [Indexed: 01/20/2023] Open
Abstract
Stroke is one of the leading causes of death and disability. Despite the high prevalence of stroke, characterizing the acute neural recovery patterns that follow stroke and predicting long-term recovery remains challenging. Objective methods to quantify and characterize neural injury are still lacking. Since neuroimaging methods have a poor temporal resolution, EEG has been used as a method for characterizing post-stroke recovery mechanisms for various deficits including motor, language, and cognition as well as predicting treatment response to experimental therapies. In addition, transcranial magnetic stimulation (TMS), a form of non-invasive brain stimulation, has been used in conjunction with EEG (TMS-EEG) to evaluate neurophysiology for a variety of indications. TMS-EEG has significant potential for exploring brain connectivity using focal TMS-evoked potentials and oscillations, which may allow for the system-specific delineation of recovery patterns after stroke. In this review, we summarize the use of EEG alone or in combination with TMS in post-stroke motor, language, cognition, and functional/global recovery. Overall, stroke leads to a reduction in higher frequency activity (≥8 Hz) and intra-hemispheric connectivity in the lesioned hemisphere, which creates an activity imbalance between non-lesioned and lesioned hemispheres. Compensatory activity in the non-lesioned hemisphere leads mostly to unfavorable outcomes and further aggravated interhemispheric imbalance. Balanced interhemispheric activity with increased intrahemispheric coherence in the lesioned networks correlates with improved post-stroke recovery. TMS-EEG studies reveal the clinical importance of cortical reactivity and functional connectivity within the sensorimotor cortex for motor recovery after stroke. Although post-stroke motor studies support the prognostic value of TMS-EEG, more studies are needed to determine its utility as a biomarker for recovery across domains including language, cognition, and hemispatial neglect. As a complement to MRI-based technologies, EEG-based technologies are accessible and valuable non-invasive clinical tools in stroke neurology.
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Affiliation(s)
- Zafer Keser
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Samuel C. Buchl
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Nathan A. Seven
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Matej Markota
- Department of Psychiatry, Mayo Clinic, Rochester, MN, United States
| | - Heather M. Clark
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - David T. Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Giuseppe Lanzino
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Robert D. Brown
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
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Asmedi A, Gofir A, Satiti S, Paryono P, Sebayang DP, Putri DPA, Vidyanti A. Quantitative EEG Correlates with NIHSS and MoCA for Assessing the Initial Stroke Severity in Acute Ischemic Stroke Patients. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.8483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND: National Institutes of Health Stroke Scale (NIHSS) and Montreal Cognitive Assessment (MoCA) measure stroke severity by assessing the functional and cognitive outcome, respectively. However, they cannot be used to measure subtle evolution in clinical symptoms during the early phase. Quantitative EEG (qEEG) can detect any subtle changes in CBF and brain metabolism thus may also benefit for assessing the severity.
AIM: This study aims to identify the correlation between qEEG with NIHSS and MoCA for assessing the initial stroke severity in acute ischemic stroke patients.
METHODS: This was a cross-sectional study. We recruited 30 patients with first-ever acute ischemic stroke hospitalized in Dr. Sardjito General Hospital, Yogyakarta, Indonesia. We measured the NIHSS, MoCA score, and qEEG parameter during the acute phase of stroke. Correlation and regression analysis was completed to investigate the relationship between qEEG parameter with NIHSS and MoCA.
RESULTS: Four acute qEEG parameter demonstrated moderate-to-high correlations with NIHSS and MoCA. DTABR had positive correlation with NIHSS (r = 0.379, p = 0.04). Meanwhile, delta-absolute power, DTABR, and DAR were negatively correlated with MoCA score (r = −0.654, p = 0.01; r = −0.397, p = 0.03; and r = −0.371, p = 0.04, respectively). After adjusted with the confounding variables, delta-absolute power was independently associated with MoCA score, but not with NIHSS (B = −2.887, 95% CI (−4.304–−1.470), p < 0.001).
CONCLUSIONS: Several qEEG parameters had significant correlations with NIHSS and MoCA in acute ischemic stroke patients. The use of qEEG in acute clinical setting may provide a reliable and efficient prediction of initial stroke severity. Further cohort study with larger sample size and wide range of stroke severity is still needed.
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Rasheed W, Wodeyar A, Srinivasan R, Frostig RD. Sensory stimulation-based protection from impending stroke following MCA occlusion is correlated with desynchronization of widespread spontaneous local field potentials. Sci Rep 2022; 12:1744. [PMID: 35110588 PMCID: PMC8810838 DOI: 10.1038/s41598-022-05604-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 01/12/2022] [Indexed: 11/26/2022] Open
Abstract
In a rat model of ischemic stroke by permanent occlusion of the medial cerebral artery (pMCAo), we have demonstrated using continuous recordings by microelectrode array at the depth of the ischemic territory that there is an immediate wide-spread increase in spontaneous local field potential synchrony following pMCAo that was correlated with ischemic stroke damage, but such increase was not seen in control sham-surgery rats. We further found that the underpinning source of the synchrony increase is intermittent bursts of low multi-frequency oscillations. Here we show that such increase in spontaneous LFP synchrony after pMCAo can be reduced to pre-pMCAo baseline level by delivering early (immediately after pMCAo) protective sensory stimulation that reduced the underpinning bursts. However, the delivery of a late (3 h after pMCAo) destructive sensory stimulation had no influence on the elevated LFP synchrony and its underpinning bursts. Histology confirmed both protection for the early stimulation group and an infarct for the late stimulation group. These findings highlight the unexpected importance of spontaneous LFP and its synchrony as a predictive correlate of cerebral protection or stroke infarct during the hyperacute state following pMCAo and the potential clinical relevance of stimulation to reduce EEG synchrony in acute stroke.
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Affiliation(s)
- Waqas Rasheed
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Anirudh Wodeyar
- Department of Cognitive Science, University of California, Irvine, CA, USA
- Department of Statistics, University of California, Irvine, CA, USA
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Ramesh Srinivasan
- Department of Cognitive Science, University of California, Irvine, CA, USA
- Department of Statistics, University of California, Irvine, CA, USA
| | - Ron D Frostig
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA.
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA.
- Department of Biomedical Engineering, University of California, Irvine, CA, USA.
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