1
|
Sieghartsleitner S, Sebastián-Romagosa M, Cho W, Grünwald J, Ortner R, Scharinger J, Kamada K, Guger C. Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system. Front Neurosci 2024; 18:1346607. [PMID: 38500488 PMCID: PMC10944934 DOI: 10.3389/fnins.2024.1346607] [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: 11/29/2023] [Accepted: 02/08/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction Brain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions. Methods Patients' functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively. Results Patients' motor function as assessed by the FMA-UE improved by an average of 4.2 points (p < 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed. Discussion The results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions.
Collapse
Affiliation(s)
- Sebastian Sieghartsleitner
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | | | - Woosang Cho
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Johannes Grünwald
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | - Rupert Ortner
- g.tec Medical Engineering Spain S.L., Barcelona, Spain
| | - Josef Scharinger
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | | | - Christoph Guger
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
- g.tec Medical Engineering Spain S.L., Barcelona, Spain
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Chiem E, Zhao K, Stark G, Ghiani CA, Colwell CS, Paul KN. Sex differences in sleep architecture in a mouse model of Huntington's disease. J Neurosci Res 2024; 102:e25290. [PMID: 38284849 DOI: 10.1002/jnr.25290] [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: 05/01/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 01/30/2024]
Abstract
Sleep and circadian rhythm disturbances are common features of Huntington's disease (HD). HD is an autosomal dominant neurodegenerative disorder that affects men and women in equal numbers, but some epidemiological studies as well as preclinical work indicate there may be sex differences in disease presentation and progression. Since sex differences in HD could provide important insights to understand cellular and molecular mechanism(s), we used the bacterial artificial chromosome transgenic mouse model of HD (BACHD) to examine whether sex differences in sleep/wake cycles are detectable in an animal model of the disease. Electroencephalography/electromyography (EEG/EMG) was used to measure sleep/wake states and polysomnographic patterns in young adult (12-week-old) male and female wild-type and BACHD mice. Our findings show that male, but not female, BACHD mice exhibited increased variation in phases of the rhythms as compared to age- and sex-matched wild-types. For both rapid-eye movement (REM) and non-rapid eye movement (NREM) sleep, genotypic and sex differences were detected. In particular, the BACHD males spent less time in NREM sleep and exhibited a more fragmented sleep than the other groups. Finally, in response to 6 h of sleep deprivation, both genotypes and sexes displayed the predicted homeostatic responses to sleep loss. These findings suggest that females are relatively protected early in disease progression in this HD model.
Collapse
Affiliation(s)
- Emily Chiem
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, California, USA
- Molecular, Cellular, Integrative Physiology Program, University of California Los Angeles, Los Angeles, California, USA
| | - Kevin Zhao
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, California, USA
| | - Gemma Stark
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Cristina A Ghiani
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Christopher S Colwell
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Ketema N Paul
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, California, USA
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| |
Collapse
|
4
|
Chen J, Xia Y, Zhou X, Vidal Rosas E, Thomas A, Loureiro R, Cooper RJ, Carlson T, Zhao H. fNIRS-EEG BCIs for Motor Rehabilitation: A Review. Bioengineering (Basel) 2023; 10:1393. [PMID: 38135985 PMCID: PMC10740927 DOI: 10.3390/bioengineering10121393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/26/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain-computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain's state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.
Collapse
Affiliation(s)
- Jianan Chen
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
| | - Yunjia Xia
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
- DOT-HUB, Department of Medical Physics & Biomedical Engineering, University College London (UCL), London WC1E 6BT, UK; (E.V.R.); (R.J.C.)
| | - Xinkai Zhou
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
| | - Ernesto Vidal Rosas
- DOT-HUB, Department of Medical Physics & Biomedical Engineering, University College London (UCL), London WC1E 6BT, UK; (E.V.R.); (R.J.C.)
- Digital Health and Biomedical Engineering, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Alexander Thomas
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
- Aspire CREATe, Department of Orthopaedics & Musculoskeletal Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (R.L.); (T.C.)
| | - Rui Loureiro
- Aspire CREATe, Department of Orthopaedics & Musculoskeletal Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (R.L.); (T.C.)
| | - Robert J. Cooper
- DOT-HUB, Department of Medical Physics & Biomedical Engineering, University College London (UCL), London WC1E 6BT, UK; (E.V.R.); (R.J.C.)
| | - Tom Carlson
- Aspire CREATe, Department of Orthopaedics & Musculoskeletal Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (R.L.); (T.C.)
| | - Hubin Zhao
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
- DOT-HUB, Department of Medical Physics & Biomedical Engineering, University College London (UCL), London WC1E 6BT, UK; (E.V.R.); (R.J.C.)
| |
Collapse
|
5
|
Bigoni C, Beanato E, Harquel S, Hervé J, Oflar M, Crema A, Espinosa A, Evangelista GG, Koch P, Bonvin C, Turlan JL, Guggisberg A, Morishita T, Wessel MJ, Zandvliet SB, Hummel FC. Novel personalized treatment strategy for patients with chronic stroke with severe upper-extremity impairment: The first patient of the AVANCER trial. MED 2023; 4:591-599.e3. [PMID: 37437575 DOI: 10.1016/j.medj.2023.06.006] [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: 05/26/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 07/14/2023]
Abstract
BACKGROUND Around 25% of patients who have had a stroke suffer from severe upper-limb impairment and lack effective rehabilitation strategies. The AVANCER proof-of-concept clinical trial (NCT04448483) tackles this issue through an intensive and personalized-dosage cumulative intervention that combines multiple non-invasive neurotechnologies. METHODS The therapy consists of two sequential interventions, lasting until the patient shows no further motor improvement, for a minimum of 11 sessions each. The first phase involves a brain-computer interface governing an exoskeleton and multi-channel functional electrical stimulation enabling full upper-limb movements. The second phase adds anodal transcranial direct current stimulation of the motor cortex of the lesioned hemisphere. Clinical, electrophysiological, and neuroimaging examinations are performed before, between, and after the two interventions (T0, T1, and T2). This case report presents the results from the first patient of the study. FINDINGS The primary outcome (i.e., 4-point improvement in the Fugl-Meyer assessment of the upper extremity) was met in the first patient, with an increase from 6 to 11 points between T0 and T2. This improvement was paralleled by changes in motor-network structure and function. Resting-state and transcranial magnetic stimulation-evoked electroencephalography revealed brain functional changes, and magnetic resonance imaging (MRI) measures detected structural and task-related functional changes. CONCLUSIONS These first results are promising, pointing to feasibility, safety, and potential efficacy of this personalized approach acting synergistically on the nervous and musculoskeletal systems. Integrating multi-modal data may provide valuable insights into underlying mechanisms driving the improvements and providing predictive information regarding treatment response and outcomes. FUNDING This work was funded by the Wyss-Center for Bio and Neuro Engineering (WCP-030), the Defitech Foundation, PHRT-#2017-205, ERA-NET-NEURON (Discover), and SNSF (320030L_197899, NiBS-iCog).
Collapse
Affiliation(s)
- Claudia Bigoni
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Elena Beanato
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Sylvain Harquel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Julie Hervé
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Meltem Oflar
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Andrea Crema
- Clinical Neuroscience, University of Geneva Medical School, 1202 Geneva, Switzerland; Bertarelli Foundation Chair in Translational Neuroengineering, Neuro-X Institute (INX) and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Arnau Espinosa
- Wyss Center for Bio and Neuroengineering, Chemin des Mines 9, 1202 Geneva, Switzerland
| | - Giorgia G Evangelista
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Philipp Koch
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany; Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | | | - Jean-Luc Turlan
- Department of Neurological Rehabilitation, Clinique Romande de Réadaptation SUVA, 1951 Sion, Switzerland
| | - Adrian Guggisberg
- Universitäre Neurorehabilitation, Universitätsklinik für Neurologie, Inselspital, University Hospital Berne, Bern, Switzerland
| | - Takuya Morishita
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Maximilian J Wessel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Sarah B Zandvliet
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Clinical Neuroscience, University of Geneva Medical School, 1202 Geneva, Switzerland.
| |
Collapse
|
6
|
Baker A, Schranz C, Seo NJ. Associating Functional Neural Connectivity and Specific Aspects of Sensorimotor Control in Chronic Stroke. SENSORS (BASEL, SWITZERLAND) 2023; 23:5398. [PMID: 37420566 DOI: 10.3390/s23125398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/09/2023]
Abstract
Hand sensorimotor deficits often result from stroke, limiting the ability to perform daily living activities. Sensorimotor deficits are heterogeneous among stroke survivors. Previous work suggests a cause of hand deficits is altered neural connectivity. However, the relationships between neural connectivity and specific aspects of sensorimotor control have seldom been explored. Understanding these relationships is important for developing personalized rehabilitation strategies to improve individual patients' specific sensorimotor deficits and, thus, rehabilitation outcomes. Here, we investigated the hypothesis that specific aspects of sensorimotor control will be associated with distinct neural connectivity in chronic stroke survivors. Twelve chronic stroke survivors performed a paretic hand grip-and-relax task while EEG was collected. Four aspects of hand sensorimotor grip control were extracted, including reaction time, relaxation time, force magnitude control, and force direction control. EEG source connectivity in the bilateral sensorimotor regions was calculated in α and β frequency bands during grip preparation and execution. Each of the four hand grip measures was significantly associated with a distinct connectivity measure. These results support further investigations into functional neural connectivity signatures that explain various aspects of sensorimotor control, to assist the development of personalized rehabilitation that targets the specific brain networks responsible for the individuals' distinct sensorimotor deficits.
Collapse
Affiliation(s)
- Adam Baker
- Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, 77 President St., Charleston, SC 29425, USA
| | - Christian Schranz
- Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, 77 President St., Charleston, SC 29425, USA
| | - Na Jin Seo
- Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, 77 President St., Charleston, SC 29425, USA
- Division of Occupational Therapy, Department of Rehabilitation Sciences, College of Health Professions, Medical University of South Carolina, 151B Rutledge Ave., Charleston, SC 29425, USA
- Ralph H. Johnson VA Health Care System, 109 Bee St., Charleston, SC 29425, USA
| |
Collapse
|
7
|
Chiem E, Zhao K, Stark G, Ghiani CA, Colwell CS, Paul KN. Sex Differences in Sleep Phenotypes in the BACHD Mouse Model of Huntington's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.28.538324. [PMID: 37162913 PMCID: PMC10168394 DOI: 10.1101/2023.04.28.538324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Sleep and circadian rhythm disturbances are common features of Huntington's disease (HD). HD is an autosomal dominant neurodegenerative disorder that affects men and women in equal numbers, but some epidemiological studies as well as preclinical work indicate there may be sex differences in disease progression. Since sex differences in HD could provide important insights to understand cellular and molecular mechanism(s), we used the bacterial artificial chromosome transgenic mouse model of HD (BACHD) to examine whether sex differences in sleep/wake cycles are detectable in an animal model of the disease. Electroencephalography/electromyography (EEG/EMG) was used to measure sleep/wake states and polysomnographic patterns in young adult (12 week-old) male and female wild-type and BACHD mice. Our findings show that male, but not female, BACHD mice exhibited increased variation in phases of the rhythms as compared to age and sex matched wild-types. For both Rapid-eye movement (REM) and Non-rapid eye movement (NREM) sleep, genotypic and sex differences were detected. In particular, the BACHD males spent less time in NREM and exhibited a more fragmented sleep than the other groups. Both male and female BACHD mice exhibited significant changes in delta but not in gamma power compared to wild-type mice. Finally, in response to a 6-hrs sleep deprivation, both genotypes and sexes displayed predicted homeostatic responses to sleep loss. These findings suggest that females are relatively protected early in disease progression in this HD model.
Collapse
Affiliation(s)
- Emily Chiem
- Department of Integrative Biology and Physiology, University of California Los Angeles
- Molecular, Cellular, Integrative Physiology program, University of California Los Angeles
| | - Kevin Zhao
- Department of Integrative Biology and Physiology, University of California Los Angeles
| | - Gemma Stark
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles
| | - Cristina A. Ghiani
- Department of Pathology and Laboratory Medicine, University of California Los Angeles
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles
| | | | - Ketema N. Paul
- Department of Integrative Biology and Physiology, University of California Los Angeles
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles
| |
Collapse
|
8
|
Balathay D, Narasimhan U, Belo D, Anandan K. Quantitative assessment of cognitive profile and brain asymmetry in the characterization of autism spectrum in children: A task-based EEG study. Proc Inst Mech Eng H 2023:9544119231170683. [PMID: 37096354 DOI: 10.1177/09544119231170683] [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: 04/26/2023]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children. Assessment of EEG signals acquired during specific cognitive tasks is more appropriate in identifying brain functional and cognitive load variations. The spectral power of EEG sub-band frequency and parameters related to brain asymmetry has the potential to be employed as indices to characterize brain functioning. Thus, the objective of this work is to analyze the cognitive task-based electrophysiological variations in autistic and control groups, using EEG acquired during two well-defined protocols. Theta to Alpha ratio (TAR) and Theta to Beta ratio (TBR) of absolute powers of the respective sub-band frequencies have been estimated to quantify the cognitive load. The variations in interhemispheric cortical power measured by EEG were studied using the brain asymmetry index. For the arithmetic task, the TBR of the LF group was found to be considerably higher than the HF group. The findings reveal that the spectral powers of EEG sub-bands can be a key indicator in the assessment of high and low-functioning ASD to facilitate appropriate training strategies. Instead of depending solely on behavioral tests to diagnose autism, it could be a beneficial approach to use task-based EEG characteristics to differentiate between the LF and HF groups.
Collapse
Affiliation(s)
- Divya Balathay
- Centre for Healthcare Technologies, Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India
| | - Udayakumar Narasimhan
- Department of Pediatrics, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu, India
| | - David Belo
- Machine Learning for Time Series at Fraunhofer Portugal AICOS, Seixal, Setubal, Portugal
| | - Kavitha Anandan
- Centre for Healthcare Technologies, Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India
| |
Collapse
|
9
|
Action Observation Therapy for Arm Recovery after Stroke: A Preliminary Investigation on a Novel Protocol with EEG Monitoring. J Clin Med 2023; 12:jcm12041327. [PMID: 36835865 PMCID: PMC9961867 DOI: 10.3390/jcm12041327] [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: 12/19/2022] [Revised: 01/31/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023] Open
Abstract
This preliminary study introduces a novel action observation therapy (AOT) protocol associated with electroencephalographic (EEG) monitoring to be used in the future as a rehabilitation strategy for the upper limb in patients with subacute stroke. To provide initial evidence on the usefulness of this method, we compared the outcome of 11 patients who received daily AOT for three weeks with that of patients who undertook two other approaches recently investigated by our group, namely intensive conventional therapy (ICT), and robot-assisted therapy combined with functional electrical stimulation (RAT-FES). The three rehabilitative interventions showed similar arm motor recovery as indexed by Fugl-Meyer's assessment of the upper extremity (FMA_UE) and box and block test (BBT). The improvement in the FMA_UE was yet more favourable in patients with mild/moderate motor impairments who received AOT, in contrast with patients carrying similar disabilities who received the other two treatments. This suggests that AOT might be more effective in this subgroup of patients, perhaps because the integrity of their mirror neurons system (MNS) was more preserved, as indexed by EEG recording from central electrodes during action observation. In conclusion, AOT may reveal an effective rehabilitative tool in patients with subacute stroke; the EEG evaluation of MNS integrity may help to select patients who could maximally benefit from this intervention.
Collapse
|
10
|
Normative Structure of Resting-State EEG in Bipolar Derivations for Daily Clinical Practice: A Pilot Study. Brain Sci 2023; 13:brainsci13020167. [PMID: 36831710 PMCID: PMC9953767 DOI: 10.3390/brainsci13020167] [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: 12/29/2022] [Revised: 01/12/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
We used numerical methods to define the normative structure of resting-state EEG (rsEEG) in a pilot study of 37 healthy subjects (10-74 years old), using a double-banana bipolar montage. Artifact-free 120-200 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands and was log-transformed. Furthermore, Shannon's spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the main frequency and amplitude of the posterior dominant rhythm. According to the age-dependent distribution of the bands, we divided the patients in the following three groups: younger than 20; between 21 and 50; and older than 51 years old. The distribution of bands and coherence was different for the three groups depending on the brain lobes. We described the normative equations for the three age groups and for every brain lobe. We showed the feasibility of a normative structure of rsEEG picked up with a double-banana montage.
Collapse
|
11
|
Lee Friesen C, Lawrence M, Ingram TGJ, Boe SG. Home-based portable fNIRS-derived cortical laterality correlates with impairment and function in chronic stroke. Front Hum Neurosci 2022; 16:1023246. [PMID: 36569472 PMCID: PMC9780676 DOI: 10.3389/fnhum.2022.1023246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 11/21/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction Improved understanding of the relationship between post-stroke rehabilitation interventions and functional motor outcomes could result in improvements in the efficacy of post-stroke physical rehabilitation. The laterality of motor cortex activity (M1-LAT) during paretic upper-extremity movement has been documented as a useful biomarker of post-stroke motor recovery. However, the expensive, labor intensive, and laboratory-based equipment required to take measurements of M1-LAT limit its potential clinical utility in improving post-stroke physical rehabilitation. The present study tested the ability of a mobile functional near-infrared spectroscopy (fNIRS) system (designed to enable independent measurement by stroke survivors) to measure cerebral hemodynamics at the motor cortex in the homes of chronic stroke survivors. Methods Eleven chronic stroke survivors, ranging widely in their level of upper-extremity motor deficit, used their stroke-affected upper-extremity to perform a simple unilateral movement protocol in their homes while a wireless prototype fNIRS headband took measurements at the motor cortex. Measures of participants' upper-extremity impairment and function were taken. Results Participants demonstrated either a typically lateralized response, with an increase in contralateral relative oxyhemoglobin (ΔHbO), or response showing a bilateral pattern of increase in ΔHbO during the motor task. During the simple unilateral task, M1-LAT correlated significantly with measures of both upper-extremity impairment and function, indicating that participants with more severe motor deficits had more a more atypical (i.e., bilateral) pattern of lateralization. Discussion These results indicate it is feasible to gain M1-LAT measures from stroke survivors in their homes using fNIRS. These findings represent a preliminary step toward the goals of using ergonomic functional neuroimaging to improve post-stroke rehabilitative care, via the capture of neural biomarkers of post-stroke motor recovery, and/or via use as part of an accessible rehabilitation brain-computer-interface.
Collapse
Affiliation(s)
- Christopher Lee Friesen
- Laboratory for Brain Recovery and Function, Dalhousie University, Halifax, NS, Canada
- Axem Neurotechnology, Halifax, NS, Canada
- School of Physiotherapy, Dalhousie University, Halifax, NS, Canada
| | - Michael Lawrence
- Laboratory for Brain Recovery and Function, Dalhousie University, Halifax, NS, Canada
- Axem Neurotechnology, Halifax, NS, Canada
- School of Physiotherapy, Dalhousie University, Halifax, NS, Canada
| | - Tony Gerald Joseph Ingram
- Laboratory for Brain Recovery and Function, Dalhousie University, Halifax, NS, Canada
- Axem Neurotechnology, Halifax, NS, Canada
- School of Physiotherapy, Dalhousie University, Halifax, NS, Canada
| | - Shaun Gregory Boe
- Laboratory for Brain Recovery and Function, Dalhousie University, Halifax, NS, Canada
- School of Physiotherapy, Dalhousie University, Halifax, NS, Canada
- School of Health and Human Performance, Dalhousie University, Halifax, NS, Canada
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada
| |
Collapse
|
12
|
Wu JY, Ching CTS, Wang HMD, Liao LD. Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications. BIOSENSORS 2022; 12:1097. [PMID: 36551064 PMCID: PMC9776100 DOI: 10.3390/bios12121097] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.
Collapse
Affiliation(s)
- Ju-Yu Wu
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli County 35053, Taiwan
- Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, South District, Taichung City 402, Taiwan
| | - Congo Tak-Shing Ching
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, South District, Taichung City 402, Taiwan
- Department of Electrical Engineering, National Chi Nan University, No. 1 University Road, Puli Township, Nantou County 545301, Taiwan
| | - Hui-Min David Wang
- Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, South District, Taichung City 402, Taiwan
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, South District, Taichung City 402, Taiwan
| | - Lun-De Liao
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli County 35053, Taiwan
- Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, South District, Taichung City 402, Taiwan
| |
Collapse
|
13
|
Liu L, Zhang Z, Zhou Y, Pu Y, Liu D, Tian J. Brain symmetry index predicts 3-month mortality in patients with acute large hemispheric infarction. Medicine (Baltimore) 2022; 101:e31620. [PMID: 36451383 PMCID: PMC9704942 DOI: 10.1097/md.0000000000031620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Quantitative electroencephalography data are helpful to predict outcomes of cerebral infarction patients. The study was performed to evaluate the value of brain symmetry index by quantitative electroencephalography in predicting 3-month mortality of large hemispheric infarction. We studied a prospective, consecutive series of patients with large supratentorial cerebral infarction confirmed within 3 days from the onset in 2 intensive care units from August 2017 to February 2020. The electroencephalography was recorded once admission. The brain symmetry index (BSI) which is divided into BSIfast and BSIslow were calculated for each electrodes pair. The outcome was mortality at 3 months after the onset. A total of 38 patients were included. The subjects were divided into the mortality group (15 patients) and survival group (23 patients). Of the BSIfast and BSIslow at each electrodes pair, higher BSIfastC3-C4, higher BSIslowC3-C4, and higher BSIslowO1-O2 were noticed in the mortality group than that in the survival group at 3 months (P = .001; P = .010; P = .009). Multivariable analysis indicated that BSIfastC3-C4 was an independent predictor of 3-month mortality (odds ratio = 1.059, 95%CI 1.003, 1.119, P = .039). BSIfastC3-C4 could significant predict 3-month mortality (area under curve = 0.805, P = .005). And when we combined BSIfastC3-C4, Glasgow Coma Scale and infarct volume together to predict the 3-month mortality, the predicted value increased (area under curve = 0.840, P = .002). BSIfastC3-C4 could independently predict the 3-month mortality of large hemispheric infarction. The combination marker which includes Glasgow Coma Scale, infarct volume, and BSIfastC3-C4 has a better diagnostic value. Further clinical trials with a large sample size are still needed.
Collapse
Affiliation(s)
- Lidou Liu
- Neurocritical care unit, Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, China
| | - Zhe Zhang
- Neurocritical care unit, Department of neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yi Zhou
- Neurocritical care unit, Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yuehua Pu
- Neurocritical care unit, Department of neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Dacheng Liu
- Neurocritical care unit, Department of neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jia Tian
- Neurocritical care unit, Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, China
- * Correspondence: Jia Tian, Neurocritical care unit, Department of Neurology, the Second Hospital of Hebei Medical University, 215 Heping West Road, Xinhua District, Shijiazhuang 050000, Hebei, China (e-mail: )
| |
Collapse
|
14
|
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.
Collapse
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.
| |
Collapse
|
15
|
Liang J, Song Y, Belkacem AN, Li F, Liu S, Chen X, Wang X, Wang Y, Wan C. Prediction of balance function for stroke based on EEG and fNIRS features during ankle dorsiflexion. Front Neurosci 2022; 16:968928. [PMID: 36061607 PMCID: PMC9433808 DOI: 10.3389/fnins.2022.968928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
Balance rehabilitation is exceedingly crucial during stroke rehabilitation and is highly related to the stroke patients’ secondary injuries (caused by falling). Stroke patients focus on walking ability rehabilitation during the early stage. Ankle dorsiflexion can activate the brain areas of stroke patients, similar to walking. The combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was a new method, providing more beneficial information. We extracted the event-related desynchronization (ERD), oxygenated hemoglobin (HBO), and Phase Synchronization Index (PSI) features during ankle dorsiflexion from EEG and fNIRS. Moreover, we established a linear regression model to predict Berg Balance Scale (BBS) values and used an eightfold cross validation to test the model. The results showed that ERD, HBO, PSI, and age were critical biomarkers in predicting BBS. ERD and HBO during ankle dorsiflexion and age were promising biomarkers for stroke motor recovery.
Collapse
Affiliation(s)
- Jun Liang
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
- Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | | | - Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
- *Correspondence: Abdelkader Nasreddine Belkacem,
| | - Fengmin Li
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
| | - Shizhong Liu
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaona Chen
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
| | - Xinrui Wang
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
| | - Yueyun Wang
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
| | - Chunxiao Wan
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
- Chunxiao Wan,
| |
Collapse
|
16
|
Zhang Y, Ye L, Cao L, Song W. Resting-state electroencephalography changes in poststroke patients with visuospatial neglect. Front Neurosci 2022; 16:974712. [PMID: 36033611 PMCID: PMC9399887 DOI: 10.3389/fnins.2022.974712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 07/22/2022] [Indexed: 11/13/2022] Open
Abstract
Background This study aimed to explore the electrophysiological characteristics of resting-state electroencephalography (rsEEG) in patients with visuospatial neglect (VSN) after stroke. Methods A total of 44 first-event sub-acute strokes after right hemisphere damage (26 with VSN and 18 without VSN) were included. Besides, 18 age-matched healthy participants were used as healthy controls. The resting-state electroencephalography (EEG) of 64 electrodes was recorded to obtain the power of the spectral density of different frequency bands. The global delta/alpha ratio (DAR), DAR over the affected hemispheres (DARAH), DAR over the unaffected hemispheres (DARUH), and the pairwise-derived brain symmetry index (pdBSI; global and four bands) were compared between groups and receiver operating characteristic (ROC) curve analysis was conducted. The Barthel index (BI), Fugl-Meyer motor function assessment (FMA), and Berg balance scale (BBS) were used to assess the functional state of patients. Visuospatial neglect was assessed using a battery of standardized tests. Results We found that patients with VSN performed poorly compared with those without VSN. Analysis of rsEEG revealed increased delta and theta power and decreased alpha and beta power in stroke patients with VSN. Compared to healthy controls and poststroke non-VSN patients, patients with VSN showed a higher DAR (P < 0.001), which was significantly positively correlated with the BBS (DAR: r = –0.522, P = 0.006; DARAH: r = –0.521, P = 0.006; DARUH: r = –0.494, P = 0.01). The line bisection task was positively correlated with DAR (r = 0.458, P = 0.019) and DARAH (r = 0.483, P = 0.012), while the star cancellation task was only positively correlated with DARAH (r = 0.428, P = 0.029). DARAH had the best discriminating value between VSN and non-VSN, with an area under the curve (AUC) of 0.865. Patients with VSN showed decreased alpha power in the parietal and occipital areas of the right hemisphere. A higher parieto-occipital pdBSIalpha was associated with a worse line bisection task (r = 0.442, P = 0.024). Conclusion rsEEG may be a useful tool for screening for stroke patients with visuospatial neglect, and DAR and parieto-occipital pdBSIalpha may be useful biomarkers for visuospatial neglect after stroke.
Collapse
|
17
|
Leonardi G, Ciurleo R, Cucinotta F, Fonti B, Borzelli D, Costa L, Tisano A, Portaro S, Alito A. The role of brain oscillations in post-stroke motor recovery: An overview. Front Syst Neurosci 2022; 16:947421. [PMID: 35965998 PMCID: PMC9373799 DOI: 10.3389/fnsys.2022.947421] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 07/13/2022] [Indexed: 11/25/2022] Open
Abstract
Stroke is the second cause of disability and death worldwide, highly impacting patient’s quality of life. Several changes in brain architecture and function led by stroke can be disclosed by neurophysiological techniques. Specifically, electroencephalogram (EEG) can disclose brain oscillatory rhythms, which can be considered as a possible outcome measure for stroke recovery, and potentially shaped by neuromodulation techniques. We performed a review of randomized controlled trials on the role of brain oscillations in patients with post-stroke searching the following databases: Pubmed, Scopus, and the Web of Science, from 2012 to 2022. Thirteen studies involving 346 patients in total were included. Patients in the control groups received various treatments (sham or different stimulation modalities) in different post-stroke phases. This review describes the state of the art in the existing randomized controlled trials evaluating post-stroke motor function recovery after conventional rehabilitation treatment associated with neuromodulation techniques. Moreover, the role of brain pattern rhythms to modulate cortical excitability has been analyzed. To date, neuromodulation approaches could be considered a valid tool to improve stroke rehabilitation outcomes, despite more high-quality, and homogeneous randomized clinical trials are needed to determine to which extent motor functional impairment after stroke can be improved by neuromodulation approaches and which one could provide better functional outcomes. However, the high reproducibility of brain oscillatory rhythms could be considered a promising predictive outcome measure applicable to evaluate patients with stroke recovery after rehabilitation.
Collapse
Affiliation(s)
- Giulia Leonardi
- Department of Physical and Rehabilitation Medicine and Sports Medicine, Policlinico “G. Martino,”Messina, Italy
| | | | | | - Bartolo Fonti
- IRCCS Centro Neurolesi Bonino-Pulejo, Messina, Italy
| | - Daniele Borzelli
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Lara Costa
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Adriana Tisano
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Simona Portaro
- Department of Physical and Rehabilitation Medicine and Sports Medicine, Policlinico “G. Martino,”Messina, Italy
| | - Angelo Alito
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
- *Correspondence: Angelo Alito,
| |
Collapse
|
18
|
Tian J, Zhou Y, Liu H, Qu Z, Zhang L, Liu L. Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method. Front Neurol 2022; 13:897734. [PMID: 35968284 PMCID: PMC9366714 DOI: 10.3389/fneur.2022.897734] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/04/2022] [Indexed: 12/04/2022] Open
Abstract
Background Better outcome prediction could assist in reliable classification of the illnesses in neurological intensive care unit (ICU) severity to support clinical decision-making. We developed a multifactorial model including quantitative electroencephalography (QEEG) parameters for outcome prediction of patients in neurological ICU. Methods We retrospectively analyzed neurological ICU patients from November 2018 to November 2021. We used 3-month mortality as the outcome. Prediction models were created using a linear discriminant analysis (LDA) based on QEEG parameters, APACHEII score, and clinically relevant features. Additionally, we compared our best models with APACHEII score and Glasgow Coma Scale (GCS). The DeLong test was carried out to compare the ROC curves in different models. Results A total of 110 patients were included and divided into a training set (n=80) and a validation set (n = 30). The best performing model had an AUC of 0.85 in the training set and an AUC of 0.82 in the validation set, which were better than that of GCS (training set 0.64, validation set 0.61). Models in which we selected only the 4 best QEEG parameters had an AUC of 0.77 in the training set and an AUC of 0.71 in the validation set, which were similar to that of APACHEII (training set 0.75, validation set 0.73). The models also identified the relative importance of each feature. Conclusion Multifactorial machine learning models using QEEG parameters, clinical data, and APACHEII score have a better potential to predict 3-month mortality in non-traumatic patients in neurological ICU.
Collapse
Affiliation(s)
- Jia Tian
- Neurocritical Care Unit, Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yi Zhou
- Neurocritical Care Unit, Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Hu Liu
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhenzhen Qu
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Limiao Zhang
- Neurocritical Care Unit, Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Lidou Liu
- Neurocritical Care Unit, Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
- *Correspondence: Lidou Liu
| |
Collapse
|
19
|
Hao Z, Zhai X, Cheng D, Pan Y, Dou W. EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics. Front Neurosci 2022; 16:848737. [PMID: 35645720 PMCID: PMC9131012 DOI: 10.3389/fnins.2022.848737] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.
Collapse
Affiliation(s)
- Zexuan Hao
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
| | - Xiaoxue Zhai
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Dandan Cheng
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Yu Pan
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
- *Correspondence: Yu Pan,
| | - Weibei Dou
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
- Weibei Dou,
| |
Collapse
|
20
|
Ren B, Yang K, Zhu L, Hu L, Qiu T, Kong W, Zhang J. Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke. Front Comput Neurosci 2022; 16:785397. [PMID: 35431850 PMCID: PMC9008254 DOI: 10.3389/fncom.2022.785397] [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: 09/29/2021] [Accepted: 02/16/2022] [Indexed: 11/13/2022] Open
Abstract
Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500–800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.
Collapse
Affiliation(s)
- Bin Ren
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Kun Yang
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Li Zhu
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Lang Hu
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Tao Qiu
- Department of Neurology, Zhejiang Provincial Hospital of Chinese Medicine, Hangzhou, China
| | - Wanzeng Kong
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Jianhai Zhang
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
- *Correspondence: Jianhai Zhang
| |
Collapse
|
21
|
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]
|
22
|
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.
Collapse
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
| | | | | |
Collapse
|
23
|
Xu R, Spataro R, Allison BZ, Guger C. Brain-Computer Interfaces in Acute and Subacute Disorders of Consciousness. J Clin Neurophysiol 2022; 39:32-39. [PMID: 34474428 DOI: 10.1097/wnp.0000000000000810] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
SUMMARY Disorders of consciousness include coma, unresponsive wakefulness syndrome (also known as vegetative state), and minimally conscious state. Neurobehavioral scales such as coma recovery scale-revised are the gold standard for disorder of consciousness assessment. Brain-computer interfaces have been emerging as an alternative tool for these patients. The application of brain-computer interfaces in disorders of consciousness can be divided into four fields: assessment, communication, prediction, and rehabilitation. The operational theoretical model of consciousness that brain-computer interfaces explore was reviewed in this article, with a focus on studies with acute and subacute patients. We then proposed a clinically friendly guideline, which could contribute to the implementation of brain-computer interfaces in neurorehabilitation settings. Finally, we discussed limitations and future directions, including major challenges and possible solutions.
Collapse
Affiliation(s)
- Ren Xu
- Guger Technologies OG, Schiedlberg, Austria
| | - Rossella Spataro
- g.tec medical engineering GmbH, Schiedlberg, Austria
- IRCCS Centro Neurolesi Bonino Pulejo, Palermo, Italy; and
| | - Brendan Z Allison
- Cognitive Science Department, University of California San Diego, La Jolla, California, U.S.A
| | - Christoph Guger
- Guger Technologies OG, Schiedlberg, Austria
- g.tec medical engineering GmbH, Schiedlberg, Austria
| |
Collapse
|
24
|
Zhang Y, Zhang Z, Luo L, Tong H, Chen F, Hou ST. 40 Hz Light Flicker Alters Human Brain Electroencephalography Microstates and Complexity Implicated in Brain Diseases. Front Neurosci 2021; 15:777183. [PMID: 34966258 PMCID: PMC8710722 DOI: 10.3389/fnins.2021.777183] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/25/2021] [Indexed: 11/17/2022] Open
Abstract
Previous studies showed that entrainment of light flicker at low gamma frequencies provided neuroprotection in mouse models of Alzheimer’s disease (AD) and stroke. The current study was set to explore the feasibility of using 40 Hz light flicker for human brain stimulation for future development as a tool for brain disease treatment. The effect of 40 Hz low gamma frequency light on a cohort of healthy human brains was examined using 64 channel electroencephalography (EEG), followed by microstate analyses. A random frequency light flicker was used as a negative control treatment. Light flicker at 40 Hz significantly increased the corresponding band power in the O1, Oz, and O3 electrodes covering the occipital areas of both sides of the brain, indicating potent entrainment with 40 Hz light flicker in the visual cortex area. Importantly, the 40 Hz light flicker significantly altered microstate coverage, transition duration, and the Lempel-Ziv complexity (LZC) compared to the rest state. Microstate metrics are known to change in the brains of Alzheimer’s disease, schizophrenia, and stroke patients. The current study laid the foundation for the future development of 40 Hz light flicker as therapeutics for brain diseases.
Collapse
Affiliation(s)
- Yiqi Zhang
- Brain Research Centre and Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Zhenyu Zhang
- Brain Research Centre and Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Lei Luo
- Brain Research Centre and Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Huaiyu Tong
- Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Sheng-Tao Hou
- Brain Research Centre and Department of Biology, Southern University of Science and Technology, Shenzhen, China
| |
Collapse
|
25
|
Yeom HG, Jeong H. F-Value Time-Frequency Analysis: Between-Within Variance Analysis. Front Neurosci 2021; 15:729449. [PMID: 34955709 PMCID: PMC8697975 DOI: 10.3389/fnins.2021.729449] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Studies on brain mechanisms enable us to treat various brain diseases and develop diverse technologies for daily life. Therefore, an analysis method of neural signals is critical, as it provides the basis for many brain studies. In many cases, researchers want to understand how neural signals change according to different conditions. However, it is challenging to find distinguishing characteristics, and doing so requires complex statistical analysis. In this study, we propose a novel analysis method, FTF (F-value time-frequency) analysis, that applies the F-value of ANOVA to time-frequency analysis. The proposed method shows the statistical differences among conditions in time and frequency. To evaluate the proposed method, electroencephalography (EEG) signals were analyzed using the proposed FTF method. The EEG signals were measured during imagined movement of the left hand, right hand, foot, and tongue. The analysis revealed the important characteristics which were different among different conditions and similar within the same condition. The FTF analysis method will be useful in various fields, as it allows researchers to analyze how frequency characteristics vary according to different conditions.
Collapse
Affiliation(s)
- Hong Gi Yeom
- Department of Electronics Engineering, Chosun University, Gwangju, South Korea
| | - Hyundoo Jeong
- Department of Mechatronics Engineering, Incheon National University, Incheon, South Korea
| |
Collapse
|
26
|
Garro F, Chiappalone M, Buccelli S, De Michieli L, Semprini M. Neuromechanical Biomarkers for Robotic Neurorehabilitation. Front Neurorobot 2021; 15:742163. [PMID: 34776920 PMCID: PMC8579108 DOI: 10.3389/fnbot.2021.742163] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023] Open
Abstract
One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.
Collapse
Affiliation(s)
- Florencia Garro
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Michela Chiappalone
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
| | | | | |
Collapse
|
27
|
Wang N, Liang J, Zhang H, Wan C, Liu S, Xu R, Ming D. Correlation Between Poststroke Balance Function and Brain Symmetry Index in Sitting and Standing Postures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6273-6276. [PMID: 34892547 DOI: 10.1109/embc46164.2021.9629668] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Balance problems are the main sequelae of stroke, which increases the risk of falling. The assessment of balance ability can guide doctors to formulate rehabilitation plans, thereby reducing the risk of falls. Studies have reported the role of resting-state EEG during sitting in the motor assessment of the upper extremity and prognosis of stroke patients. However, the above research in the sitting posture lacks specificity in evaluating the balance ability of the lower limbs. Herein, this article investigated whether EEG was different in sitting and standing positions with different difficulty levels and validated the feasibility of EEG in assessing body balance ability. The resting-state EEG signals were collected from 11 stroke patients. The pairwise-derived brain symmetry index (pdBSI) was used to identify the differences in EEG-quantified interhemispheric cortical power asymmetry observable in healthy versus cortical and subcortical stroke patients by calculating the absolute value of the difference in power at each pair of electrodes. Subsequently, we computed the pdBSI over different frequency bands. Balance function was assessed using the BBS (Berg Balance Scale). Stroke survivors showed higher pdBSI (1-25 Hz) values in standing posture compared to sitting (p <0.05) and the pdBSI was significantly negatively correlated with BBS (r = -0.671, p =0.034). Additionally, the pdBSI within beta band was also significantly negatively correlated with BBS (r = -0.711, p=0.017). In conclusion, stroke brain asymmetry in standing posture was significantly more severe and the pdBSIs in 1-25Hz and beta hand were related to balance function. BBS and NIHSS was significantly negatively correlated (r = -0.701, p = 0.024), and NIHSS was significantly correlated with age (r = 0.822, p = 0.004). The present study suggests that stroke can seriously affect the body's balance ability. Compared with the sitting posture, the asymmetry of cortical energy in the standing posture can better assess the patient's balance ability.
Collapse
|
28
|
Almarzouki HZ, Alsulami H, Rizwan A, Basingab MS, Bukhari H, Shabaz M. An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:1233166. [PMID: 34745488 PMCID: PMC8566034 DOI: 10.1155/2021/1233166] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/09/2021] [Accepted: 10/12/2021] [Indexed: 02/07/2023]
Abstract
In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study's aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3-10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3-10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha (p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta (p < 0.001), alpha (p < 0.01), and beta-1 (p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.
Collapse
Affiliation(s)
- Hatim Z. Almarzouki
- Department of Radiology, Faculty of Medicine, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Hemaid Alsulami
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Ali Rizwan
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mohammed S. Basingab
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Hatim Bukhari
- Department of Industrial and Systems Engineering, College of Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Mohammad Shabaz
- Arba Minch University, Arba Minch, Ethiopia
- Department of Computer Science Engineering, Chandigarh University, Punjab, Ajitgarh, India
| |
Collapse
|
29
|
Mrachacz-Kersting N, Ibáñez J, Farina D. Towards a mechanistic approach for the development of non-invasive brain-computer interfaces for motor rehabilitation. J Physiol 2021; 599:2361-2374. [PMID: 33728656 DOI: 10.1113/jp281314] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 03/05/2021] [Indexed: 12/11/2022] Open
Abstract
Brain-computer interfaces (BCIs) designed for motor rehabilitation use brain signals associated with motor-processing states to guide neuroplastic changes in a state-dependent manner. These technologies are uniquely positioned to induce targeted and functionally relevant plastic changes in the human motor nervous system. However, while several studies have shown that BCI-based neuromodulation interventions may improve motor function in patients with lesions in the central nervous system, the neurophysiological structures and processes targeted with the BCI interventions have not been identified. In this review, we first summarize current knowledge of the changes in the central nervous system associated with learning new motor skills. Then, we propose a classification of current BCI paradigms for plasticity induction and motor rehabilitation based on the expected neural plastic changes promoted. This classification proposes four paradigms based on two criteria: the plasticity induction methods and the brain states targeted. The existing evidence regarding the brain circuits and processes targeted with these different BCIs is discussed in detail. The proposed classification aims to serve as a starting point for future studies trying to elucidate the underlying plastic changes following BCI interventions.
Collapse
Affiliation(s)
| | - Jaime Ibáñez
- Department of Bioengineering, Centre for Neurotechnologies, Imperial College London, London, UK
- Department of Clinical and Movement Neuroscience, Institute of Neurology, University College London, London, UK
| | - Dario Farina
- Department of Bioengineering, Centre for Neurotechnologies, Imperial College London, London, UK
| |
Collapse
|
30
|
Jee S. Brain Oscillations and Their Implications for Neurorehabilitation. BRAIN & NEUROREHABILITATION 2021; 14:e7. [PMID: 36742108 PMCID: PMC9879411 DOI: 10.12786/bn.2021.14.e7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 02/14/2021] [Accepted: 03/05/2021] [Indexed: 11/08/2022] Open
Abstract
Neural oscillation is rhythmic or repetitive neural activities, which can be observed at all levels of the central nervous system (CNS). The large-scale oscillations measured by electroencephalography have long been used in clinical practice and may have a potential for the usage in neurorehabilitation for people with various CNS disorders. The recent advancement of computational neuroscience has opened up new opportunities to explore clinical application of the results of neural oscillatory activity analysis to evaluation and diagnosis; monitoring the rehab progress; prognostication; and personalized rehabilitation planning in neurorehabilitation. In addition, neural oscillation is catching more attention to its role as a target of noninvasive neuromodulation in neurological disorders.
Collapse
Affiliation(s)
- Sungju Jee
- Department of Rehabilitation Medicine, College of Medicine, Chungnam National University, Daejeon, Korea.,Daejeon Chungcheong Regional Medical Rehabilitation Center, Chungnam National University Hospital, Daejeon, Korea.,Daejeon Chungcheong Regional Cardiocerebrovascular Center, Chungnam National University Hospital, Daejeon, Korea
| |
Collapse
|
31
|
Insausti-Delgado A, López-Larraz E, Omedes J, Ramos-Murguialday A. Intensity and Dose of Neuromuscular Electrical Stimulation Influence Sensorimotor Cortical Excitability. Front Neurosci 2021; 14:593360. [PMID: 33519355 PMCID: PMC7845652 DOI: 10.3389/fnins.2020.593360] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/30/2020] [Indexed: 12/13/2022] Open
Abstract
Neuromuscular electrical stimulation (NMES) of the nervous system has been extensively used in neurorehabilitation due to its capacity to engage the muscle fibers, improving muscle tone, and the neural pathways, sending afferent volleys toward the brain. Although different neuroimaging tools suggested the capability of NMES to regulate the excitability of sensorimotor cortex and corticospinal circuits, how the intensity and dose of NMES can neuromodulate the brain oscillatory activity measured with electroencephalography (EEG) is still unknown to date. We quantified the effect of NMES parameters on brain oscillatory activity of 12 healthy participants who underwent stimulation of wrist extensors during rest. Three different NMES intensities were included, two below and one above the individual motor threshold, fixing the stimulation frequency to 35 Hz and the pulse width to 300 μs. Firstly, we efficiently removed stimulation artifacts from the EEG recordings. Secondly, we analyzed the effect of amplitude and dose on the sensorimotor oscillatory activity. On the one hand, we observed a significant NMES intensity-dependent modulation of brain activity, demonstrating the direct effect of afferent receptor recruitment. On the other hand, we described a significant NMES intensity-dependent dose-effect on sensorimotor activity modulation over time, with below-motor-threshold intensities causing cortical inhibition and above-motor-threshold intensities causing cortical facilitation. Our results highlight the relevance of intensity and dose of NMES, and show that these parameters can influence the recruitment of the sensorimotor pathways from the muscle to the brain, which should be carefully considered for the design of novel neuromodulation interventions based on NMES.
Collapse
Affiliation(s)
- Ainhoa Insausti-Delgado
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- International Max Planck Research School (IMPRS) for Cognitive and Systems Neuroscience, Tübingen, Germany
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Eduardo López-Larraz
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Bitbrain, Zaragoza, Spain
| | - Jason Omedes
- Instituto de Investigación en Ingeniería de Aragón (I3A), Zaragoza, Spain
- Departamento de Informática e Ingeniería de Sistemas (DIIS), University of Zaragoza, Zaragoza, Spain
| | - Ander Ramos-Murguialday
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Neurotechnology Laboratory, TECNALIA, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
| |
Collapse
|
32
|
Xue J, Ren F, Sun X, Yin M, Wu J, Ma C, Gao Z. A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding. Neural Plast 2020; 2020:8863223. [PMID: 33505456 PMCID: PMC7787825 DOI: 10.1155/2020/8863223] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 10/22/2020] [Accepted: 11/04/2020] [Indexed: 12/11/2022] Open
Abstract
Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.
Collapse
Affiliation(s)
- Juntao Xue
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Feiyue Ren
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Xinlin Sun
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Miaomiao Yin
- Department of Neurorehabilitation and Neurology, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin 300350, China
| | - Jialing Wu
- Department of Neurorehabilitation and Neurology, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin 300350, China
| | - Chao Ma
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Zhongke Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| |
Collapse
|
33
|
Sebastián-Romagosa M, Cho W, Ortner R, Murovec N, Von Oertzen T, Kamada K, Allison BZ, Guger C. Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients-A Feasibility Study. Front Neurosci 2020; 14:591435. [PMID: 33192277 PMCID: PMC7640937 DOI: 10.3389/fnins.2020.591435] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 09/10/2020] [Indexed: 12/21/2022] Open
Abstract
Introduction Numerous recent publications have explored Brain Computer Interfaces (BCI) systems as rehabilitation tools to help subacute and chronic stroke patients recover upper extremity movement. Recent work has shown that BCI therapy can lead to better outcomes than conventional therapy. BCI combined with other techniques such as Functional Electrical Stimulation (FES) and Virtual Reality (VR) allows to the user restore the neurological function by inducing the neural plasticity through improved real-time detection of motor imagery (MI) as patients perform therapy tasks. Methods Fifty-one stroke patients with upper extremity hemiparesis were recruited for this study. All participants performed 25 sessions with the MI BCI and assessment visits to track the functional changes before and after the therapy. Results The results of this study demonstrated a significant increase in the motor function of the paretic arm assessed by Fugl-Meyer Assessment (FMA-UE), ΔFMA-UE = 4.68 points, P < 0.001, reduction of the spasticity in the wrist and fingers assessed by Modified Ashworth Scale (MAS), ΔMAS-wrist = -0.72 points (SD = 0.83), P < 0.001, ΔMAS-fingers = -0.63 points (SD = 0.82), P < 0.001. Other significant improvements in the grasp ability were detected in the healthy hand. All these functional improvements achieved during the BCI therapy persisted 6 months after the therapy ended. Results also showed that patients with Motor Imagery accuracy (MI) above 80% increase 3.16 points more in the FMA than patients below this threshold (95% CI; [1.47–6.62], P = 0.003). The functional improvement was not related with the stroke severity or with the stroke stage. Conclusion The BCI treatment used here was effective in promoting long lasting functional improvements in the upper extremity in stroke survivors with severe, moderate and mild impairment. This functional improvement can be explained by improved neuroplasticity in the central nervous system.
Collapse
Affiliation(s)
| | - Woosang Cho
- g.tec Medical Engineering GmbH, Schiedlberg, Austria.,Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,International Max Planck Research School for Neural & Behavioral Sciences, Tübingen, Germany
| | - Rupert Ortner
- g.tec Medical Engineering Spain SL, Barcelona, Spain
| | - Nensi Murovec
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Tim Von Oertzen
- Department of Neurology 1, Kepler Universitätsklinik, Linz, Austria
| | | | - Brendan Z Allison
- Department of Cognitive Science, University of California, San Diego, San Diego, CA, United States
| | - Christoph Guger
- g.tec Medical Engineering Spain SL, Barcelona, Spain.,g.tec Medical Engineering GmbH, Schiedlberg, Austria
| |
Collapse
|