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Tam PK, Oey NE, Tang N, Ramamurthy G, Chew E. Facilitating Corticomotor Excitability of the Contralesional Hemisphere Using Non-Invasive Brain Stimulation to Improve Upper Limb Motor Recovery from Stroke-A Scoping Review. J Clin Med 2024; 13:4420. [PMID: 39124687 PMCID: PMC11313572 DOI: 10.3390/jcm13154420] [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: 06/29/2024] [Revised: 07/18/2024] [Accepted: 07/25/2024] [Indexed: 08/12/2024] Open
Abstract
Upper limb weakness following stroke poses a significant global psychosocial and economic burden. Non-invasive brain stimulation (NIBS) is a potential adjunctive treatment in rehabilitation. However, traditional approaches to rebalance interhemispheric inhibition may not be effective for all patients. The supportive role of the contralesional hemisphere in recovery of upper limb motor function has been supported by animal and clinical studies, particularly for those with severe strokes. This review aims to provide an overview of the facilitation role of the contralesional hemisphere for post-stroke motor recovery. While more studies are required to predict responses and inform the choice of NIBS approach, contralesional facilitation may offer new hope for patients in whom traditional rehabilitation and NIBS approaches have failed.
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Affiliation(s)
- Pui Kit Tam
- Division of Rehabilitation Medicine, Department of Medicine, National University Hospital, Singapore 119228, Singapore; (P.K.T.); (N.E.O.); (N.T.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
| | - Nicodemus Edrick Oey
- Division of Rehabilitation Medicine, Department of Medicine, National University Hospital, Singapore 119228, Singapore; (P.K.T.); (N.E.O.); (N.T.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
| | - Ning Tang
- Division of Rehabilitation Medicine, Department of Medicine, National University Hospital, Singapore 119228, Singapore; (P.K.T.); (N.E.O.); (N.T.)
| | - Guhan Ramamurthy
- BG Institute of Neurosciences, BG Hospital, Tiruchendur, Tuticorin 628216, Tamil Nadu, India;
| | - Effie Chew
- Division of Rehabilitation Medicine, Department of Medicine, National University Hospital, Singapore 119228, Singapore; (P.K.T.); (N.E.O.); (N.T.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
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Ding Q, Chen J, Zhang S, Chen S, Li X, Peng Y, Chen Y, Chen J, Chen K, Cai G, Xu G, Lan Y. Neurophysiological characterization of stroke recovery: A longitudinal TMS and EEG study. CNS Neurosci Ther 2024; 30:e14471. [PMID: 37718708 PMCID: PMC10916444 DOI: 10.1111/cns.14471] [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: 07/05/2023] [Revised: 08/25/2023] [Accepted: 09/03/2023] [Indexed: 09/19/2023] Open
Abstract
AIMS Understanding the neural mechanisms underlying stroke recovery is critical to determine effective interventions for stroke rehabilitation. This study aims to systematically explore how recovery mechanisms post-stroke differ between individuals with different levels of functional integrity of the ipsilesional corticomotor pathway and motor function. METHODS Eighty-one stroke survivors and 15 age-matched healthy adults participated in this study. We used transcranial magnetic stimulation (TMS), electroencephalography (EEG), and concurrent TMS-EEG to investigate longitudinal neurophysiological changes post-stroke, and their relationship with behavioral changes. Subgroup analysis was performed based on the presence of paretic motor evoked potentials and motor function. RESULTS Functional connectivity was increased dramatically in low-functioning individuals without elicitable motor evoked potentials (MEPs), which showed a positive effect on motor recovery. Functional connectivity was increased gradually in higher-functioning individuals without elicitable MEP during stroke recovery and influence from the contralesional hemisphere played a key role in motor recovery. In individuals with elicitable MEPs, negative correlations between interhemispheric functional connectivity and motor function suggest that the influence from the contralesional hemisphere may be detrimental to motor recovery. CONCLUSION Our results demonstrate prominent clinical implications for individualized stroke rehabilitation based on both functional integrity of the ipsilesional corticomotor pathway and motor function.
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Affiliation(s)
- Qian Ding
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
- Department of Rehabilitation Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
- Guangzhou Key Laboratory of Aging Frailty and NeurorehabilitationGuangzhouChina
| | - Jixiang Chen
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Shunxi Zhang
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Songbin Chen
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Xiaotong Li
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Yuan Peng
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Yujie Chen
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Junhui Chen
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Kang Chen
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Guiyuan Cai
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Guangqing Xu
- Department of Rehabilitation Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Yue Lan
- Department of Rehabilitation Medicine, Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
- Guangzhou Key Laboratory of Aging Frailty and NeurorehabilitationGuangzhouChina
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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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Sheng B, Chen X, Cheng J, Zhang Y, Xie SSQ, Tao J, Duan C. A novel scoring approach for the Wolf Motor Function Test in stroke survivors using motion-sensing technology and machine learning: A preliminary study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107887. [PMID: 37913714 DOI: 10.1016/j.cmpb.2023.107887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND AND OBJECTIVE Human-administered clinical scales, such as the Functional Ability Scale of the Wolf Motor Function Test (WMFT-FAS), are widely utilized to evaluate upper-limb motor function in stroke survivors. However, these scales are generally subjective and labor-intensive. To end this, we proposed a novel scoring approach for the motor function assessment. METHODS The proposed novel scoring approach mainly contained one Microsoft Kinect v2, one customized motion tracking system, and one customized intelligent scoring system. Specifically, the Kinect v2 was used to capture stroke survivors' functional movements, the motion tracking system was developed for recording the gathered movement data, and the intelligent scoring system (kernel: feed-forward neural network, FFNN) was developed to evaluate movement quality and provide corresponding WMFT-FAS scores. Several methods have been applied to enhance the approach's usability, such as singular spectrum analysis and multi-ReliefF method. RESULTS Sixteen stroke survivors and ten healthy subjects were recruited for validation. Inspiring results of the proposed approach were achieved when compared with the clinical scores provided by a physiotherapist: 0.924 ± 0.027 for accuracy, 0.875 ± 0.063 for F1-score, 0.915 ± 0.051 for sensitivity, 0.969 ± 0.013 for specificity, 0.952 ± 0.038 for AUC, 0.098 ± 0.037 for mean absolute error, and 0.214 ± 0.078 for root mean squared error. CONCLUSIONS The results indicate that the proposed novel scoring approach can provide objective and accurate assessment scores, which can help physiotherapists make individualized treatment decisions.
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Affiliation(s)
- Bo Sheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Xiaohui Chen
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Jian Cheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Yanxin Zhang
- Department of Exercise Sciences, The University of Auckland, Auckland, 1010, New Zealand
| | - Shane Sheng Quan Xie
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Jing Tao
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Chaoqun Duan
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.
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Hanakawa T, Hotta F, Nakamura T, Shindo K, Ushiba N, Hirosawa M, Yamazaki Y, Moriyama Y, Takagi S, Mizuno K, Liu M. Macrostructural Cerebellar Neuroplasticity Correlates With Motor Recovery After Stroke. Neurorehabil Neural Repair 2023; 37:775-785. [PMID: 37882368 DOI: 10.1177/15459683231207356] [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: 10/27/2023]
Abstract
BACKGROUND Motor recovery varies across post-stroke individuals, some of whom require a better rehabilitation strategy. We hypothesized that macrostructural neuroplasticity of the motor control network including the cerebellum might underlie individual differences in motor recovery. Objectives. To gain insight into the macrostructural neuroplasticity after stroke, we examined 52 post-stroke individuals using both the Fugl-Meyer assessment and structural magnetic resonance imaging. METHODS We performed voxel-based lesion symptom mapping and cross-sectional voxel-based morphometry to correlate the motor scores with the lesion location and the gray matter volume (GMV), respectively. Longitudinal data were available at ~8 and/or 15 weeks after admission from 43 individuals with supratentorial lesions. We performed a longitudinal VBM analysis followed by a multiple regression analysis to correlate between the changes of the motor assessment scores and those of GMV overtime. RESULTS We found a cross-sectional correlation of residual motor functioning with GMV in the ipsilesional cerebellum and contralesional parietal cortex. Longitudinally, we found increases in GMV in the ipsilesional supplementary motor area, and the ipsilesional superior and inferior cerebellar zones, along with a GMV decrease in the ipsilesional thalamus. The motor recovery was correlated with the GMV changes in the superior and inferior cerebellar zones. The regaining of upper-limb motor functioning was correlated with the GMV changes of both superior and inferior cerebellum while that of lower-limb motor functioning with the GMV increase of the inferior cerebellum only. CONCLUSIONS The present findings support the hypothesis that macrostructural cerebellar neuroplasticity is correlated with individual differences in motor recovery after stroke.
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Affiliation(s)
- Takashi Hanakawa
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan
- Tokyo Metropolitan Rehabilitation Hospital, Tokyo, Japan
| | - Fujiko Hotta
- Tokyo Metropolitan Rehabilitation Hospital, Tokyo, Japan
| | - Tatsuhiro Nakamura
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Keiichiro Shindo
- Tokyo Metropolitan Rehabilitation Hospital, Tokyo, Japan
- Department of Rehabilitation Medicine, Hikarigaoka Hospital, Takaoka, Japan
| | - Naoko Ushiba
- Tokyo Metropolitan Rehabilitation Hospital, Tokyo, Japan
- Department of Rehabilitation Medicine, Setagaya Memorial Hospital, Tokyo, Japan
| | | | | | | | - Syota Takagi
- Tokyo Metropolitan Rehabilitation Hospital, Tokyo, Japan
| | - Katsuhiro Mizuno
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
- Department of Physical Rehabilitation, National Center Hospital, National Center of Neurology and Psychiatry Hospital, Kodaira, Japan
| | - Meigen Liu
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
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Zhang X, Nan J, Xu M, Chen L, Ni G, Ming D. PSIs of EEG With Refined Frequency Decomposition Could Prognose Motor Recovery Before Rehabilitation Intervention. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3760-3771. [PMID: 37721877 DOI: 10.1109/tnsre.2023.3316210] [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: 09/20/2023]
Abstract
Stroke often leads to permanent impairment in motor function. Accurate and quantitative prognosis of potential motor recovery before rehabilitation intervention can help healthcare centers improve resources organization and enable individualized intervention. The context of this paper investigated the potential of using electroencephalography (EEG) functional connectivity (FC) measures as biomarkers for assessing and prognosing improvement of Fugl-Meyer Assessment in upper extremity motor function ( ∆FMU) among participants with chronic stroke. EEG data from resting and motor imagery task were recorded from 13 participants with chronic stroke. Three functional connectivity methods, which were Pearson correlation measure (PCM), weighted Phase Lag Index (wPLI) and phase synchronization index (PSI), were investigated, under three regions of interest (inter-hemispheric, intra-hemispheric, and whole-brain), in two statues (resting and motor imagery), with 15 refined center frequencies. We applied correlation analysis to identify the optimal center frequencies and pairs of synchronized channels that were consistently associated with ∆FMU . Predictive models were generated using regression analysis algorithms based on optimized center frequencies and channel pairs identified from the proposed analysis method, with leave-one-out cross-validation. We found that PSI in the Alpha band (with center frequency of 9Hz) was the most sensitive FC measures for prognosing motor recovery. Strong and significant correlations were identified between the predictions and actual ∆FMU scores both in the resting state ( [Formula: see text], [Formula: see text], N=13) and motor imagery ( [Formula: see text], [Formula: see text], N=13). Our results suggested that EEG connectivity measured with PSI in resting state could be a promising biomarker for quantifying motor recovery before motor rehabilitation intervention.
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Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach. Brain Sci 2022; 13:brainsci13010071. [PMID: 36672052 PMCID: PMC9856795 DOI: 10.3390/brainsci13010071] [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: 11/25/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome. METHODS We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy. RESULTS Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified. CONCLUSIONS The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT. SIGNIFICANCE The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.
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Milani G, Antonioni A, Baroni A, Malerba P, Straudi S. Relation Between EEG Measures and Upper Limb Motor Recovery in Stroke Patients: A Scoping Review. Brain Topogr 2022; 35:651-666. [PMID: 36136166 DOI: 10.1007/s10548-022-00915-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 09/04/2022] [Indexed: 11/25/2022]
Abstract
Current clinical practice does not leverage electroencephalography (EEG) measurements in stroke patients, despite its potential to contribute to post-stroke recovery predictions. We review the literature on the effectiveness of various quantitative and qualitative EEG-based measures after stroke as a tool to predict upper limb motor outcome, in relation to stroke timeframe and applied experimental tasks. Moreover, we aim to provide guidance on the use of EEG in the assessment of upper limb motor recovery after stroke, suggesting a high potential for some metrics in the appropriate context. We identified relevant papers (N = 16) from databases ScienceDirect, Web of Science and MEDLINE, and assessed their methodological quality with the Joanna Briggs Institute (JBI) Critical Appraisal. We applied the Preferred Reporting Systems for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) Framework. Identified works used EEG to identify properties including event-related activation, spectral power in physiologically relevant bands, symmetry in brain dynamics, functional connectivity, cortico-muscular coherence and rhythmic coordination. EEG was acquired in resting state or in relation to behavioural conditions. Motor outcome was mainly evaluated with the Upper Limb Fugl-Meyer Assessment. Despite great variability in the literature, data suggests that the most promising EEG quantifiers for predicting post-stroke motor outcome are event-related measures. Measures of spectral power in physiologically relevant bands and measures of brain symmetry also show promise. We suggest that EEG measures may improve our understanding of stroke brain dynamics during recovery, and contribute to establishing a functional prognosis and choosing the rehabilitation approach.
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Affiliation(s)
- Giada Milani
- IIT@Unife Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Ferrara, Italy.,Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Annibale Antonioni
- Unit of Clinical Neurology, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | - Andrea Baroni
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | - Paola Malerba
- Battelle Center for Mathematical Medicine and Center for Biobehavioral Health, The Ohio State University, Columbus, OH, USA
| | - Sofia Straudi
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy. .,Department of Neuroscience and Rehabilitation, University of Ferrara, Via Luigi Borsari 46, 44121, Ferrara, Italy.
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Vatinno AA, Simpson A, Ramakrishnan V, Bonilha HS, Bonilha L, Seo NJ. The Prognostic Utility of Electroencephalography in Stroke Recovery: A Systematic Review and Meta-Analysis. Neurorehabil Neural Repair 2022; 36:255-268. [PMID: 35311412 PMCID: PMC9007868 DOI: 10.1177/15459683221078294] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
BACKGROUND Improved ability to predict patient recovery would guide post-stroke care by helping clinicians personalize treatment and maximize outcomes. Electroencephalography (EEG) provides a direct measure of the functional neuroelectric activity in the brain that forms the basis for neuroplasticity and recovery, and thus may increase prognostic ability. OBJECTIVE To examine evidence for the prognostic utility of EEG in stroke recovery via systematic review/meta-analysis. METHODS Peer-reviewed journal articles that examined the relationship between EEG and subsequent clinical outcome(s) in stroke were searched using electronic databases. Two independent researchers extracted data for synthesis. Linear meta-regressions were performed across subsets of papers with common outcome measures to quantify the association between EEG and outcome. RESULTS 75 papers were included. Association between EEG and clinical outcomes was seen not only early post-stroke, but more than 6 months post-stroke. The most studied prognostic potential of EEG was in predicting independence and stroke severity in the standard acute stroke care setting. The meta-analysis showed that EEG was associated with subsequent clinical outcomes measured by the Modified Rankin Scale, National Institutes of Health Stroke Scale, and Fugl-Meyer Upper Extremity Assessment (r = .72, .70, and .53 from 8, 13, and 12 papers, respectively). EEG improved prognostic abilities beyond prediction afforded by standard clinical assessments. However, the EEG variables examined were highly variable across studies and did not converge. CONCLUSIONS EEG shows potential to predict post-stroke recovery outcomes. However, evidence is largely explorative, primarily due to the lack of a definitive set of EEG measures to be used for prognosis.
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Affiliation(s)
- Amanda A Vatinno
- Department of Health Sciences and Research, College of Health Professions, 2345Medical University of South Carolina (MUSC), Charleston, SC, USA
| | - Annie Simpson
- Department of Health Sciences and Research, College of Health Professions, 2345Medical University of South Carolina (MUSC), Charleston, SC, USA
- Department of Healthcare Leadership and Management, College of Health Professions, 2345MUSC, Charleston, SC, USA
| | | | - Heather S Bonilha
- Department of Health Sciences and Research, College of Health Professions, 2345Medical University of South Carolina (MUSC), Charleston, SC, USA
| | - Leonardo Bonilha
- Department of Neurology, College of Medicine, 2345MUSC, Charleston, SC, USA
| | - Na Jin Seo
- Ralph H. Johnson VA Medical Center, Charleston, SC, USA
- Department of Health Sciences and Research, 2345MUSC, Charleston, SC, USA
- Division of Occupational Therapy, Department of Rehabilitation Sciences, MUSC, Charleston, SC, USA
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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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Association between aphasia severity and brain network alterations after stroke assessed using the electroencephalographic phase synchrony index. Sci Rep 2021; 11:12469. [PMID: 34127750 PMCID: PMC8203681 DOI: 10.1038/s41598-021-91978-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 06/02/2021] [Indexed: 12/03/2022] Open
Abstract
Electroencephalographic synchrony can help assess brain network status; however, its usefulness has not yet been fully proven. We developed a clinically feasible method that combines the phase synchrony index (PSI) with resting-state 19-channel electroencephalography (EEG) to evaluate post-stroke motor impairment. In this study, we investigated whether our method could be applied to aphasia, a common post-stroke cognitive impairment. This study included 31 patients with subacute aphasia and 24 healthy controls. We assessed the expressive function of patients and calculated the PSIs of three motor language-related regions: frontofrontal, left frontotemporal, and right frontotemporal. Then, we evaluated post-stroke network alterations by comparing PSIs of the patients and controls and by analyzing the correlations between PSIs and aphasia scores. The frontofrontal PSI (beta band) was lower in patients than in controls and positively correlated with aphasia scores, whereas the right frontotemporal PSI (delta band) was higher in patients than in controls and negatively correlated with aphasia scores. Evaluation of artifacts suggests that this association is attributed to true synchrony rather than spurious synchrony. These findings suggest that post-stroke aphasia is associated with alternations of two different networks and point to the usefulness of EEG PSI in understanding the pathophysiology of aphasia.
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Okazaki YO, Nakagawa Y, Mizuno Y, Hanakawa T, Kitajo K. Frequency- and Area-Specific Phase Entrainment of Intrinsic Cortical Oscillations by Repetitive Transcranial Magnetic Stimulation. Front Hum Neurosci 2021; 15:608947. [PMID: 33776666 PMCID: PMC7994763 DOI: 10.3389/fnhum.2021.608947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/19/2021] [Indexed: 11/13/2022] Open
Abstract
Synchronous oscillations are ubiquitous throughout the cortex, but the frequency of oscillations differs from area to area. To elucidate the mechanistic architectures underlying various rhythmic activities, we tested whether spontaneous neural oscillations in different local cortical areas and large-scale networks can be phase-entrained by direct perturbation with distinct frequencies of repetitive transcranial magnetic stimulation (rTMS). While recording the electroencephalogram (EEG), we applied single-pulse TMS (sp-TMS) and rTMS at 5, 11, and 23 Hz over the motor or visual cortex. We assessed local and global modulation of phase dynamics using the phase-locking factor (PLF). sp-TMS to the motor and the visual cortex triggered a transient increase in PLF in distinct frequencies that peaked at 21 and 8 Hz, respectively. rTMS at 23 Hz over the motor cortex and 11 Hz over the visual cortex induced a prominent and progressive increase in PLF that lasted for a few cycles after the termination of rTMS. Moreover, the local increase in PLF propagated to other cortical areas. These results suggest that distinct cortical areas have area-specific oscillatory frequencies, and the manipulation of oscillations in local areas impacts other areas through the large-scale oscillatory network with the corresponding frequency specificity. We speculate that rTMS that is close to area-specific frequencies (natural frequencies) enables direct manipulation of brain dynamics and is thus useful for investigating the causal roles of synchronous neural oscillations. Moreover, this technique could be used to treat clinical symptoms associated with impaired oscillations and synchrony.
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Affiliation(s)
- Yuka O Okazaki
- RIKEN CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako, Japan.,Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan.,Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Japan
| | - Yumi Nakagawa
- RIKEN CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako, Japan
| | - Yuji Mizuno
- RIKEN CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako, Japan.,Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Takashi Hanakawa
- RIKEN CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako, Japan.,Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan.,Department of Integrated Neuroanatomy and Neuroimaging, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Keiichi Kitajo
- RIKEN CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako, Japan.,Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan.,Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Japan
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Intensive Care Admission and Management of Patients With Acute Ischemic Stroke: A Cross-sectional Survey of the European Society of Intensive Care Medicine. J Neurosurg Anesthesiol 2021; 34:313-320. [PMID: 33587531 DOI: 10.1097/ana.0000000000000761] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 12/31/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND No specific recommendations are available regarding the intensive care management of critically ill acute ischemic stroke (AIS) patients, and questions remain regarding optimal ventilatory, hemodynamic, and general intensive care unit (ICU) therapeutic targets in this population. We performed an international survey to investigate ICU admission criteria and management of AIS patients. METHODS An electronic questionnaire including 25 items divided into 3 sections was available on the European Society of Intensive Care Medicine Web site between November 1, 2019 and March 30, 2020 and advertised through the neurointensive care (NIC) section newsletter. This survey was emailed directly to the NIC members and was endorsed by the European Society of Intensive Care Medicine. RESULTS There were 214 respondents from 198 centers, with response rate of 16.5% of total membership (214/1296). In most centers (67%), the number of AIS patients admitted to respondents' hospitals in 2019 was between 100 and 300, and, among them, fewer than 50 required ICU admission per hospital. The most widely accepted indication for ICU admission criteria was a requirement for intubation and mechanical ventilation. A standard protocol for arterial blood pressure (ABP) management was utilized by 88 (58%) of the respondents. For patients eligible for intravenous thrombolysis, the most common ABP target was <185/110 mm Hg (n=77 [51%]), whereas for patients undergoing mechanical thrombectomy it was ≤160/90 mm Hg (n=79 [54%]). The preferred drug for reducing ABP was labetalol (n=84 [55.6%]). Other frequently used therapeutic targets included: blood glucose 140 to 180 mg/dL (n=65 [43%]) maintained with intravenous insulin infusion in most institutions (n=110 [72.4%]); enteral feeding initiated within 2 to 3 days from stroke onset (n=142 [93.4%]); oxygen saturation (SpO2) >95% (n=80 [53%]), and tidal volume 6 to 8 mL/kg of predicted body weight (n=135 [89%]). CONCLUSIONS The ICU management of AIS, including therapeutic targets and clinical practice strategies, importantly varies between centers. Our findings may be helpful to define future studies and create a research agenda regarding the ICU therapeutic targets for AIS patients.
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Wann EG, Wodeyar A, Srinivasan R, Frostig RD. Rapid development of strong, persistent, spatiotemporally extensive cortical synchrony and underlying oscillations following acute MCA focal ischemia. Sci Rep 2020; 10:21441. [PMID: 33293620 PMCID: PMC7722868 DOI: 10.1038/s41598-020-78179-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 11/18/2020] [Indexed: 11/09/2022] Open
Abstract
Stroke is a leading cause of death and the leading cause of long-term disability, but its electrophysiological basis is poorly understood. Characterizing acute ischemic neuronal activity dynamics is important for understanding the temporal and spatial development of ischemic pathophysiology and determining neuronal activity signatures of ischemia. Using a 32-microelectrode array spanning the depth of cortex, electrophysiological recordings generated for the first time a continuous spatiotemporal profile of local field potentials (LFP) and multi-unit activity (MUA) before (baseline) and directly after (0-5 h) distal, permanent MCA occlusion (pMCAo) in a rat model. Although evoked activity persisted for hours after pMCAo with minor differences from baseline, spatiotemporal analyses of spontaneous activity revealed that LFP became spatially and temporally synchronized regardless of cortical depth within minutes after pMCAo and extended over large parts of cortex. Such enhanced post-ischemic synchrony was found to be driven by increased bursts of low multi-frequency oscillations and continued throughout the acute ischemic period whereas synchrony measures minimally changed over the same recording period in surgical sham controls. EEG recordings of a similar frequency range have been applied to successfully predict stroke damage and recovery, suggesting clear clinical relevance for our rat model.
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Affiliation(s)
- Ellen G Wann
- 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
| | - Anirudh Wodeyar
- Department of Cognitive Science, University of California, Irvine, CA, USA
- Department of Statistics, University of California, Irvine, CA, 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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Zhang X, D’Arcy R, Chen L, Xu M, Ming D, Menon C. The Feasibility of Longitudinal Upper Extremity Motor Function Assessment Using EEG. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5487. [PMID: 32992698 PMCID: PMC7582505 DOI: 10.3390/s20195487] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/19/2020] [Accepted: 09/21/2020] [Indexed: 11/16/2022]
Abstract
Motor function assessment is crucial in quantifying motor recovery following stroke. In the rehabilitation field, motor function is usually assessed using questionnaire-based assessments, which are not completely objective and require prior training for the examiners. Some research groups have reported that electroencephalography (EEG) data have the potential to be a good indicator of motor function. However, those motor function scores based on EEG data were not evaluated in a longitudinal paradigm. The ability of the motor function scores from EEG data to track the motor function changes in long-term clinical applications is still unclear. In order to investigate the feasibility of using EEG to score motor function in a longitudinal paradigm, a convolutional neural network (CNN) EEG model and a residual neural network (ResNet) EEG model were previously generated to translate EEG data into motor function scores. To validate applications in monitoring rehabilitation following stroke, the pre-established models were evaluated using an initial small sample of individuals in an active 14-week rehabilitation program. Longitudinal performances of CNN and ResNet were evaluated through comparison with standard Fugl-Meyer Assessment (FMA) scores of upper extremity collected in the assessment sessions. The results showed good accuracy and robustness with both proposed networks (average difference: 1.22 points for CNN, 1.03 points for ResNet), providing preliminary evidence for the proposed method in objective evaluation of motor function of upper extremity in long-term clinical applications.
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Affiliation(s)
- Xin Zhang
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (X.Z.); (M.X.)
- Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada;
| | - Ryan D’Arcy
- Schools of Engineering Science and Computer Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada;
| | - Long Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
| | - Minpeng Xu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (X.Z.); (M.X.)
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (X.Z.); (M.X.)
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
- Tianjin International Joint Research Center for Neural Engineering, Tianjin 300072, China
| | - Carlo Menon
- Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada;
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